Cortical processing of pain: the role of habituation

Cortical processing of pain: the role of habituation

C.J. Vossen

UM

P

Šcopyright Carine Vossen, Maastricht 2018 Design Cover by Brigitte Bazuin Illustration in introduction by Greet Mommen Lay out Datawyse Printed and bound by Datawyse | Universitaire Pers Maastricht ISBN 978-94-6295-943-9

UNIVERSITAIRE

PERS MAASTRICHT

Š2018 All rights reserved. No parts of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or otherwise, without prior written permission of the author.

Cortical processing of pain the role of habituation Proefschrift Ter verkrijging van de graad van doctor aan de Universiteit Maastricht, op gezag van de Rector magnificus, Prof. dr. Rianne M. Letschert, volgens het besluit van het College van Decanen, in het openbaar te verdedigen op woensdag 27 juni 2018 om 14.00 uur

door

Catherine Jeanne (Carine) Vossen geboren op 25 januari 1979 te Geleen

Promotoren: Prof. dr. E.A. Joosten Prof. dr. J. van Os, Universiteit Utrecht/ Universiteit Maastricht Copromotor: Dr. R. Lousberg Beoordelingscommissie: Prof. dr. T.A.M.J. van Amelsfoort (voorzitter) Prof. dr. W.F.F.A. Buhre Prof. dr. ir. H.J. Hermens (Universiteit Twente) Prof. dr. D.E.J. Linden Dr. H.A. van Suijlekom (Catharina Ziekenhuis)

The research presented in this thesis was conducted at the school for Mental Health and Neuroscience in a collaboration between the Department of Psychiatry and Neuropsychology and the Department of Anesthesiology and Pain Medicine.

Paranimfen: Drs. C.P. Vossen Drs. G. Sellenraad

Contents Chapter 1

General introduction

Chapter 2

The Use of Event-Related Potentials in Chronic Back Pain Patients

23

Chapter 3

Introducing the event-related fixed-interval area (ERFIA) multilevel technique: A method to analyze the complete epoch of event-related potentials at single trial level

51

Does habituation differ in chronic low back pain subjects compared to pain-free controls? A cross-sectional pain rating ERP study reanalyzed with the ERFIA multilevel method

75

The Influence of Pain Hypervigilance on Cortical Processing and Habituation to Painful Stimuli in Healthy Subjects: A cross-sectional pain-ERP study

97

Chapter 4

Chapter 5

9

Chapter 6

Does pain hypervigilance further impact the lack of habituation to pain in individuals with chronic pain? A cross-sectional pain-ERP study 117

Chapter 7

General discussion Summary Samenvatting Valorisation Epiloog/dankwoord Curriculum Vitae List of publications

139 155 159 163 167 171 173

7

Chapter

1

General introduction

It's far more important to know what person the disease has than what disease the person has.

(Hippocrates)

9

General introduction

The rationale for this thesis At the outset of my residency in anesthesiology, I observed that patients who were undergoing seemingly identical surgeries varied substantially with regard to their pain experience and subsequent need for analgesic treatment. Anesthesiologists have many analgesics and advanced techniques at their disposal, such as epidural analgesia and peripheral nerve blocks. Despite the availability of these sophisticated analgesic tools, anesthesiologists fail to reduce the pain to acceptable levels in a substantial number of patients. In fact, 40% to 75% of patients suffer from moderate to severe pain during the 1–3 first several days after surgery, many of whom develop chronic pain.

Notably, evidence has emerged that patients who experience severe pain during the acute postop3 erative phase are at risk of developing chronic postoperative pain.

The issues that are related to the variability in pain experience and insufficient pain management are not limited to postoperative surgical patients. According to a large European survey, approximately 18% of the Dutch population suffers from chronic pain with 4 various causes, 56% of who report that their pain is managed inadequately. Due to pain, an average of 13 workdays are lost annually, decreasing productivity and costing billions 4 of euros in the Netherlands. Thus, more insight is needed into the wide variability in pain experiences in acute and chronic pain—particularly its factors and underlying mechanisms. Identification of these factors can guide the development of new, tailored therapeutic options that prevent the transition from acute to chronic pain.

Multidimensionality of pain The definition of pain includes a description of the variety in pain experiences—namely, its subjective nature and multidimensionality. The official definition of pain per the International Association for the Study of Pain (IASP) is “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in 5 terms of such damage.” When pain persists or recurs for more than 3 to 6 months, it is 6 regarded as chronic. th

Our understanding of the mechanisms of pain has progressed significantly. In the 17 century, pain was viewed by Descartes as a passive one-way transduction system of noxious 7 stimuli to the brain (Figure 1). However, today, the pain experience is seen as a broader concept, in which nociceptive inputs to the brain do not simply correlate linearly with the pain experience. In addition to noxious sensory inputs, genetics, cognitive beliefs, expecta8 tions, attention, mood, prior experiences, and context shape the final pain experience.

11

Chapter 1 The pain experience is preceded by a complex neurophysiological process, which can be roughly divided into 3 steps. First, a noxious stimulus is converted into an electrophysiological response by A-delta and C-fibers. These nociceptors are peripheral nerve endings that are present in many tissues, such as skin, muscles, joints, and viscera. In the second stage, the electrophysiological nociceptive signal enters the dorsal horn of the spinal cord via primary afferent neurons through the dorsal roots, in which it is altered and transmitted to the brainstem and thalamus. In the final step, the pain experience arises from connections between the thalamus and higher cortical areas, in which the initial 9 nociceptive response is integrated.

The pain experience can be modified at various levels. According to the gate control theory, nociceptive information in the dorsal horn can be modified by peripheral sensory input or supraspinal mechanisms. This ‘gate’ opens when nociceptive input reaches a threshold that surpasses the elicited inhibition, thereby activating pathways that lead to 7 the experience of pain and pain-related behavior.

This theoretical concept provides a neural basis for the possibility that psychological factors influence pain in top-down manner. In this context, the multidimensional model of pain by Loeser describes pain as layers of an ‘onion’, the center of which is nociception, followed by pain sensation, emotions, and suffering and ending with pain behavior 10,11 at the outermost layer (see Figure 2).

The multidimensional pain model recognizes that the pain experience can be influenced by replacing pain behaviors with desirable behaviors to lessen the suffering.

Considering the multidimensionality of pain, recent pain research has been focusing on how the transition from acute to chronic pain occurs. Greater insight into this process— the chronification of pain—could lead to novel preventive measures preoperatively if surgery is required or during the acute phase of pain. One of the mechanisms that is believed to be imbalanced in the chronification of pain is habituation.

Habituation Habituation is defined as a mechanism in which a behavioral response to a repeatedly 12,13 presented (noxious) stimulus decreases.

Habituation is considered a basic and general form of learning that can appear after a wide range of stimuli (visual, auditory, sensory) and occurs in humans down to single-celled organisms. Habituation allows 14 irrelevant information to be filtered. In pain research, habituation is viewed as one of the key top-down processes in antinociception that may constitute a protective mecha8,15 nism that prevents the chronification of pain.

Several studies have demonstrated a deficit in habituation or the inability to habituate to painful experiences in various 16–18 chronic pain populations.

12

General introduction Pain behaviors Suffering Emotions

Pain sensations

Tissue damage

Figure 1. Descartes’s one-way transduction model of pain.

Figure 2. Loeser’s multidimensional model of pain.

Pain hypervigilance Another factor that influences the pain experience is pain hypervigilance. Hypervigilance, a concept that originated in cognitive psychology, is defined as a constant scanning of 19 the body for somatic and pain sensations. Excessive attention specifically to pain sensations is termed pain hypervigilance. Pain hypervigilance—the attention toward pain—is 20 believed to have a significant function in the modification of the pain experience. Distraction can mitigate the perceived pain intensity, and conversely, heightened attention 21–24 or hypervigilance can increase the pain report by focusing on the pain.

Subjective pain hypervigilance can be measured using the Pain Vigilance and Awareness 25 Questionnaire (PVAQ). This questionnaire has been validated for several pain popula26–30 tions and pain-free subjects.

In habituation, when the novelty of a stimulus decreases, the response declines. In this context, Schuh-Hofer and colleagues hypothesized that habituation may act as an ‘at31 tentional’ filter. It is conceivable that pain hypervigilance—the general heightened attention toward pain—impairs the mechanism of habituation to pain. There is, however, no experimental evidence to support this hypothesis.

Measuring pain Pain can be measured using several tools, such as questionnaires and neuroimaging techniques.

13

Chapter 1

Questionnaires Due to its subjective nature, it is not possible to measure pain directly, and thus, its assessment can be challenging. The end-product of nociception, the pain experience, can be evaluated using questionnaires and pain diaries. Several methods measure pain 32 intensity, an unidimensional aspect of pain. A common tool for pain intensity is the 33,34 numerical rating scale (NRS).

Patients are asked to rate their pain on a scale of 0 to 10: a 0 indicates no pain, and 10 is the most severe pain imaginable. On the visual analog scale (VAS), patients are asked to mark their pain intensity on a 100-mm line, in which 0 means no pain and 100 mm is the worst imaginable pain. Using the verbal rating scale (VRS), patients rate their pain by choosing 1 of 4 adjectives: none, mild, moderate, and severe.

Although pain intensity assessments are easy to use, their interpretation can be difficult, because the experience of pain is multidimensional and is influenced by many 35 factors, such as mood and context. Such psychosocial aspects can be measured on questionnaires, such as the Brief Pain Inventory (BPI) and Multidimensional Pain Inven36–39 tory (MPI).

The BPI was developed to measure the subjective intensity of pain and 40 the resulting impairments. The BPI has been validated for several conditions, such as cancer, osteoarthritis, diabetic neuropathy, inflammatory bowel disease, and low back 41–45 pain, and has been translated to and validated in many languages.

In addition to pain intensity and pain interference, the MPI analyzes general activity level, coping of pain, social support, and potential reinforcement of pain behaviors by the patient’s 38,46 significant other.

Although the psychometric properties of these questionnaires are considered to be sound, self-report questionnaires rely on the ability to make valid judgments of the perceived pain, rendering them subject to various biases, such as 47,48 recall bias and interviewer bias.

Neuroimaging techniques Because questionnaires are a subjective evaluation of pain, researchers have attempted to develop ‘objective’ pain-related (psycho)physiological parameters. During the past several decades, they have focused on how the brain processes nociceptive input, the structures that are involved, and how the conscious experience of pain arises from the cortical processing of nociceptive input. Many noninvasive neuroimaging techniques have been adopted in the endeavor to attempt to objectively evaluate the pain experience in the brain, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG), which provide complementary information on the processing of nociceptive input in the brain.

14

General introduction fMRI and PET detect indirect responses to painful stimuli by measuring the hemodynamic and metabolic responses to them, respectively. These modalities have particularly good spatial resolution, and many brain structures that are involved in the processing of pain have been identified by fMRI and PET—often collectively termed ‘the pain matrix’ or ‘pain signature’ and roughly divided into the lateral and medial pain 8,49 pathways.

The brain structures in the lateral pain pathway involve sensorydiscriminatory areas, including the primary somatosensory cortex (S1), secondary somatosensory cortex (S2), thalamus, and posterior regions of the insula. The brain structures that are associated with the medial pain pathway include affective, cognitive, and evaluative structures, such as the anterior sections of the insula, anterior cingulate 50,51 cortex (ACC), and prefrontal cortex (PFC).

In contrast to fMRI and PET, EEG has excellent temporal resolution and is thus able to monitor the temporal aspects of pain. The time-locked EEG responses to painful stimuli are termed event-related potentials (ERPs) (Figure 3). As of 1970, many reports had investigated pain-ERPs. Pain-ERPs can be elicited by various stimuli, such as brief intracutaneously applied electrical stimuli, contact heat stimuli, and brief radiant pulses using an 52 infrared laser. As these stimuli are applied, the ongoing EEG is measured, and the resulting EEG segments, called epochs, are selected, based on the stimulus markers in the ‘raw’ EEG. After several processing techniques (baseline and electrooculogram (EOG) 53 correction), these segments are analyzed further (Figure 3). Specific ERP components 54–56 correlate well with subjective pain estimates.

In particular, the N2 and N2-P2 peakto-peak amplitude in the pain-ERP are linked to characteristics of the stimulus, such as 55,57–60 intensity.

Such N2 and P2 peaks can be identified by averaging blocks of stimuli.

1 Stimuli

2 EEG registration

4 Averaging

3 Segmentation

Grand average pain-ERP

- 10 -5

amplitude µV

0 5 10 15 20

-200 -150 -100 -50

0

50

100 150 200 250 300 350 400 450 500

milliseconds

Figure 3. Basic concept of measuring and processing event-related potentials (ERPs). Illustration by Greet Mommen.

15

Chapter 1

ERP analysis Traditionally, as discussed, peaks and their latencies have been the focus of research with regard to pain-ERPs. In theory, however, in addition to N2 and P2 peaks, each poststimulus point on the waveform may provide meaningful information regarding the processing of pain. Ideally, the entire variability in amplitudes at each latency point after a stimulus should be explained using a series of variables that modify the amplitude.

Based on this idea, an alternative method for analyzing ERPs will be introduced—the event-related fixed-interval area (ERFIA) multilevel technique, which examines the poststimulus epoch more broadly, instead of centring on particular peaks.

Based on its high temporal resolution, event-related EEG can be used to study habituation to painful stimuli. Previous studies on habituation and pain have primarily evaluated between-session habituation. However, with the introduction of multilevel analysis at the single-trial level, within-session habituation can be modelled. Also, by multilevel analysis, various time courses of habitation can be detailed. Following the pioneering work of Vossen H. and colleagues, 3 pathways of habituation are assessed in this thesis 61 (Figure 4). The most basic course of habituation is a stable decline in response—i.e., a linear decline. But, other forms might garner interest, such as an inverse relationship, in which an initial rapid decline is followed by a slower decrease in response over time.

The last course of habituation, mathematically defined by a quadratic function, might be important in the transition from acute to chronic pain. An initial decline that reverses to an increase in response might represent sensitization processes or increase in the pain experience. In this thesis, the term dishabituation is used specifically for this quadratic function. In the literature, dishabituation is commonly defined as recovery of the 62 habituated response after another (usually strong) stimulus. Thus, we use the term dishabituation when the initial habituation reverses in the form of a quadratic function.

The ERFIA multilevel technique, in which these habituation courses are used, will be expounded on in the next two chapters.

Linear (-a*x + b)

Inverse (1/x)

Quadratic (x2)

Figure 4. Three functions for modeling habituation.

In conclusion, these modalities provide various types of information on how the brain processes pain. The advantages and drawbacks of each method should be considered, based on the subject of interest.

16

General introduction

Research questions and this thesis With event-related EEGs, time-related cortical responses to pain can be measured objectively. However, not all information is used in such analyses—particularly the nonpeak-related poststimulus areas of the event-related EEG. As an initial step, we developed an adapted method to include non-peak-related ERP information into our analysis.

Using this approach, habituation, hypothesized to be involved in the chronification of pain, can be studied in greater detail. In habituation, when the novelty of a stimulus 31 decreases, the response declines. In pain hypervigilance, heightened attention toward pain is hypothesized to impair or reduce ‘normal’ habituation, because attention is less disengaged from painful stimuli.

The major goal of this thesis is to obtain greater insight into the relationship between habituation and chronic pain. Our second objective will be to examine the function of pain hypervigilance in this link, thus helping explain the variability in pain experience.

Based on these objectives, we formulated the following research questions: 1. Is it possible to develop an alternative event-related EEG method that analyzes nonpeak-related poststimulus information?

2. In which poststimulus areas do stimulus intensity and habituation influence the cortical processing of pain in pain-free controls?

3. Does habituation in the cortical processing of pain differs between individuals with chronic pain and pain-free controls?

4. Does pain hypervigilance impact the cortical processing of painful stimuli and its habituation in pain-free controls?

5. Is the association between chronic pain and habituation moderated by pain hypervigilance?

Overview of this thesis Chapter 2 addresses research question 1 and describes the development of an EEG analysis method that includes non-peak-related poststimulus areas, called the ERFIA multilevel technique. This chapter presents the pilot study results of an existing EEG dataset that comprises healthy participants and chronic low back pain sufferers. Part one of the chapter is a general review of event-related potentials and factors that influence the pain-ERP. Part two covers the development of ERFIA multilevel technique.

Chapter 3 expounds on research question 2 and further examines the ERFIA multilevel technique in 76 pain-free controls. The analyses are expanded to a wider poststimulus range and to more EEG electrodes. Also, the hypothesis that the ERFIA multilevel technique generates comparable results with those of (multilevel) peak analyses is tested.

17

Chapter 1 Then, the effects of stimulus intensity, previous stimulus intensity, and (nonlinear) habituation are studied in non-peak-related areas of the pain-related somatosensory ERP. In Chapter 4, research question 3 is addressed. Because habituation has been demonstrated to be impaired in chronic pain, the difference in habituation in chronic pain subjects (n=65) versus pain-free controls (n=76) is examined. Three habituation courses—linear, inverse, and quadratic—are studied in relation to chronic pain status.

Research question 4 is addressed in Chapter 5 to study the impact of pain hypervigilance on cortical processing and habituation to painful stimuli in 42 pain-free controls.

Chapter 6 discusses research question 5, wherein the hypothesis that the association between habituation and chronic pain is moderated by pain hypervigilance is studied.

Chapter 7 discusses the findings of the previous chapters with respect to methods and future directions.

18

General introduction

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1998;107(4):227-253. doi:10.1016/S0013-4694(98)00075-3.

53. Luck SJ. An Introduction to the Event-Related Potential Technique. MIT Press; 2005.

54. Chen ACN, Richard Chapman C, Harkins SW. Brain evoked potentials are functional correlates of induced pain in man. Pain. 1979;6(3):365-374. doi:10.1016/0304-3959(79)90054-X. 55. Bromm B, Meier W. The intracutaneous stimulus: a new pain model for algesimetric studies. Methods Find Exp Clin Pharmacol. 1984;6(7):405-410. http://www.ncbi.nlm.nih.gov/pubmed/6503475.

56. Miltner W, Johnson Jr. R, Braun C, Larbig W. Somatosensory event-related potentials to painful and nonpainful stimuli: effects of attention. Pain. 1989;38(3):303-312. http://www.ncbi.nlm.nih.gov/pubmed/ 2812841.

57. Bromm B, Treede RD. Laser-evoked cerebral potentials in the assessment of cutaneous pain sensitivity in normal subjects and patients. Rev Neurol. 1991;147(10):625-643. http://www.ncbi.nlm.nih.gov/pubmed/ 1763252.

58. Becker DE, Haley DW, Urena VM, Yingling CD. Pain measurement with evoked potentials: combination of subjective ratings, randomized intensities, and long interstimulus intervals produces a P300-like confound. Pain. 2000;84(1):37-47. http://www.ncbi.nlm.nih.gov/pubmed/10601671.

59. Iannetti GD, Zambreanu L, Cruccu G, Tracey I. Operculoinsular cortex encodes pain intensity at the earliest stages of cortical processing as indicated by amplitude of laser-evoked potentials in humans.

Neuroscience. 2005;131(1):199-208. doi:10.1016/j.neuroscience.2004.10.035.

60. Granovsky Y, Granot M, Nir RR, Yarnitsky D. Objective correlate of subjective pain perception by contact heat-evoked potentials. J Pain. 2008;9(1):53-63. doi:10.1016/j.jpain.2007.08.010.

61. Vossen H, Van Breukelen G, Hermens H, Van Os J, Lousberg R. More potential in statistical analyses of event-related potentials: a mixed regression approach. Int J Methods Psychiatr Res. 2011;20:e56-e68. doi:10.1002/mpr.348.

62. Thompson RF. Habituation: A history. Neurobiol Learn Mem. 2009;92(2):127-134. doi:10.1016/j.nlm.

2008.07.011.

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The Use of Event-Related Potentials in Chronic Back Pain Patients

Modified from publication: Vossen CJ, Vossen HGM, Van de Wetering W, Marcus MAE, Van Os J, Lousberg R. The use of event-related potentials in chronic back pain patients. Norasteh A, ed.

Low Back Pain. 2012:1–22.

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The Use of Event-Related Potentials in Chronic Back Pain Patients

Part 1 Introduction to event-related potentials in pain Introduction Chronic back pain is one of the most common pain syndromes, with a lifetime incidence of 60% to 90%. An important question in the field of chronic pain is how acute pain transits to a chronic pain state: why do some persons develop chronic pain while others do not? Approximately 10% to 20% of patients with chronic low back pain (CLBP) still 1 have persisting complaints after 6 weeks. If more insight is gained into chronification mechanisms and, as a consequence, the ability to predict which individual with acute pain develops chronic pain, it may become possible to intervene in the process at an early stage.

In acute pain states, pain is often causally related to physical damage, whereas this relationship is less pronounced in chronic pain states. With increasing duration of pain complaints, other factors, such as psychological, cognitive, and environmental factors, 2 are likely to become more involved. As a result, pain is conceptualized as a multidimensional phenomenon, making pain measurement complex. However, due to the subjective nature of the pain experience, it cannot be measured directly. In fact, only derivatives of pain can be measured. The most frequently measured aspect of pain is its intensity. Two often used measures are the Visual Analog Scale (VAS) and Numeric Rating Scale (NRS). Despite some limitations, their psychometric properties have been demon3,4 strated to be adequate. To evaluate several other components of pain, pain-related aspects, and risk factors for chronic back pain (such as fear avoidance, inadequate coping strategies, etc.), clinicians use questionnaires. Although many of these instruments provide reliable and valid results (for an overview, see the Handbook of Pain Assessment, edited by Turk & Melzack, 2011), all subjective measures have the potential for 5,6 several forms of bias. In an attempt to measure relatively unbiased pain responses, a large number of studies in the 70s and 80s investigated the usefulness of psychophysiological recordings. The results of these studies showed that small but significant correlations could be demonstrated between the subjective pain experience on the one hand and skin conductance, heart rate, electromyography, and finger pulse volume on the 7 other. The most promising results, however, were obtained from experiments studying event (pain)-related potentials (ERPs), a measure that is derived from electroencephalography (EEG). This technique has been used to study cerebral responses to (non-)noxious stimuli and to gain more insight into the cortical processing of pain. PainERP studies are typically performed in a laboratory setting under strict experimental control. In contrast to the other aforementioned psychophysiological measures, specific 8–10 ERP components correlate relatively highly with subjective pain estimates.

In addition, ERPs have been shown to contain information not only on pain intensity but also on many other important (pain-related) factors, such as habituation, personality, and

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Chapter 2

coping strategies. In other words, making use of ERPs, a large number of aspects of the pain construct, interrelations, and mechanisms can be quantified and studied (see Loeser’s ‘onion’ model in figure 1).

Pain questionnaires and pain scores

Pain behaviors Suffering Emotions

Pain sensations

Event Related Potentials

Tissue damage

Figure 1. Loeser’s ‘onion’ model.

This chapter starts with a general description of event-related potentials and their components. Second, factors related to the pain-ERP, such as personality and genetics, are discussed. Special attention will be paid to methods of analyzing the ERP signal. After some discussion of methodological considerations, we will propose an alternative ERP analysis. In addition, we will present preliminary data to illustrate the usefulness of this alternative method. In the last section, some future directions of ERP will be discussed.

ERP structure The ERP represents a cortical response to a specific stimulus—for instance, a sound, a light signal, or a pain stimulus. Event-related potentials are regarded as manifestations of specific (psycho)physiological, stimulus-related processes. An ERP is derived from 11 EEG. Electrodes are attached to specific locations on the scalp. Potential differences between the scalp electrodes and a reference electrode are sampled at a certain frequency, most commonly between 500 and 5000 Hz (cycles per second). The essence of an ERP is that the signal (the cortical reaction to the stimulus) has to be discriminated from background EEG noise. The procedure to achieve this goal is to compute an average of a number of time-locked EEG samples, called epochs or segments. As a general rule, it can be stated that the larger the number of epochs, the better the signal-to-

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The Use of Event-Related Potentials in Chronic Back Pain Patients noise ratio. Within each person or condition, the averaging procedure results in a voltage-by-time graph. When averaging ERPs from different persons or conditions, the result is called a grand average.

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In figure 2, a grand average pain-ERP (Cz location) is shown. This ERP was obtained from a paradigm in which subjects received a series of 150 painful and non-painful shocks of 12 10 ms in duration. As can be seen from figure 2, three peaks can be clearly distinguished. The most common way to describe these peaks is by polarity and latency (time in ms after stimulus onset). For instance, the large positive peak between 250 and 300 ms is called P300. There is also a system that numbers the sequence of the peaks; e.g. the second positive peak is called P2. In this ERP, P300 is the second positive peak, and thus P2 and P300 are abbreviations pointing to the same peak. In ERP experiments, researchers try to explain the meaning of the peaks. Which (stimulus) characteristics or processes are ‘responsible’ for the amplitude and latency of the peaks? Early peaks, also called ‘exogenous’ components, are believed to represent stimulus parameters, such as the intensity and other properties of the stimulus. Later components are thought to be 13 representations of ‘endogenous’ processes, such as attention. Further, it is known that (slight) differences in the paradigm that is used (not only with respect to the stimulus but also to instructions, environmental characteristics, time of the day, etc.) result in changes in the ERP. These changes pertain to latency and amplitude effects and even profound morphological changes. It should be noted that an ERP has a high temporal but low spatial resolution. The latter means that it is difficult to draw valid conclusions 14 on the underlying cerebral source that is generating the electrical activity.

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Chapter 2

Influences on the pain-evoked potentials In the following paragraphs, we will discuss the influence of a variety of stimulus-related and person- related factors on the pain-ERP. Special attention will be paid to the predictive relationship of pain-ERPs to the clinical experience of low back pain.

Stimulus intensity and subjective pain experience Intensity is an essential property of a stimulus. Is there a relationship between the intensity of a stimulus and peaks of the ERP? Accumulating evidence confirms the rela8,15,16 tionship between stimulus intensity and increased peak values of N200 and P300.

Does an increase in certain peak amplitudes relate to the amount of pain that a subject experiences? One of the first studies that investigated this association demonstrated that an increase in the N200 and P300 peak amplitudes was accompanied by an in17 crease in VAS scores (r = 0.67-0.77). These results were replicated by Chen and Garcia9,18 Larrea. These authors also found a strong linear association (r = 0.67 and r = 0.41, respectively) between the N200/P300 peak-to-peak amplitude and subjective pain ratings. Miltner and colleagues, however, could not find such a relationship when investi10 gating habituation of noxious stimuli. They observed a significant decrease in peak-topeak amplitudes of the N150-P360 across trials, but without a corresponding effect on VAS ratings. They suggested that the association between ERP amplitudes and subjective ratings might not be as strong as was claimed previously.

The subjective pain experience seems to be limited not only to these peak amplitude effects. Kanda and colleagues discovered a late positive component around 600 ms that was associated with pain report, which they called the ‘intensity assessment-related 19 potential’ (IAP). The IAP was not influenced by intensity, suggesting that this component solely reflects the psychological processes of pain. A recent study, again investigating the relationship between stimulus intensity, peak amplitudes, and subjective pain 20 experience, was performed by Vossen and colleagues. Their methodological comments on common pain-ERP analyses relate to the problems inherent in the averaging technique (see also Part 2 of this chapter). The authors argued that averaging eliminates any unwanted ‘noise’ in the ERP, but in doing so, it assumes no difference between repeated trials. This assumption cannot hold, since single trials likely differ from one another because of processes, such as habituation. Moreover, averaging across trials eliminates all information about possible within-subject correlations between ERP and subjective pain. As an alternative, they introduced multilevel random regression analysis, applied on pain-ERP, making it possible to model time (habituation), stimulus intensity, and their random within-person effects. The findings of this study show that the relationships between these three variables are confounded and moderated by several other variables, such as the intensity of the previous stimulus. This means that a certain pain rating after a stimulus also depends on the intensity of the previous stimulus,

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The Use of Event-Related Potentials in Chronic Back Pain Patients which makes clinical sense. A pain patient who is asked to evaluate his/her perceived pain is highly likely to base this evaluation on previous pain experiences. In sum, the relationship between stimulus intensity, ERP peaks, and pain experience is probably far more complex than previously thought.

Habituation and pain-evoked potentials Habituation is the process that refers to a decrease in a behavioral response to a re21 peatedly presented stimulus. It could be hypothesized that altered habituation might be an explanation for the chronification of pain. It is thought that chronic pain patients may have a deficit in habituation or even an inability to habituate to painful experiences, resulting in persistent pain. Older studies, using pain rating as an outcome measure, reported mixed results. One study investigated the habituation difference between 22 CLBP patients and controls, using eight successive trials of the cold pressure test.

Healthy controls could be divided into a subgroup that habituated over trials and a subgroup that sensitized. The CLBP group did not habituate or sensitize over time. Additionally, they found a lower pain tolerance in CLBP patients while reporting higher pain ratings. It was hypothesized that CLBP patients had already undergone a learning process in which sensitization had taken place. Arntz and colleagues also studied habitua23 tion in CLBP patients and controls. They did not observe a difference in habituation between the groups, measured by pain intensity ratings, EMG, and heart rate. A third study from Peters and colleagues confirmed the results of Brands & Schmidt but did not 24 find differences in physiological measures, such as heart rate and skin conductance.

More recently, Smith and colleagues reported differences in habituation of subjective 25 pain ratings between women with fibromyalgia and pain-free controls. They found that women with fibromyalgia habituated at a lower rate to repeated heat stimuli.

In addition, there are some recent studies that have used ERP as a measure to study habituation. They are suggestive of a deficit in habituation in chronic pain patients, although different chronic pain populations were used. Valeriani and colleagues studied 26 habituation in response to painful CO2 laser stimulation in migraine patients. They found reduced habituation of ERP amplitudes in migraine sufferers compared to painfree controls. Another study found that patients with migraine did not show any habitu27 ation, whereas healthy controls did. Vossen et al. studied habituation in a group of chronic low back pain patients compared to pain-free controls, measuring ERP in re12 sponse to 20 painful stimuli. They found a significant interaction between group and trial number on the P300 component at C4 and T4. This means that chronic low back pain patients appeared to habituate to a lesser degree than pain-free controls. They also examined the influence of state-depression on habituation, using the BDI score.

The results revealed a significant three-way interaction between BDI, group, and trialinverse, suggesting that the difference in habituation between groups depends on the level of depression. Only in the presence of depression did CLBP patients show a deficit in

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Chapter 2 habituation. Interestingly, a recent study in fibromyalgia patients also found evidence for reduced habituation of the N200 vertex component, facilitated by the presence of 28 symptoms of depression. In conclusion, habituation seems to be different in chronic back pain patients compared to controls but is probably also influenced by factors, such as depression.

Influence of neuroticism on pain-evoked potentials ‘Personality’ can be defined as a dynamic and organized set of characteristics possessed by a person that uniquely influence his or her cognitions, motivations, and behaviors in 29 various situations. Individuals with different personalities will differ in reaction to a specific situation. Likewise, it is conceivable that persons with diverse personality structures will react differently to a pain stimulus. This theoretical claim is frequently being confirmed in clinical practice. There is a large variety in ‘pain behaviors’ when patients are confronted with painful medical procedures (injections, stitches, etc.). One of the most important personality factors that are known to influence the experience of pain is 30 neuroticism. Neuroticism is defined as a tendency to experience negative emotions in 31 stressful situations. One of the most commonly used questionnaire measuring neuroticism, is the NEO-Big 5, which also gives information on six neuroticism facets—namely 32 anxiety, impulsivity, depression, self-consciousness, irritability, and vulnerability.

There are several mechanisms that explain the hypothesized relationship between neuroticism and pain. In the first explanation, the relationship between neuroticism and pain is thought to arise from over-reporting of pain-related complaints, an exaggerated expression of disturbance, and a more focused view on bodily states. Persons with high levels of neuroticism tend to be more aware of their bodily states than others and thus 33 report more physical complaints. Costa and McCrae suggest that in patients with high levels of neuroticism, physical complaints can be viewed as exaggerations of bodily 31 concerns, linking neuroticism to hypochondria. In yet another explanation, neuroticism can be seen as a vulnerability factor. When a patient is confronted with a stressor, such as low back pain, patients with high neuroticism levels already might perceive pain as threatening at lower thresholds, which consequently may evoke catastrophic 34 thoughts. In a study on a large sample (n = 1441) of CLBP patients, Bendebba found a 35 correlation between the severity of perceived pain and psychological distress. A correlation between psychological distress and the duration of the complaint, however, could not be demonstrated.

Note that the aforementioned studies are based on data derived from questionnaires.

ERP can be used as an additional tool to get more insight into the mechanism(s) of pain and neuroticism. Vossen et al. performed a study in 75 healthy subjects in which they studied the influence of neuroticism and two NEO-big 5 facets (depression and anxiety) 36 on the pain-ERP. They found that subjects with relatively high neuroticism scores

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The Use of Event-Related Potentials in Chronic Back Pain Patients showed more positive ERP amplitudes. These amplitude effects were observed frontally in a broad latency range, from 250 to 1500 ms, with significant effects between 340400 ms, 730-860 ms, and 1240-1450 ms, suggesting stronger pain processing. Comparing the effects of the neuroticism facets anxiety and depression, opposite effects were found. Anxiety was associated with a negative effect (enlarging negative amplitudes) early in the ERP (100-200 ms), whereas depression exerted an opposite effect in the same latency range. Another study of 14 healthy participants also found anxiety to be 37 related with a larger N140 component. In addition, the authors also observed no effect of anxiety in the P300 range. It is known that the N140 increases when attention to 38 a stimulus is heightened. It is plausible that participants focus their attention more when they are more anxious, thus increasing the amplitude of N140. This would support the idea of a greater focus on bodily states in anxious patients. In conclusion, the number of pain-ERP studies investigating the relationship between personality factors (especially neuroticism) is relatively small. More experimental data are needed to unravel mechanisms involving pain report, personality, and cortical pain processing.

The influence of gene polymorphisms on pain-evoked potentials There is a rising interest in genetic factors in pain research, as they likely explain a substantial portion of the inter-individual differences in pain perception and response to 39 pain treatment. Twin studies give the opportunity to determine the proportion of variability in pain response that is accounted for by genes (heritability).

Heritability for migraine has been estimated at 35%, 55% for menstrual pain, and 33% 40–43 to 50% for low back pain.

The main aim in pain genetics, however, is to identify the actual genes and gene polymorphisms that influence the pain pathways. Linkage and association studies have attempted to identify specific genes that affect the peripheral nervous system through the voltage-gated sodium channels on the one hand and genes that affect the central nervous system and modulate sensory-discriminatory and affective-evaluative elements of pain perception that affects the central nervous system on 44 the other (for an extensive overview, see Foulkes & Wood, 2008). Many candidate genes have been proposed, but the effects of these genes are small and even together, if true, explain only a fraction of the heritability involved. A possible explanation for this is the complexity of pain as a phenotype. Measurement of the pain experience plays an important part in this. In human studies, three single-nucleotide polymorphisms (SNPs) have been proposed to impact pain perception: COMT Val158Met (rs4680), BDNF Val66Met (rs6265), and OPRM A118G (rs1799971). COMT Val158Met is a gene polymorphism that alters the activity of the COMT enzyme, which degrades catechol45 amines, such as dopamine, epinephrine, and norepinephrine. It has been demonstrated that Met/Met homozygotes have decreased mu-opioid system activation in re46,47 sponse to pain, however, further replication is required.

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that supports the growth, differentiation, and survival

31

Chapter 2 of neurons in both the peripheral and the central nervous system. BDNF is released when nociceptors are activated and is involved in the activity-dependent pathogenesis 48,49 One piece of of nociceptive pathways, which may lead to chronification of pain. genetic variation within the BDNF gene is a valine-to-methionine substitution at codon 66 (Val66Met), resulting in reduced secretion of the BDNF protein and impaired BDNF signalling. The Met carriers are believed to be more sensitive to pain; however, here, replication in large and systematic studies is also required.

Experimental designs represent a particularly powerful approach to study genetic effects on psychological phenotypes, as they allow for controlled conditions and investi50 gation of underlying mechanisms. Lรถtsch and colleagues studied the influence of the G allele of the OPRM1 A118G polymorphism on ERP pain processing of experimental pain 51 stimuli. This polymorphism replaces adenine with guanine, increasing the receptor 52,53 affinity of b-endorphin 3-fold, resulting in decreased pain responses.

Lรถtsch and colleagues concluded that ERP amplitudes (N1 component) of carriers of the G allele were, on average, half as high as the amplitude of the non-carriers, suggesting lower pain processing for the G allele carriers. In a more recent study, we investigated the influence of the COMT Val158Met, BDNF Val66Met, and BDNF Val66Met polymor54 phisms on pain using ERPs. The sample of this study consisted of chronic low back pain patients, as well as healthy controls. The results suggest that the COMT Val158Met and the BDNF Val66Met polymorphisms influence the cortical processing of experimental electrical pain stimuli. However, no main gene effects were observed. Rather, genetic effects appeared to be moderated by the concurrent presence of chronic pain complaints. In the presence of chronic pain, the COMT Met allele and the BDNF Met allele augmented cortical pain processing at the N2 and P1 components, respectively, whilst reducing pain processing in pain-free controls. The findings of Lรถtsch and colleagues concerning the OPRM1 A118G polymorphism could not be replicated the study of Vossen. The influence of chronic pain complaints on gene effects may indicate a geneenvironment interaction and may even implicate epigenetic modification. It is clear that genetic findings remain preliminary, and well-conducted systematic studies with larger samples sizes are required. Up to now, limited attention has gone out to geneenvironment interplay in pain research, especially with event-related potentials as pain measure. In future studies investigating gene-environment interplay, the complexity of 55 the phenotype and the overall small direct effect of genes should be considered. Longitudinal designs using event-related potentials can contribute to the study of genetic influences in causal pathways of the chronification of pain.

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The Use of Event-Related Potentials in Chronic Back Pain Patients

The predictive relationship of pain-ERPs to clinical experience of pain Since chronic low back pain is a very common problem and accompanied by high costs in health care, it is important to be able to predict the likelihood of developing chronic disabling back pain. In 2010, Chou and Chelleke performed a systematic review of 20 studies to investigate the usefulness of individual risk factors for chronification in low 56 back pain. Identified risk factors, although individually relatively weak, were maladaptive pain coping behaviors, nonorganic signs, functional impairment, a poor general health status, and presence of psychiatric comorbidities. They also reviewed riskpredicting instruments, which are usually based on self-reported questionnaires. To date, no instrument has been used routinely and no recommendations exist, since evidence is insufficient. Could the pain-ERP serve as a predictor for chronic low back pain?

The experimentally induced pain-ERP has been demonstrated to be a relatively objec16,57,58 tive measure of experimental pain compared to subjective pain ratings.

An important issue, however, concerns the relevance and translation of the experimentally induced pain-ERP to pain in daily life. Stated in another way, can the pain-ERP serve as a predictor for clinical pain? There are two fundamental problems, which are related to the meaning of experimentally induced pain and its generalizability to pain in daily life.

First, the characteristics of experimentally induced pain stimuli are typically not comparable with those of clinical pain (e.g., the intensity and duration). Second, in an experimental environment, the subject has at least partial control of the experimentally induced pain (escape is possible by stopping the participation), a controllability that cannot be exerted in a clinical setting. Consequently, a straightforward translation of experimentally induced pain to clinical pain is simply not possible. Nevertheless, eventrelated potentials have already been used to predict depression, awakening from a 59–61 coma, and in the discrimination of Alzheimer’s disease from controls.

The prediction of pain, using ERPs, has not been studied intensively. To our knowledge, only one study 62 investigated the prediction of chronic low back pain complaints. Event-related potentials in response to experimental pain were measured in 75 CLBP patients. The ERP mean amplitudes of the peaks (N1, P1, N2, P3) served as predictors for the mean pain ratings, registered during a 2-week period after the experiment. The N2 component of Cz and C4 appeared to be significantly related to the daily pain ratings, collected over 2 consecutive weeks. Surprisingly, the ERP variables related more strongly to the clinical pain ratings than the accompanying subjective ratings of the experimental pain stimuli.

Although care must be taken in the interpretation of these results, the findings suggest that it might be possible to make inferences on clinical pain, based on experimentally derived pain-ERPs. More studies are needed to confirm these results and to investigate the usefulness of predicting long-term complaints in patients with acute or chronic back pain.

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Chapter 2

Part 2 Introduction of an alternative method for the analysis of painERP Analyzing event-related potentials In the last two decades, the underlying principles behind the methods of analyzing ERPs have not been changed essentially. In the first paragraph, we will describe the most commonly used method. In the second paragraph, we will discuss several issues concerning the methodology and as a consequence we would like to introduce an alternative method and present preliminary results.

Common methods in analyzing pain-ERPs In an experimental ERP paradigm, stimuli are repeated to allow averaging of the epochs and to compare different experimentally induced conditions. Although many variants 13,63 are possible, the most common procedure of ERP analysis is as follows : The first step is to filter the raw EEG data. The second step is the creation of segments or epochs, based on markers of the stimuli in the EEG. The duration of these epochs varies, but is usually between 500 and 1500 ms. The third step concerns identification of invalid epochs. Epochs are qualified as valid or not, depending on whether an epoch is likely to be confounded by an artifact: electrical activity that does not arise from the brain—for example, an eyelid movement, tension of the muscles in the head and neck, or electrical activity from the heart. Basically, there are two procedures for dealing with invalid epochs. The first method is a rejection of invalid epochs in the computation of the averaged ERP. This simply reduces the maximum number of analyzable segments, and as a result, information is lost. The second option is a correction for confounding effects by commonly accepted statistical algorithms, such as the so-called Gratton and 64 Coles ocular correction. To date, it is not clear which of these two methods is preferable. After the averaging of all epochs per individual (step 4), a grand average of ERP segments across subjects (per experimental condition) is calculated (step 5). The next action is to carefully identify peaks with their corresponding latency windows: a time range surrounding a specific peak. The seventh step is to apply these ‘peak latency windows’ at the within-subject epoch level: within the defined latency window, the maximum (or minimum) amplitude is determined. These maximum amplitudes form the input for the computation of the peak average for each individual. The final action is to use these maximum or minimum amplitudes as a dependent variable in statistical ana65 lyses, such as ANOVA.

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The Use of Event-Related Potentials in Chronic Back Pain Patients

Methodological considerations Although this procedure of ERP analysis is plausible, functional, and generally accepted, there are some critical issues that need to be considered, particularly given recent developments in statistics that may provide superior analytical approaches. First, each time-locked EEG segment consists of the aimed signal and a noise element (all background ongoing EEG activity). Averaging of the trials will separate the signal from the noise, because the signal element is thought to be constant in every trial, while the noise element is considered to be random. However, one can dispute the fact that a signal is constant over trials in pain experiments, since processes, such as habituation, 36,66 play an important role.

In addition, within-subject variance (trial-to-trial variance) is lost by averaging, which may contain clinically important information on cortical processes. Second, it is known that in consecutive trials, the latency of maximum peak values is likely to differ. Although it is possible to take the variability of latency into account in the analysis (as a covariate), this solution is not ideal, since the trial-to-trial latency information is lost. A third unsolved problem is how to deal with peak values located on the borders of the latency window. A final critical point regards the fact that peaks contain information on many processes: it is generally known that P300 is sensitive for attention, evaluation, stimulus intensity, and many other stimulus-related and 67 person related factors. Multilevel random regression analysis tackles the problems associated with averaging and habituation. Nonetheless, the methodological problems concerning peak definition and peak measurement cannot be solved with multilevel analysis. Without a doubt, averaged maximized peak values carry important information. However, theoretically spoken, each (!) latency point contains meaningful information. To be able to analyze amplitude information that is not related to peaks, area under the curve (AUC) can be computed for specific latency ranges. Usually, AUC is applied to quantify peaks as well as to calculate averaged group differences located on 13 the flanks/limbs of a peak. AUC is not often applied in pain-ERPs, since it has been 68 postulated that more noise is introduced when averaging trials. However, when using AUCs of single-trial data, the problem of introducing noise is substantially reduced.

Introducing an alternative method In this section, we will discuss an alternative method for analyzing ERP data. We will present preliminary results using this method in a previously used dataset of CLBP patients and pain-free controls.

Fixed-interval AUC segment analysis From a statistical point of view, the main goal of ERP analysis is to explain variance in the pain-ERP as much as possible, using a series of predictors. It seems reasonable to 35

Chapter 2 focus not only on the explanation of maximum peak amplitudes but also on effects in other latencies. This assertion is supported by the fact that several above-cited publica19,36 We felt that tions report stimulus- and person-related effects in non-peak latencies. the concept of AUC is valuable but should be applied to small fixed intervals, independent of peaks. This implicates a partitioning of the whole epoch in small event-related fixed interval areas (ERFIAs). To illustrate this line of reasoning, three averaged painERPs are presented. In the first picture, three ERPs are depicted, each representing another level of stimulus intensity. As can be observed, there are intensity effects on P1 and N2 and a large effect on P2: the larger the intensity, the larger the peak amplitude.

However, the intensity effects are not limited to these peaks. The effect on the P2 already starts at approximately 200 ms and lasts at least until 500 ms. Also, the intensity effect between P1 and N2 cannot be ignored. The second graph illustrates the effects on habituation. The three ERPs represent three blocks of trials delivered in the experiment. Again, habituation is not restricted visually to the peaks. Also, habituation seems to reduce the amplitude. In the third graph, two grand averages are shown of ERPs on Cz: one from a pain-free control group (n = 76) and the other from a group of chronic low back patients (n = 75). There seem to be small amplitude group effects on the P1 and N2 but not on the P2. In addition, there seem to be non-peak-related group effects.

Care must be taken in the interpretation of differences observed in these grand averages, since they represent a reduced, oversimplified representation of the pain experience. In the intensity ERPs, the information on habituation is averaged out and vice versa. Based on these observations, we performed a number of (unpublished) pilot analyses on ERP datasets of previous studies to determine a pragmatic width of AUC segments (ERFIAs). This led to our choice of segments of 20 ms. In our view, this seems to be a reasonable compromise between specific AUC segments that are too large on the one hand and segments that are too small, resulting in multiple testing problems on the other. We decided to reanalyze part of the data pertaining to the PhD thesis, defended by H. Vossen.12 We focused the preliminary analysis on three electrodes, namely C3, C4, and Cz, because these locations represent the sensorimotor cortex and are of 69 anatomical importance in pain processing. Also, we restricted the range to 0-500 ms poststimulus. The reanalysis took place in an explorative, hypothesis-generating fashion.

Basically, we were interested in to what degree the proposed ERFIA method would yield significant relationships between stimulus intensity and habituation and to what degree these findings would correspond to known results, based on peak analyses. In addition, there was one special point of interest: Do the ERPs of chronic pain patients differ from pain-free controls, analyzed with fixed-interval AUCs?

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Figure 4. Grand ERPs of the three consecutive blocks of stimuli on Cz.

CLBP patients vs. pain-free controls -10

-5

amplitude μV

0

Control 5

CLBP 10

15

20

-200

-150

-100

-50

0

50

100

150

200

250

300

350

400

450

500

milliseconds

Figure 5. Grand ERPs of CLBP patients and a pain-free control group on Cz.

37

Chapter 2

Study design The dataset we are using is based on previously collected raw EEG data. For a detailed 20 description of the protocol, we refer the studies by Vossen et al. Here, a summary of the design is given. Seventy-six pain-free subjects and 75 patients with chronic low back pain participated in the study. All CLBP subjects suffered from low back pain for at least 6 months and were recruited from the general population. All subjects underwent a rating paradigm of 150 semi-randomly presented electrical stimuli. The used stimuli, administered intracutaneously on the top of the left middle finger, consisted of electrical pulses, each with a duration of 10 ms, and an inter-stimulus interval (ISI) ranging from 9 to 11 seconds. Before starting the experiment, the sensory and pain threshold were determined. In the experiment, five different intensities, based on the participant’s pain threshold, were administered. The five used intensities were -50% and -25% below the pain threshold, the pain threshold itself (0%), and 25% and 50% above the pain threshold. After each stimulus, subjects were asked to rate the intensity on a numeric rating scale (NRS) ranging from 0 to 100. During the entire experiment, EEG was recorded with a 1000-Hz sampling rate. The ERP epochs were selected from the continuous EEG and segmented at 200 ms prior to the stimulus to 500 ms poststimulus. For each stimulus, we calculated 20-ms ERFIAs in the range of 0 to 500 ms. ERFIA segments with EOG activity exceeding +25 mA or -25 mA were excluded from the analysis. The calculated ERFIAs were used as dependent variables in a multilevel random regression model (see equations 1 and 2). This resulted in 25 separate multilevel regression analyses per electrode. All analyses were performed with SPSS 18.0.

1 Multilevel regression model with main effects: Yti = β0 + β1*intensitylinear + β2*triallinear + β3*trialquadratic + β4*trialinverse + β5*group + β6*age + β7*gender + β8*sensation threshold + β9*pain threshold + β10*intensitylinear of previous trial + eti + u1*intensitylinear +u2 *triallinear + u3* trialinverse + u4* trialquadratic

2 Multilevel regression model with three group-interaction effects: Yti = β0 + β1*intensitylinear + β2*triallinear + β3*trialquadratic + β4*trialinverse + β5*group +β6*age + β7*gender + β8*sensation threshold + β9*pain threshold + β10*intensitylinear of previous trial + β11*group*intensitylinear + β12*group*triallinear +β13*group*trialquadratic + β14*group*trialinverse + β15*group*intensitylinear of previoustrial + β16*group*pain threshold + β17*group*sensory threshold + eti +u1*intensitylinear + u2 *triallinear + u3* trialinverse + u4* trialquadratic

38

The Use of Event-Related Potentials in Chronic Back Pain Patients

Preliminary results First, some general group characteristics are presented in Table 1. The CLBP patients report much more pain and pain interference. In addition, as might be expected, their mood is negatively affected, which is expressed in a higher depression score.

Table 1. Characteristics of the two experimental groups.

Pain-free control (N = 76)

CLBP patients (N = 75)

T-value/χ2

P-value

Age

34.68

42.11

3.15

0.002

Gender male

26

34

2.26

0.09

Gender female

50

41

Pain magnitude (SF-36)

1.65

3.51

13.73

0.000

Pain interference (SF-36)

1.18

2.23

9.84

0.000

Depression (BDI)

3.35

7.09

5.8

0.000

Because of the preliminary aspect of the analyses, a full description of all results is beyond the scope of this chapter. Therefore, the focus will be on the robust and salient results of the analyses.

We present the results of four independent variables, applied in the first multilevel model (see equation 1): intensity, the intensity of the previous trial, habituation (analysed with a linear contrast), and group (CLBP patients versus pain-free controls). The results for the 20-ms periods between 0 and 500 ms are shown in Figures 6-9.

Group 4

3

T-value

2

1

Cz C3

0

C4 -1

-2

-3

20

40

60

80

100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

Figure 6. T-values per 20-ms AUC of the variable group (CLBP vs pain-free).

39

Chapter 2 Intensity 20

15

T-value

10

Cz

5

C3 C4

0

-5

-10

20

40

60

80

100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

Figure 7. T-values per 20-ms AUC of the variable intensity.

Habituation linear 8 6 4

T-value

2 Cz

0

C3 C4

-2 -4 -6 -8

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

Figure 8. T-values per 20-ms AUC of the variable habituation linear.

Previous trial intensity 4 3 2 1

T-value

0

Cz

-1

C3

-2

C4

-3 -4 -5 -6

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

40

The Use of Event-Related Potentials in Chronic Back Pain Patients Figure 9. T-values per 20-ms AUC of the variable previous trial intensity.

On the vertical axis, the t-value of the variable is depicted, and the horizontal axis represents latency (i.e. the 25 consecutive 20-ms ERFIAs). The variable intensity, habituation (linear), and previous trial intensity show profound and long-lasting, significant effects, as shown in the figures 7-9. Note a significant negative effect of stimulus intensity on all three electrodes from 60 to 120 ms and a very strong positive effect from 160 to 400 ms. Remarkably, two positive intensity processes in the latency range between 100 and 400 ms are apparent (from 140-200 ms and from 200-400 ms). In addition, an asymmetry can be observed between C4 and C3/Cz in the 140 to 200-ms range, where C4 demonstrates no clear significance. In contrast to the results presented by Vossen and colleagues, where no main effects of the previous trial intensity variable were found in peak amplitude analyses, the ERFIAs show a large and consistent negative 20 effect from 320 to 500 ms at all three electrodes. There are some small, significant, positive effects from 180 to 260 ms. With respect to linear habituation, large and longlasting significant t-values can also be identified. The linear habituation emerges from the 100-140 ms range as a significant positive effect and becomes significantly negative in the range from 200 to 380 ms. Although less pronounced, the linear habituation Tcurve appears to be opposite compared to the intensity T-curve. Whereas intensity has an amplitude-inflating effect in the range from 140 to 400 ms, linear habituation has the opposite effect. Although not displayed, significant effects were observed for the inverse variables habituation (1/trial) and quadratic habituation. Inverse habituation had a clear, significant (t-values between -2 and -4) amplitude-reducing effect during a latency period of 360 to 500 ms, and quadratic habituation showed strong, significant amplitude-inflating effects (t-values up to 4) in the range of 200 to 300 ms. No convincing main effect of group, independent of the effect of all other variables in the model, could be demonstrated. The only area of interest appears to be located on C4 and is situated between 140 and 200 ms, but taking the number of tests into account (3x25=75), this effect is questionable.

Finally, we investigated whether significant interaction effects with the group variable existed (see equation 2). A priori, based on findings in an earlier study by Vossen, we expected a group*habituation interaction showing CLBP patients to have a reduced habituation. We were also interested in whether the intensity effect was modified by group. In order to limit the number of figures, we present the results on the group by intensity and group by linear habituation interactions (note that group was coded “1” for CLBP patients and “0” for pain-free controls). As can be seen from Figures 10 and 11, the group effect may depend on stimulus intensity (especially in the 200-300 ms range) as well as linear habituation (320-440 ms, especially on Cz). There were also clear significant effects (t-values between 2 and 3 between 320 and 440 ms) for the group by quadratic habituation (no graphs included).

41

Chapter 2 Group * Intensity 3

2

T-value

1

0

Cz -1

C3 C4

-2

-3

-4

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

Figure 10. T-values per 20-ms ERFIAs of the interaction group*intensity.

Group * Habituation linear 4

3

T-value

2

Cz

1

C3 C4

0

-1

-2

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500

milliseconds

Figure 11. T-values per 20-ms ERFIAs of the interaction group*habituation linear.

Discussion of the preliminary findings In the previous paragraphs, several critical points were discussed in relation to ERP peak measures, followed by the proposal of an alternative approach based on AUC, analysed with multilevel random regression techniques. Next, a number of explorative analyses were performed. We are aware of the fact that the results are merely explorative, since we only analyzed three cranial locations and restricted the analyses to a 500-ms poststimulus time range. However, given these limitations, we think the analyses show promising results and illustrate the proof of concept.

42

The Use of Event-Related Potentials in Chronic Back Pain Patients As a first step, we investigated whether the ‘new’ method would yield comparable results, with respect to already established relationships between peaks of the pain-ERP, stimulus intensity, and habituation. Reviewing our results, the answer seems to be affirmative. Consistent results were found for all three central electrodes. A large number of the 20-ms ERFIA segments were significantly related to stimulus intensity and habituation. When examining the areas in which we would expect significant results for the intensity and habituation variables a priori, the alternative method produced results 16,58 comparable to other studies.

For example, the main effect of the variable intensity was very significant in the N2 range and the P3 range. A similar observation could be 28 made for linear habituation in the N2 range.

In addition to these basic ‘validating’ analyses, we were also interested in whether ERPpain processing of CLBP patients differs from pain-free controls. No clear main group effect could be observed. However, the results strongly suggest that the effects on intensity and linear and quadratic habituation depend on being a CLBP patient or not.

An interesting observation was that the group*intensity interaction took place at an earlier latency range compared to the group*habituation interaction in the ERP. The group*intensity interaction effect is situated in the latency range of the P3 and probably can be replicated using peak amplitudes. The group*habituation effect, however, is situated after the P3 and, as a consequence, most likely cannot be found in peak analyses. These off-peak effects may be valuable in the search for chronification mechanisms.

All group interaction effects are based on a contrast of a subclinical CLBP population to pain free-controls. The choice of this CLBP group may be disputed, since this subclinical group is likely to be heterogeneous with respect to underlying pathology. Nonetheless, the CLBP group clearly differs from the control group with regard to the key variable pain (see Table 1). The analyses of the ERFIA segments seem to produce more pronounced and significant results, compared to the peak results published in the thesis of 12 Vossen. This can be concluded not only from the very large t-values (up to 14!) but also from the prolonged latency effects. A typical example is the very broad latency window (from 160 to 420 ms) of the main effect on intensity. Also, effects of linear habituation are significantly embedded in a large range of consecutive ERFIA segments.

Interestingly, intensity of a previous stimulus, indicative for a ‘memory’ of painful events, showed a significant long-lasting influence (see Figure 5). Since no apparent peaks emerge after 300 ms in the averaged pain-ERP (see Figure 1), the ERFIA method seems to be more useful to detect such late effects than the peak method. This is demonstrated by the fact that we could not find a main effect of the previous stimulus 12 intensity in earlier analyses. By plotting the t-values of consecutive ERFIAs, we observed another advantage in the interpretation of the results. In time, variables become more significant and reach a ‘peak significance’ followed by a decrease. This information gives insight into the start and end of an influential effect of a variable. To illustrate,

43

Chapter 2 intensity seems to have two main effects in the latency range of 140 to 400 ms. One could speculate that this t-value graph (see Figure 3) is indicative for two ‘intensity’ processes.

Some critical aspects need to be considered in the application of the proposed ERFIA method. First, a large number of consecutive ERFIA segments may result in an unacceptable number of statistical tests. In our view, a rigid correction method for multiple testing, such as the Bonferroni correction, would increase the risk of rejecting ‘real’ effects in this early, explorative phase of the study. However, an appropriate correction for multiple testing is required. Another critical point concerns the optimal width of ERFIAs. In the present study, we used fixed segments of 20 ms. In order to get a general impression of effects within a relatively large window (500 ms or more), we judge the 20-ms criterion to be appropriate. When investigating small effects, one could argue for the use of smaller areas. Enlargement of the width would reduce the number of tests but may introduce more noise. A third note is related to EOG rejection. In the present analyses, we used a ±25-μV criterion to reject AUCs. It remains to be investigated whether this criterion is optimal. In handling confounded EOG segments, the use of multilevel analyses is especially worthwhile, since all valid, analyzable segments are included, whereas in analysis of variance, a whole subject would have been excluded in the case of too many invalid segments. Finally, one major disadvantage of both peak and AUC measures is the poor spatial resolution. Other techniques in analyzing ERP have been developed to overcome this problem. As an example, probabilistic independent component analysis (PICA) has been applied to gain more insight in the source of underlying multimodal and modality-specific neural activities.70–72 Also, many fMRI studies and magnetoencephalography studies are emerging, with high spatial resolu14,73–75 tion.

Combining ERP methods with fMRI will allow investigation of pain processing in a temporal as well as a spatial superior fashion.

Conclusion In conclusion, without doubting the importance of maximized data derived from peak analyses, one could express doubt whether this approach represents a too large reduction and oversimplification of the poststimulus cortical processing. In this respect, the present alternative method seems to be more appropriate. A direct comparison of the methods is difficult, if not impossible, since the ERFIA method is based on fixed latency intervals for all trials, whereas in the peak method, the latency of the maximum amplitude differs per trial. Future research has to clarify when to use peak amplitude analysis and in which situations a fixed-AUC method is more suitable.

Using ERP measures, many interesting insights in cortical processing of pain are emerging, such as habituation processes, genetic influences, and influences of personality.

44

The Use of Event-Related Potentials in Chronic Back Pain Patients These phenomena may contribute to finding explanations for the transition of acute pain to chronic (low back) pain states. Nevertheless, longitudinal research designs are necessary to study this process in detail, as well as a combination of ERP with other methods, such as fMRI, and magnetoencephalography. Furthermore, the application of mixed regression will enable a better understanding of the variance in the pain-ERP. Once the pain-ERP and its underlying cortical processes are understood more completely, the path to remediation in clinical practice is open. Then, the development of diagnostic tools could be in reach.

Acknowledgments We are grateful to Jacco Ronner and his colleagues, Department of Instrumentation, Faculty of Psychology and Neuroscience, Maastricht University, for their technical assistance and programming.

45

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58. Bromm B, Jahnke MT, Treede RD. Responses of human cutaneous afferents to CO2 laser stimuli causing pain. Exp Brain Res. 1984;55(1):158–166. Available at: http://www.ncbi.nlm.nih.gov/pubmed/6086371.

59. Kemp AH, Hopkinson PJ, Stephan BCM, et al. Predicting severity of non-clinical depression: preliminary findings using an integrated approach. J Integr Neurosci. 2006;5(1):89–110. doi:10.1142/s0219 635206001069.

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The Use of Event-Related Potentials in Chronic Back Pain Patients 60. Daltrozzo J, Wioland N, Mutschler V, Kotchoubey B. Predicting coma and other low responsive patients outcome using event-related brain potentials: A meta-analysis. Clin Neurophysiol. 2007;118(3):606–614. doi:10.1016/j.clinph.2006.11.019.

61. Benvenuto J, Jin Y, Casale M, Lynch G, Granger R. Identification of diagnostic evoked response potential segments in Alzheimer’s disease. Exp Neurol. 2002;176(2):269–276. doi:10.1006/exnr.2002.7930.

62. Vossen H, Van Os J, Hermens H, Lousberg R. The Predictive Value of Pain Event-Related Potentials for the Clinical Experience of Pain. J Integr Neurosci. 2010;09(01):1–10. doi:10.1142/S0219635210002354.

63. Mouraux A, Iannetti GD. Across-trial averaging of event-related EEG responses and beyond. Magn Reson Imaging. 2008;26(7):1041–1054. doi:10.1016/j.mri.2008.01.011.

64. Gratton G, Coles MGH, Donchin E. A new method for off-line removal of ocular artifact.

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65. Hoormann J, Falkenstein M, Schwarzenau P, Hohnsbein J. Methods for the quantification and statistical testing of ERP differences across conditions. Behav Res Methods. 1998;30(1):103–109. doi:10.3758/ bf03209420.

66. Woestenburg JC, Verbaten MN, van Hees HH, Slangen JL. Single trial erp estimation in the frequency domain using orthogonal polynomial trend analysis (OPTA): Estimation of individual habituation. Biol Psychol. 1983;17(2):173–191. doi:10.1016/0301-0511(83)90018-2.

67. Zaslansky R, Sprecher E, Tenke CE, Hemli JA, Yarnitsky D. The P300 in pain evoked potentials. Pain.

1996;66(1):39–49. Available at: http://www.ncbi.nlm.nih.gov/pubmed/8857630.

68. Picton TW, Bentin S, Berg P, et al. Guidelines for using human event-related potentials to study cognition:recording standards and publication criteria. Psychophysiology. 2000;37(2):127–152. Available at: http://view.ncbi.nlm.nih.gov/pubmed/10731765.

69. Kupers R, Kehlet H. Brain imaging of clinical pain states: a critical review and strategies for future studies.

Lancet Neurol. 2006;5(12):1033–44. doi:10.1016/S1474-4422(06)70624-X. 70. Jung TP, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ. Analysis and visualization of single-trial event-related potentials. Hum Brain Mapp. 2001;14(3):166–185. doi:10.1002/hbm.1050.

71. Makeig S, Jung TP, Bell AJ, Ghahremani D, Sejnowski TJ. Blind separation of auditory event-related brain responses into independent components. Proc Natl Acad Sci U S A. 1997;94(20):10979–84. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9380745. Accessed July 23, 2016.

72. Mouraux A, Iannetti GD. Nociceptive laser-evoked brain potentials do not reflect nociceptive-specific neural activity. J Neurophysiol. 2009;101(6):3258–3269. doi:10.1152/jn.91181.2008.

73. Bromm B. Brain images of pain. News Physiol Sci. 2001;16:244–249. Available at: http://www.ncbi.nlm.nih.gov/pubmed/11572930.

74. Stancak A, Alghamdi J, Nurmikko TJ. Cortical activation changes during repeated laser stimulation: A magnetoencephalographic study. PLoS One. 2011;6(5). doi:10.1371/journal.pone.0019744.

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3

Introducing the event-related fixed-interval area (ERFIA) multilevel technique: A method to analyze the complete epoch of event-related potentials at single trial level

Published as: Vossen CJ, Vossen HGM, Marcus MAE, Van Os J, Lousberg R. Introducing the event related fixed interval area (ERFIA) multilevel technique: A method to analyze the complete epoch of event-related potentials at single trial level. PLoS One. 2013;8(11). doi:10.1371/journal.pone.0079905.

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Abstract In analyzing time-locked event-related potentials (ERPs), many studies have focused on specific peaks and their differences between experimental conditions. In theory, each latency point after a stimulus contains potentially meaningful information, regardless of whether it is peak-related. Based on this assumption, we introduce a new concept which allows for flexible investigation of the whole epoch and does not primarily focus on peaks and their corresponding latencies. For each trial, the entire epoch is partitioned into event-related fixed-interval areas under the curve (ERFIAs). These ERFIAs, obtained at single trial level, act as dependent variables in a multilevel random regression analysis. The ERFIA multilevel method was tested in an existing ERP dataset of 85 healthy subjects, who underwent a rating paradigm of 150 painful and non-painful somatosensory electrical stimuli. We modeled the variability of each consecutive ERFIA with a set of predictor variables among which were stimulus intensity and stimulus number. Furthermore, we corrected for latency variations of the P2 (260 ms). With respect to known relationships between stimulus intensity, habituation, and painrelated somatosensory ERP, the ERFIA method generated highly comparable results to those of commonly used methods. Notably, effects on stimulus intensity and habituation were also observed in non-peak-related latency ranges. Further, cortical processing of actual stimulus intensity depended on the intensity of the previous stimulus, which may reflect pain-memory processing. In conclusion, the ERFIA multilevel method is a promising tool that can be used to study event-related cortical processing.

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique

Introduction In psychophysiological pain research, the event-related potential (ERP), a time-locked derivative of the electroencephalogram (EEG), is frequently used as an objective mea1,2 sure of pain. Since 1970, many reports have investigated the pain-ERP. In particular, the N2 and N2-P2 peak-to-peak amplitude in the pain-ERP are associated with stimulus 1,3–6 7,8 characteristics, such as intensity, and processes, such as attention and habitua9–13 tion.

To identify and examine these peaks in the ERP, several methods have been developed, of which domain averaging across trials and conditions has been the most 14,15 commonly used in the past.

In this type of analysis, the continuous EEG signal is partitioned into stimulus-related time segments (epochs). Invalid epochs, confounded 16 by artifacts, such as eye blinks, are identified and removed or corrected. Next, an averaging procedure of all valid epochs is performed intraindividually (all epochs of a single person) and interindividually (all epochs of a group or experimental condition), resulting in averaged ERPs. On visual inspection of an averaged ERP, several positive and negative peak amplitudes can be identified in the poststimulus period. Around these averaged peak amplitudes, time windows are defined to determine the maximum (or minimum) amplitude of the peaks per subject or condition. In addition, latencies (time after stimulus onset) of these peak values are determined. These stimulus-related peak 17 and latency values serve as dependent variables in statistical analyses, such as ANOVA. There are, however, certain disadvantages of this procedure. First, the method of averaging assumes that the ERP waveform is stable over time with respect to amplitude and 18,19 latency. Consequently, by averaging, across-trial variability of the ERP is lost.

Acrosstrial variability in amplitude, however, may be important—for example, in the study of habituation to repeated stimuli. Another problem is the trial-to-trial variability in laten20 cy, so-called latency jitter. In the worst case, when latencies of these peaks vary considerably across trials, specific peaks may be undetectable after averaging (for an extensive review, see Mouraux and Iannetti, 2008). Further, when using ANOVA as a statistical technique to analyze ERP data, subjects are deleted list-wise when one or more missing values occur. However, missing data—for example, due to EOG artifacts—are common in ERP analysis, which can lead to a considerable loss of analyzable cases. In recent years, several advances have been made in signal processing and statistical analysis of ERPs. Methods have been developed to enhance the signal-to-noise ratio and 18,21,22 reduce latency jitter, such as the use of continuous wavelet transform (CWT) and 23 independent component analysis (ICA). Furthermore, automatic single-trial measurements of the filtered waveforms, using a multiple linear regression have been developed, in which peaks of single trials are estimated and derived from the parameters 6,21,24,25 of the across-trial filtered waveform.

With respect to the statistical analysis of ERPs, the introduction of multilevel random regression analysis has been proposed. A recent ERP study by Vossen and colleagues

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Chapter 3 26

demonstrated the superiority of multilevel analysis over repeated measures ANOVA. Unlike ANOVA, multilevel analysis takes into account the hierarchical structure of ERP 26,27 data, in which trials are nested within subject.

In addition, in multilevel analysis, all valid EOG artifact-free trials can be included, and cases are not deleted list-wise. Finally, random effects and nonlinear contrasts can be incorporated. Thus, person-by-time 26 effects, such as habituation and its nonlinear properties, can be modeled. Altogether, many advances in the analysis of event-related potentials have been made. However, the main focus was always on peaks and their latencies. Undoubtedly, (maximized) peak 1,7,28,29 values carry relevant information on various processes.

Theoretically, however, each poststimulus point on the waveform contains potentially meaningful information, regardless of whether it is peak-related. Ideally, one would want to explain the entire variability in amplitudes at each latency point after a stimulus using a series of variables that modify the amplitude. Stated mathematically, the variability of amplitudes on a specific poststimulus latency point is a function of stimulusrelated variables and variables that pertain to all other ongoing brain processes. From this perspective, EEG data should be analyzed on a single-trial level and as ‘raw’ and untransformed as possible. The area under the curve measure (AUC) may be useful for avoiding an excessive amount of analyses (there are an infinite number of latency 30 points). In ERP research, the AUC method has not been applied often, and when it has 31 been used, it is related to areas around peaks. Based on these considerations, this article presents an alternative method of analyzing event-related EEG data, which focuses on poststimulus fixed-interval areas, independent of peaks, wherein the concept of AUC is applied at single-trial level and analyzed by multilevel regression analysis. In practice, for each single trial, the poststimulus EEG information is partitioned into small fixed-interval AUC segments (See Figure 1). These event-related fixed-interval AUCs (ERFIAs) are nested within subjects and should, therefore, be analyzed using a multilevel regression technique —ie, we attempt to explain the variance in event-related EEGs for every fixed-area poststimulus on 2 levels: between subjects and within subject.

32 Pilot analyses have suggested that this method is productive.

Based on the results of the pilot study, we hypothesized that the ERFIA multilevel method generates results that are comparable with those of (multilevel) peak analyses.

Specifically, we expect that ERFIAs in the N2-P2 region correlate significantly with stimulus intensity. To test this hypothesis, we reanalyzed an existing ERP dataset of a (non-) 26 noxious stimulus rating paradigm. In addition, the effects of stimulus intensity, previous stimulus intensity, and (nonlinear) habituation were explored in non-peak-related areas of the pain-related somatosensory ERP up to 1500 ms poststimulus.

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique Ongoing raw EEG

Segments

• Segmentation after events • Baselinecorrection

• Partitioning into fixed 20ms intervals • AUC computations

Event-Related Fixed Interval Areas (ERFIAs)

• EOG rejection of individual ERFIAs

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• Multilevel analysis for all ERFIAs

Figure 1. The ERFIA multilevel method.

The processing steps from the ‘raw’ EEG to ERFIAs serving as the dependent variable for multilevel analysis.

First, the EEG is partitioned into event-related segments, and then a baseline correction is made. Next, the segments are partitioned in 20-ms intervals, and the area under the curve for every interval for all trials is calculated. As a third step, an EOG rejection is carried out for all ERFIAs separately. Finally, the valid ERFIAs per fixed interval serve as a dependent variable in the multilevel analysis.

Materials and Method Ethics Statement Approval was obtained from the medical ethics committee of the Academic Hospital Maastricht, on January, 6th, 2005. All subjects gave their verbal and written informed consent prior to the study, after having read a document with detailed information of the study and having discussed any possible concerns with the researcher.

Subjects Eighty-five pain-free subjects participated in the study, ranging in age from 18 to 65 years. Exclusion criteria were a history of chronic pain complaints, the use of psychoactive drugs and the use of analgesics less than 8 hours prior to the experiment. Participation was rewarded with 25 euros on completion of the study.

Stimuli Electrical pulse stimuli (duration 10 milliseconds) were applied intracutaneously on the 3 left middle finger, per Bromm and Meier. Using this method, a small lumen in the epidermis was prepared, using a dental gimlet, ensuring that the procedure was not painful. In the prepared lumen, a golden electrode was placed and fixed with tape. Two grounding copper laces were attached around the prepared finger and wrist. First, the sensation and pain thresholds were determined by gradually increasing the intensity of the stimulus, starting at zero intensity. The first intensity that was consciously experienced was defined as the sensation threshold; the first intensity that was experienced as painful was defined as the pain threshold. This procedure was repeated 3 times to generate a reliable measurement. Based on the difference between a subject’s sensation and pain thresholds, 5 stimulus intensities were presented in a rating paradigm.

One of the 5 intensities was equal to the pain threshold, against which the other inten55

Chapter 3 sities were defined: -50%, -25%, +25%, and +50% of the difference between the sensation and pain thresholds (threshold range). The maximum stimulus intensity never exceeded 5 mA.

Paradigm 3

One hundred fifty stimuli were presented in a rating paradigm. The 5 stimulus intensities were presented semi-randomly. Blocks of 15 stimuli were administered, in which each intensity occurred 3 times. Inter-stimulus intervals (ISIs) ranged between 9 and 11 seconds. Subjects were asked to rate the intensity of each stimulus on a scale from 0 (no sensation) to 100 (the most excruciating pain imaginable).

EEG recording All EEG recordings were conducted in an electrically- and sound-shielded cubicle (3*4 m2). Ag/AgCl electrodes were placed on Fz, Cz, Pz, C3, C4, T3, and T4 using the interna33 tional 10-20 system. Impedances were maintained below 5 kΩ. A reference electrode was placed on each ear lobe. To check for possible vertical eye movements, an electrooculogram (EOG) electrode was placed 1 centimeter under the midline of the right eye. A ground electrode was placed at Fpz. All electrodes were fixed using 10-20 conductive paste. Neuroscan 4.3 software was used to record EEGs.

Procedure Before the start of the experiment, the subjects were informed about the purpose of the study. Subjects were told that they would undergo EEG registration while receiving various intensities of electric shocks—some painless, some painful. After informed consent forms were signed, EEG electrodes were attached, and the shock electrode was 3 placed on the top of the left middle finger as described by Bromm and Meier. Next, the sensation and pain thresholds were determined, after which the rating paradigm was initiated.

Data reduction and computation of ERFIAs EEGs were recorded at a 1000-Hz sampling rate using Neuroscan 4.3. Trials were segmented from the continuous EEG, from 200 ms before the stimulus to 1500 ms poststimulus. Data were offline band-pass filtered (0-50Hz) and baseline-corrected (interval -200 ms to 0 ms) using BrainVision Analyser 2.0, Brain Products, München, Germany.

The filtered data segments were exported to Microsoft Office Excel 2007. Twentymillisecond ERFIAs were calculated from 0 to 1500 ms poststimulus, resulting in 75 ERFIAs per trial per EEG electrode per subject. Additionally, maximum and minimum values of the EOG channel were selected per 20-ms ERFIA. Next, the ERFIAs and maximum and minimum EOG values of all 7 electrodes were imported into SPSS 18.0. Single

56

Introducing the event-related fixed-interval area (ERFIA) multilevel technique ERFIAs with EOG activity that exceeded Âą25ÂľV were excluded from the multilevel analyses. The number of rejected ERFIAs ranged from 5.6% to 24%, depending on ERFIA interval and location. After EOG rejection, a minimal amount of 8600 ERFIAs was available for multilevel analysis.

Statistical analyses Multilevel random regression analyses were carried out separately for each EEG electrode. Trial number (1-150 stimuli) was considered the repeated measure. Subjects represented the highest level in the model, and the 20-ms ERFIAs were the dependent variable (see Figure 2). As shown in Figure 1, ERFIAs were derived from single trials and were computed independently of averaged waveforms. One of the advantages of multilevel analysis is that it models time or trial effects with a nonlinear function of trial number. As in the study by Vossen and colleagues, habituation was modeled by 3 time 26 effects. First, a linear effect was modeled, assuming a linear decrease or increase of the dependent variable (of a particular ERFIA) over time. Second, an inverse relationship was included, representing a rapid decline in the consecutive ERFIAs, followed by a gradual decline or plateau phase—i.e., habituation of the initial trials is more pronounced than that later in the experiment. Third, a quadratic function, representing a 34 sensitization process (or dishabituation) after an initial habituation, was modeled.

Thus, habituation was modeled in three ways: linear habituation (trial number), fast habituation (inverse relationship, computed as 1/trial), and dishabituation (parabolic 26 relationship, computed as trial*trial). The full multilevel model comprised the following independent variables (fixed factors): actual stimulus intensity, previous stimulus intensity, the interaction between actual stimulus intensity and previous stimulus intensity, trial, trialinverse, trialquadratic, age, gender, and absolute stimulus intensity level of the difference between sensation threshold and pain threshold. We made the assumption that subjects differ from each other in their response to the 5 intensities and with regard to habituation. Thus, random effects, such as a random intercept and a random slope for intensity and (linear) trial number, were also included. The Scaled Identity covariance structure was used in the multilevel analyses. The analyses were performed separately for each 20-ms ERFIA for all 7 cranial sites, resulting in 75 (1500 ms/20 ms) * 7 (cranial locations) = 525 multilevel models. For this large number of statistical tests, a correction for multiple testing should be performed. We chose not to define a specific p-value for statistical significance, due to the partially explorative aspect of the analyses.

Instead, we considered relatively long-lasting effects (3 or more consecutive 20-ms ERFIAs) with p-values values <= 0.05 as significant.) Single ERFIAs were considered significant when the p-value exceeded 0.0007 (with a corresponding t-value of 3.43), based on Bonferroni correction for the complete epoch, obtained by dividing a significance level of 0.05 by the number of ERFIAs (75). The full multilevel model is described in the appendix A. All statistical analyses were performed with SPSS 18.0.

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Event-related EEG information

Multilevel analysis

ERFIAs

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Figure 2. Multilevel analyses for event-related EEG. Valid ERFIAs for each fixed interval serve as the dependent variable. Trials are nested within subjects and are selected as repeated measures. Subjects represent the highest level in the model, and random factors can be modeled, such as stimulus intensity and trial number. Conditions can be incorporated as fixed factors in the model.

Results Subject characteristics Eighty-five subjects participated in the study; 9 were excluded because they had significant pain in the previous week or had consumed more than 5 units of alcohol on the evening before the experiment. Ultimately, there were 76 analyzable cases, 26 men (34.2%) and 50 women (65.8%). The mean age of the participants was 34.8 years (SD = 13.7).

Grand averaged EEG response for 5 stimulus intensities and habituation In Figure 3, the grand averages are displayed for the 5 stimulus intensities and linear habituation at Cz. Notably, intensity and habituation affected not only the N2 and P2 peaks but also their slopes and nonpeak-related latencies. Note that these graphs are for illustrative purposes onlyâ&#x20AC;&#x201D;the averages were not used in the ERFIA multilevel analyses.

Interpretation of the model parameters from the ERFIA multilevel analyses T-values were plotted for each of the fixed variables of the multilevel models (y-axis) for the 75 consecutive ERFIAs (x-axis) for all 7 EEG electrodes (Figures 4, and 5) to identify significant effects. Figures 4 and 5 show the results for stimulus intensity, linear habituation, fast habituation (trialinverse), dishabituation (trialquadratic), the difference between

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique the sensory and pain thresholds and the actual stimulus intensity* previous stimulus intensity interaction.

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A) Grand average for 5 stimulus intensities. B) Grand averages for 5 consecutive blocks of 30 stimuli. Both peak- and non-peak related EEG information can differ between the conditions. Note that these averages are only for illustrative purposes and were not used for the computation of event-related fixed-interval areas (ERFIAs) at single trial level.

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Figure 4. T-value graphs of the stimulus intensity related variables.

These graphs show the results of the 2 fixed variables and one interaction of the multilevel model, respectively. A) Stimulus intensity, B) the interaction between the actual stimulus intensity with the previous stimulus intensity, and C) the difference between sensory and pain threshold. On the horizontal axis, the ERFIAs of 75 consecutive 20-ms intervals (0-1500 ms poststimulus) are displayed, with corresponding t-values of the fixed variable from the multilevel analyses on the vertical axis. T-values above 2 or below -2 have a corresponding p-value of 0.05.

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique

Stimulus intensity For stimulus intensity, a significant effect was observed from 100 to 160 ms poststimulus for all electrodes except Pz (Figure 4a). Negative t-values indicate that stronger intensities are accompanied by larger negative ERFIAs. Further, a robust, long-lasting effect was observed for all electrodes from 220 to 360 ms poststimulus, indicating that stronger intensities result in more positive ERFIAs. In the range of 1120 to 1400 ms, a third intensity effect was apparent for all electrodes except T3 and T4. The variable previous stimulus intensity did not have independent main effects. However, the interaction between actual and previous stimulus intensity was highly significant and persisted from 380 to 660 ms poststimulus (Figure 4b).

Difference between pain and sensory thresholds Significant effects for the difference between pain and sensory thresholds were observed from 200 to 260 ms for Fz, Cz, C4, and T4. Between 360 and 420 ms, significant negative t-values were visible for all electrodes except T4 (Figure 4c).

Habituation: linear, inverse, quadratic For linear habituation, significant positive t-values developed between 100 and 140 ms, except for Pz. On all electrodes, significant negative t-values were observed between 200 and 360 ms, except for T4. For Fz, Pz, Cz, and C3, the effect was nearly continuously prolonged until 560 ms (Figure 5a). The effect of fast habituation occurred from 340 to 480 ms primarily on Pz and to a lesser extent on Cz and C4 (Figure 5b). The effect of dishabituation was seen on all electrodes, predominantly between 200 and 260 ms (Figure 5c).

Random effects Finally, all random effects (intercepts and random slopes for intensity and triallinear) were significant in all models, indicating that both intercepts and slopes varied significantly between subjects.

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Figure 5. T-value graphs of the habituation variables.

These graphs show the results of the 3 habituation variables. A) Linear habituation, B) fast habituation (inverse), and C) dishabituation (quadratic). On the horizontal axis, the ERFIAs of 75 consecutive 20-ms intervals (0-1500 ms poststimulus) are displayed, with corresponding t-values of the fixed variable from the multilevel analyses on the vertical axis. T-values above 2 or below -2 have a corresponding p-value of 0.05.

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique

Post-hoc analyses It is generally known that the latency of the P2 peak varies between trials, the so-called latency jitter. This P2 latency variability is thought to be the result of both peripheral, as 6,28 well as cognitive factors.

Translating this phenomenon into ERFIAs means that the shift of the latency of a peak on the x-axis is inextricably related to changes in ERFIAs on the y-axis. Thus, it could be hypothesized that the P2-latency variability may confound the intensity and habituation effects on ERFIAs. To investigate this issue, we computed the latency of the P2 at single trial level with the use of the BrainVision Analyser 2.0, Brain Products, MĂźnchen, Germany. Using the peak export module of the BrainVision Analyser software, the maximum amplitude and corresponding latency were determined for each trial, applying a latency window from 100 to 400 ms. Next, we added the latencies of the P2 for each trial to the original multilevel dataset and incorporated P2-latency as a predictor variable in the multilevel model (see Appendix A). The results of the main effect of latency variation on ERFIAs are depicted in Figure 6. When examining the t-value curves, we observed a highly significant, sinus shaped effect around the P2. The p-values of the main effects of the other variables remained almost unchanged compared to the original model. For illustrative purposes, the difference in t-value between the models (for intensity and linear habituation) is depicted in Figure 7 for Fz, Cz and Pz. All other electrodes showed similar differences.

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Figure 6. T-value graph of the P2 latency variable.

This graph shows the result of the P2 latency variable of the post-hoc analyses. On the horizontal axis, the ERFIAs of 75 consecutive 20-ms intervals (0-1500 ms poststimulus) are displayed, with corresponding t-values of the fixed variable from the multilevel analyses on the vertical axis. T-values above 2 or below -2 have a corresponding p-value of 0.05.

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Chapter 3 Intensity: Fz, Cz, Pz

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Figure 7. Influence of correction for P2 latency variability on the model.

These graphs show the differences between the original model and the model of the post-hoc analyses in which the P2 latency variability is incorporated. A) Differences between the models for variable stimulus intensity, at Fz, Cz and Pz. B) Differences between the models for variable stimulus intensity, at C3, C4, T3 and T4. C) Differences between the models for variable linear habituation, at Fz, Cz and Pz. D) Differences between the models for variable linear habituation, at C3, C4, T3 and T4. On the horizontal axis, the ERFIAs of 75 consecutive 20-ms intervals (0-1500 ms poststimulus) are displayed, with corresponding t-values of the fixed variable from the multilevel analyses on the vertical axis.

Discussion In this analysis, the value of a novel analysis of event-related EEG using ERFIAs was examined. This approach was applied to EEG data of a paradigm in which a series of 150 electrical stimuli with 5 intensities was delivered. The first goal was to determine whether the ERFIA multilevel method would yield comparable results with peak analyses of previous studies regarding the effect of stimulus intensity and habituation in the N2 and P2 regions. In addition, we determined the influence of other variables on the complete 1500-ms epoch.

Stimulus intensity Many studies have reported a significant relationship between stimulus intensity and the amplitude of the N2 and P2 peaks. The N2 peak becomes more negative with in1,3,6,26,35 creasing stimulus intensity, whereas the P2 peak becomes more positive.

Figure

64

Introducing the event-related fixed-interval area (ERFIA) multilevel technique 4a shows that ERFIAs in the N2 and P2 ranges are significantly related to stimulus intensity in the same direction as in the peak analyses. In the ERFIA analysis, the effect of stimulus intensity is not solely expressed in a single peak value but appears to be embedded in a broader range. Further, by ERFIA analysis of the complete 1500-ms epoch, we discovered a strong, long-lasting, ultra-late stimulus intensity effect (from 1000 to 1500 ms) at 5 electrodes. It is unclear what this effect reflectsâ&#x20AC;&#x201D;for example, attention, 36 pain evaluation, or C-fiber activation. Future research is required to clarify this issue.

Modeling habituation In general, habituation is defined as a behavioral response decrement that results from repeated stimulation. The decrease is usually a negative exponential function of the 37,38 number of stimulus presentations.

Three forms of habituation can be distinguished overall: an initial rapid decline in response, a linear decrease in response, and an increase in response that reappears after an initial decrement (dishabituation). Habitua10 tion to pain can be observed at the level of the subjective pain experience and at the 9,13,26 cortical level, expressed in the N2 and P2 peaks of pain-related EEGs. Like Vossen 26,32 and colleagues, we included 3 forms of habituation in the model.

Regarding linear habituation, significant effects were seen from 100 to 160 ms and 180 to 580 ms (Figure 5a). For example, as trial number increased, the ERFIAs became less negative in the N140 range. Similarly, ERFIAs became less positive in the 200-560 ms range as trial number roseâ&#x20AC;&#x201D;i.e. positive ERFIAs returned more quickly to baseline in this range. Notably, intensity and linear habituation influenced ERFIAs oppositely. The effects of linear habituation were similar to those of the multilevel peak analyses of Vossen, who demonstrated significant effects of linear habituation on the N2 and P2 26 peaks at all electrodes (Fz, Cz, Pz, C3, C4, T3, T4). These findings are also consistent with the literature, indicating that habituation is embedded in the N2 and P2 peaks.

Valeriani and colleagues studied habituation processes in migraine sufferers and pain13 free controls, applying CO2 laser stimulation. In the pain-free control group, habituation on the N2/P2 components was observed. Another study observed that fibromyalgia 9 patients experience reduced habituation on the N2 component. In addition to linear habituation, we also found significant effects for both fast (inverse) habituation and (quadratic) dishabituation (Figures 5b and 5c), consistent with earlier findings on peak analyses, but less pronounced compared with the effects of stimulus intensity and linear habituation. Significant effects of linear habituation and dishabituation were noted in the 100-140 ms region, in contrast to fast habituation, implying that the habituation process takes place in several parts of the epoch and is a complex function over time.

Perhaps other functions may fit the data better than the 3 we used and this could be an interesting topic for future research. Overall, the results from this study demonstrate that the influence of stimulus intensity and habituation is not merely limited to specific peak values. Both variables appear to exist in a much broader latency range than com-

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Chapter 3 monly presumed. The possibility of investigating the effects in late nonpeak-related areas is perhaps the key advantage of the ERFIA technique. Because a deficit in habituation might partially explain chronification of pain, the ERFIA multilevel random regression method can be used to study habituation to stimuli in pain populations compared with pain-free controls. Ideally, longitudinal studies should be performed to examine habituation changes over time in the chronification of pain.

Post-hoc analyses Latency jitter is a phenomenon related to peaks. Between trials, the latency of a peak varies. Latency jitter of the P2 may be associated to peripheral as well as cognitive fac6,28,39 tors.

First, it seems logical that the fixed-interval areas under the curve (ERFIAs) are influenced by a change in latency of a peak. Second, it may be hypothesized that the jitter of the P2 confounds the effects of stimulus intensity and habituation on ERFIAs. To test these two critical issues, we incorporated the P2 latency variability in the multilevel model. Regarding the main effect of the P2 latency variability, we observed a highly significant, sinus shaped effect around the P2 (Figure 6). This shape makes sense: first, exactly on the averaged P2-peak a zero effect of latency was found. The average peak value corresponds to the mean value of latency. The variable latency exerts a significant negative main effect on ERFIAs before the averaged P2 peak. In other words, in the region before the averaged P2 peak, ERFIAs are negatively adjusted when peak latency increases. In the post stimulus region after the averaged P2 peak, ERFIAs are positively adjusted with increasing peak latencies. Thus, a shift in the latency of a peak on the xaxis has a profound effect on the y-axis, i.e. ERFIAs. Although the effect of the P2 latency variable is highly significant, the t-value plots of all other variables in the model remained almost identical. Thus, despite the large main effects of the variable P2 latency on ERFIAs, there was no confounding effect on the other variables. In the post-hoc analyses, we showed that it is possible to account for latency variations of peaks in the ERFIA multilevel method. In future work, relevant latency jitter of other peaks can also serve as predictor variable.

The ERFIA multilevel method: critical evaluation Although ERFIAs appear to be promising in the study of pain-related somatosensory ERPs, certain critical issues must be addressed. One concern is the optimal width of AUC segments. In this study, we used fixed 20-ms segments to obtain a general impression of effects of the complete 1500-ms poststimulus range. The width of such a segment should be small enough to obtain sufficient specificity but should not be too small to result in excessive significance testing. When a study focuses on a specific small poststimulus range, however, it could be argued that this time range should be partitioned into smaller segments, permitting a more detailed examination of the influence of predictor variables. Another issue, related directly to AUC width, concerns multiple testing.

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique Because ERFIAs of a specific poststimulus interval range acts as a dependent variable in a multilevel model, the risk of an unacceptably high number of statistical tests increases as the ERFIA width becomes smaller. In this article, we examined the complete (1500 ms) epoch with 75 20-ms ERFIAs. Thus, as outlined in the methods section, when applying a Bonferroni correction, only p-values that are smaller than 0.0007 are statistically significant. This correction for multiple testing, however, seems to be too rigid. In this report, we proposed the combination of the strict Bonferroni correction for single ERFIAs with a more tolerant, less stringent level of significance for 3 or more consecutive ERFIAs. Future research should examine whether other methods, such as permutation testing and bootstrapping, are more appropriate than the Bonferroni method for correcting multiple testing of ERFIAs. A third issue relates to the rejection of ERFIAs due to eye movements (EOG). In the analyses, a ±25 μV EOG rejection criterion for each 20-ms ERFIA range was used. After EOG rejection, the minimum amount of ERFIAs that were available in a specific 20-ms range was 8600, which has been deemed sufficient for performing multilevel analyses. It is unclear whether the EOG criterion should be extended to surrounding ERFIAs. For example, in the analysis of an ERFIA at 140 ms, one could consider implementing an EOG rejection for 100-180 ms. Apart from this issue, the use of multilevel analyses is particularly advantageous in handling confounded EOG segments, since all ‘valid’, analyzable segments are included, whereas in the analysis of variance, all observations that pertain to a given subject would be excluded if there are too many invalid segments. It is unknown whether the results are influenced by an adjustment of the EOG rejection criterion. In the post-hoc analyses, we used a straightforward method to determine the latency of the P2 peaks at single trial level, by using a fixed latency window. Of course, other more sophisticated methods for the determination of latencies of peaks, for example as described by Hu and colleagues, can be applied for this purpose.6

New insights with the ERFIA technique Notwithstanding the critical issues above, we gained several insights into cortical processing of (painful) electrical stimuli with the use of ERFIA method. Plotting the t-values of a predictor variable against consecutive ERFIAs has an advantage in the interpretation of the results. In the examination of all consecutive ERFIAs of the poststimulus period, predictor variables become more significant, reach a maximum significance level, and subsequently decrease. This pattern gives insight into the approximate onset and end of an influential effect of a variable. For example, intensity appears to have 2 main effects in the latency range of 140-380 ms (Figure 4a), which suggests 2 ‘intensity’ processes. A novel finding was the prolonged, significant interaction between actual stimulus intensity and previous stimulus intensity, which emerged from 380 to 660 ms on all electrodes. Notably, there was no main effect of previous stimulus intensity, suggesting that previous stimulus intensity is only meaningful in relation to the actual stim-

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Chapter 3 ulus intensity and that —i.e., cortical processing of the actual stimulus intensity is modified considerably by the intensity of the previous stimulus. Thus, the brain may make a “comparison” with previous stimulus intensity information, possibly reflecting stimulusrelated memory processes. At approximately 400 ms, this interaction effect was significant, after the main effect of the actual stimulus intensity diminished, suggesting two separate ‘intensity’ processes. The negative t-values of the interaction can be interpreted as follows: higher previous stimulus intensities result in less positive effect on ERFIAs of the current stimulus intensity, and vice versa—i.e., there appears to be a cortical tendency to adjust the actual stimulus intensity in the opposite direction, depending on the level of the previous intensity. As an analogy, one hand is placed in hot water and the other in cold water for a short time, and then both hands are put in warm water; the warm water will feel rather hot to the hand that was immersed in cold water and relatively cold to the other. More research is required to study the influence of previous stimuli on the processing of the actual stimulus. In addition, greater differences between pain and sensory thresholds or “sensory-pain threshold gap”, result in a greater increase of ERFIAs between 200 and 260 ms, and a reduction in ERFIAs between 360 to 420 ms. The clinical implication of this finding and its relation to the experience of pain may be an interesting topic for future research. Given the robust effect on the eventrelated EEG it seems to be reasonable to include this variable in future analyses. Because the ERFIA multilevel method is a processing technique for raw EEG data, the generalizability of the ERFIA multilevel method seems obvious and can be applied to all types of stimulus-related EEG information. The application of this method to other areas of event-related EEGs and whether the variability of the ERFIA information can be linked to meaningful stimulus- and subject information remains to be determined. In conclusion, the ERFIA multilevel method enables us to examine the complete poststimulus period, including non-peak-related information, ultra-late information, and model time-dependent variables, such as habituation, in a refined manner. The multilevel ERFIA method will likely contribute to the unraveling of mechanisms of painrelated cortical processing. This method can be applied to all other forms of eventrelated cortical processing, including other types of noxious stimulation.

Acknowledgements We are grateful to Dr. Wolfgang Viechtbauer, Department of Psychiatry and Psychology, Maastricht University Medical Centre, for statistical advice.

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Chapter 3

Appendix A Two multilevel models. The general model and the post-hoc model corresponding to the analyses of the article “Introducing the Event-Related Fixed-Interval Area (ERFIA) multilevel technique: a method to analyze the complete epoch of event-related potentials at single trial level”.

The multilevel model: Yti = ß0 + ß1⋅intensitylin + ß2⋅triallin + ß3⋅trialquad + ß4⋅trialinvers + ß5⋅age + ß6⋅gender + ß7⋅difference_pain_and_sensory_threshold + ß8⋅intensitylin_previous_trial + ß9⋅intensitylin ⋅intensitylin_previous_trial + eti + u0i + u1 intensitylin + u2 triallin The multilevel model for the post-hoc analysis: Yti = ß0 + ß1⋅intensitylin + ß2⋅triallin + ß3⋅trialquad + ß4⋅trialinvers + ß5⋅age + ß6⋅gender + ß7⋅difference_pain_and_sensory_threshold + ß8⋅intensitylin_previous_trial + ß9⋅intensitylin ⋅intensitylin_previous_trial + ß10⋅P2 latency + eti + u0i + u1 intensitylin + u2 triallin where: t = time point (1 to 150) i = subject intensity = -2 = -50%, -1 = -25%, 0 = 0%, 1 = 25% and 2 = 50% trial = 150 trial numbers, centered from –75 to +75 age = centered continuous variable in years gender = dichotomous variable, -1 = man, 1= woman difference pain and sensory threshold = absolute pain threshold - absolute sensation threshold intensity previous trial = -2 = -50%, -1 = -25%, 0 = 0%, 1 = 25% and 2 = 50% P2 latency = centered continuous variable in ms eti = error variance for subject i at time point t. This error term is subdivided into a random intercept, a random slope for intensitylin, and a random slope for triallin The model must be interpreted as follows: ß0 = the outcome mean (amplitude) for the intensity equal to the pain threshold (intensity = 0) at trial number 75 for a male (gender =0) subject with a mean age ß1 = the mean difference between intensities ß2 = the mean change in linear contrast over the trial ß3 = the mean change in quadratic contrast over the trial ß4 = the mean change in inverse contrast over the trial ß5 = the mean change in amplitude per year ß6 = the mean difference between men and women

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Introducing the event-related fixed-interval area (ERFIA) multilevel technique ß7 = the mean pain and sensory threshold difference ß8 = the relationship between the intensity of the previous trial and the amplitude of the present trial ß9 = the interaction between the effect of the intensity of the previous trial and the intensity of the present trial ß10 = the mean change in latency

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4

Does habituation differ in chronic low back pain subjects compared to pain-free controls?

A cross-sectional pain rating ERP study reanalyzed with the ERFIA multilevel method

Published as: Vossen CJ, Vossen HGM, Joosten EA, van Os J, Lousberg R. Does habituation differ in chronic low back pain subjects compared to pain-free controls? A cross-sectional pain rating ERP study reanalyzed with the ERFIA multilevel method. Medicine (Baltimore).

2015;94(19):e865. doi:10.1097/ MD.0000000000000865.

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Abstract The objective of the present study was to investigate cortical differences between chronic low back pain (CLBP) subjects and pain-free controls with respect to habituation and processing of stimulus intensity. The use of a novel event-related fixed-interval areas (ERFIA) multilevel technique enables the analysis of event-related electroencephalogram (EEG) of the whole poststimulus range at a single trial level. This technique makes it possible to disentangle the cortical processes of habituation and stimulus intensity. In a cross-sectional study, 78 individuals with CLBP and 85 pain-free controls underwent a rating paradigm of 150 non-painful and painful somatosensory electrical stimuli. For each trial, the entire epoch was partitioned into 20-ms ERFIAs, which acted as dependent variables in a multilevel analysis. The variability of each consecutive ERFIA period was modeled with a set of predictor variables, including 3 forms of habituation and stimulus intensity. Seventy-six pain-free controls and 65 CLBP subjects were eligible for analysis. CLBP subjects showed a significantly decreased linear habituation at 340 to 460 ms in the midline electrodes and C3 (p < .05) and had a significantly more pronounced dishabituation for the regions of 400 to 460 ms and 800 to 820 ms for all electrodes, except for T3 and T4 (p < .05). No significant group differences for stimulus intensity processing were observed. In this study, group differences with respect to linear habituation and dishabituation were demonstrated. By means of the ERFIA multilevel technique, habituation effects were found in a broad post stimulus range and were not solely limited to peaks. This study suggests that habituation may be a key mechanism involved in the transition process to chronic pain. Future studies with a longitudinal design are required to solve this issue.

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Does habituation differ in chronic pain subjects compared to pain-free controls?

Introduction Chronic pain may be considered as a nosological entity in its own right, in which neuro1â&#x20AC;&#x201C;5 plastic alterations in the central nervous system occur. An alteration in habituation is proposed as one of the mechanisms involved in chronic pain. Habituation is the process 6,7 that refers to a decrease in a behavioral response to a repeatedly presented stimulus.

Habituation can be observed and measured both at the subjective experiential level (pain report) as well as at the psychophysiological level. In this respect, event-related potentials (ERPs) may be a useful tool in studying the process of habituation. ERPs are time-locked responses to stimuli derived from an ongoing electroencephalogram (EEG). ERPs may contribute to the understanding of the cortical processing of pain and possible differences between subjects with chronic pain and healthy subjects. Commonly, the intensity of (non-)noxious stimuli (laser, heat, or electrical) is positively associated with the second 8,9 negative and second positive ERP peak, termed N2 and P2. In other words, higher stimu9â&#x20AC;&#x201C;12 lus intensities are typically accompanied by larger N2 and P2 peak amplitudes. Because blocks of stimuli are generally averaged between conditions, the possible influence of 13,14 habituation may be disregarded in the case of averaging.

It is therefore not clear if and how the process of habituation influences different conditions and group effects. Two recent developments enable a more detailed study of the relationship between ERPs on the one hand and stimulus intensity and habituation to stimuli on the other. First, Vossen 15 and colleagues proposed the use of a multilevel technique in the analysis of ERPs. A multilevel approach has several advantages over commonly used ANOVA techniques, especially when studying the process of habituation. Not only does multilevel analysis consider the hierarchical nature of ERP data, in which trials are nested within subjects, but also person-by-time effects can be studied, the latter through the incorporation of ran15,16 dom effects and nonlinear contrasts.

A second development was the recent introduction of event-related fixed-interval areas (ERFIAs) under the curve analyzed with multilevel analysis. This technique is based on the idea that not only maximized peaks may contain relevant information, but also fundamentally each post stimulus latency point may carry relevant information, regardless of whether it is peak-related or not. In the ERFIA multilevel technique, the entire post stimulus epoch is partitioned into 20-ms ERFIAs for each 17,18 single trial.

The ERFIA multilevel technique enables the investigation of the influence of predictor variables of interest on the whole epoch at single trial level, and thereby taking person-by-time effects between epochs such as habituation into account. In a sample of (n = 76) pain-free subjects analyzed with the multilevel ERFIA method, the results showed that cortical processing of both habituation and stimulus intensity was associated with post stimulus areas broader than peaks. In addition, stimulus intensity processing 17 was influenced by the previous stimulus intensity in a broad range. Altogether, the analytical developments and first results gave the impetus to reanalyze habituation processes in an existing ERP-dataset of a pain-rating paradigm of chronic low back pain (CLBP) subjects and pain-free controls. The aim of the present study is to investigate whether the 77

Chapter 4 cortical processing of habituation, stimulus intensity, and the interaction of the previous intensity with the actual stimulus intensity differs between subjects with CLBP and painfree controls, using the ERFIA multilevel technique. This study is mainly explorative in nature, because of the novelty of the ERFIA multilevel technique. Nevertheless, some a priori hypotheses were postulated based on existing ERP literature. First, with respect to habituation, Valeriani and colleagues reported a reduced habituation of the N2/P2 ampli19 tudes in patients diagnosed with migraine compared to pain-free controls. In addition, we found linear habituation effects in the ranges from 100 to 140 ms and 200 to 560 ms 17 in healthy subjects. Based on the finding of Valeriani and our previous results, a reduced linear habituation for CLBP subjects was postulated in the ranges from 100 to 140 and 200 to 560 ms. Habituation was modeled in 3 ways—linear habituation, fast habituation (inverse relationship), and dishabituation (quadratic relationship). However, no a priori hypotheses were made for fast habituation and dishabituation. Second, regarding stimulus intensity, it has been suggested that the peak-to-peak amplitudes are larger in chronic 20 pain patients compared to pain-free controls. Higher vertex amplitudes were observed 20–22 in fibromyalgia patients compared to controls.

Based on these studies, a group x stimulus intensity interaction was expected, in which the effect of stimulus intensity on ERFIAs in the N2 and P2 peak regions is more positive for CLBP subjects compared to pain-free controls. Third, previously we demonstrated a strong and robust effect (range 380-660 ms) of the previous stimulus intensity on the processing of the actual stimulus intensity, 17 which suggests a kind of ‘‘stimulus-related memory process’’. Therefore, it was decided to investigate whether this phenomenon would be different between subjects with CLBP and pain-free controls. This hypothesis was tested by the inclusion of a 3-way interaction (group x actual stimulus intensity x previous stimulus intensity) in the model.

Materials and methods Subjects Eighty-five pain-free subjects and 78 CLBP subjects, ranging in age from 18 to 65 years, were enrolled in the study between November 2005 and April 2007. Subjects with low back pain were included in the study if they had an anamnestic history of nonmalignant low back pain for at least 6 months without other interfering pain complaints. Pain-free subjects were recruited if they had no chronic pain complaints during the past 6 months and did not use any analgesic or psychotropic medication during this period. For both groups exclusion criteria were the consumption of analgesics < 8 h before the start of the experiment and/or the structural use of psychoactive drugs, such as antidepressants, antipsychotics, antiepileptics, and opioids. Participation was rewarded with 25 euros upon completion of the study. Approval was obtained from the Medical Ethics Committee of Maastricht University Medical Centre. All subjects gave their verbal and

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Does habituation differ in chronic pain subjects compared to pain-free controls? written informed consent before the study. Subjects were recruited by means of a flyer from the general population of Maastricht.

Stimuli Intracutaneous electrical pulse stimuli with a duration of 10 ms were administered on 10 the left middle finger, per Bromm and Meier. Using this method, a small lumen in the epidermis was prepared, using a dental gimlet, ensuring that the procedure was not painful. In the prepared lumen, a golden electrode was placed and fixed with tape. Two grounding copper laces were attached around the prepared finger and wrist. First, the sensation and pain thresholds were determined by gradually increasing the intensity of the stimulus, starting at zero intensity. The first intensity that was consciously experienced was defined as the sensation threshold. Next, the first intensity that was experienced as painful was defined as the pain threshold. This procedure was repeated 3 times to generate a reliable measurement. Based on the difference between a subjectâ&#x20AC;&#x2122;s sensation and pain thresholds, 5 stimulus intensities were presented in a rating paradigm. One of the 5 intensities was equal to the pain threshold, against which the other intensities were defined: -50%, -25%, +25%, and +50% of the difference between the sensation and pain thresholds (threshold range). The maximum stimulus intensity never exceeded 5mA.

Paradigm 10

One hundred fifty stimuli were presented in a rating paradigm. The 5 stimulus intensities were presented semi-randomly. Blocks of 15 stimuli were administered, in which each intensity occurred 3 times. Interstimulus intervals (ISIs) randomly varied between 9 and 11 s. Subjects were asked to rate the intensity of each stimulus on a scale from 0 (no sensation) to 100 (the most excruciating pain imaginable). The subject was told that the intensity of the first stimulus would be exactly equal to the calibrated pain threshold and should be rated as 60.

EEG Recording All EEG recordings were conducted in an electrically- and sound-shielded cubicle (3x4 m2). Ag/AgCl electrodes were placed on Fz, Cz, Pz, C3, C4, T3, and T4 using the interna23 tional 10 to 20 system. Impedances were maintained < 5 kâ&#x201E;Ś. A reference electrode was placed on each ear lobe. In order to check for possible vertical eye movements, an electrooculogram (EOG) electrode was placed 1 cm under the midline of the right eye. A ground electrode was placed at Fpz. All electrodes were fixed using 10-20 conductive paste. Neuroscan 4.3 software was used to record EEGs.

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Chapter 4

Procedure Before starting the experiment, subjects were informed about the purpose of the study.

Subjects were told that they would undergo an EEG registration while receiving various intensities of electric shocks—some painless and some painful. After completion of the 24,25 informed consent forms, the SF-36 questionnaire was completed.

Next, EEG electrodes were placed on the subjects, and the shock electrode was attached to the top of 10 the left middle finger, per Bromm and Meier. Then, the sensory and pain thresholds were determined, after which the rating paradigm was initiated.

Data reduction and computation of ERFIAs EEGs were recorded at a 1000-Hz sampling rate. Trials were segmented from the continuous EEG, from 200 ms before the stimulus to 1500 ms post stimulus. Data were offline band-pass filtered (0–50 Hz), and baseline-corrected (interval -200 to 0 ms) using BrainVision Analyser 2.0, Brain Products, München, Germany. The filtered data segments (per millisecond) were exported to Microsoft Office Excel 2007. Areas under the curve amplitude sum scores of 20 consecutive milliseconds were calculated from 0 to 1500 ms post stimulus, resulting in 75 ERFIAs per trial, per EEG electrode, and per subject. Additionally, maximum and minimum values of the EOG channel were selected per 20-ms ERFIA. Next, the ERFIAs of all 150 trials of all 7 electrodes of the subjects were imported into SPSS 20.0. Single ERFIAs with EOG activity that exceeded ±25 mV were excluded from the multilevel analyses.

Statistical analyses 2

Subject characteristics were analyzed using independent t tests and χ tests. Multilevel random regression analyses were performed separately for each EEG electrode. Trial number (1–150 stimuli) was considered the repeated measure. Subjects represented the highest level in the model, and the 20-ms ERFIAs served as the dependent variables. The dependent variables were assessed for normality. Habituation was modeled in 3 ways, namely linear habituation, fast habituation, and dishabituation. First, linear habituation was modeled as trial number (triallinear), assuming a linear decrease or increase of the dependent variable (of a particular ERFIA) over time (trial number). Second, fast habituation was modeled as an inverse relationship (trialinverse), representing a rapid decline, followed by a gradual decline or plateau phase— that is, habituation of the initial trials is more pronounced than later in the experiment. The inverse relationship was computed as 1 divided by trial number (1/trial number). Third, dishabituation was modeled as a quadratic function, representing a sensitization process (or dishabituation) after an initial habituation. This parabolic relationship was computed as trial number x trial number 15,17 (trailquadratic).

The full multilevel model comprised the following independent variables (fixed factors): actual stimulus intensity, triallinear, trialinverse, trialquadratic, age, gender, previ-

80

Does habituation differ in chronic pain subjects compared to pain-free controls? ous stimulus intensity, difference between pain and sensory thresholds (diff_pain_sens), and group (CLBP, coded as 1 vs pain-free coded as 0). The following 2-way interactions were modeled (Table 1): actual stimulus intensity x previous stimulus intensity, group x actual stimulus intensity, group x triallinear, group x trialinverse, group x trialquadratic, and group x diff_pain_sens. Finally, one 3-way interaction (group x actual stimulus intensity x previous stimulus intensity) was incorporated. With respect to random effects in the model, we made the assumption that subjects differ from one another in their response to the 5 intensities and with regard to habituation. Consequently, random effects, such as a random intercept and a random slope for intensity and trial number, were also included. The scaled identity covariance structure was used in the multilevel analyses. The analyses were performed separately for each 20-ms ERFIA period for all 7 cranial sites, resulting in 75 (1500 ms/20 ms) x 7 (cranial locations) = 525 multilevel models. For this large number of statistical tests, a correction for multiple testing should be performed. We chose not to define a specific p-value for statistical significance, due to the partially explorative aspect of the analyses. Instead, we considered relatively long-lasting effects (3 or consecutive >20-ms ERFIAs) with p-values ≤ .05 as significant. Single ERFIAs were considered significant when the p-value was ≤ .0007 (with a corresponding t-value of 3.43), based on Bonferroni correction for the complete epoch, obtained by dividing a significance level of .05 by the 17 number of ERFIAs (n = 75). The full multilevel model is described in Appendix B. All statistical analyses were performed with SPSS 20.0. Because the present study pertains a reanalysis of a dataset, no a priori study size calculation was made. To visualize and create an overview of the results, so-called ERFIA predictor blots were constructed. ‘Blots’ are tables in which the columns represent the 75 consecutive 20-ms ERFIAs, and the rows represent the EEG electrodes of a given predictor. In each row, cells were given a color when t-values were < -2 or > 2. Additionally, a plus or minus sign was added to each cell to indicate whether the t-value was positive or negative.

Table 1. Summary of the main, random and interaction factors of the multilevel model.

Main (fixed) factors Age Gender Group (CLBP1 vs pain-free) Linear habituation (trial number) Fast habituation (1/trial number) Dishabituation (trial*trial) Sensory and pain threshold difference (Diff_pain_sens) Actual stimulus intensity Previous stimulus intensity

Interaction factors

Random factors

Linear habituation*group Fast habituation*group Dishabituation*group Diff_pain_sens*group

Trial number

Actual Stimulus intensity*group Previous stimulus intensity*group Actual stimulus intensity*Previous stimulus intensity Actual stimulus intensity*Previous stimulus intensity*group

Actual stimulus intensity

1

CLBP = chronic low back pain

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Chapter 4

Results Sample characteristics From the 85 pain-free subjects, 9 were excluded for one of the following reasons: relevant pain complaints in the past week, consumption of more than 5 units of alcohol on the evening before the experiment or EEG-related technical errors. Thus, 76 pain-free subjects were analyzed: 26 men (34.2%) and 50 women (65.8%). Of the 78 CLBP subjects, 65 patients were analyzable. Thirteen individuals were excluded because of a variety of reasons: technical problems in the EEG measurement, use of painkillers or sleep medication the day before the experiment, other accompanying pain complaints, and excessive structural alcohol use. Table 2 summarizes the group characteristics.

Pain-free controls were on average 6.1 years younger (p = .01). In addition, the CLBP group showed a statistically significant higher score on the items ‘‘pain magnitude’’ and ‘‘pain interference’’ of the SF-36 scale (Table 2). Grand averages of ERP differences between CLBP and pain-free subjects are presented with respect to overall (Figure 1A), stimulus intensity (Figure 1B), and habituation (Figure 1C) differences between the groups.

Table 2. Characteristics of chronic low back pain subjects and pain-free controls eligible for analysis.

Pain-free controls

CLBP1 subjects

N

76

65

Gender male/ female (n)

26/ 50

32/ 33

.07

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34.8 (13.7)

40.9 (15.3)

.01

Pain threshold (mA)

1.1 (0.9)

1.2 (1.1)

.40

Pain magnitude (SF-362 item, sd)

1.7 (0.9)

3.5 (0.9)

< .001

Pain interference (SF-36 item, sd)

1.2 (0.5)

2.2 (0.9)

< .001

1

p-value

2

CLBP: chronic low back pain, SF-36: short-form, 36 items.

Habituation The main effects of linear habituation were observed in 3 latency ranges: an early positive effect between 100 and 140 ms, a second opposite (negative) effect between 200 and 320 ms, and a third negative effect at 580 to 640 ms. These effects could be observed at all electrodes except T4, in which the third region of linear habituation was located at 940 to 1220 ms (Figure 2A). Interaction effects between group and linear habituation were mainly seen in the region of 340 to 460 ms in the midline (Fz, Cz, and Pz) and C3 (Figure 2B). Fast habituation effects (inverse function) were rather scattered in the blot. However, clustered effects were seen at approximately 1220 to 1440 ms for most electrodes (Figure 2C). Interaction effects between fast habituation and group were mainly located at Cz, C3, and T3 (Figure 2D). The main effects of dishabituation (quadratic function) were situated in the same regions as linear habituation.

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Does habituation differ in chronic pain subjects compared to pain-free controls?

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Figure 1. Grand averages for chronic low back pain subjects versus pain-free controls at Cz of 150 intracutaneous electrical stimuli. A) Overall differences, B) grand averages of 2 stimulus intensities (50% below and 50% above pain threshold), and C) grand averages for habituation at Cz of 150 intracutaneous electrical stimuli. Of the 5 blocks, only blocks 1, 3, and 5 are shown (30 stimuli for each block).

83

Chapter 4 However, the direction of effects was the opposite and lasted only until 280 ms (Figure 2E). Again, in the region of T4, a late effect was observed at 940 to 1220 ms. Interactions of dishabituation with group were observed in the regions of 400 to 460 ms and 800 to 820 ms for all electrodes, except for T3 and T4 (Figure 2F). As an example, the model of 320 ms at Cz was chosen to visualize the 3 habituation interaction effects.

Based on the model regression estimates, the habituation effects at Cz were calculated for the CLBP group and the pain-free controls (Figure 3). These figures illustrate that pain-free subjects show a faster linear habituation, have a faster inverse habituation, and display less dishabituation compared to the CLBP group. For dishabituation, the top of the parabola was calculated for both groups. This top was located at trial number 22 for the CLBP group and on trial number 34 for the pain-free control group.

Stimulus intensity There were no statistically significant effects for either the interaction between group and actual stimulus intensity or for the interaction between group and previous stimulus intensity, or for the 3-way interaction of group x actual stimulus intensity x previous stimulus intensity. Consequently, these interaction terms were removed from the model, resulting in a reduced model (Appendix B, model 2). The main effects of the variable stimulus intensity are depicted in Figure 2G. Significant effects were found in several latency ranges, but were especially pronounced between 100 to 340 ms and 1040 to 1500 ms. ERFIAs were not marked in case significant stimulus intensity interaction effects with previous stimulus intensity occurred (Figure 2H). The interaction between actual stimulus intensity and previous stimulus intensity was statistically significant in the latency range 400 to 680 ms for all electrodes (Figure 2H).

Difference between pain and sensory threshold The main effects of the pain and sensory threshold difference were observed in the early latencies until 260 to 280 ms (Figure 2I). In these latency ranges, an increase in the ‘‘pain-sensory gap’’ corresponded with more positive ERFIAs. An interaction effect with group was found between 260 and 320 ms at T4, 360 to 500 ms at T3, 400 to 560 ms at C3, and from 440 to 560 ms at Cz, respectively (Figure 2J).

Random Effects All random intercepts were significant in all models, indicating that intercepts varied significantly between subjects (p < .001). Slopes were significant (p < .05) in the majority of models, indicating that slopes varied significantly between subjects. Significant slopes were found for 72% of the models for stimulus intensity and 76% of the models for habituation. Nonsignificant random effects for the slopes were only found for ERFIAs after 700 ms post stimulus.

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Discussion Differences in cortical processing pertaining to a rating paradigm using electrical stimuli were explored between subjects with CLBP and pain-free controls. The results suggested that CLBP subjects habituated to a lesser extent to repetitive stimuli than pain-free controls for linear habituation in the region of 340 to 460 ms. Cortical processing of different stimulus intensities, ranging from 50% below the pain threshold to 50% above, was equal between CLBP subjects and pain-free controls, and the influence of the previous stimulus intensity on the actual stimulus intensity did not differ between CLBP subjects and pain-free controls. These group interaction effects are discussed consecutively in the light of available literature below.

Habituation Three types of habituation were investigated, namely linear habituation (modeled with trial number), fast habituation (modeled with 1/trial number), and dishabituation (modeled with trial number x trial number). The results showed that linear habituation and dishabituation are influenced by CLBP status (Figure 2B, D, and F). To gain more insight into these different habituation processes, the course of habituation for CLBP subjects and pain-free controls was calculated, based on the model parameters (Figure 3). Overall, CLBP patients habituated less than pain-free controls in all 3 mathematically modeled forms of habituation. The differences of the top (trial number 22 vs 34) of the parabola (Figure 3C) indicate that the habituation process of the CLBP group is shorter compared to the pain-free controls. Although comparisons of the present results with those of other studies are difficult to make (different populations, different stimulation paradigms, and different analysis techniques), some similarities can be observed. Valeriani and colleagues found reduced habituation of ERP amplitudes in migraine compared to pain-free controls 19 in response to painful CO2 laser stimulation. Research from De Tommaso and coworkers also showed a decreased habituation in the peak-to-peak amplitude in migraine patients and a decreased habituation in the N1, N2, and P2 peaks in fibromyalgia patients in a next 20,26 study.

The question arises why habituation may be different in CLBP subjects. One of 21,27 the explanations is the idea of a ‘‘windup’’ mechanism and central sensitization.

The disability to habituate may be the result of changes in the modulation of nociceptive in28–30 put, associated with central sensitization.

The present results may support this hypothesis. Furthermore, the fact that significant effects also appear on electrodes such as Fz and Pz suggests that higher cortical processes, such as affective, evaluative, and cognitive processes, may play a role in the relationship between habituation and pain, which is in line with the notion of multidimensionality of the pain experience. In this perspective, investigation of the influence of coping, pain vigilance, pain catastrophizing, and mood on the cortical processing of pain is essential in future research.

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Stimulus intensity Inconsistent reports exist regarding the comparison of peak-to-peak amplitudes between chronic pain populations and pain-free controls. Several studies reported higher amplitudes (N2–P2 component) in fibromyalgia patients and tension-type headache patients 20,21,31 compared to pain-free controls.

On the contrary, Diers and colleagues found a 30 lower P260 component in CLBP patients compared to pain-free controls. Valeriani and co-workers did not find any amplitude differences between patients with migraine, ten19 sion-type headache patients, and a control group. Noteworthy, all these studies applied only a single intensity level. In the present study, we did not observe a difference between the groups with respect to the cortical processing of 5 different stimulus intensities. Obvious explanations would be the different chronic pain populations between studies and the difference in number of stimulus intensities applied. However, the most plausible explanation may be related to the fact that only in the present study, the estimates for stimulus intensity were corrected for the influence of habituation. Not taking into account habituation may have confounded the results of previous studies. In addition, in a multilevel analysis, within-subject variability (random intercepts and slopes) is modeled, estimating fixed effects more precisely than analyses ignoring random effects.

Given the highly significant random effects in the present study, the intensity effects may be considered more accurate. The 3-way interaction group x previous stimulus intensity x actual stimulus intensity was not significant. This finding suggests that a ‘stimulus intensity-related memory process’ is not different between CLBP subjects and pain-free controls. The 2-way interaction previous stimulus intensity x actual stimulus intensity, however, remained highly significant in the present analyses, in which CLBP subjects were added to the dataset, compared to the results of pain-free subjects alone in the previous study.17 These robust effects contribute to the notion that the short-term ‘stimulusintensity-related memory process’ reflects a basic phenomenon in stimulus processing.

In other words, the previous stimulus intensity may ‘resonate’ within the brain, thereby affecting the processing of the present intensity. Hence, we suggest the effect of the previous stimulus intensity upon the processing of the present intensity should be taken into account in future analyses of intensity-related information.

Pain–sensory threshold With respect to possible group differences in sensory and pain thresholds, the literature is limited and inconsistent, if only because different pain populations have been included across studies. Peters and colleagues noted a higher pain threshold for experi32 mental pain in CLBP patients as compared to control subjects. Other studies, however, reported significantly lower pain thresholds in fibromyalgia patients, regional pain syn21,33 drome patients, and chronic back pain patients.

In a recent study of cortical processing of electrical noxious stimuli, performed in healthy subjects, a main effect related

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Figure 3. Habituation differences for chronic low back pain subjects versus pain-free controls, based on model estimates for the 20-ms event-related fixed-interval area at 320 ms on Cz. A) Linear habituation (trial number), B) fast habituation (1/trial number), and C) dishabituation (trial number x trial number).

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Does habituation differ in chronic pain subjects compared to pain-free controls? to the difference between the sensory and the pain threshold was observed at 360 to 17 420 ms post stimulus. This suggests that a relationship exists between the extent of the interval (‘sensory–pain gap’) between these 2 stimulus thresholds on the one hand, and the cortical processing of (non-)painful stimuli on the other. Present results indicate that the effect of this ‘sensory–pain gap’ differs between the 2 groups notably around 400 to 560 ms post stimulus for Cz, C3, and T3. A clear explanation for these findings cannot be given, because the experiment was not designed to investigate this issue.

Nevertheless, it seems to be worthwhile to include the ‘sensory–pain gap’ in future analyses.

Discrepancies between multilevel model outcomes and grand averaged ERP graphs Inspecting the grand averaged graphs (Figure 1), compared to the results of the multilevel analysis, may lead to different conclusions. For example, no main effect for group was found in the multilevel models, whereas a difference between the groups seems apparent in the grand averaged graph (Figure 1). However, the present study shows that the ERP needs to be statistically corrected for the influence of several variables such as habituation, stimulus intensity, and the influence of previous stimulus intensity on present stimulus intensity processing. In conclusion, grand averaged ERPs cannot adequately express the complexity of the cortical processing of (non-)painful stimuli.

Limitations A cross-sectional design does not allow conclusions on causal interferences. Therefore, the results of the present study should be interpreted with caution. Although the present study population was relatively large in view of an ERP study, some considerations need to be taken into account. First, the CLBP sample was selected from the general population. Group interaction effects with the variables of interest may have been stronger in a purely clinical CLBP population. On the other hand, the heterogeneous CLBP group of this study may reflect chronic pain complaints of the general population more accurately. Second, the age distribution differed significantly between the CLBP group and pain-free group. The CLBP group was on average 6 years older than the painfree controls and this difference could have influenced the results. In recent literature, aging has been reported to reduce N2 to P2 amplitudes, and to increase latencies in 34,35 laser and electrical ERPs. The mean age difference between the studied groups was, compared to the difference in our study, much larger, 27.8 and 40.6 years, respectively.

To correct for the potential age influences on habituation, age was added as a covariate in the analyses in the present study. Furthermore, it could be questioned whether this statistically significant group difference of 6 years is clinically relevant. Third, this study investigated the cortical differences between the 2 groups based on 7, mainly central, cranial locations. In future research, the number of EEG electrodes should be enlarged, 91

Chapter 4 especially in the frontal and parietal areas (F3–F4/P3–P4). This would allow further investigation of potential lateral effects. A fourth possible limitation of our study concerns the correction for multiple testing. Because of the large number of statistical tests performed in this study, we used the strict Bonferroni criterion for single isolated ERFIAs. A less stringent cutoff point for significance was applied in the case of 3 or more consecutive ERFIAs with a p-value < .05. In this way, an attempt was made to find a balance between rejecting too many relevant influences, on the one hand, and the risk of accepting too many ‘‘small’’ and clinically non-relevant but significant effects, on the other. Inspection of the ERFIA predictor blots (Fig. 2) show several ‘‘long-lasting’’ effects (ranging 60 to 140 ms) in the interaction (group x habituation) analyses. Without doubt, future research is needed to replicate the explorative interaction findings of the present study.

In conclusion, to our knowledge, this is the first study to investigate possible group differences between CLBP subjects and pain-free controls with respect to habituation and stimulus intensity processing, using the ERFIA multilevel technique. In contrast to stimulus intensity, group differences were found in 3 types of habituation (linear, fast, and dishabituation). Hence, habituation may be a promising key variable to gain more insight into the chronification mechanisms of pain. Future experimental studies with a longitudinal design are undoubtedly needed.

Acknowledgements We are grateful to Dr. Wolfgang Viechtbauer, Department of Psychiatry and Psychology, Maastricht University Medical Centre, for statistical advice.

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Appendix B Model 1: The full multilevel model: Yti = ß0 + ß1⋅intensitylin + ß2⋅triallin + ß3⋅trialquadratic + ß4⋅trialinverse + ß5⋅age + ß6⋅gender + ß7⋅difference_pain_and_sensory_threshold + ß8⋅intensitylin_previous_trial + ß9⋅intensitylin⋅intensitylin_previous_trial + ß10⋅group⋅triallin + ß11⋅group⋅trialinverse + ß12⋅group⋅trialquadratic + ß13⋅group⋅difference_pain_and_sensory_threshold + ß14⋅group⋅intensitylin + ß15⋅group⋅intensitylin_previous_trial + ß16⋅group⋅intensitylin⋅intensitylin_previous_trial + eti + u0i + u1⋅intensitylin + u2⋅triallin

Model 2: The reduced multilevel model: Yti = ß0 + ß1⋅intensitylin + ß2⋅triallin + ß3⋅trialquadratic + ß4⋅trialinverse + ß5⋅age + ß6⋅gender + ß7⋅difference_pain_and_sensory_threshold + ß8⋅intensitylin_previous_trial + ß9⋅intensitylin ⋅intensitylin_previous_trial + ß10⋅group⋅triallin + ß11⋅group⋅trialinverse + ß12⋅group⋅trialquadratic + ß13⋅group⋅difference_pain_and_sensory_threshold + eti + u0i + u1⋅intensitylin + u2⋅ triallin where: t = time point (1 to 150) I = subject intensity = -2 = -50%, -1 = -25%, 0 = 0%, 1 = 25% and 2 = 50% trial = 150 trial numbers, centered from –75 to +75 age = centered continuous variable in years gender = dichotomous variable, -1 = man, 1= woman difference pain and sensory threshold = absolute pain threshold - absolute sensory threshold intensity previous trial = -2 = -50%, -1 = -25%, 0 = 0%, 1 = 25% and 2 = 50% eti = error variance for subject i at time point t. This error term is subdivided into a random intercept, a random slope for intensitylin, and a random slope for triallin The model must be interpreted as follows: ß0 = the outcome mean (amplitude) for the intensity equal to the pain threshold (intensity = 0) at trial number 75 for a male (gender =0) subject with a mean age ß1 = the mean difference between intensities ß2 = the mean change in linear contrast over the trial ß3 = the mean change in quadratic contrast over the trial ß4 = the mean change in inverse contrast over the trial ß5 = the mean change in amplitude per year

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Chapter 4 ß6 = the mean difference between men and women ß7 = the mean pain and sensory threshold difference ß8 = the relationship between the intensity of the previous trial and the amplitude of the present trial ß9 = the interaction between the effect of the intensity of the previous trial and the intensity of the present trial ß10 = the interaction between group and the linear component of habituation ß11 = the interaction between group and the inverse component of habituation ß12 = the interaction between group and the quadratic component of habituation ß13 = the interaction between group and the pain-sensory threshold gap ß14 = the interaction between group and actual stimulus intensity ß15 = the interaction between group and previous stimulus intensity ß16 = the 3-way interaction between group, actual stimulus intensity and previous stimulus intensity u0i = individual variance from the average intercept u1 = individual variance from the average slope ß1 of intensitylin u2 = individual variance from the average slope ß2 of triallin

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5

The Influence of Pain Hypervigilance on Cortical Processing and Habituation to Painful Stimuli in Healthy Subjects: A cross-sectional pain-ERP study

Submitted for publication as : Vossen CJ, Luijcks R, Joosten EA, van Os J, Lousberg R. The Influence of Pain Hypervigilance on Cortical Processing and Habituation to Painful Stimuli in Healthy Subjects: A cross-sectional pain-ERP study.

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Abstract Pain hypervigilance can be detected at trait level in pain-free individuals and may represent a predisposing factor for chronic pain. Habituation to pain is impaired in several chronic pain populations. This experimental study investigated the influence of pain hypervigilance on cortical processing and habituation to painful stimuli. Forty-six painfree participants underwent a habituation pain rating protocol of 25 painful somatosensory electrical stimuli, while recording EEG. Pain hypervigilance was assessed with the Pain Vigilance Awareness Questionnaire (PVAQ). At single trial level, the whole onesecond post stimulus epoch was partitioned in 20-ms Event-Related Fixed-Interval Areas (ERFIAs). ERFIAs were used as dependent variables in multilevel models in which the PVAQ score was the variable of primary interest. PVAQ interaction effects with trial number were modelled to assess the influence of PVAQ on habituation. Forty-two subjects were eligible for analysis. Significant main effects for PVAQ were seen from 440580 ms, meaning that high PVAQ scores were associated with more positive single trial areas for that region, compared to low scores. The habituation course over 25 stimuli differed significantly between the two PVAQ groups from 480-600 ms. Pain hypervigilance impacts cortical processing of painful stimuli, suggesting that pain hypervigilance may modulate the pain experience through altered cortical habituation.

Perspective This study shows that pain hypervigilance impacts cortical processing of pain and habituation to painful stimuli, suggesting that pain hypervigilance may modulate the pain experience. Insight in psychological mechanisms influencing pain processing and habituation could help the clinician identify psychological vulnerability to pain and may contribute to the management of pain.

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Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation

Introduction Hypervigilance is defined as the constant scanning of the body for somatic and pain 1 sensation. An excessive attention specifically to pain sensations is termed pain hyper2 vigilance. Pain hypervigilance may be one of the mechanisms through which pain3–6 related fear can result in reports of higher pain intensity. Pain hypervigilance can be 3 assessed by the Pain Vigilance Awareness Questionnaire (PVAQ). This questionnaire was originally developed to assess attention to pain in chronic pain patients, and was 7,8 later also validated for non-clinical and pain-free study populations.

Hypervigilance can already be apparent in pain-free individuals and as such may be a 9–11 predisposing factor in the onset of chronic pain disorders.

Rolmann postulated that hypervigilant individuals may be biologically predisposed and that hypervigilance may 9,12 serve as a risk factor for the development of chronic pain syndromes. Pain hypervigilance is thought to be associated with central sensitization, a mechanism in the nocicep13,14 tive system, leading to increased clinical and experimental pain.

However, a pain experience not only results from nociceptive but also from antinociceptive mecha15 nisms. One of the most fundamental forms of learning is habituation. Habituation is 16,17 defined as a physiological decrease in response to repetitive sensory stimulation.

In pain research, habituation is a process that is thought to function as a central antino18,19 ciceptive mechanism.

Consequently, an impaired habituation to pain imbalances nociceptive and antinociceptive mechanisms, and may result in a more severe pain experience. Both mechanisms are influenced by cognitive processes, such as fear and 5,20 hypervigilance.

In the context of the current investigation, two questions were addressed: (1) Since hypervigilance may represent a risk factor for the development of chronic pain, do hypervigilant pain-free individuals already display an altered cortical processing to painful stimuli compared to non-hypervigilant pain-free individuals? (2) Does pain hypervigilance moderate antinociceptive mechanisms through a decrease in habituation to painful stimuli?

One of the tools to investigate cortical processing of pain and habituation are Event21 Related Potentials (ERPs). Pain-ERPs are time-locked responses to somatosensory 22 painful stimuli, derived from a continuous electroencephalogram (EEG). Specific deflections in the pain-ERP, such as the N2 and N2-P2 peak-to-peak amplitude in the pain22–25 ERP are thought to be related to stimulus characteristics, such as intensity , and 26,27 28–30 processes, such as attention and habituation.

Recently, a new technique, called the Event-Related Fixed-Interval Area (ERFIA) multilevel technique, was developed for 31 the analysis of ERPs. This method enables the study of the whole post-stimulus epoch of an ERP and the habituation of repeated stimuli in a flexible way. For example, to study habituation, a linear function for stimulus number can be incorporated in the

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Chapter 5 model to investigate a linear decline of 20-ms areas at a fixed post stimulus interval (i.e.

ERFIAs), over the course of 25 stimuli. Consequently, by modelling a parabolic (quadratic) function, an initial decrease and later increase in response over the course of 25 stimuli can be investigated, representing an impaired habituation. In this article we will use the term linear habituation for the linear function and dishabituation for the quadratic function. In addition, this type of modelling creates the possibility to investigate the impact of psychological variables in relation to habituation on the whole epoch.

The aim of the present study was to investigate the two abovementioned issues in a habituation protocol of 25 somatosensory electrical stimuli: (1) Does a relationship exist between pain hypervigilance in pain-free individuals and event-related fixed-interval areas after painful stimuli? Since a higher PVAQ score is associated with a higher pain intensity report, it was hypothesized that a high PVAQ score would be associated with a more negative N2 region and a more positive P2 region; (2) Does pain hypervigilance influence the habituation course of painful stimuli? It was hypothesized that a higher PVAQ score would be associated with a decreased linear habituation or more pronounced quadratic dishabituation. No a priori assumption concerning the latency range could be made, since no previous results exist. As secondary outcome, pain report, measured with NRS, was investigated.

Methods Participants The study was approved by the Medical Ethics Committee of the Maastricht University Medical Centre (NL40284.068.12/METC 12-3-015). Participants consisted of a sample of the general population of Maastricht and were recruited using flyers. The flyers were delivered in 5 different neighborhoods in Maastricht. Inclusion criteria were an age between 18 and 65 years and a good understanding of the Dutch language. Exclusion criteria were: (1) structural use of psychoactive medications such as antipsychotics, antidepressants, antiepileptics and/or anxiolytics during the past year, (2) regular use of alcohol > 10 U/day during the past year, (3) epilepsy, (4) psychotic disorder, (5) a visual or hearing disability, (6) analphabetism or dyslexia. Subjects were asked not to take alcoholic beverages the evening prior to the experiment and to refrain from caffeinecontaining beverages three hours before the start of the experiment.

Before the experiment, written informed consent was obtained. Compensation for the period of the whole experiment was 50 euros.

Of a total of 111 subjects, participating in the original study, 46 subjects were pain free.

The selection was made based on the following criteria: (1) no pain complaints at the moment of the experiment (Brief Pain Inventory), nor during the six months before the

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Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation experiment (Short Form Health Survey). In addition, participants did not use pain medication. Since the present study was part of a larger dataset, no a priori study size calculation was made.

Questionnaires Before the EEG measurement, subjects were asked to complete the following three questionnaires: (1) Short-Form Health Survey (SF-36), consisting of 36 items, in order to 32 evaluate the general health status and in particular the subscale bodily pain, (2) Brief Pain Inventory Short Form (BPI-SF), which consists of 9 items, exploring pain com33,34 plaints, (3) The Pain Vigilance Awareness Questionnaire (PVAQ) which has 16 items, and considers the pain behavior with respect to attention to pain and attention to 8 changes in pain. In addition to these questionnaires, questions regarding age, sex, pain complaints in the last six months, nature of pain complaints and medication use were also included.

Electro-shocker and stimuli An electro-shocker (type Shocko-100-AA-20, developed by Maastricht Instruments BV and approved for use in experimental studies) was used to deliver electroshocks. Electrical pulse stimuli (duration 10 milliseconds) were applied intracutaneously on the left 35 middle finger, as per Bromm and Meier. Using this method, a small lumen in the epidermis was prepared with a dental gimlet. Care was taken that the procedure was not painful. In the prepared lumen, a golden electrode was placed and fixed with tape. A tm grounding wrist strap (3M wrist strap, WBB-AFWS61M) was attached to the wrist proximally to the prepared finger. In order to determine the intensity of the stimuli for the habituation protocol, the sensation and pain thresholds were measured. The sensation and pain thresholds were determined by gradually increasing the intensity of the stimulus, starting at zero intensity. The first intensity that was consciously experienced was defined as the sensation threshold; the first intensity that was experienced as painful was defined as the pain threshold. This procedure was repeated 3 times to generate a reliable measurement.

Habituation protocol Based on the subjectâ&#x20AC;&#x2122;s difference between the sensation and pain thresholds, a stimulus 25% above pain threshold was calculated as follows: Delivered habituation stimulus = pain threshold + 0.25*(pain threshold â&#x2C6;&#x2019; sensation threshold).

This intensity level was experienced as painful, nonetheless still acceptable. The habituation protocol consisted of 25 identical stimuli, with durations of 10 milliseconds. Inter-

101

Chapter 5 stimulus intervals (ISIs) ranged between 9 and 11 seconds. Subjects were instructed that they would undergo a series of stimuli and that the task was to determine differences between the stimuli. The intensity of the stimuli was unknown to the subject, as was the number of stimuli that would be administered. Subjects were asked to rate the intensity of each stimulus on a scale from 0 (no sensation) to 100 (the most excruciating pain imaginable). For standardization purposes, subjects were asked to rate the first stimulus as 60. In addition, subjects were instructed to wait for two seconds after each stimulus before rating the intensity of the stimulus.

EEG measurement 2

The EEG recordings took place in an electrically and sound-shielded cubicle (7.1 m ). For the EEG recording, the BrainAmp Amplifier and BrainVision Software were used. EEG recordings were sampled at 1000 Hz. Ag/AgCl electrodes were placed on respectively Fz, F3, F4, Cz, C3, C4, T3, T4, Pz, P3, P4, Oz, O1 and O2, using the international 10-20 36 system. Reference electrodes were placed on the earlobes. A ground electrode was fixed at Fpz. To measure vertical eye movements, electrooculogram (EOG) electrodes were placed 1 centimeter under the midline of the right and left eye. All electrodes were fixed using conductive paste.

Data processing With Analyzer 2.0 (Brain Products, MĂźnchen, Germany) software, trials were segmented from the continuous EEG, from 200 ms before the stimulus to 1000 ms post stimulus. Data were offline band-pass filtered (0-50 Hz) and baseline-corrected (interval -200 to 0 ms). For each subject, the data (microvolts) for each millisecond (from -200 to 1000 ms) of all electrodes and EOG channels were imported into SPSS 21.0. Subsequently, a multilevel dataset was built for each subject, using a syntax file in which the following calculations were made: (1) twenty-millisecond event-related fixed-interval areas (ERFIAs) were calculated from 0 to 1000 ms post stimulus, partitioning the whole epoch in 50 consecutive 20-ms areas (ERFIAs) per trial per EEG electrode per subject (2) Additionally, maximum and minimum values of the EOG channel were selected per 20-ms ERFIA. (3) Questionnaire data were added to the dataset. Next, all cases were added in order to obtain a full multilevel dataset. EOG left and right activity were included per 20-ms ERFIA in the analysis as covariates.

Statistical Analysis Multilevel regression analyses were carried out separately for each EEG electrode and for every 20-ms ERFIA period. The 20-ms ERFIAs were used as the dependent variable.

The dependent variables were assessed for normality. Subjects represented the highest level in the model and trial number (1-25 stimuli) was the repeated measure within

102

Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation subject. We made the assumption that subjects differ from one another with regard to habituation. Consequently, random effects, such as a random intercept and a random slope for trial number were included. An AR-1 covariance structure was used, assuming that trials next to each other are more correlated compared to trials further apart. As in previous studies, linear habituation (trial number) and dishabituation (parabolic rela31,37 tionship, computed as trial*trial) were modelled.

A linear function with a negative coefficient represents a linear decline in ERFIAs over the course of 25 stimuli. A quadratic function, in which the parabola opens upward represents initial habituation after which a sensitization process (or dishabituation) occurs. For the research question, the main effect of PVAQ on the post-stimulus EEG was investigated, using the following basic model: ERFIAs of a specific 20-ms range and electrode (ERFIAs20-ms range, location) served as the dependent variable, which was modelled as a function of the following fixed factors: trial number, trialquadratic, PVAQmedian split, age, sex, pain threshold, sensation threshold, EOG left and EOG right. For the second research question, the investigation of the influence of pain hypervigilance on habituation and dishabituation, two two-way interactions were incorporated in the model, namely PVAQmedian split*habituationlinear and PVAQmedian split*habituationquadratic.

The analyses were performed separately for each 20-ms ERFIA (0-1000 ms post stimulus) for all 14 cranial locations. For this large number of statistical tests, a correction for multiple testing should be performed. However, the analyses are explorative in nature.

Therefore, we chose not to define a specific p-value for statistical significance. Instead, we considered only robust effects (3 or more consecutive 20-ms ERFIAs) with p-values â&#x2030;¤ .05 as significant. For low-power interaction effects, we considered robust effects with p-values â&#x2030;¤ .10 as significant.

The results were summarized in so-called ERFIA predictor blots, in which the columns represent 50 consecutive 20-ms ERFIAs, and the rows represent the EEG electrodes of a given predictor. In each row, cells were given a color when t-values were < -2 or > 2. A red color indicated a positive significant t-value, a blue color a significant negative tvalue. All statistical analyses were performed with SPSS 21.0. Also, the influence of pain hypervigilance on the secondary outcome of pain report, measured with NRS, was investigated, as was time course. NRS was the dependent variable and modelled as a function of the following variables: trial number, trialquadratic, PVAQmedian split, age, sex, pain threshold, sensation threshold, trial number*PVAQmedian split and trialquadratic*PVAQmedian split. An overall course of NRS across the 25 stimuli was constructed and compared between the two PVAQ groups.

103

Chapter 5

Results Subject characteristics A total of 111 subjects were enrolled in the study. The inclusion took place from April 2012 until August 2014. Of a total of 111 participants, 46 subjects were pain free. Of these 46 subjects, 42 were eligible for analysis, one subject was excluded because of inadequate EEG measurement and three subjects had missing data on the PVAQ questionnaire. The PVAQ was dichotomized around the median split of a score of 29. In Table 1, the characteristics of the analyzable subjects are summarized. The group with a high PVAQ score did not significantly differ from the low PVAQ score group regarding age, sex, sensation threshold, pain threshold and NRS. Table 1. Characteristics of the study participants.

Total

PVAQ low

PVAQ high

p-values

N

42

21

21

PVAQ score mean (sd)

28 (13.6)

16.5 (6.7)

39.5 (7.5)

p <0.0001

Age years mean (sd)

35.8 (16.9)

35.4 (17.8)

36.3 (16.5)

ns

Sex M/F

19/23

8/13

11/10

ns

Sensation threshold mA

0.34 mA

0.32 (0.18)

0.35 (0.15)

ns

Pain threshold mA

1.13 mA

1.1 (0.68)

1.2 (0.63)

ns

NRS 0-100 (sd)

57 (9.8)

57 (8.7)

56 (11.1)

ns

NRS 64 62 60

NRS

58 56

PVAQ low PVAQ high

54 52 50 48

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Trial number

Figure 1. Pain intensity (NRS) of the 25 trials for PVAQ high and PVAQ low.

104

Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation

Pain hypervigilance and NRS In Figure 1, the mean NRS course for both PVAQ groups over 25 trials is depicted. A small decline in NRS for both PVAQ groups can be observed. The difference between the mean NRS scores of the two PVAQ groups was not statistically significant, nor was the interaction between trial number and NRS score. Stated otherwise, there were no different time courses of the NRS scores for the groups. There was no difference in NRS between the groups, nor did NRS course significantly differ between the groups.

Main effects of pain hypervigilance and habituation on the event-related EEG Significant positive main associations between PVAQ and event-related EEG were mainly seen in the region from 440 to 580 ms, except for F3, F4, C4, and T3 (Figure 2). This indicates that high PVAQ scores were associated with larger areas for that region, compared to low scores. In Figure 3A, a grand average ERP is depicted for participants with a high and a low PVAQ score at Cz. In the region of 460-560 ms, an overall statistically significant difference for the PVAQ groups was observed for Cz (Figure 3A). With respect to habituation, for trial number (linear habituation) and trialquadratic (quadratic habituation), significant main effects were observed in a broad range of 140 to 460 ms for all electrodes. For the electrodes Fz, F3, and F4, main habituation effects were also seen in the area from 620 to 640 ms (Figure 2B and 2C). In addition, significant main effects for sensory and pain thresholds after 400 ms were apparent across almost all electrodes (Figure 2D and 2E).

Pain Hypervigilance in relation to (dis)habituation To study the influence of pain hypervigilance on habituation, interaction effects of PVAQ with linear habituation and quadratic habituation (dishabituation) were added to the model. Interaction effects of pain hypervigilance with linear habituation and quadratic trial number effect were seen especially in the latency range from 480 to 600 ms (Figure 4). A positive coefficient is indicative for dishabituation, representing a parabola which opens upward. For the electrodes F4 and T4, significant effects were observed in a larger latency range. Significant robust effects were seen for F4, Cz, Pz, P3, P4, and T4.

No effects were found for Fz, F3, T3, Oz, and O1 (Figure 4).

With respect to the results of random effects, the intercept was significant in almost all models, whereas the random slope for trial number was significant in approximately 50% of the models.

105

140

120

240

220

200

180

160

280

260

1.15

0.75 0.97 -0.6

Pz

400

380

360

320

1.1

0.7 1.16 1.54

1.3 0.92 1.02 1.47 0.98 0.72

1.3 0.92 1.02 1.47 0.98 0.72

540

520

500

480

460

440 1.7 2.01 2.76

560

660

640

620

600 0.7 0.58 0.43 0.41 1.23 1.25 1.45 1.64

0.8 1.19 1.75 1.69 2.01 1.29

Linear habituation

B.

-1 0.81 0.41

0.87 0.56 -0.2 -2.1 1.16 0.46

O2

-1.5 -1.7 -1.2

-0.7 -1.3 -0.5 0.82 0.62 0.28 -2.6 -3.1 -2.2 -1.4 -4.5 -6.7 -6.1 -5.9

-2.1 -1.4 -1.7 -1.3 -0.7 0.18 -2.1 -2.3

Cz

C3

C4

340

320

300

-3 -2.6 -2.3 -3.1 -3.5

-0.4 -0.6 -0.4 0.82 0.75 -0.2 -3.8 -4.2 -4.2 -1.9

360

0.3 0.41 1.11 0.44

1.9 1.48 2.51 1.49 1.12 1.43 1.66 1.49 1.81 1.54

1.2 0.95 1.01 0.87 0.38 0.42 0.63 0.22 -0.1

2.4 1.48

0.5 0.16 0.47 0.66 0.96 1.11

1.9 1.48 2.51 1.49 1.12 1.43 1.66 1.49 1.81 1.54

0.9 0.61 0.69 0.57 0.58

2.4 1.48

2.5 2.37

1.7 1.99 2.3

2.5 2.54 3.39 3.35 2.69 1.81 0.95 0.87 1.03

-1 -0.3 -0.6 0.34 -0.1 1.54 2.44

1.9 0.44 0.72

0.4 0.17

-3

1 0.49

-0 -3

0.4 -0.7 0.29 -0.1 -1.3

1 -0.4 0.48 -0.3

1.8 1.39 1.03 1.08

0.3 1.77 2.23

Dishabituation

C.

2.45

0.56 0.18 0.43 0.89 1.61 0.52 1.06 1.89 2.37

0.23 0.15 -0.3 0.92 1.71 0.38 0.99 2.08 1.23 0.94 1.48 2.39

0.87 -0.1 0.05 2.56 2.07 -0.4 1.12 1.81 3.79 3.33 1.55 2.29 3.46 5.06

O2

4.8 3.74 2.87 4.04

620

600

580

1.7 1.54 3.14 2.62 1.34 1.31 1.74

3.5 2.76

0.2

760

3

0.2 -0.3 -1.8 -0.9 -0.1 0.58 0.54

-0 -0.3 -1.3 -0.1 0.05 0.61 0.74 -1 -0.7 -0.2 0.61 -0.6 0.17 0.05 0.16 -1.2 0.05 -0.2 -0.1 0.31

-1 0.26 -0.7 -0.5 -0.7 0.11 0.15 0.43 0.59 0.64 -0.4 0.36 0.26 1.67 1.26

-1 -0.5 -0.2 0.41

-1 -0.7 -1.4 -0.2 -0.2 -0.3 -0.7 0.65 0.53 0.25 -0.4 -0.8 -0.5 0.37 0.72

-1 -0.7 -1.4 -0.2 -0.2 -0.3 -0.7 0.65 0.53 0.25 -0.4 -0.8 -0.5 0.37 0.72

-0 -0.4 -0.4 0.32 -0.1 -0.4 -0.2 1.09

-0 1.43 0.71

0.8 1.57 -0 -0.8 -0.7 -0.9 -0.6 -1.1 -0.7 -0.7 -0.1 -1.1 -1.1 -0.4 -0.3 -1.3 0.04 -0.4 -0.3 0.35

0.7 -0.6 -1.5 -0.1 -0.4 0.58 -0.8 0.33 -0 0.75 -0.7 -1.6

4.2 3.65 2.16 2.41 2.72 3.19 2.86 2.08

2.8 2.97 2.27 1.81 0.44

1.1 1.21 2.52 0.75 -0.7

3.5 3.53 3.85 2.45 2.34 2.54 3.24 2.23 3.37 2.17 2.62 2.58 2.04 0.74 0.42 1.21 2.12 0.72 -1.5

0.3 0.67

-1 0.19

-1 -0.5 -0.8

0.2 1.94 0.72 -0.7 0.39 -1 0.59 -0.2 0.57 -0.2 0.72 -0.2 0.14 1.43

0.3 -1.1 0.04 0.62 0.12 0.08 -0.2 1.73 0.93 2.37 0.83 0.74 1.49 0.9 0.39 -0.2 -1.8 -1.6 -1.3

-0

-0 -0.5 0.59

2.6 3.01 3.14 2.74 2.47 1.18 1.68 0.85 0.13 0.32 0.45 -0.3 -1.1 -0.3 0.16 -0.4 0.12 0.22 -0.4 0.34 0.02 0.38 -0.4 -0.3 0.75 -0.3 -0.3 -0.8 -0.8 0.62 -0.1 0.25

1.2 -0.7 -0.1 -0.2 -0.2 -0.9 -1.1 -0.6 0.28 0.53 -0.6 0.15 0.77 0.68 0.96 -0.1 0.43 1.28 1.55 2.36 1.28 1.24 2.35 2.85 2.83

2 1.74 2.16 2.69 3.92 3.84 4.31 2.02 2.14 2.33 2.81 2.36 1.91 1.94 2.31 2.97 1.53 0.27 1.02 0.76 2.08 -0.1 -1.4 0.04 0.72 -0.3 -1.8 -0.9 -0.3 -0.1 -0.4 -0.1 -0.6

2.3 1.95 3.62 2.87 3.88 2.05 2.81 4.04 2.95 3.35

800

-0 0.89 -1.1 -2.1 -0.8 -1.3 -1.1 -2.2 -1.1 -1.4 -1.4 -1.5 -0.9 -1.3 -1.3 -1.1 -0.3 -1.2 0.49 -0.3 -0.2 0.67 2 2.16 1.74 1.61 1.08 0.91 0.37

3.8 2.91 2.96 3.46 2.72 2.39 1.78 1.37 1.16 0.02 0.47

3.8 3.91 2.11 3.15 3.58 2.38 2.64 1.76 2.85 3.31 3.52 2.34 1.28 1.74

1.7 2.35 3.29 3.71 3.28 3.42

2.2 0.51 -0.5 2.36

2

720

3 3.23 3.34 3.43 3.41 2.54 2.78 3.43 2.71 1.47 0.77 0.34 0.45 -0.4 0.08 -0.6 -0.7 -1.5 -1.9 -0.8 -1.3 -0.3 -0.6 -0.7

-1

840

-1 -0.9 -0.9 -1.2 -0.5 -0.4 -0.4 0.38

780 -2 -1.4 -1.1 -0.9 -1.7 -0.7 -1.5 -0.8 2 1.05 1.26 0.35 0.69 -0.3 -0.2 -1.1 -1.5 -0.8

4 4.78 3.83 2.39 1.55 1.34 1.31 0.68 0.52 -0.1 0.03 -1.1

3.3 3.41 4.39 4.68 4.13 1.86

4.2 3.96 3.62

3 1.72 0.57 3.63 5.56 5.28 4.71 3.47 4.08 3.68

0.5 1.36

2.4

640

3 3.12 0.87 4.18 4.92 3.78 3.38 3.58 2.32 2.45 2.43 1.96 2.07 2.48 3.11 1.06 0.46 -0.8 -1.2 -1.9 -1.5 -1.6 -2.1 -2.2 -3.1 -2.2 -1.3 -1.1

1 1.84 1.07 0.12 2.04 4.54 5.04 4.61 3.31 3.94 3.56 3.64 3.64 4.02 4.37 4.17

2.4

O1

20

Oz

40

T4

60

0.39 0.46 0.66 0.69 0.75 2.15

80

T3

120

1.31 0.94 0.28 2.18 1.43 -0.5

140

0.49 0.85 0.21 -0.2 0.36 -0.3

160

0.94 0.83 0.53 0.36 0.96 -0.2 0.55 1.36 1.31 0.39 1.74 3.27 4.48 3.81 3.15 3.41 3.77 3.57 3.22 3.32 3.54 3.24 2.16 2.02 1.33 1.49 0.75 0.96

180

P4

200

P3

220

Pz

240

1.56 1.18 1.24 1.34 0.89 -0.4 2.12 2.35 1.54 1.79 2.72 4.38 3.91

260

C4

280

0.34 0.97 0.13 -1.1 -0.3 -0.5

300

1.14 1.46 0.77 -0.2 0.25 -0.3 1.77 2.33 0.81 0.01 2.01 3.98

320

0.37 0.59 0.14 -0.6 -0.7 -0.9 2.66

340

C3

380

Cz

660

0.9 0.61 -1.5 -1.5 -1.9 -1.8 -2.3 -2.9 -2.3 -1.4 -0.7

700

3 3.12 0.87 4.18 4.92 3.78 3.38 3.58 2.32 2.45 2.43 1.96 2.07 2.48 3.11 1.06 0.46 -0.8 -1.2 -1.9 -1.5 -1.6 -2.1 -2.2 -3.1 -2.2 -1.3 -1.1

1.4 1.24

480

F4

420

-1 0.13 2.73 3.41 2.67 1.03 3.35 3.98 3.87 2.22 2.48 2.16 2.84 3.17 1.72 1.76 2.86 2.14

500

0.34 0.02 -0.1 -1.2

520

0.37 0.59 0.14 -0.6 -0.7 -0.9 2.66

540

Fz

560

F3

860

-0 -0.4 1.39 0.13 0.15 1.36 0.21 -0.4 -0.1 -0.1 0.57 -1.5 -0.5 -2.3 -0.5 -0.8 -1.6

880

-2 -0.6 -0.5 -1.1 -1.7 -0.6 1.77 0.24

-3 -1.9 -1.5 -0.2 -1.1 -1.3 -2.1 -0.6 0.68 -0.6 -0.2

-3

-3 -2.4

360

-3 -2.9 -2.4 -2.1 -2.1 -2.3 -2.6 -1.3 0.11 -0.9 -0.6 -1.5 0.48 1.63 0.22 -0.1 0.82 2.11 1.58 0.91 0.32 0.84 0.46 0.71 0.17 0.93 -0.4 0.17 -1.7 -0.2 0.69 -0.5

O2

920

-3 -3.4 -2.2 -2.5 -2.2 -2.5 -2.4

Oz

O1

940

-3 -3.2

-0.3 -0.2

-0.9 0.04 -0.4 -2.6 -1.5

100

960

-4 -4.2

-0.5 -0.3 -0.6 -0.6 -0.9 -0.8 -1.6 -1.8 -2.3 -2.3 -2.1 -2.9 -3.6 -4.8 -4.2 -4.8 -2.7 -2.9

-0 -1.6 -1.4 -3.8 -3.6 -2.1 -3.3 -4.5 -5.9 -4.5 -4.1 -2.9 -3.1 -3.5 -3.4

400

980

-0 -0.9 -1.3 -0.5 -1.4 -1.7 -1.1 -1.2 -1.7 -2.7 -3.9 -4.4

440

0.8 0.44 -0.4 0.48 0.55 1.05 1.09 -0.4 0.17 0.14

0.4 -0.6 -0.9 -0.7 -1.1 0.05 -0.4 -1.5 -1.5 -2.5 -0.9 -1.3 -2.4

2 0.58 1.18 1.21 1.64 1.21 1.83 1.25 1.34 0.79 1.56 1.67 0.91 0.57 1.74 0.25

0.4 0.22 0.86 1.13 0.51 -0.3 -0.7

460

-1 -1.3 -0.8 -0.1 -0.3 -0.6 0.18 1.11 0.33 -0.2 0.09 -0.4 -0.3 0.66 -0.1 0.11 -0.3

680

-3 -2.8 -2.4

-3 -3.1 -1.6 -0.5 -1.1 -0.8 1.28 0.18

0.4

0.7 0.01 0.27 0.49 0.36 -0.9 0.42 -1.3 -0.2 -0.7 -1.5

740

-3 -3.3

-3 -1.8 -2.8

-4 -3.6 -4.5 -3.5 -3.2 -3.3 -2.5 -2.1 -1.3 -0.7 -0.5 0.47 -0.1 0.35 0.04 1.17

-4 -4.8 -2.9 -3.4 -3.7 -2.6

0.7

1.5 1.87 1.72 2.95 1.71 2.15 1.91 1.93 1.54 1.71 1.69 1.54 0.43 1.54 -0.3 0.73 0.37 -0.4

1.2 0.68 -0.1 1.05 0.31 0.53

-0 1.21 -0.3 0.81 0.65 0.72 0.11 -0.1 -0.4 -0.4 -0.5 0.57 -0.1 -0.1 -1.7 -1.1

1 0.37 1.06 1.29 1.85 2.23 1.42 1.63 0.63 1.23 1.14 1.55

1.1

-4 -3.1 -1.8 -1.9 -1.3 -1.1 -0.8 -0.6 -0.2 -0.2 0.82 1.71 0.64 0.78 -0.1 1.44 0.14 0.62

-4 -4.8 -4.4 -3.8 -3.5 -3.4 -2.9 -1.9 -1.6 -0.9

0.4 0.51 0.28 -0.7 -0.8

0.4 0.51 0.28 -0.7 -0.8

0.3 0.38 0.41 -0.3 -0.2 0.33 1.69 0.78 0.06 -0.7 -0.4

2.1 1.09 0.92 0.76 0.22 1.16 -0.1 -0.1 0.18 0.48 -0.7 -0.7 -0.4

2.1 1.09 0.92 0.76 0.22 1.16 -0.1 -0.1 0.18 0.48 -0.7 -0.7 -0.4

820

-4 -3.8 -4.5 -2.8 -2.9 -4.4 -3.4 -3.7

-0 -2.9

3

3

-1 -0.3 -1.4 -0 -0.6 -1.1 -0.6 -1.9 -0.8 -0.4 -0.2 0.09 -0.7

900

-1 -0.8 -0.6 -2.6 -2.9 -2.2 -0.7

-3 -4.2 -4.6 -4.3 -3.8

-2.6 -2.1 -1.7 -3.2 -2.6 -1.8 -1.8 -1.7 -2.8 -2.6

T4

-2

-0.6 -0.7

T3

-1 -2.1

-1.8 -1.2 -0.6 -2.1 -0.9 0.59 -0.4

P4

-5 -3.6

0.4 -0.1 1.35 0.97 -0.6 0.31 -0.4

-1 0.11 -1.1

-0 0.63 0.65 0.73

0.7 0.24 -0.2 -0.3 -0.7 -1.1 -1.8 -0.8 -0.5

0.1 -0.5 -1.2 -1.2 -0.9 -0.5

0.6 0.51 0.22 0.38 -0.4 0.07 -0.3 0.02

0.5 1.32 2.23 1.71 1.39 1.22 2.12 0.94 1.95 1.14 1.19 0.84 0.52 -0.1 0.38 0.61 1.66 0.32 0.25 -0.4 -0.3

1.7 2.33 2.24

1.7 2.33 2.24

-4 -4.2 -4.6 -3.4 -1.8 -0.8 -0.6 -0.7 -0.1 -0.2 0.36

-2 -2.2 -2.7 -0.4 0.35 1.41 1.78 2.44 1.87

-2 -2.2 -2.7 -0.4 0.35 1.41 1.78 2.44 1.87

-6 -5.8 -3.7 -4.6 -4.5 -4.5 -4.2 -4.2

-4 -5.4

0.8 0.93 0.78 0.99 0.51 0.53

0.1 0.45 0.1 -0.2 -0.3 -0.5

0.5 0.48 0.78 0.19 0.48 0.03 -0.5 -0.3 -0.9 -0.8 -0.9 -0.7 -0.6 -0.4

2.5 3.84 2.25 1.86 2.72 2.45 1.68 1.46 1.09 0.98 2.24 1.62 0.41 1.56 0.76

-2 -2.3 -3.4 -5.3 -4.6 -3.8 -3.4 -3.4 -3.8 -3.7 -2.8 -2.8 -3.2 -2.2 -0.9 0.08 0.45 0.32

0.3 -0.4 -0.8 -1.3 -0.6 -2.3

-0.7 -1.2 -0.6 0.15 0.02 0.29 -1.1 -1.4 -1.1 -0.4 -2.7 -5.3

P3

0.4

0.1 -0.8

-1 -0.8 -0.3 -0.1 -0.1 -0.3

1.3 0.68 0.53 -0.1 -0.2 -0.4 0.01 0.33 -0.1 0.06 0.86

0.9 0.79 0.44 -0.3 -0.4 -0.3 -0.5

2.1 2.17 2.48 3.14 3.12 3.02 2.73 3.06 3.24 3.01 2.12 1.16 0.54 1.59 0.62 1.15 1.47 1.36 1.49 0.49 0.44 0.21

400

-4 -4.4 -4.3 -3.9 -3.7 -4.6 -4.5 -3.7 -1.5 -1.4 -0.3 -0.7 0.07 -0.4 0.33 0.51 1.17 1.66 1.02

-0 -0.2 0.38 -1.6 -2.1 -1.3 -0.6 -2.8 -4.9 -5.7 -4.7 -3.2 -4.4 -4.7 -4.4

1.5

1.1 0.73 0.81 0.72 0.15 0.98 0.83 0.82 0.75 1.19 0.97 -0.5 -0.2 -0.2 -0.1 0.28 -0.2

1.7 1.84 2.46 2.23 2.46 0.87 0.87 1.34 1.85 1.78 1.93 1.05 1.48

420

-2 -1.9 -2.7 -1.8 -0.9 -0.4 -0.2 -0.1 1.97 1.76 2.08 1.93 2.27 2.77 2.21 1.27 0.56 0.99 0.61 0.95 1.17

-5 -5.7 -4.6 -4.2 -3.9 -2.4 -2.7 -2.8 -2.1

-5 -5.7 -4.6 -4.2 -3.9 -2.4 -2.7 -2.8 -2.1

-0.4 -0.6 -0.4 0.82 0.75 -0.2 -3.8 -4.2 -4.2 -1.9

180

-0.4 -0.1 0.13 1.45 1.11 -1.1 -3.8 -4.4 -3.7 -2.1 -4.2 -4.9 -4.7

240

F4

260

F3

140

1

-1 -1.2 0.89 0.96 0.18 1.48 2.12 1.55 1.31 0.92 1.61 1.81

280

Fz

160

1.6 1.29

440

1.9 1.43 1.31 0.98

460

1.8

480

0.7 -0.4 -0.8 1.37 0.43 0.39 0.74 0.77 0.83 0.78 0.86 1.84 1.29

500

1.4 1.05 -0.5 -0.8 1.07 0.43 -0.5 0.47 0.65 -0.6 -0.4 1.92 1.25 0.71 1.49 2.56

1.65 -0.2 -1.4 -0.7 1.32 0.15 -0.1 0.97

520

0.72 1.29 2.03 -0.5 0.28 0.24 1.09 1.65 0.27 -0.7 -0.3 0.32 0.22 0.29

120

O1

540

T4

-0 -0.3 -0.5 -0.8 0.58 -0.1 -0.1 -0.3 0.55 0.15 -0.2 0.29 1.05 0.63 0.89 0.61 1.44 1.61

560

-0 0.12 -0.3 -0.9 -0.6

580

-1 0.35 -0.9 -1.4 -0.5

200

2.1 1.01 0.06 0.17 0.09

600

2.32

380

0.4 0.27 0.04 0.49 0.97 0.95 1.05 0.59 1.28

620

T3

1.3 1.12 -0.2 -0.6 1.84 1.37 1.19 0.94 0.08 -1.6 -0.7 0.33 -0.2 -0.4 1.47 1.86 1.77 0.94 0.43 1.07 1.63 3.13 2.11 2.01 1.76 1.59 2.55 2.49

P4

220

1.9 2.35 2.38 0.99 0.86 0.59 0.35 1.31 1.46 1.25 2.17 1.21 1.01 0.82 0.17 0.13 -0.1 -0.3 -0.6 -0.2 0.22 -0.2

2.8 2.76 2.42 0.88 0.42 0.28 0.41 1.09 1.28 1.06

1.2 2.23 1.97 1.62 0.76

640

1.8 1.98 2.42 0.68 0.65

0.4 -0.8 -0.5 0.38 -0.2 -0.3 1.18 1.93 1.58 0.89 0.83 1.51 1.97 1.92 2.52 2.91 2.57 2.48

660

1.3 0.86 -0.6 -0.6 0.25 -0.1 0.38 1.2 0.66 -0.5 -0.4 0.59 -0.1 -0.4 1.06 1.14 1.02 0.65 0.46 1.35 1.81 1.29 2.09 2.31 2.33 2.11

680

0 2.28 1.72 1.16 0.89

700

1.1 0.09 -0.8 1.42 1.43 1.52

-1 -0.7 -0.4 -0.7

680

1.9 1.33 0.87 0.93 0.67 1.15 0.89 1.31 1.43 1.61 1.89

1.5 0.57 -1.3 -0.6 1.81 1.46 1.01

-1

700

1.9 1.33 0.87 0.93 0.67 1.15 0.89 1.31 1.43 1.61 1.89

P3

Pz

580

2.4 2.14 2.26 2.51 1.92

1 1.13 1.68 1.56 1.79 1.63

1 1.13 1.68 1.56 1.79 1.63

720

Oz

720

1.8 2.25 2.26 1.99 2.82 2.19 2.35 1.68 0.82 0.61 0.69 0.57 0.56 0.45 0.89 0.74 1.31 1.18

1000

1.2 1.26 1.04 0.55 -0.5 -0.5 0.08 0.01 -0.1 0.97 1.68 1.66 0.91 1.67 1.65 1.97 1.62 1.66 2.91 2.06 2.12 1.75 2.42 2.17 0.95 0.73 1.06 0.46 1.46 1.93 1.63 2.02 1.37 1.92 1.48 0.24 0.48 1.11 0.58 0.54 0.87 0.84 0.73 0.86 0.79

C4

340

0.9 1.89 1.77

-0 0.66

420

1.4 1.27 1.17 1.26 1.36 1.02

740 740

-1 -0.7 1.24

1.31 0.93 -0.4 -0.2 1.52 1.71 0.89 0.77 0.54 -0.3 -0.5 -0.4 -0.5 -0.1

1.23 -0.3

-0

-0 0.66

Cz

1.1 1.81 0.42 0.09 0.27

-0

C3

-1 0.88 1.98 1.45

1.1 1.81 0.42 0.09 0.27

760 760

0.55 0.42 -0.8

-1 0.88 1.98 1.45

780 780

F4

300

1.7 -0.4 -0.6 -0.5 1.36 2.25 1.31 0.91 1.49 0.38 0.61 0.15 0.02 0.14 0.99

800 800

0.55 0.42 -0.8

820 820

F3

840 840

Fz

860 860

PVAQ 880 880

20

20

900 900

40

40

920 920

60

60

940 940

80

80

960 960

100

100

980 980

1000

106 1000

A.

Chapter 5

360

340

320

300

280

260

240

220

200

180

160

140

80

60

40

20

900

880

860

840

820

800

780

760

740

680

640

620

600

580

-2 -1.8 -2.4 -3.2 -3.4 -3.3 -2.9 -3.5 -2.3 -1.5 -1.9 -2.4 -1.3 -0.7 -0.8 -1.3 -0.8 -1.2 -0.5 -0.3 -0.6

-0 -0

-0 -0

-0 -0.2 -0.4 -0.2 -0.1 -0.3 -0.6 -0.4 -0.6

-2

0.1 0.15 0.48 -0.1

0.5 0.53 -0.4 0.51 0.43 0.15 -0.2 0.18 0.29

0.5 0.78 0.49

-0 -0.1 -0.2 0.24 -0.9 -0.3 -0.2 -0.4 -1.8 -1.5 -1.4 -0.8 -1.1 -1.4 -0.6 -0.6 -0.5 -0.3 0.16 -1.1 -0.9 -0.8 -1.6 -2.1 -0.6 -1.2 0.34 0.02 -1.2 -0.1 1.77 0.42 1.65 0.65 0.78 0.51 0.58

0.33 -0.2 0.82 -0.8 -0.3 0.41 -0.1 -0.4 0.34 0.69

Pain threshold

O2

E. 260

240

220

180

160

120

80

60

40

20

580

560

520

-0.5 0.89 -0.5 -0.4 0.91 0.42 0.44 -0.3 0.52 0.73 1.35 1.23 1.09 0.29 0.32 0.34 -0.2 -0.2 -0.4 0.45 0.91 1.21 1.14 1.68 1.66

0.56 0.05 -0.2 -0.1 -0.9

P4

740

700

680

660

640

620

1.2 1.11 0.53 0.39 1.52 1.45

2.8 2.39 2.82 2.34 1.81 1.96 1.47

2 2.16 1.77 1.76 2 2.16 1.77 1.76

1.6 1.64 1.51 1.37 0.3 0.84 1.24 1.06 0.72

1.5 1.39 1.8 1.52 0.99 0.34

2.1 1.86 2.18

1

0.7 2.84 0.34 0.04 1.34 0.55 1.07 -0.7 0.37 0.28 0.44 0.11 -0.2

1.5 2.02 -0.1 0.04 0.38 1.33 0.28 0.02

1.4 1.26 1.53 1.75 1.86 -0.6 0.31 0.43 1.82 0.62 0.07 2 2.01 1.77 1.59 1.64 0.88 1.05 0.26 1.48 1.71 2.16 2.42 1.24 1.67

2.5 0.92 1.61 2.09 1.73 0.69 0.84 1.15 0.62 0.14 1.53 1.74 2.37

1.2 0.73 1.08 0.78 1.35 1.07

2.7 2.58 2.56 1.64 1.61 2.02 1.77 1.57 0.85 0.51 0.84 0.67 0.61

0.5 0.37 0.62 0.95 0.75 0.87

1.9 2.21 3.01 2.45 1.64 1.94 1.79 1.96 0.98 1.09 1.07 1.76 1.28 1.28

1.8 1.61 2.15 2.15 2.66 2.15 1.73 2.69 2.01 2.03 1.58 1.48 1.99 2.28 1.84 1.22 1.78 1.11

1.9 3.04 3.22 2.51 2.84 2.78 2.35

2.5 1.99 2.43 1.58

1.3 1.24 2.08

0.6 1.25

3.1 1.94 3.1 1.94

2.8 3.24 3.31 2.85 2.85 2.11 2.22 2.54 3.58 2.53 2.03 2.16 2.53 2.23 2.01 2.01 1.21 1.96 1.46 1.55

3.4 3.02 3.37 3.93 2.71 2.88 2.21 2.07 2.18 3.19 2.49 1.85 2.15 2.19 1.67

0.4 0.87 0.66 0.88 1.33 2.06 2.02 2.68 3.18 2.28 2.66 3.12 2.67 1.82 1.97 1.58 0.97 0.82 1.53

0.4 -0.2 -0.9

2.9 3.16 3.38 2.58 2.9 3.16 3.38 2.58

1.9 2.65 3.35 2.47 1.57 2.04 1.75 1.37 1.26 1.06 1.24 1.81 1.46 1.46

Figure 2 A to E: ERFIA predictor blots of the variables of the main model. Columns represent consecutive 20-ms ERFIAs whereas rows display cranial locations. Cells with significant results are colored (p < .05) and the plus or minus sign expresses the direction of the relationship.

-0 -0.1 -0.1

0.4 0.12 -0.8 -0.7

0.2 -0.5 -0.2 -0.1 -0.4 0.92 0.32 0.53 0.77

0.5 0.74 1.24 0.15 0.29 0.27 0.88 0.95 1.34 1.72 0.95 1.05

-0.2 0.64 0.01 1.02 1.26 0.46 0.83 0.63 0.23 0.83 1.62 1.25 1.06 0.16 0.31

0.8 0.67

0.6 0.72 1.27 0.62 -0.2 0.41 1.41 1.54 1.46 -0.4 0.17

O2

0.91 0.65 0.57 -1.2 -0.4 -0.5 -0.5 -0.3 0.54 0.26 0.65

760

2.2 3.13 2.28 2.82 3.34 2.98 3.55 3.17 2.21 1.72 1.75 1.64 1.93 2.84 1.56 1.43 1.92 1.35 1.07

1.3 1.42 2.03 2.38 2.25 2.43

-0 0.76 0.96 1.04 1.81 2.34

0 -0.8 0.63

-0 -1.5 -0.3 -0.4 -0.4 0.75 0.17 0.64 -0.2 0.12 -0.2 -0.3 -0.1 -0.9 0.04 -0.5

0.09 0.59 -0.5 -0.1 0.98 0.72 1.46 0.22 -0.5 0.38 0.92 1.04 0.87 -0.1

820

2 2.05 2.44 2.22 2.33 2.18 1.61 1.51 1.32 1.57 2.15 2.96 1.83 1.12

T3

1 1.29 0.48 0.47 0.59 0.25 -0.3 -0.9 -0.2

0.98 0.76 -0.2 1.26

600

0.6 1.44 1.93 2.09 2.41 2.64 3.01 3.11

0.4 0.81 1.16 1.36 1.61 2.62

0.33 -0.1 -1.1 0.49 0.37

0.2 0.77 0.32 0.11 -0.5 -0.3 0.37 1.27 0.94 0.84 0.24 0.22 -0.3 -0.7 -0.4 -0.9 -0.1 0.11

P3

T4

720

2.7 2.63 2.63 2.61 2.91 2.99 2.44 2.23 1.86

-0.3 1.29 -0.8 -1.6 -0.1 -0.5 -0.4 -0.3 0.39 0.37 1.06 1.09 0.76 0.31 -0.1 0.01 -0.3 -0.6 -0.5 -0.2 0.63 1.29 1.81 1.54 1.54 2.32 3.17 2.71 3.12

Oz

780

3.4 3.09 3.32 3.52 3.89 3.49 3.26 3.42 2.69 2.56 2.62 3.34 2.98

0.37 0.49 -0.5

O1

800

3.4 3.09 3.32 3.52 3.89 3.49 3.26 3.42 2.69 2.56 2.62 3.34 2.98

C4

0.6 0.73 0.67 -0.1 -0.8 -0.7 -0.6 -0.8 -1.1 -0.9 -0.2 0.25

2.3 2.64

-0 0.04 1.11 1.51 1.51 2.39 2.21 2.52 3.31

Pz

0 0.15 -0.2 -0.2 0.12 1.38

840

0.4 1.21 1.73 2.06 2.49 2.03 2.94 3.11 3.52 3.29 3.35 3.38 3.81 3.73 3.41 2.95 2.42 1.95 2.55 3.12 2.36 1.88 2.17 2.68 2.17 2.08 2.22 2.17 2.31 1.84 2.06

540

-0 0.04 1.11 1.51 1.51 2.39 2.21 2.52 3.31

0.6 0.05

-1 -0.7 -0.3 -0.3 0.43

-1 -0.7 -0.3 -0.3 0.43

0.27 0.13 -0.7 0.07 0.12 -0.6 -0.6 -0.4 -0.3 -0.6

280

C3

300

0.17 -0.2 -0.4 -0.6 -0.3 -0.3 -0.7 -0.3 0.42 0.77 1.28 1.16 0.89 0.04 -0.7 -1.3 -1.4 -1.2 -1.2 -0.5 0.26 0.97 1.46 1.73 1.84

320

-0 -0.4 -0.5 0.16 -0.4 -0.3 -0.2 0.12 -0.4 -0.6 -0.4 0.23

340

Cz

100

-1 -1.7 -0.2

360

-0.3 -0.8

380

-0.2 -0.9 -2.2 -1.8 -0.6 0.09 -1.1 -1.5 -1.1 -1.2 -0.9 -0.7 -0.6

400

-0.2 -0.9 -2.2 -1.8 -0.6 0.09 -1.1 -1.5 -1.1 -1.2 -0.9 -0.7 -0.6

420

Fz

440

F3

500

F4

860

-2 -0.7 -0.9 -0.7 -0.8 -1.6 -1.1 -1.3 -1.4 -0.9 -1.2 -0.9 -0.2 -0.2 -0.7 -0.9 1.37 0.99 0.91 -0.2

880

0.8 1.17 0.19 -0.5 -1.1 0.07 -0.7 -0.7 -0.1 0.41 -0.8 -1.4 -0.8 0.46 -0.8 -1.2

460

1.4 1.84 1.41 1.51 1.15 1.49 1.09

-1.4 -0.2 0.54 -0.9 0.09 -0.1 -1.1 -1.2

480

0.4 -0.3 -0.5 0.35 0.85 0.33 1.12 -0.6 0.98 0.91 0.46 0.19 0.86 0.84

900

-1 -1.2 -0.4 -0.6 -1.4 -0.2 -0.6

920

-0 -1.1 -1.3 -0.4 -0.3 -0.6 -0.8 -0.5 -0.2 -0.3 0.26 -0.1

O1

140

-1 -0.1 -0.6 -0.7 -1.1 -1.4 -1.3 -1.5 -0.3 0.14 -0.3 0.39 -0.3 -0.2 -0.3 -0.2 -0.1 -0.4 -0.3 -0.1 1.02 1.35 0.34 0.38 0.87 1.17 0.88 1.56

960

-0 1.28 1.35 0.46 0.22 -0.1 0.34 -0.6 -0.1 0.18

-0.8 -0.2 -1.1 0.13 1.41 0.69

-1.1 -0.1 1.02 0.18 0.26 0.18 -0.5

T4

Oz

-1 -0.7 -0.1 0.51 0.15 0.01 0.29 0.76 0.67 0.42 0.56 0.89

-1 0.37 0.53 0.23 0.37 -0.1 0.47 0.56 0.12 0.17 0.44 -0.2

-2 -1.6 -0.5 -0.5 -1.1 -0.5 -0.2

-3 -2.6 -1.6 -1.1 -0.6 0.27 -0.5

-0 -0.8 0.79 1.98 0.86 0.59 -0.3 -0.4 -0.9 -1.1 -0.9 -0.7 -0.6 -1.9 -1.5 -2.1 -1.6 -1.5 -1.2 -0.7 -0.9 -0.4 -0.7 -2.3 -1.4 -2.4 -2.1 -2.4 -1.9 -1.2 -0.6 -1.1

0.2 0.19 -0.7 -0.7 -0.4 -0.4 -1.6 -1.7 -2.1 -2.8 -2.5 -2.1 -1.8 -1.4 -1.8 -1.3 -1.8 -2.9 -2.8

-0 -0.4 -0.9 -0.2 0.58 0.59 0.29 -0.1 0.64 0.65

-2 -2.2 -3.5 -2.9 -3.2 -3.5 -1.7 -2.3 -0.8 -0.3 -0.7 -1.4 -0.6 -0.4 -0.1 -0.7 -0.3 0.22 -0.3 -0.5 -0.1 0.27 -0.3

-1 -0.4 -0.1 -0.7 -0.8 -0.6 -0.8 -0.2 -0.6 0.19 -0.7

-1 -1.5 -0.8 0.02

-1 -1.3 -2.3

-2 -2.6 -2.8 -3.1 -2.8 -1.7 -1.9 -1.2 -0.5

980

-0 -0.7 0.11 1.21 0.51 0.04 -0.1 0.01 -0.8

920

200

-1 -0.6 0.71 0.42

940 940

-1.9 -0.7

660

2 0.68 0.37 -0.4 -0.3 -1.2 -1.2 -0.6 -0.3 -1.2 -1.9 -1.9 -1.4 -2.9 -2.5 -2.1 -1.8 -1.8 -1.2 -1.4 -2.8 -2.7 -3.3 -2.4

0.4 1.38 0.76 0.56

700

-2 -1.8 -2.3 -2.9 -3.4 -3.5 -3.4 -2.7 -3.2 -1.8

-2 -2.5

980

1000

T3

1.5 0.33 -0.4 0.11 -0.2 0.01

720

-2 -1.7 -1.9 -1.3 -1.4 -2.9 -2.6 -2.6 -2.3 -1.5 -1.4 -1.7

-0.9 0.37 1.95 0.55 -0.2 -0.2 -0.5 -0.6 0.77

-2

-3 -2.3 -3.3 -3.2 -2.8 -2.1

0.12 0.25

-1 -1.8 -2.1

P3

-1 -0.7

0.2 -0.1 -0.8 -0.7 0.01 -0.3 -1.4 -1.7 -1.9 -1.6 -2.5

0.3 -0.3 -0.4 -0.8

P4

2.8 0.89

-0.9 0.48 2.06 0.65 -0.4 -0.3 -0.3 0.02 0.83 1.29 0.63 0.64

0.5 -0.2 0.03 0.07 -0.3 0.73

-0.5 0.27 1.05

0.04 0.27 1.84 0.84 0.06 0.34 -0.1 -0.5 0.28 1.79 0.55 0.02 0.06 -0.3 -0.9 -1.2 -1.3 -0.4 -1.3 -2.1 -3.1 -2.8 -3.5 -2.8 -2.3

C3

1000

-2 -3.2 -3.1 -3.4 -3.3 -3.1 -2.8 -1.8 -0.9 -1.7 -1.6 -0.7 -0.1 -0.4 -0.7 -0.4 -0.4 -0.6 -0.5 -0.1 -0.1 -0.8

960

-2 -1.8 -2.4 -3.2 -3.4 -3.3 -2.9 -3.5 -2.3 -1.5 -1.9 -2.4 -1.3 -0.7 -0.8 -1.3 -0.8 -1.2 -0.5 -0.3 -0.6

-1 -1.6 -2.2 -2.8 -2.6 -3.7 -2.9 -2.9 -2.3 -2.3 -2.2

C4

-1

Pz

-0 0.31 1.24 0.32 -0.1 -0.2 -0.5 -1.1 -1.1

560

-3 -3.4 -3.8 -3.2 -3.1 -2.2 -2.3 -2.4

-0.6 0.59 1.28 1.12 0.35 -0.4 -0.1

400 -2

420

Cz

380

0.7 0.53 -0.2 0.26 0.31 0.21 1.85 1.19 0.51 -0.1 -0.7 -0.9 -1.3 -1.3 -1.1 -1.4

440

-0.2 1.01 0.98

460

-0.4 -0.3 1.93 0.93 0.53 0.11 1.15 0.91 0.94 2.65 1.79 0.42 -0.2 -0.4 -0.8 -1.2 -0.7 -1.5 -1.6 -1.9 -2.8 -2.4 -3.2 -2.4 -2.6 -2.2 -2.2

480

-0.4 -0.3 1.93 0.93 0.53 0.11 1.15 0.91 0.94 2.65 1.79 0.42 -0.2 -0.4 -0.8 -1.2 -0.7 -1.5 -1.6 -1.9 -2.8 -2.4 -3.2 -2.4 -2.6 -2.2 -2.2

500

Fz

520

F3

540

F4

100

Sensory threshold

120

D.

Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation

107

Chapter 5

Post-hoc analyses In order to test the robustness of the interaction effects found in the region from 480 to 600 ms, we performed some post-hoc analyses. The sum of the ERFIAs 480 to 600 ms was calculated at trial level and was used as dependent variable for the interaction model. Six of the 14 electrodes showed significant PVAQ*trial interaction effects, namely F4, Cz, Pz, P3, T4 and O2. In Table 2, the estimates for the calculations of the habituation course are given for Cz and T4. Figure 3A and 3C show the averaged ERPs of the two PVAQ groups for Cz and P4 in which 25 trials are averaged, and time effects cannot be visualized. The right side of Figure 3 (Figures 3B and 3D), displays the computed (dis)habituation time course for low and high PVAQ scores for the 480 to 600 ms range for the 25 stimuli, based on the model estimates for the linear and quadratic time functions.

3A

-20

3B

Cz

Cz 300

-15 250

ÂľVolts

-5 0

PVAQ_low

5

PVAQ_high

10

AUC 480-600ms

-10

200 PVAQ low

150

PVAQ high 100

15 50

20

0

-200 -160 -120 -80 -40 20 60 100 140 180 220 260 300 340 380 420 460 500 540 580 620 660 700 740 780 820 860 900 940 980

25

milliseconds

3C

-8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 trial number

3D

T4

100

T4

-6 50

ÂľVolts

-2 0

PVAQ_low PVAQ_high

2 4

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

PVAQ low PVAQ high

-50

-100

-200 -160 -120 -80 -40 20 60 100 140 180 220 260 300 340 380 420 460 500 540 580 620 660 700 740 780 820 860 900 940 980

6 8

AUC 480-600ms

-4

-150

trial number

milliseconds

Figure 3 A to D: The influence of PVAQ on habituation for Cz and T4 at 480 to 600 ms. On the left the grand averaged ERPs of Cz (3A) and T4 (3C) for both PVAQ groups are depicted. On the right, the AUC time courses over 25 stimuli (3B and 3D) for both PVAQ groups are visualized. These time courses are based on model estimates summarized in Table 2.

108

360

340

320

300

280

260

240

180

160

140

120

100

80

60

40

20

0.39 -0.6 -0.9 -1.3 -0.2 -2.1 -1.4 0.11 1.39 -0.6 -0.4 0.91 0.34 -0.2 -1.2 -0.3 0.16 -0.1 -1.4

0.28 -0.7 -1.1 -0.8 0.83 -0.9 -1.1 0.66 0.63 -0.6 -0.7 0.22 -0.6 -1.3 -1.8 -1.4 -1.1

0.57 -1.3 -1.3 -1.7

P3

P4

540

520

640

600

580

900

880

860

840

820

800

780

760

740

720

700

680 -2 -1.7

-2 -0.7 -1.1 -2.9 -2.6 -1.9 -1.5 -1.2 -0.8 -1.4

-1 -1.1 -0.8 -0.1 -0.6

0.2 0.45 0.37 -0.2 0.22 0.68 -0.1 -1.1 -0.5 -0.3 0.01 -0.6 -0.5 -0.5 0.16 -0.5 0.62 -0.1 -0.3

-1 -1.4 -1.2 -1.6 -0.5

-0 -0.2 -0.6

-1 -2.2 -1.6 -0.9 -0.2 -0.6 -1.9

-2 -2.3 -2.8 -1.5 -1.7 0 0.27 -0.3 -0.5 0.04 1.04 -0.2

-1 -1.1 -0.5 -0.3 -0.1

-1 -0 -1.8

-1 -0.6

-1.6 -1.5 -2.2 -0.9 -0.9 -1.4 -1.1

-1 -1.7 -0.3 -0.7 -0.5 -0.8 -0.7 -0.7 -1.2

Interaction PVAQ*dishabituation

B.

200

180

160

140

120

100

80

60

40

20

360

340

320

300

280

260

240

220

-0.5 0.57 0.69 0.91

-0.4 0.67 0.85

-0.5 1.28 0.89 1.46 0.74 1.67 1.08 -0.3 -1.6 0.49 -0.4 -1.3 -0.4 -0.4

1.52

2.08 2.18 1.38 1.34 1.87

-0.6 0.98 1.27

-0.2 0.18 0.82 0.42 -0.3 0.81

0.26 2.05 1.24 1.17 0.15 0.45 0.94 -0.4 -1.4 0.72 0.95

Pz

P3

P4

T3

T4

Oz

O1

O2

1.3 0.12

0 -0 0.33

600

580

500

480

460

440

1.2

0.8 0.11 0.67 0.52

0.7

-0 0.34 -0.5 0.34 0.38 0.98 0.22 0.24

-0 0.63 0.73

1 0.75 1.12 0.86 0.79 1.58 1.48 1.26

0.6 0.76

0.9 1.29 1.18 1.99 1.17 0.56

0.4 1.27 1.32 0.09

0.7 1.35 -0.3 0.43 0.88 0.41

0.9 1.33 -0.4 0.67 1.08 0.55 1.42 -1 0.03 1.13 0.02 0.04 0.41 1.14 1.06 1.42

0.6 0.65 0.69 -0.1 0.39 0.52 -0.2 0.57 -0.4 -0.6 0.66 1.14 -0.7 0.44

-0 -0.9 -0.1 1.74 1.75 1.48 1.24 0.91 0.79 1.61 1.12

-1 -1.9 -0.1

1.5

1 1.81 1.75 1.95 2.31 2.16 1.44 3.01

0.1 -0.7 0.41 1.12 1.41 0.78 0.62 0.53 0.44 2.04

-1 0.59 1.06 -0.4 -1.6 -0.3 0.86 1.27 1.09 0.64 0.68 0.21 1.08 0.84 1.02 1.02 0.26 -0.3 0.89 1.05 -0.5 -0.9 -0.1 1.05 -0.4 0.08 0.68 0.56 -0.1 0.68 1.51 0.93

2.2 1.33 -0.8 -1.3

-0 -0.9 0.24 1.73 0.93 -0.7 -0.1 1.19 1.46

0.4 -1.5 -0.9 0.66 0.85 -0.2 -0.2 -0.2

1.4 1.09 1.79 1.12 1.44 1.93 1.43 1.88 1.6 0.69 1.92 1.69 2.35 1.17 1.18 1.68 1.67 1.48

0.1 0.86 1.06 1.38

0.9 0.59 0.34 0.73 1.12 1.3 1.01 1.55 0.25 0.58 1.32 1.19 0.99

-0 0.71 0.85 1.31

1.1 1.05 1.32 1.05 0.53 0.63 1.57 1.27 1.63 0.35

1.7 2.02 0.52 0.83 1.35

1.4 0.68 0.93 0.81 0.82 1.23 0.79

1.8 1.48 0.88

0.8

1 1.06 1.32 1.52 0.82 1.32 1.76 1.56 1.78 0.76

0.3 0.37 0.03 1.04 0.63

1.9 2.34 2.76 1.42 1.59 1.99 1.02 0.76 0.82 0.73 1.39

0.9 0.21 -0.1 0.02 1.64 1.83 2.15 2.52 1.81 1.97 1.63

1.5 0.41 0.54 0.81 0.41 0.89 0.27 -0.2 0.21 0.93 0.55 0.44 1.42 1.41 0.58

0.6 0.55 1.14 0.26 -1.3 -1.7 0.58 1.18 -1.2 -1.9 -0.8 1.58 1.17 -1.1

0.5 0.88 0.7 -0.3 0.37 0.78 1.6 1.16 1.22 1.03 0.77 1.34

0.5 0.03 -0.4 0.31 1.05 0.52 0.16 0.19 0.67 0.63 0.61 0.09

2.3 2.23 1.72 0.96 0.92 0.74 1.11 1.34 0.67 0.73 0.97 1.72 1.61 0.93 1.37 1.56 2.05 1.07 0.83 1.44 1.31 1.77

-1 -0.7 0.62 0.53 -0.6 -0.1 -0.4 1.06 1.96 1.82 1.89 1.42 1.58

0.8 0.31 0.41 1.78

-0

0.9 0.85 0.62 1.06 0.91 2.64 1.82 0.86 0.44 0.96 1.57 0.51 0.45 0.29 0.52

1.6 2.61 2.42 2.15 1.67 1.23 2.07 1.92 2.33 0.85 1.41 3.18 2.73 1.99 1.76 1.49 1.06

1.2 0.42 0.38 1.89 0.83 2.09 1.17 1.05 1.14 1.55 1.25

0.5 1.02 0.67 0.55 -0.1 1.01 1.78 2.03 1.51 1.62 1.75 1.96 1.35 0.84

1.8 1.34 0.87 0.79 1.78 1.34 0.2 -0.2

0.5 0.38

640

-0 0.68 1.16 0.71 0.02 0.51 0.11 0.33 1.17 1.16 1.39 2.14 2.84 2.52 3.16 2.68 1.93 2.91 2.08 1.15 0.45 1.56 2.58 2.45 1.26 1.45 2.05 3.25 1.73 1.94 1.48

1.7 0.81 0.46 1.11 0.94

1.5 0.28 0.42 -0.6 1.88 0.98

0.7 -0.5 0.76 1.67 -0.3 -0.4

0.6 1.01 -0.9 -0.8 0.73 0.68 -0.1 0.46 0.87

-0 1.76 1.45 -0.3 -1.4 0.68 0.41 -0.8 -0.6 -0.3 1.21 0.38 -0.2 0.08

0.6 -0.9

520

0.6 0.76 1.52 0.84 1.33 0.76 0.88 0.75 0.29 0.61 0.09 2.12 1.86 2.19 2.16 1.64

-0 -1.1 1.53 -0.5 -0.3 0.17 0.17 0.62 -0.3

0.6 -0.4

620

1.6 1.35 0.77

660

0.1 -1.2 0.38 -0.8 -0.2 0.92 -0.3 0.72 1.74 0.89 0.11 0.06

680

Figure 4 A and B: ERFIA predictor blots of the interactions PVAQ with linear habituation and quadratic habituation (dishabituation). Columns represent consecutive 20ms ERFIAs whereas rows display cranial locations. Cells with significant results are colored (p < .05) and in a lighter shade (p < .10) The plus or minus sign expresses the direction of the relationship.

0.2 -0.2

0.91 1.19 0.49 1.45 1.13 1.33 0.95

C4

1.7

560

1.2 -0.6 0.59 -0.1 0.96 1.01 0.85

540

-0 0.73 0.07 0.37 0.21 0.75 0.87 1.76 1.84

-0.4 0.47 0.68 0.52 -0.8 0.81 0.94 0.11 0.09 0.79 0.61 -0.5 0.84 0.63 1.72 1.28 1.38 0.75 1.29 1.25

-0

-0.1 0.54 0.39 1.41 0.83 1.85

0.6 0.05 -0.8 -0.6 0.18 -0.9 -0.3 0.15 0.57 -0.7 0.17 0.77

0.01 0.87 -0.1 0.64 1.14

0.1 0.89

C3

0.1 -0.5

Cz

0.1 0.68 0.23 1.22

F4

0.4 -0.1 -0.4 0.23 -0.3 -0.3 0.02 -0.3 -1.7 0.07

-1 -0.8 0.32 0.19 0.36 0.63 1.52 0.16 0.23 0.26 0.25

380

0.26 0.78 -0.1

-0 0.75 -0.8 0.62 0.58 0.65 0.04 -0.8 -1.4 0.43

400

F3

Fz

-1

0 -0.2 -0.9 -0.8

700

-1 0.73 -0.3 -0.9 -1.3 0.38 -0.4 -0.9 -0.4 -1.1 0.9 0.05 -1.1 0.23

720

-2 -1.2 -0.8 -0.7 -0.7 -0.6 0.39 -0.2 -0.5 0.13 -0.6 0.55 0.72 -0.6

740

1.3 -0.2 -1.1 -1.4 -1.1

760

-1 -0.8 0.95

780

-1 0.79 1.04

840

-2

-0.5 -2.1 -1.3 -1.3 -0.4 -0.6 -0.6 0.44 1.16 -0.9 -0.9 0.17 0.66 -0.8 -1.4 -0.5 0.65 -0.2 -1.4 -1.2 0.28 0.86 -0.2 -1.8 -1.8 -1.4 -1.1 -0.8 -0.8 -1.8 -0.9 -0.3 -1.1 -1.3 -0.1 -0.8 -1.2 0.65 -0.4 -0.7 -0.3

860

1.2 0.55 -0.8 -0.7 0.69 -0.2 -0.4

0.24 -0.1 -0.9 -0.7 0.13 -1.1 -0.4

O2

800

-0 -0.5 -0.4 0.18 -0.4 -1.2 -0.8 -1.2

O1

420

-1 -0.5 -0.6 -0.4 -1.2 -0.8 -0.9 -0.9 -0.1 0.23 -0.8 -0.9 0.88 1.09 0.25 -1.2 0.41

880

-1 -1.3

920

-0

940

0.4 1.42

960

1.5 0.21 -1.3 -0.7 1.12 0.75 -0.8 -0.9

0.42 -0.9 -1.1 -0.8 -0.5 -1.6 -0.1 1.09 1.52 -0.5 -0.9 1.61

Oz

-2 -1.1 -0.3 -1.3 -1.6 -0.9 -0.5 -0.9 -0.3 -0.9 -1.5 -1.6 -1.9 -2.5 -3.3 -3.1 -3.8 -3.2 -2.4 -3.2 -2.3 -1.3 -0.7 -1.9 -2.7 -2.6 -1.4 -1.4 -2.2 -3.2 -1.6 -2.2 -1.6 -1.2 -1.7 -1.8 -2.1 -2.5 -2.1 -1.1 -2.8

-1 -0.5 -0.9 -0.2 0.29 -0.1 -0.9 -0.7 -0.2 -1.3 -1.6 -0.4 0.22 0.09 0.92 0.09 0.03 -0.3 0.05

-1 -0.9 -1.2 -1.1 -0.4 -0.4 -1.3

-2 -0.9 -0.6 -0.6 -0.5 -1.2 -1.6 -1.8 -0.3 -0.6 -1.2 -1.6 -0.6

-1 -1.3

-1 -0.6 -1.3 -0.1 -0.4 -0.8 -0.6 -0.3

-1 -0.2 0.27 -0.1 -1.8 -1.9 -2.2 -2.4 -1.8 -1.9 -1.5 -1.2 -0.6 -0.7 -0.6 -0.6 -1.1 -0.5 0.22 -0.5 -0.8 -1.4 -0.6 -1.1 -0.7 -1.5 -0.9 -1.1 -1.5

-2 -2.2 -1.6 -1.6 -1.5 -1.7 -1.1 -0.8 -0.8 -0.7 -0.9 -1.3 -0.6

-1 -1.2 -0.9 -0.2 0.06 0.38 -0.7 -0.4 -0.5 0.18 -0.4 -0.2 -0.7 0.36 -0.4 -0.6 -1.2 -0.6 -0.4

-2 -1.9 -1.4 -0.8 -0.6 -0.3 -0.7 -1.1 -0.3 -0.3 -0.6 -1.3 -1.4 -0.6 -0.9 -0.9 -1.7 -0.7 -0.7 -1.1 -0.7 -1.1

1.5 -0.7 0.21 1.38 -0.1 -0.1 -0.1 0.22 0.94 0.56 -0.9 -0.7 0.48 0.07 0.17 -1.2 -2.2 -1.9 -1.9 -1.5 -1.6 -1.8 -1.4 -0.8

-1 0.44 -0.7 -1.5 0.14 0.41 -1.6 -0.6 -0.2 -0.8 -0.7 -1.3 -0.2 -0.4

920

820

-2 -2.5 -1.5 -1.4 -2.2 -1.9 -0.6 -0.4 0.34

940

900

-1 -0.3 0.01

960 980

T4

-1 0.15

620

-0 -1.1 0.22 -0.5 -1.5 -0.7

-2 -0.9 -2.2 -1.2 -0.9

-1 -0.9 -0.5 -0.1 -1.2

980

1000

T3

-1 -2.1

-1 0.07 1.24 -1.6 0.43 0.29 -0.4 -0.5 -0.7 0.17 0.15 -0.6 -0.7

Pz

-1 -1.6 -1.3 -1.7

-1.1 -1.2

C4

-1 -0.3 -0.2 -0.8 -0.7 0.51 -0.8 -0.8 -1.8 -1.3 -1.4 -0.8 -1.4 -1.5 -1.3 -0.3 -0.3

0.34 -0.6

0.9 -0.9

660

-0 -0.2 -0.8 -0.3 -0.5 -0.3 -0.9 -1.2 -2.1 -2.1 -1.8 -2.6 -2.5 -2.3 -1.7 -1.2

C3

-1 -0.7

560

-2 -1.9 -0.8 0.42 -1.5 -0.3 0.06 -0.7 -0.9 -1.4 -0.8 -1.2 -0.8 -0.9 -0.8 -0.3 -0.5 -0.1 -2.2 -1.9 -2.3 -2.2 -1.5

-0 0.78 0.35 -0.2 -0.8 0.95 -0.2 -0.8

1000

-0 -1.4 -1.2 -0.9 -1.7 -1.2 -0.7 -0.5 -0.2 -0.6 -0.5 -0.5 -0.7 -0.7 -2.4 -1.6 -0.9 -0.4 -0.7 -1.3 -0.3 -0.1 -0.1 -0.3 -0.1 -0.4

0.2 -0.7 -0.7 -1.7 -0.8

380

-0.1 -1.1 -0.2 -0.7 -1.4 -0.8 -0.2 0.84 0.86

400

Cz

420

F4

440

-0.1 -1.1 -0.1 -0.3 0.49 0.57 -0.3 0.58 0.56 0.06 0.39 1.81 0.11 -0.1 -0.3 -0.1 -0.9 0.02 0.38 -0.3 -1.2 0.01 1.35 -0.4 0.57

460

-0.1 -1.1 0.51 -0.8 -0.6 -0.6 -0.2 0.76 1.58 -0.5 1.03 0.97 -0.2 -0.2 -0.2 -0.6 -1.4 -0.3 -0.5 -0.4 -0.7 -1.3 0.52 -0.6

480

F3

500

Fz

200

Interaction PVAQ*linear habituation 220

A.

Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation

109

Chapter 5 Table 2. Model estimates for AUC 480-600 ms of Cz and T4.

EEG electrode

Variable

β

CI 95%

p-value

Cz

Intercept

81.10

-76.42 - 238.62

0.307

Trial number

6.53

-6.34 - 19.40

0.319

Trial numberquadratic

-0.14

-0.62 - 0.32

0.534

PVAQ median split

162.71

44.67 - 280.75

0.007*

PVAQ*trial number

-18.25

-36.48 - -0.02

0.05*

PVAQ*Trialquadratic

0.68

0.01 - 1.34

0.046*

Intercept

-132.43

-260.10 - 4.79

0.042

Trial number

11.30

1.06 - 21.56

0.031

Trial numberquadratic

-0.33

-0.70 - 0.04

0.079

PVAQ median split

215.41

104.91- 325.90

<0.001*

PVAQ*trial number

-26.77

-41.48 - -12.06

<0.001*

PVAQ*Trialquadratic

0.83

0.30 â&#x20AC;&#x201C; 1.35

0.002*

T4

Discussion This study investigated two related questions concerning pain hypervigilance in painfree individuals: Is pain hypervigilance associated with cortical processing of painful stimuli, and, if so, does pain hypervigilance moderate the level of habituation to painful stimuli? With regard to the first question, the answer appeared to be affirmative. A main effect for pain hypervigilance was demonstrated from 440 to 580 ms post stimulus. A high PVAQ score resulted in a larger (more positive) AUC, which may be interpreted as a stronger processing of the stimulus. The range in which the association was found, however, did not correspond with the a priori expected regions (N140, P300), which are thought to embed pain-related information. Moreover, the PVAQ main effect was independent of pain-related variables such as habituation (time course) processes as well as pain and sensation threshold.

Secondly, our data indicate that pain hypervigilance interacts with habituation in the region from 480 to 600 ms. In addition, post-hoc analyses of the sum of these ERFIAs demonstrate that the influence of PVAQ on habituation appears to be relatively robust.

Figures 3B and 3D show the difference in the habituation course over the 25 trials between high and low PVAQ for Cz and T4. In the high PVAQ group, the habituation course is a parabola that opens upward, whereas the parabola opens downward in the low PVAQ group. The interpretation of these opposite habituation parabolic curves is as follows: the high PVAQ group shows increasingly positive ERFIAs in the second part of the experiment. This is indicative of a stronger cortical processing of the stimuli. The opposite process, where the parabola opens downward, a relative decrease in AUC size is apparent for the low PVAQ group in the second part of the experiment. All other electrodes show a similar opposite habituation pattern, some being significant, others

110

Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation not. It is important to understand the difference between the averaged ERPs (Figure 3A and 3C) and the habituation curves. The averaged ERPs indicate an overall (uncorrected) difference between groups, however, optical differences need to be interpreted with caution. First, a ‘traditional’ averaged ERP does not display information on the variance. Second, trial effects inherently disappear through averaging. A typical example can be seen on the T4 electrode of the averaged ERP (Figure 3C). From 480 to 600 ms, a difference between the high and low PVAQ group is practically invisible. The habituation course in this range, however, differs highly significantly between the groups (Table 2).

In sum, an averaged ERP only reveals part of the whole story. It is of great importance to analyze the ERP with a multilevel technique in which single trials are nested within subjects. In this way, dynamic time changes in the brain can be unraveled, resulting in a more in-depth understanding of the cortical processing of painful stimuli.

Although an effect of pain hypervigilance on cortical processing is demonstrated, this association could not be ‘translated’ to an effect on NRS pain report (Figure 1). This discrepancy in findings may be related to several factors. First, it is known that the so38–41 called threat value of a stimulus influences the pain experience.

In our study, the pain experiences were provoked within an experimental setting. In such a setting the participant has an intrinsic control on the experiment by being able to stop the experiment at any time. Therefore, the threat value in this experiment may be perceived as relatively low. Second, Crombez and colleagues questioned whether hypervigilance 42 directly amplifies the experience of pain. The idea is that hypervigilance rather intensifies escape or avoidance tendencies, and that amplification of pain can be a result of 42,43 repeated failure to distract from pain.

The NRS scores in this experiment only measures the intensity aspect of pain, although it is conceivable that hypervigilance may influence other behavioral and affective aspects of pain. Third, hypervigilance may act as a risk factor in healthy, pain-free individuals in whom enhanced pain processing in 9 the EEG is already manifest, but the actual reporting of pain is still unaffected. Certain conditions or triggers such as acute or chronic pain or an emotional state such as anxiety and depression, need to be present before hypervigilance as a risk factor may result 44–46 in higher perceived pain intensity.

In accordance with this line of thought, a study

by Lautenbacher demonstrated that hypervigilance was a predictive factor, for postoperative pain, accounting for 17% of the variance, in a preoperatively pain-free study 10 population.

In the present study, which included pain-free subjects, a relationship of PVAQ with pain report was not found. However, in chronic pain patients, pain hypervigilance is 3,5,6,47 associated with a more pronounced pain experience.

In addition, previous ERP research provides evidence that several chronic pain populations display a decreased 29,30,37,48 habituation compared to pain-free controls.

Taken together, a mechanistic process in the development of (chronic) pain may unravel as follows: starting with a high level of pain hypervigilance, antinociceptive processes such as habituation may be

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Chapter 5 reduced. After being exposed to repeated painful events, a hypervigilant subject may be more vulnerable to develop chronic pain. It would therefore be instructive to attempt to replicate this study in a sample of chronic pain subjects.

Limitations The overall PVAQ score of our study population (mean 28) was significantly lower than the reported mean in the Dutch validation study in pain free individuals (mean 34.4), 8,10 but did not differ from a healthy presurgical group with a mean PVAQ score of 32.

The high PVAQ group of our study had a mean score of 39.5 (SD 7.4), which is comparable to reported means in chronic pain populations such as fibromyalgia (mean 40, SD 2 12.1). Therefore, we consider the contrast between the PVAQ groups in our study to be adequate.

Pain hypervigilance was measured with the PVAQ questionnaire, which investigates an explicit form of hypervigilance. Hypervigilance is thought to be a partly automatic pro42 cess, which is unintentional, uncontrollable and unconscious. We did not account for the implicit form of hypervigilance which can be measured with attentional experiments such as the dot-probe task. The results only show that PVAQ has an effect on the habitation of 25 painful electrical stimuli. However, we do not know if this effect is restricted to painful stimuli or that it is a general effect on habituation of various stimuli. A next study should incorporate a non-painful condition to evaluate, whether the PVAQ effect is specific for painful stimuli. Due to the explorative nature of this study, the number of tests was large. Strict corrections, however, may be too conservative and may discard relevant findings. In the future, the number of tests can be decreased by testing a specific latency range based on previous findings. For PVAQ we suggest the range of 480 to 600 ms. As a first attempt, a post-hoc analysis was performed for the region of interest in which PVAQ showed a significant effect. In this experiment, only 14 EEG electrodes were used. Consequently, spatial information could not be assessed. A combined EEG study with fMRI would be of interest to investigate both temporal and spatial information regarding the influence of pain hypervigilance on habituation.

Conclusions To our knowledge, this is the first study to investigate the influence of pain hypervigilance, measured with the PVAQ questionnaire, on cortical processing of painful stimuli, as well as its habituation. An main effect of pain hypervigilance was demonstrated from 440 to 580 ms post stimulus. In addition, pain hypervigilance interacted with the habituation course in the region from 480 to 600 ms. In conclusion, pain hypervigilance impacts cortical processing of pain and is associated with dishabituation to painful stim-

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Influence of Pain Hypervigilance on Cortical Processing of pain and its Habituation uli in healthy subjects, suggesting that hypervigilance may modulate the pain experience through altered cortical habituation.

Acknowlegdements We are grateful to Marga Schnitzeler, for the recruitment of participants, performing the EEG measurements and data management and to Dr. Wolfgang Viechtbauer for statistical advise, both from the Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre.

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Does pain hypervigilance further impact the lack of habituation to pain in individuals with chronic pain?

A cross-sectional pain-ERP study

Vossen CJ, Luijcks R, Van Os J, Joosten EA, Lousberg R. Does pain hypervigilance further impact the lack of habituation to pain in individuals with chronic pain?

A cross-sectional pain ERP study. J Pain Res. 2018;11. doi:10.2147/JPR.S146916.

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Abstract Aim: In chronic pain, habituation is believed to be impaired, and pain hypervigilance can enhance the pain experience. The goal of this study was to determine whether pain hypervigilance further worsens habituation of event-related potentials, measured in a pain rating protocol of 25 painful somatosensory electrical stimuli, in patients with chronic pain.

Methods: Pain hypervigilance was assessed with the Pain Vigilance Awareness Questionnaire (PVAQ) and analyzed using the ERFIA multilevel technique, which enables one to study within-session habituation. In a cohort of 111 participants, 33 reported chronic pain. This chronic pain group was compared with 33 pain-free individuals, matched for age and sex.

Results: The relationship between pain status and habituation was not moderated by pain hypervigilance. Chronic pain status affected linear habituation and dishabituation (quadratic function) from 220 to 260 ms for nearly all electrodes and from 580 to 640 ms for frontal electrodes. The effect of pain hypervigilance on habituation was observed primarily from 480 to 820 ms poststimulus for right-side and central electrodes.

Conclusion: Pain hypervigilance and chronic pain independently influence habituation to painful stimuliâ&#x20AC;&#x201D;not synergistically. To confirm that these effects are mediated by separate pathways, further research is required, in which EEG is combined with other modalities with adequate spatial resolution, such as fMRI.

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Does pain hypervigilance further impact the lack of habituation to pain?

Introduction The perception of pain does not result solely from incoming noxious information—other factors, such as cognition, emotions, and attention, affect the perception of pain and its subsequent behavior. For instance, acute pain can serve as a protective mechanism, signaling the body to rest, allowing healing to commence. However, in other circumstances, such as in dangerous situations, pain must be ignored to meet a higher-order 1 goal that is prioritized over momentary pain. Thus, the brain modulates this perception through the facilitation or inhibition of nociceptive input. As a result, pain perception arises from a balance between nociceptive and antinociceptive mechanisms.

In chronic pain—pain that persists or recurs for more than 3 to 6 months—acute war2 nings are less functional. Phenomena such as peripheral and central sensitization faci3 litate the development of chronic pain. On the other hand, inhibitory pathways, among 4 which habituation, may protect against chronic pain. There are indications that the 5,6 balance between nociceptive and antinociceptive processes is distorted. One of the inhibitory (antinociceptive) pathways that is believed to be impaired in chronic pain is habituation. Habituation is a decrease in the behavioral response which occurs after repetition of a variety of types of stimuli and is considered to be an elementary form of 7–9 learning.

Habituation to painful stimuli might protect against the development of 5,10,11 12 chronic pain.

Habituation is thought to serve as an attentional filter in pain. Impaired habituation has been demonstrated in several chronic pain populations, such as 13–16 in groups with low back pain, migraine, and fibromyalgia.

Attention is also believed to facilitate nociceptive mechanisms. Paying attention to nociceptive input renders it more painful. Thus, a cognitive factor that has been proposed to increase pain perception is a preoccupation or heightened attention to pain, 17 also known as pain hypervigilance. Attention to pain can be measured by the Pain 18 Hypervigilance and Awareness Questionnaire (PVAQ). This questionnaire is validated for healthy subjects as well as several chronic pain populations. It is assumed that hy18–21 pervigilance to pain-related stimuli increases pain experiences.

Pain hypervigilance 22 has also been suggested to predispose healthy individuals to chronic pain.

Based on their superior temporal resolution, event-related potentials (ERPs) are suitable for the assessment of habituation to pain. ERPs are time-locked cortical responses to (painful) stimuli that are derived from ongoing EEG activity. ERPs are EEG-segments (socalled epochs) around time markers of delivered stimuli in the ongoing EEG. Recently, the event-related fixed-interval area (ERFIA) multilevel technique was introduced to 23 analyze ERPs, having several advantages in the study of habituation. Multilevel analyses of ERPs allow one to examine the within-session time course—i.e., habituation— 24 over trials, whereas other techniques merely analyze between-session habituation.

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Chapter 6 Also, person-by-time effects and their nonlinear properties can be modeled. For example, in studying various forms of habituation, a linear function for stimulus number can be incorporated into the model to analyze linear declines over 25 stimuli. By modeling a parabolic (quadratic) function, an initial decrease and subsequent rise in the response over 25 stimuli can be examined, representing impaired habituation. In this article, we will use the terms “linear habituation” for the linear function and “dishabituation” for the quadratic function.

By subdividing the poststimulus epoch into small fixed-interval areas (ERFIAs), the time 23 course over trials can be studied for various poststimulus latencies. For instance, the manifestation of habituation might differ at 300 ms versus 400 ms poststimulus. Two ERP studies analyzed several aspects of habituation, chronic pain, and pain hypervigilance using the ERFIA multilevel method. The first study, conducted in individuals with chronic low back pain and pain-free controls, reported that linear and nonlinear habitu13 ation depended on chronic pain status over a broad poststimulus range. The second trial demonstrated that pain hypervigilance impacts the cortical processing of painful stimuli and its habituation in pain-free subjects (Vossen, submitted 2017).

Based on this collective evidence, chronic pain and habituation are linked, and pain hypervigilance and habituation are associated in pain-free subjects. However, the extent to which chronic pain, pain hypervigilance, and habituation are related is unknown.

Thus, we hypothesize that the impaired habituation that is observed in chronic pain is affected by pain hypervigilance, in which the attention to pain increases.

In this study, we tested the hypothesis that the effects of pain status on habituation are moderated by pain hypervigilance, as measured with the PVAQ, expecting that greater pain hypervigilance in chronic pain further impairs habituation of event-related EEGs compared with pain-free hypervigilant subjects. In addition, the relationship between pain hypervigilance, chronic pain, and habituation might become apparent at the cortical and behavioral levels. Thus, we speculated that higher pain hypervigilance scores and chronic pain status increases the pain intensity report of stimuli, as measured on a numerical rating scale (NRS), and alters the course of NRS scores over 25 consecutive trials.

Methods Participants This study was a subset of a larger project that evaluated psychophysiological reactivity as a predictor of change in pain complaints. The original study was approved by the medical ethics committee of Maastricht University Medical Centre (NL40284.068.12/ METC 12-3-015). The study participants consisted of a sample of the general population of Maastricht recruited using flyers that were distributed throughout 5 neighborhoods

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Does pain hypervigilance further impact the lack of habituation to pain? in Maastricht. The inclusion criteria were age between 18 and 65 years and a good understanding of Dutch. The exclusion criteria were: (1) structural use of psychoactive medications, such as antipsychotics, antidepressants, antiepileptics, and anxiolytics, during the past year, (2) regular use of alcohol > 10 U/day during the past year, (3) epilepsy, (4) psychotic disorder, (5) visual or hearing disability, and (6) analphabetism or dyslexia. Subjects were only included if they did not consume any alcoholic beverages the evening prior to the experiment and refrained from caffeine-containing beverages 3 hours before the start of the experiment. Before the experiment, written informed consent was obtained. Subjects were rewarded with 50 euros for their participation after completion of the entire experiment.

Out the 111 subjects in the original study, 33 had chronic pain complaints, based on the SF-36 and BPI questionnaires. These 33 patients were frequency-matched for age and sex with pain-free controls from the same dataset. The pain-free participants were selected per the following criteria: (1) no pain complaints at the time of the experiment, (2) no pain complaints in the 6 months before the experiment, and (3) no chronic use of pain medication in the previous 6 months.

Questionnaires Prior to the habituation experiment, subjects were asked to complete 3 questionnaires in an adjacent room in the laboratory: (1) Short-Form Health Survey (SF-36), consisting of 36 items, to assess their general health status and, in particular, the bodily pain sub25 scale; (2) Brief Pain Inventory Short Form (BPI-SF), which comprises 9 items on pain 26,27 complaints; and (3) Pain Vigilance Awareness Questionnaire (PVAQ), which contains 16 items on pain behavior regarding attention to pain and attention to changes in 18 pain. The PVAQ consists of 16 items that are rated on a 6-point scale (0-5). A combined sum score after inversion of two questions between zero and five is calculated, with a maximum score of 80. The Dutch version of the PVAQ has a good internal con28,29 sistency and a fair test-retest reliability.

For this questionnaire there are no defined cut-off points. Higher scores indicate a higher degree of pain hypervigilance. Additional questions were asked concerning subject characteristics (age, sex), pain complaints in the last 6 months, location of pain complaints, and medication use.

Electroshocker and stimuli An electroshocker (Shocko-100-AA-20, developed by Maastricht Instruments BV and approved for use in experimental studies) was used to apply the electrical stimuli. Electrical pulse stimuli (duration 10 milliseconds) were delivered intracutaneously to the left 30 middle finger, per Bromm and Meier. With a dental gimlet of 1mm, a small lumen in the epidermis was carefully prepared, ensuring that the procedure was not painful. A concentric gold electrode with a diameter of 0.9 mm and a 1 mm extension was at-

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Chapter 6 tached to the prepared lumen and fixed with tape. Proximal to the prepared finger, a tm grounding wrist strap (3M wrist strap, WBB-AFWS61M) was placed around the wrist.

Next, the sensation and pain thresholds were measured to determine the intensity of the stimuli for the habituation protocol by gradually increasing the intensity of the stimulus, starting at 0 and increasing by steps of 0.05 mA. The first intensity that was consciously experienced was defined as the sensation threshold; the first intensity that was regarded as painful was considered to be the pain threshold. Subjects were asked to say “stop” as soon as they perceived the intensity of the stimulus as painful. This procedure was performed 3 times in total to generate a reliable measurement. The maximum stimulus intensity never exceeded 5 mA.

Habituation protocol Based on the subject’s difference between the sensation and pain thresholds, a stimulus that was 25% above the pain threshold was calculated as follows: Delivered habituation stimulus = pain threshold + 0.25*(pain threshold − sensation threshold).

The intensity of the habituation stimulus was experienced as painful but nevertheless tolerable. The habituation protocol comprised 25 identical stimuli, with durations of 10 milliseconds. The interstimulus intervals (ISIs) varied between 9 and 11 seconds. Subjects were informed that they would experience a series of stimuli and were instructed to determine the differences between stimuli. The intensity and number of stimuli were unknown to the subject. Subjects were asked to verbally rate the intensity of each stimulus on a scale from 0 (no sensation) to 100 (the most severe pain imaginable). For standardization purposes, subjects were asked to score the first stimulus as 60.

EEG measurement The EEG measurements took place in an electrically and sound-shielded cubicle (7.1 2 m ). EEGs were recorded with the BrainAmp Amplifier using BrainVision and sampled at 1000 Hz. Using the international 10-20 system, Ag/AgCl electrodes were placed on Fz, 31 F3, F4, Cz, C3, C4, T3, T4, Pz, P3, P4, Oz, O1, and O2, respectively. Reference electrodes were placed on the earlobes, and a ground electrode was fixed at Fpz. To measure vertical eye movements, electrooculogram (EOG) electrodes were placed 1 centimeter under the midline of the right and left eyes. All electrodes were fixed with conductive paste.

Data processing In Analyzer 2.0 (Brain Products, München, Germany), trials were segmented from the continuous EEG, from 200 ms before the stimulus to 1000 ms poststimulus, and were

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Does pain hypervigilance further impact the lack of habituation to pain? offline band-pass-filtered (0-50 Hz) and baseline-corrected (interval -200 to 0 ms) (Figure 1). For each subject and the 25 stimuli within a subject, the data (microvolts) for each millisecond between -200 to 1000 ms for all electrodes and EOG channels were imported into SPSS 21.0. Subsequently, a multilevel dataset was constructed for each subject, using a syntax file in which the following calculations were made: (1) 20millisecond event-related fixed-interval areas (ERFIAs) were calculated from 0 to 1000 ms poststimulus, resulting in 50 ERFIAs per trial per EEG electrode per subject, (2) maximum and minimum values of the EOG channel were selected per 20-ms ERFIA, and (3) questionnaire data were added to the dataset. Next, all cases were added to obtain a full multilevel dataset. Left and right EOG activity was included per 20-ms ERFIA in the analysis as covariates.

Ongoing raw EEG

• Segmentation after events for each EEG electrode within a subject (-200 ms to 1000 ms) • Baseline-correction (-200 ms to 0 ms)

Segments

• Partitioning into fixed 20-ms intervals (from 0 ms to 1000 ms = 50 segments) • AUC computations

Event-Related Fixed Interval Areas (ERFIAs)

• EOG rejection of individual ERFIAs

Dependent variable

• Multilevel analysis for all ERFIAs per electrode

Figure 1. The processing steps from the ‘raw’ EEG to ERFIAs serving as the dependent variable for multilevel analysis. First, the EEG is partitioned into epochs, and then a baseline correction is made. Next, the segments are divided into 20-ms intervals, and the area under the curve for every interval for all trials is calculated. An EOG rejection is carried out for all ERFIAs separately. In the last step, the valid ERFIAs per fixed interval serve as a dependent variable in the multilevel analysis.

Statistical analysis Multilevel regression was performed separately for each EEG electrode and every 20ms ERFIA period. The dependent variable in the multilevel model comprised all 20-ms ERFIAs at a particular poststimulus latency from all trials and subjects. The dependent variables, thus consisting of 50 consecutive 20-ms ERFIAs, were assessed for normality.

Subjects represented the highest level in the model, and trial number (1-25 stimuli) was the repeated measure within each subject. Based on the assumption that habituation can differ between subjects, random effects, such as a random intercept and a random slope for trial number, were included. An AR-1 covariance structure was used, assuming that trials that were nearer to each other correlated better than those that were further apart.

As in previous studies, linear habituation (trial number) and dishabituation (parabolic 13,23 relationship, computed as trial*trial) were modeled.

A linear function with a negative coefficient reflects a linear decline in ERFIAs over 25 stimuli. A quadratic function, in which the parabola opens upward, represents initial habituation, after which sensitiza-

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Chapter 6 tion (or dishabituation) occurs over 25 stimuli. To address our research question, the main effects of PVAQ and pain status (chronic pain versus pain-free) on the poststimulus EEG were measured, using the following basic model: ERFIAs of a specific 20-ms range and electrode (ERFIAs20-ms range, location) constituted the dependent variable, which was modeled as a function of the following fixed factors: PVAQmedian split, pain status, trial number, trialquadratic, age, sex, pain threshold, sensation threshold, EOG left, and EOG right.

To answer our research question with regard to whether the impact of pain status on linear and quadratic habituation is influenced by PVAQ score, 2 3-way interactions and 5 2-way interactions were added to the model: PVAQmedian split*pain status*habituationlinear, PVAQmedian split*pain status *habituationquadratic, PVAQmedian split*habituationlinear, PVAQmedian split*habituationquadratic, pain status*habituationlinear, pain status*habituationquadratic, and PVAQmedian split*pain status.

For the PVAQ variable, the group-specific—instead of overall—median split was used to ensure that the relationship between PVAQ and group was separated.

The analyses were performed separately for each consecutive 20-ms ERFIA period (0 – 1000 ms poststimulus) for all 14 cranial locations. The large number of statistical tests necessitated a correction for multiple testing, but the analyses were exploratory. Thus, we chose not to define a specific p-value for statistical significance. Alternatively, we considered only robust effects (3 or more consecutive 20-ms ERFIAs) with p-values ≤ .05 to be significant. For low-power interaction effects, we considered robust effects with p-values ≤ .10 to be significant.

The results were summarized in ERFIA predictor blots, in which the columns represented 50 consecutive 20-ms ERFIAs and the rows represented the EEG electrodes of a given predictor. In each row, cells were given a color when t-values were < -2 or > 2.

Red indicated a positive significant t-value, whereas blue denoted a significant negative t-value.

The influence of pain status and pain hypervigilance on pain report, as measured on an NRS, was examined, as was their effect on the time course. A basic model was used, in which NRS was the dependent variable and modeled as a function of the following variables: pain status, PVAQmedian split, trial number, trialquadratic, age, and sex. Next, the following interactions were added to analyze the influence of PVAQ and pain status on the time course of the NRS variable: PVAQmedian split*pain status *habituationlinear, PVAQmedian split*pain_status*habituationquadratic, PVAQmedian split*habituationlinear, PVAQmedian split*habituationquadratic, pain status*habituationlinear, pain status*habituationquadratic, and PVAQmedian split*pain status.

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Does pain hypervigilance further impact the lack of habituation to pain?

For illustrative purposes, an overall course of NRS across the 25 stimuli was constructed for the 2 groups (chronic pain versus pain-free). All statistical analyses were performed with SPSS 21.0 (Figure 2).

Results Subject characteristics A total of 111 participants were enrolled from April 2012 until August 2014. Of this cohort, 33 participants reported chronic pain complaints and were matched for age with 33 pain-free individuals. Table 1 summarizes the characteristics of the analyzable participants. The mean PVAQ score was 32 (range 7 to 59). The median split for pain hypervigilance score—30 for the pain-free group and 35 for the chronic pain group. The mean PVAQ score differed significantly between groups. Table 2 shows the location of pain in the chronic pain participants, based on the Brief Pain Inventory; 13 of these participants had pain in more than 1 area.

Table 1. Characteristics of the study participants. PVAQ = pain hypervigilance questionnaire score; NRS = numerical rating scale.

Total

Pain-free

Chronic pain

N

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PVAQ score mean (sd)

31.6 (12.1)

28.1 (13.1)

35.1 (10.1)*

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PVAQ median

p-values p <0.012

Age years mean (sd)

42.1 (17.0)

39.1 (16.7)

45.0 (17.0)

ns

Sex M/F

25/41

15/18

10/23

ns

Sensation threshold mA

0.29 (0.16)

0.26 (0.14)

0.32 (0.18)

ns

Pain threshold mA

0.93 (0.52)

0.82 (0.52)

1.03 (0.52)

ns

NRS (of the 25 stimuli) 0-100 (sd)

57 (9.6)

56 (10.2)

58 (9.1)

ns

Table 2. Pain locations in the chronic pain group. Note that 13 subjects suffered from pain in more than 1 location.

Location

Abdomen

Back

Thorax

Lower extremities

Upper extremities

Neck and shoulder

Head/face

N (%)

4 (12%)

9 (27%)

2 (6%)

12 (36%)

7 (21%)

13 (36%)

3 (9%)

Pain hypervigilance and the NRS As a priori hypothesized, in the main model, ‘trial number’ was significantly negatively (p = 0.041) associated with NRS, thus indicating a significant linear decrease of the NRS scores over the course of 25 trials, i.e. representing habituation. No significant differences were noted between the chronic pain and pain-free groups, with respect to the

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Chapter 6 course of NRS scores over 25 stimuli. Nor did pain hypervigilance and pain status influence the time course of the NRS scores in the interaction model (Figure 2).

Main influence of chronic pain status and pain hypervigilance on the ERP In the primary multilevel model, chronic pain status had a robust, significant positive main effect (p <0.05) at 3 or more consecutive ERFIAs between 300 and 400 ms for all electrodes, except for P4 and the occipital locations (Figure 3A). This means that ERFIAs in this region are greater for the chronic pain group compared to the pain-free subjects.

Regarding pain hypervigilance, a significant positive association was observed only for F3 from 940 to 980 ms (Figure 3B).

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Figure 2. Numerical rating scale (NRS) course of 25 trials for pain-free subjects and participants with chronic pain.

Main influence of habituation on the ERP In the main model, significant effects of both linear habituation and quadratic habituation were seen in a broad range of the ERP for all electrodes. Linear habituation was seen from approximately 160 to 460 ms and quadratic habituation 180 to 480 ms (Figure 3C and 3D). In addition, a late significant effect of linear and quadratic habituation was observed from 580 to 660 ms for Fz, and F3 (Figure 3C and 3D).

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Figure 3. ERFIA predictor blots of the variables pain status (A), PVAQ (B), linear habituation (C) and quadratic habituation (D) of the main multilevel model. Columns represent consecutive 20-ms ERFIAs, and rows display cranial locations. Cells with significant results are colored (p < 0.05), and the plus or minus sign expresses the direction of the relationship. Cells in red indicate a positive association, and blue cells denote a negative association

0.03 0.70 1.09 -1.22 -0.63 -0.06 -1.10 -0.18 0.61 -0.24 -0.70 -0.75 -0.39 0.11 0.56 1.04 0.98 1.54 1.42 1.01 0.58 0.95 0.92 0.31 0.67 -0.07 0.39 1.16 0.46 0.61 -0.03 0.28 -0.13 0.57 -0.36 0.54 -0.31 -0.83 -0.16 -0.47 -0.06 -0.09 -0.44 -0.36 0.07 0.40 0.18 -0.49 0.28 0.16

0.37 0.54 0.01 -1.15 0.09 0.37 -0.28 -0.18 0.64 0.64 -0.94 -0.95 -0.48 -0.36 0.55 0.78 0.46 0.91 1.39 1.81 1.17 1.54 1.66 1.67 1.68 0.81 1.28 2.07 1.31 0.96 1.24 1.29 1.00 0.96 0.56 0.24 0.50 0.16 0.25 0.01 -0.64 -0.26 -0.07 -0.88 -0.75 -0.36 -0.12 -0.53 0.04 -1.06

1.37 0.88 0.44 0.13 -0.05 0.40 -0.76 -0.03 1.12 0.39 -0.14 -0.15 -0.22 -0.43 0.40 1.02 0.99 1.35 1.39 1.14 0.74 1.16 1.13 1.09 1.52 0.95 1.44 1.78 0.89 0.93 0.65 1.00 0.77 0.98 0.14 0.06 -0.04 0.25 0.09 -0.05 -0.28 -0.30 0.57 -0.70 -0.53 0.07 0.47 -0.36 0.66 0.21

0.27 0.61 0.85 0.13 0.65 0.31 0.32 -0.38 -1.01 -1.87 -1.37 -0.78 -0.03 -0.60 0.92 1.76 1.18 1.03 0.74 1.02 0.66 2.18 1.34 1.27 0.08 0.73 0.66 0.73 0.50 0.44 0.09 0.99 0.81 0.77 0.71 0.82 0.35 0.23 0.45 0.32 0.43 0.28 -0.48 -0.52 0.55 0.53 0.82 0.46 -0.11 0.08

1.22 -0.26 0.15 -0.42 -0.64 -0.57 0.34 -1.06 -1.05 -0.85 -1.83 -1.60 -1.74 -1.21 -0.88 -0.47 -0.82 -1.03 -1.24 0.08 -0.14 0.26 0.44 1.15 0.58 0.84 0.48 1.22 0.72 1.01 0.51 1.56 0.87 0.58 0.83 0.53 0.85 0.29 0.74 -0.02 -0.20 0.39 0.37 -0.04 -0.25 -0.46 0.17 -0.18 -0.17 -0.66

1.23 1.12 0.07 -1.10 0.19 0.61 -0.37 -0.36 0.22 -1.01 -1.41 -1.48 -1.10 -0.64 0.59 1.17 1.19 0.74 0.35 0.18 -0.07 0.71 0.52 0.32 0.79 -0.04 0.43 0.92 0.28 0.35 -0.27 -0.05 0.05 0.20 -0.05 -0.04 -0.10 -0.23 0.33 -0.19 -0.25 -0.01 -0.19 -0.59 -0.05 0.44 0.41 0.35 0.33 0.17

1.39 0.18 -0.64 -1.50 0.89 1.66 -0.14 -0.36 0.15 -0.33 -1.27 -1.24 -1.51 -1.02 -0.05 0.28 0.19 0.25 0.41 0.48 0.54 0.88 1.20 1.12 1.44 0.79 1.21 1.81 1.31 0.95 1.17 1.10 0.55 0.95 1.03 0.72 1.23 0.70 0.58 0.46 0.00 0.33 0.76 0.37 0.26 0.78 1.19 0.62 0.66 -0.21

0.39 -0.24 -0.63 -0.55 1.17 2.08 0.00 -0.20 0.29 -0.17 -0.65 -0.97 -1.11 -0.97 0.27 0.90 0.87 0.60 0.57 0.28 0.40 0.72 0.65 0.57 1.07 0.52 0.98 1.22 0.65 0.27 0.36 0.50 0.08 0.61 0.24 0.05 0.18 0.16 0.21 -0.04 -0.46 0.11 0.47 -0.14 -0.22 0.49 1.12 1.07 0.42 0.23

2.07 0.27 -0.68 -1.10 0.41 2.22 0.40 -0.78 -0.41 -1.07 -1.40 -1.51 -1.56 -0.90 0.64 1.27 1.48 0.55 0.15 0.24 0.80 1.10 0.84 0.88 0.97 0.74 1.06 1.62 0.69 1.10 0.48 0.66 0.79 0.81 1.30 0.79 1.06 0.62 1.30 1.24 0.91 1.03 1.80 1.12 0.77 1.78 1.49 1.06 1.49 1.37

2.02 -0.36 -1.88 -1.79 1.26 2.80 0.70 -1.07 -1.36 -1.16 -1.27 -1.95 -1.98 -1.59 -0.36 0.32 0.02 0.20 0.46 0.88 0.70 0.70 0.63 1.20 1.35 0.95 1.01 1.69 1.15 1.10 1.06 1.14 0.48 1.22 1.35 1.11 1.49 1.29 1.38 1.13 1.20 1.21 1.85 1.30 1.22 1.76 1.68 1.89 1.38 1.33

1.92 0.53 -0.88 -1.07 1.32 2.49 0.41 -0.95 -0.91 -1.22 -1.05 -1.80 -1.87 -1.03 0.09 1.00 0.89 0.63 0.41 0.45 0.52 0.68 0.59 1.17 0.96 0.85 1.11 1.64 0.83 0.44 0.15 0.66 0.29 0.86 0.86 0.98 1.04 0.68 0.90 0.53 0.64 1.00 1.54 1.14 1.03 1.46 1.62 1.99 1.76 1.61

2.50 -0.29 -0.49 -0.02 1.45 2.55 1.10 -0.30 0.31 0.19 -0.04 -0.70 -1.22 -0.85 0.36 1.15 1.32 0.87 1.07 1.02 1.27 2.22 2.06 1.04 1.69 1.30 1.91 1.76 1.37 1.28 1.38 1.55 1.82 1.46 2.23 1.25 1.91 1.98 1.55 2.04 1.65 1.59 1.95 1.54 1.98 1.75 1.76 1.86 1.76 2.03

1.88 0.69 -0.86 -1.41 1.72 1.83 1.25 -0.30 0.28 -0.20 -0.25 -0.95 -1.53 -0.95 -0.68 0.02 -0.15 -0.10 0.13 0.66 0.21 0.66 0.58 1.09 1.28 1.11 1.25 1.29 0.79 0.82 0.45 1.07 1.07 1.05 1.45 1.48 1.78 2.07 1.77 1.31 1.57 1.97 2.65 1.77 2.19 1.79 2.16 2.11 2.15 1.92

2.05 -0.16 -0.79 -0.72 1.60 2.52 0.88 -0.69 -0.19 -0.03 0.12 -0.88 -1.63 -1.12 -0.18 0.74 0.68 0.64 0.50 0.56 0.69 1.23 1.42 1.51 1.62 1.53 1.66 1.60 1.02 0.76 0.57 0.94 0.82 1.09 1.15 1.02 1.32 1.18 1.24 0.77 1.05 1.48 1.90 1.73 1.51 1.47 1.75 1.88 1.63 2.14

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-1.46 -0.72 -0.16 -0.17 -1.06 -0.37 0.35 0.03 -0.51 -1.16 -1.54 -0.22 0.34 0.54 1.14 0.73 1.16 1.40 2.15 0.78 -0.24 -0.15 -0.56 -0.36 -0.27 -0.55 -0.83 -0.82 -1.00 -0.77 0.61 0.71 0.07 0.37 0.60 0.54 0.20 -0.10 -0.34 0.56 0.97 1.06 0.81 0.51 1.23 0.81 0.68 1.72 1.91 1.15

-0.45 0.08 0.01 -1.16 -0.42 1.16 0.48 0.01 -0.33 -0.75 -0.40 0.76 0.36 0.70 1.07 1.26 2.10 1.74 1.92 1.50 0.80 0.21 0.28 -0.06 0.00 -0.38 -0.38 -0.56 -0.42 -0.26 0.43 0.47 0.44 -0.22 0.43 1.15 0.58 0.17 -0.30 0.64 0.98 1.23 0.90 1.55 2.09 1.52 1.29 2.05 2.42 2.51

-0.98 -0.29 -0.41 -0.99 -1.32 -0.02 -0.15 -0.10 -0.74 -1.02 -1.04 0.18 0.31 0.59 0.89 0.38 1.11 1.26 1.60 0.27 -0.56 -0.25 -0.47 -0.63 -0.70 -0.57 -0.93 -1.06 -0.64 -0.39 0.46 0.26 -0.14 -0.55 -0.18 -0.02 -0.08 -0.45 -0.52 -0.22 0.55 0.57 0.39 0.28 0.59 0.74 0.66 1.57 1.37 1.16

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0.40 -0.53 -0.58 1.50 0.75 0.89 0.05 -0.04 0.00 0.43 -0.05 0.87 0.44 0.79 1.68 2.66 2.61 2.57 2.48 2.75 1.57 1.25 1.94 1.29 1.13 0.70 0.67 0.25 0.14 0.48 0.17 0.72 0.31 0.39 0.60 0.83 0.60 0.73 0.95 1.16 0.68 0.63 1.07 1.17 1.14 0.93 1.10 0.82 1.71 1.55

-0.77 0.62 -0.13 -1.17 -1.35 -0.78 -0.07 -0.12 -0.53 -0.93 -0.69 -0.62 -0.34 0.45 1.25 1.68 2.21 2.02 1.95 1.28 0.44 0.28 -0.29 -0.09 0.12 -0.68 -0.69 -0.65 -0.69 -0.37 0.33 0.30 0.06 -0.35 -0.23 0.10 -0.31 -0.07 -0.36 -0.07 0.35 0.44 0.40 0.04 0.75 0.27 0.47 0.70 0.61 0.63

0.13 0.55 -0.05 -1.35 -0.94 -0.07 -0.10 0.00 -0.83 -0.84 -0.46 -0.08 -0.15 0.45 1.41 1.87 2.34 2.32 2.52 1.83 0.77 0.47 0.53 0.35 0.23 -0.26 -0.18 -0.20 -0.32 -0.14 0.05 0.36 -0.30 -0.29 -0.28 0.07 -0.30 0.24 0.02 0.34 0.81 0.46 0.87 0.99 1.27 0.51 0.88 1.50 1.81 1.68

-1.14 -0.13 0.32 -1.37 -1.09 -0.89 -0.53 -0.18 -0.56 -0.62 -0.20 -0.15 0.02 0.63 1.54 2.00 2.30 2.41 2.35 1.40 0.38 0.27 0.04 0.25 0.19 -0.24 -0.10 -0.28 -0.23 0.23 0.40 0.50 -0.12 -0.24 -0.13 0.23 -0.15 -0.09 -0.33 0.33 0.50 0.57 0.85 0.48 1.05 0.77 0.96 1.17 1.10 1.53

0.11 0.96 0.30 -0.65 -0.73 -1.18 0.15 0.02 -0.41 -0.35 -0.18 -0.85 -0.52 0.37 1.57 2.69 3.00 2.20 1.87 0.98 0.74 0.55 0.06 -0.16 -0.46 -0.73 -0.64 -0.74 -0.63 -0.48 -0.42 -0.09 -0.53 -0.38 -0.23 -0.10 -0.05 0.14 0.00 -0.09 0.50 0.35 0.24 -0.05 0.38 -0.11 0.28 0.84 0.44 0.29

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Fz F3 F4 Cz C3 C4 Pz P3 P4 T3 T4 Oz O1 O2

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A. Main effect of pain status

Does pain hypervigilance further impact the lack of habituation to pain?

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Figure 3. ERFIA predictor blots of the variables pain status (A), PVAQ (B), linear habituation (C) and quadratic habituation (D) of the main multilevel model. Columns represent consecutive 20-ms ERFIAs, and rows display cranial locations. Cells with significant results are colored (p < 0.05), and the plus or minus sign expresses the direction of the relationship. Cells in red indicate a positive association, and blue cells denote a negative association

-0.48 -0.16 -1.07 -0.50 0.05 -0.97 0.31 0.85 1.97 3.36 1.29 0.86 2.30 2.88 3.99 3.24 1.91 2.19 1.94 2.30 1.18 1.32 2.43 2.54 1.19 1.00 -0.05 0.64 0.52 1.39 0.13 -1.60 0.60 0.20 -0.33 -1.50 -1.25 -1.28 -0.32 0.55 -1.43 -1.56 -0.52 -1.08 -0.32 -0.02 -0.35 -1.28 -0.24 0.80

-0.62 -0.45 -0.47 -0.17 -0.79 -0.69 0.06 0.69 -0.14 1.36 1.82 1.12 2.02 2.22 2.26 3.17 1.95 2.40 1.72 3.16 2.22 2.51 2.33 2.50 2.30 1.78 1.89 0.78 0.93 0.89 0.29 -1.11 0.94 0.72 0.88 -0.45 0.68 0.60 -0.79 0.05 0.28 0.18 -0.87 -0.92 0.50 0.82 0.93 -0.22 0.02 1.45

-1.25 -0.77 -0.72 -0.60 0.14 -0.13 0.25 0.47 1.24 1.80 1.86 0.49 1.99 2.35 2.40 3.17 1.30 1.80 1.49 2.00 1.36 1.46 2.38 2.49 2.61 1.30 0.61 0.77 0.47 1.11 -0.31 -1.36 0.32 0.83 0.68 -0.76 -0.33 -0.43 0.32 -0.22 -0.51 -0.52 -1.53 -0.62 -0.32 0.15 0.58 -0.74 -0.86 0.35

2.26 1.79 0.71 2.63 2.02 0.58 0.91 0.26 2.06 2.50 2.63 0.79 2.19 2.49 2.50 4.29 4.21 3.84 3.40 2.88 2.91 1.88 2.48 0.72 1.22 0.38 0.28 -0.80 0.69 -0.50 -1.16 -0.65 -0.75 -0.18 -0.15 -0.15 -0.45 0.34 0.30 0.10 0.48 0.66 0.79 0.30 0.30 0.16 -0.62 0.78 0.53 0.15

0.25 0.60 0.36 1.28 0.06 1.94 1.49 2.48 0.49 -0.01 1.53 1.98 2.83 1.91 2.56 2.46 2.28 2.59 1.73 2.39 2.85 3.71 2.49 1.34 1.79 0.21 -0.05 -0.49 -0.39 -0.13 -1.08 -0.77 -0.19 0.20 0.00 0.13 0.57 0.77 0.65 1.15 0.64 0.59 0.75 1.27 2.59 2.10 1.15 1.49 3.21 3.60

-0.54 0.41 -0.24 0.47 -0.31 -1.61 -1.54 -0.28 0.74 2.05 1.98 1.32 2.70 2.54 3.60 3.60 2.69 3.75 2.63 2.28 2.19 1.47 1.94 1.45 1.20 1.15 -0.09 0.10 -0.66 -0.50 -1.27 -1.79 -0.89 -0.57 -0.79 -1.59 -0.90 -1.14 0.14 -0.50 -0.96 -0.98 -1.54 -0.78 -0.26 -0.47 -0.64 -0.97 -0.24 -0.06

-0.71 0.11 -0.53 -0.59 -1.45 -1.28 -0.95 0.10 -0.01 0.89 1.57 2.11 2.79 2.74 2.64 3.02 3.18 3.55 2.63 3.24 3.53 2.72 2.62 2.53 2.10 1.62 1.40 0.85 0.01 -0.25 -1.67 -1.17 -0.14 0.04 0.56 -0.92 0.20 -0.15 -0.19 -0.36 -0.50 0.31 -0.74 -0.28 1.07 0.15 0.96 0.26 0.62 1.93

-0.68 -0.07 -1.21 -0.75 -0.60 -1.79 -1.22 0.00 -0.23 0.57 1.43 0.96 2.40 2.19 2.47 3.51 3.30 3.86 2.38 2.31 2.49 2.32 2.00 2.23 1.48 1.29 0.72 1.15 0.22 -0.15 -1.68 -1.94 -0.96 -0.80 -0.88 -2.10 -0.90 -1.51 -0.79 -1.39 -1.72 -1.17 -1.63 -0.98 0.36 -0.71 0.04 -0.81 -0.44 0.60

0.14 0.98 0.57 0.37 -0.66 -1.14 0.28 0.89 0.85 2.51 2.53 2.30 3.16 2.98 3.26 3.87 4.56 4.19 2.50 2.75 3.15 2.21 2.04 1.19 1.47 1.28 0.19 0.19 -0.55 -1.41 -1.81 -1.71 -1.02 -0.02 -0.03 -1.08 -0.78 -0.72 0.50 0.54 0.42 -0.23 0.48 0.08 1.13 0.23 -0.37 -0.45 0.22 0.28

-0.50 0.62 -0.39 -0.48 -1.39 -1.38 0.92 1.40 1.23 1.81 2.63 2.87 3.29 4.06 2.89 3.68 4.58 3.67 3.17 3.31 3.99 3.14 2.99 2.47 1.52 1.49 0.66 0.57 -0.56 -0.95 -1.93 -0.81 -0.99 -0.57 0.00 -1.23 -0.47 -0.66 -0.50 -0.33 -0.55 0.23 0.54 0.79 1.19 0.69 0.75 0.60 1.76 1.93

-0.02 0.61 -0.26 -0.75 -1.07 -1.57 0.05 1.25 0.46 1.15 1.81 2.02 3.27 2.81 2.86 4.09 5.03 5.04 3.37 3.67 3.92 2.83 2.90 1.99 1.64 1.30 1.18 0.83 -0.07 -0.93 -1.62 -1.63 -1.06 -0.03 -0.67 -1.76 -0.87 -0.99 -0.51 -0.77 -0.98 -0.68 -0.27 -0.04 1.29 -0.09 -0.22 -0.04 0.70 0.82

-0.55 0.95 -0.15 -0.49 -0.67 0.42 1.20 1.62 2.31 2.30 3.25 2.42 3.05 2.68 2.41 2.78 2.57 2.90 1.79 1.52 2.10 2.55 2.78 1.52 1.43 0.73 -0.75 -0.75 -1.64 -1.70 -2.13 -1.73 -1.86 -0.14 -0.42 -1.12 -0.35 -0.37 1.15 0.84 1.16 0.76 1.37 0.90 0.99 0.42 0.19 0.52 1.27 0.07

0.14 0.26 -0.01 0.02 -1.20 -0.44 1.92 2.54 2.70 2.30 4.09 3.08 2.52 2.90 2.43 2.73 3.07 3.27 1.97 2.44 3.13 3.24 2.61 0.87 0.31 -0.22 -0.55 -0.79 -2.03 -2.26 -2.67 -2.80 -2.17 -1.01 -1.19 -0.46 -0.62 -1.14 -0.28 0.16 -0.91 0.16 0.64 0.23 0.59 0.10 -0.04 -0.19 1.11 1.17

0.30 0.25 -0.04 -1.00 -1.48 -0.18 0.87 2.00 2.94 2.19 3.21 2.89 2.88 2.27 2.83 3.32 4.29 4.56 2.66 2.37 3.21 2.51 2.90 2.25 1.46 0.25 -0.74 -1.07 -1.04 -1.54 -2.17 -2.25 -2.11 -0.51 -0.50 -1.11 -0.50 -1.06 0.48 0.29 -0.37 0.04 1.17 0.59 0.76 0.65 -0.44 0.31 1.29 0.96

960

0.51 -0.11 0.97 0.48 0.42 0.50 -0.68 -0.59 -2.20 -3.91 -1.62 -1.78 -3.21 -3.84 -4.65 -4.06 -2.93 -3.12 -2.52 -2.83 -1.45 -1.70 -2.64 -2.58 -0.95 -0.92 0.06 -0.34 -0.39 -0.68 0.18 1.70 -0.21 0.23 0.97 1.83 1.76 1.76 1.03 0.32 2.04 1.97 0.73 1.35 0.70 0.44 0.65 1.33 0.40 -0.49

0.63 0.36 0.48 0.17 0.81 0.42 -0.12 -0.61 0.00 -1.68 -1.86 -1.49 -2.90 -3.18 -2.99 -3.98 -2.76 -3.69 -2.46 -3.76 -2.50 -2.63 -2.65 -2.59 -2.05 -1.92 -1.98 -0.53 -0.80 -0.31 -0.33 1.20 -0.55 -0.30 -0.17 0.94 -0.40 -0.22 1.30 0.31 0.11 -0.04 0.97 1.35 -0.20 -0.50 -0.75 0.47 0.16 -0.99

1.20 0.56 0.68 0.80 0.18 -0.26 -0.49 -0.37 -1.61 -2.20 -2.20 -1.23 -2.86 -3.31 -3.03 -3.76 -2.18 -2.85 -2.13 -2.52 -1.76 -1.72 -2.59 -2.55 -2.33 -1.45 -0.42 -0.43 -0.11 -0.15 0.72 1.48 0.24 -0.02 0.25 1.43 0.94 1.17 0.30 0.94 1.01 1.01 1.83 1.17 0.70 0.44 -0.15 1.05 1.07 -0.06

-2.23 -1.99 -0.60 -2.31 -1.42 -0.50 -0.42 0.33 -2.09 -2.78 -2.85 -1.36 -2.82 -3.15 -3.10 -4.91 -4.86 -4.35 -3.69 -2.98 -2.73 -1.96 -2.14 -0.56 -0.74 -0.16 -0.12 0.65 -0.70 0.48 1.38 0.73 0.70 0.33 0.24 0.42 0.89 0.14 0.06 0.20 0.10 -0.21 -0.33 0.09 0.24 0.39 1.13 -0.60 -0.42 0.34

-0.21 -0.75 -0.28 -1.42 -0.19 -1.91 -1.50 -2.34 -0.44 -0.24 -1.80 -2.26 -3.73 -2.71 -2.93 -2.91 -2.72 -3.02 -1.81 -2.49 -2.43 -3.50 -1.71 -0.66 -1.10 0.12 0.55 0.93 0.82 0.34 1.39 1.10 0.33 -0.13 0.30 -0.01 -0.58 -0.51 -0.36 -1.03 -0.29 -0.42 -0.65 -1.11 -2.48 -1.75 -1.16 -1.09 -3.01 -3.19

0.39 -0.75 0.15 -0.44 0.81 1.85 2.10 0.94 -0.89 -2.27 -2.45 -2.17 -3.61 -3.58 -4.29 -4.38 -3.79 -4.77 -3.24 -2.65 -2.16 -1.49 -1.76 -1.08 -0.72 -0.72 0.44 0.30 0.89 1.25 1.57 2.00 1.42 1.03 1.49 2.24 1.42 1.83 0.53 1.20 1.59 1.34 1.90 1.29 0.68 1.04 1.08 1.24 0.51 0.62

0.83 -0.23 0.44 0.55 1.62 1.35 1.24 0.38 0.06 -1.14 -2.00 -2.78 -3.78 -3.84 -3.45 -4.01 -4.30 -4.69 -3.31 -3.72 -3.55 -2.78 -2.42 -2.50 -1.73 -1.46 -1.22 -0.36 0.12 0.68 1.78 1.31 0.50 0.31 0.13 1.42 0.26 0.80 0.78 0.73 0.96 -0.12 0.79 0.57 -0.81 0.29 -0.73 0.04 -0.44 -1.31

0.71 0.00 1.08 0.63 0.68 1.80 1.62 0.59 0.13 -0.77 -1.93 -1.72 -3.31 -3.28 -3.31 -4.35 -4.46 -5.00 -3.15 -2.76 -2.66 -2.39 -1.76 -2.09 -1.17 -0.99 -0.33 -0.58 0.14 0.89 2.06 2.23 1.52 1.47 1.75 2.78 1.54 2.35 1.60 2.01 2.41 1.60 2.01 1.53 -0.01 1.25 0.34 1.07 0.56 -0.08

-0.49 -1.08 -0.56 -0.12 1.24 1.68 0.27 -0.40 -0.93 -2.60 -2.96 -2.75 -3.74 -3.60 -3.76 -4.31 -5.14 -4.63 -2.70 -2.76 -2.98 -1.81 -1.32 -0.48 -0.75 -0.65 0.18 0.12 0.92 1.86 2.06 1.96 1.56 0.51 0.51 1.62 1.20 1.35 0.15 0.09 0.11 0.54 -0.14 0.34 -0.55 0.36 0.93 0.63 0.00 0.30

0.47 -0.66 0.50 0.64 1.70 1.69 -0.70 -1.19 -1.29 -2.20 -3.11 -3.60 -4.10 -5.04 -3.75 -4.39 -5.38 -4.32 -3.58 -3.52 -3.70 -2.97 -2.49 -2.08 -0.89 -1.04 -0.22 -0.09 0.64 1.26 2.02 0.98 1.21 0.89 0.40 1.53 0.87 1.02 0.95 0.59 0.84 -0.07 -0.52 -0.75 -1.13 -0.58 -0.74 -0.59 -1.70 -1.27

-0.13 -0.81 0.17 0.68 1.25 2.07 0.55 -0.70 -0.59 -1.36 -2.17 -2.60 -4.02 -3.65 -3.50 -4.78 -5.81 -5.69 -3.83 -3.89 -3.91 -2.72 -2.38 -1.50 -1.02 -0.76 -0.74 -0.50 0.30 1.39 1.81 1.82 1.39 0.48 1.05 2.11 1.27 1.67 1.09 1.17 1.48 0.96 0.47 0.30 -1.02 0.30 0.52 0.11 -0.53 -0.17

220

D. Main effect of quadratic habituation

Fz F3 F4 Cz C3 C4 Pz P3 P4 T3 T4 Oz O1 O2

960

-0.24 -0.22 0.43 1.47 1.93 0.05 -1.27 -2.32 -3.67 -2.80 -3.63 -3.24 -3.40 -2.81 -3.16 -3.47 -4.57 -4.90 -2.92 -2.43 -3.02 -2.24 -2.08 -1.58 -0.80 0.30 1.09 1.33 1.12 1.71 2.13 1.97 2.01 0.48 0.61 1.12 0.50 1.18 -0.21 -0.08 0.52 -0.17 -1.23 -0.65 -0.69 -0.75 0.41 -0.31 -1.33 -0.51

980 980

Fz F3 F4 Cz C3 C4 Pz P3 P4 T3 T4 Oz O1 O2

1000 1000

128

200

C. Main effect of linear habituation

Chapter 6

Does pain hypervigilance further impact the lack of habituation to pain?

Interaction effects of chronic pain, pain hypervigilance, and habituation on the ERP The association between chronic pain status and linear and quadratic habituation did not depend on pain hypervigilanceâ&#x20AC;&#x201D;i.e. the two three-way interactions (chronic pain status *linear habituation*PVAQ and chronic pain status *quadratic habituation*PVAQ ) were not significant. In general, the influence of pain hypervigilance on the ERP was not significantly affected by chronic pain status (interaction PVAQ*chronic pain status) (Figure 4A). Three or more consecutive 20-ms ERFIAs were seen in the latency range 860 to 920 ms only for P4 and T4. With respect to the separate influence of chronic pain status and PVAQ, chronic pain status affected the habituation course independently of pain hypervigilance at several time latencies (Figures 4B and 4C). Pain hypervigilance also significantly impacted the habituation course independently of chronic pain status (Figures 4D and 4E).

Discussion In this study, the association between pain hypervigilance and habituation to pain, as measured using cortical responses to 25 intracutaneously delivered painful stimuli, was examined in 33 pain-free and 33 chronic pain participants. Our hypothesis that the effect of pain status on habituation would be further impacted by pain hypervigilance was not supported. Thus, the association between pain status and habituation does not appear to be moderated by pain hypervigilance. Further, the influence of pain hypervigilance and chronic pain status on habituation independently impacted the cortical processing of pain, suggesting two separate mechanisms.

Chronic pain status, hypervigilance, and their relationship with habituation These findings imply that chronic pain and pain hypervigilance influence habituation in an additive rather than synergistic manner, prompting the study of whether other psychological factors, such as anxiety and depression, act similarly on habituation in chronic pain. Greater insight into these psychological interactions might improve targeted interventions for chronic pain.

Chronic pain status affected linear habituation and dishabituation (quadratic function) between 220 and 260 ms for nearly all electrodes, consistent with other studies in which 14â&#x20AC;&#x201C;16 impaired habituation was observed in the P2 region in chronic pain.

In addition, chronic pain had a robust effect on habituation between 580 and 640 ms in the frontal electrodes (F3, F4, and Fz), in contrast to a previous study that examined the influence of 13 chronic low back pain in 150 electrical stimuli with 5 intensities (Figure 4). In our study, the influence of group on habituation was noted primarily from 340 to 460 ms.

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0.30 -0.92 0.12 -0.15 -1.10 -0.52 -1.46 -0.44 0.25 0.02 -1.92 -2.47 -0.82 -0.56 -0.28 1.24 0.01 -0.14 0.95 0.81 0.24 0.00 1.22 1.54 0.52 1.18 2.19 2.14 3.28 2.08 1.96 2.40 2.45 1.29 1.59 1.89 1.99 1.50 1.87 1.36 0.34 0.39 -0.02 0.22 0.26 1.05 -0.01 -0.28 -0.07 -0.26

-1.74 -0.31 -0.47 -2.86 -2.04 -0.60 -0.20 -0.56 -0.71 -0.58 -0.53 -1.67 -1.56 -2.15 -1.21 -0.69 -0.17 0.10 0.44 -0.50 0.09 1.02 0.74 0.70 -1.13 0.04 0.99 0.75 0.19 -0.88 0.58 0.54 0.45 0.45 0.64 1.52 1.46 1.44 1.70 1.39 0.31 -0.24 -0.37 -0.27 -0.67 0.88 -1.10 -0.73 0.35 -0.78

O2

-0.82 -0.65 -0.86 -1.82 -2.26 -0.83 -0.25 -1.80 -0.01 -0.67 -2.12 -2.79 -2.14 -0.69 -1.00 -0.25 0.95 -0.02 1.25 0.19 0.73 0.27 1.38 1.09 0.56 1.52 3.00 0.74 2.45 1.31 1.39 1.33 0.31 1.44 1.21 1.25 1.22 0.73 1.18 0.46 1.37 0.87 0.13 1.17 1.36 1.37 0.59 -0.24 0.27 -0.59

-1.29 -0.02 -0.68 -0.92 -1.69 -1.16 -0.26 0.14 -0.29 -0.12 -0.83 -2.38 -1.69 -2.03 -1.79 -1.08 -0.30 -0.14 0.06 -0.48 0.46 1.07 1.30 0.58 -1.44 0.14 0.87 1.03 -0.31 -0.26 1.03 1.24 1.43 0.74 1.36 2.03 1.53 0.38 0.50 0.66 0.39 -0.32 0.08 0.39 -0.48 0.04 -1.66 -0.76 0.45 -0.34

-0.66 -0.14 -0.05 -1.97 -2.51 -1.80 -0.16 -0.96 -0.43 0.05 -0.60 -2.50 -2.37 -1.55 -1.72 -1.61 -0.39 -0.82 -0.43 -0.96 1.15 0.46 0.90 0.26 -0.52 0.66 2.07 1.04 -0.15 -0.16 0.42 1.79 1.57 0.61 0.00 1.15 0.62 0.68 0.95 0.38 -0.22 0.06 -0.74 -0.07 -2.10 -0.75 -2.11 -0.49 -0.91 -0.64

Oz

O1

T4

-1.26 -1.28 -0.15 -1.21 -2.12 -1.33 -1.24 -0.76 0.12 0.88 -1.59 -3.90 -3.72 -1.87 -1.17 -1.49 -0.87 -1.04 -1.47 -1.29 -0.59 -0.88 0.70 0.86 0.26 0.30 2.01 1.09 1.04 0.65 0.88 2.29 1.52 1.35 0.01 1.05 0.24 0.33 0.26 0.79 -0.23 0.42 0.47 -0.28 -0.48 0.22 -1.51 -0.64 -0.83 -1.56

-2.74 -0.94 -0.55 -2.39 -1.82 -1.48 -1.11 -0.89 0.10 0.46 -1.36 -2.80 -1.89 -0.63 -0.89 -0.56 0.05 -0.53 0.21 -0.06 -0.04 0.48 0.77 1.53 0.51 0.80 1.57 1.13 1.27 0.49 1.73 1.88 0.81 1.22 1.36 1.09 0.88 1.47 1.64 1.87 0.37 -0.09 0.88 0.12 0.26 1.39 -0.79 -0.48 0.02 -0.97

-1.14 -0.76 -0.51 -0.93 -2.00 -1.85 -1.54 0.28 1.21 0.10 -2.24 -3.10 -2.79 -0.78 -2.46 -2.65 -0.34 -0.22 -0.11 -1.05 -0.71 -0.02 0.34 0.56 -0.14 -0.22 2.42 1.34 1.14 1.26 1.55 1.82 1.70 1.25 -0.57 1.48 0.97 -0.07 0.31 0.57 0.65 0.23 -1.15 -0.64 -0.54 0.92 -0.14 -1.69 -0.87 -0.57

P3

P4

T3

-1.96 -0.72 -0.66 -1.82 -1.60 -0.76 -1.91 -1.32 0.32 0.02 -1.71 -2.96 -1.53 -0.68 -0.97 0.36 0.17 0.32 0.38 0.85 0.72 0.56 2.17 2.63 1.67 1.36 1.99 2.67 1.89 1.65 1.86 2.10 1.59 1.94 1.45 1.60 1.60 1.46 2.16 1.65 0.72 0.41 0.51 0.29 -0.05 1.66 0.04 -0.46 -0.17 -0.66

-1.61 -1.14 -1.17 -1.13 -2.11 -1.92 -1.22 -0.71 0.15 0.39 -1.56 -3.41 -3.28 -1.48 -1.34 -1.07 -0.93 -0.88 -0.71 -0.86 -0.54 -0.59 0.72 0.82 -0.01 0.04 1.31 1.07 1.06 0.24 1.86 2.36 1.51 1.19 1.16 1.89 1.16 0.75 0.70 1.45 0.16 -0.06 0.43 -0.19 -0.17 0.69 -1.43 -0.42 -0.26 -1.12

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Pz

-1.58 -1.35 -0.88 -1.20 -2.07 -1.39 -1.46 -1.17 0.43 0.57 -1.63 -3.21 -2.51 -1.22 -0.68 -0.77 -0.66 -0.33 -0.52 -0.98 -0.29 -0.89 1.40 1.53 0.47 1.08 1.87 2.13 1.57 0.79 1.13 2.93 2.24 2.48 1.21 1.68 1.13 1.21 1.06 1.54 0.61 0.07 -0.28 -0.46 0.64 1.82 -0.12 -0.55 -0.50 -1.18

-1.26 -1.26 -0.77 -0.38 -1.43 -1.03 -1.30 -1.13 0.34 1.15 -1.96 -3.62 -3.00 -1.22 -1.64 -0.77 -2.03 -0.34 -1.09 -1.01 -0.93 -0.91 1.59 0.51 0.51 0.19 2.13 1.23 1.33 0.88 0.88 3.42 1.85 2.23 0.54 1.82 1.16 1.16 0.81 1.71 0.45 0.14 0.05 -0.31 0.66 1.11 -0.39 -0.22 -0.82 -0.95

Cz

C3

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0.10 -0.11 -0.02 -0.21 -0.80 -0.25 -1.36 -0.88 1.00 0.92 -1.53 -2.03 -1.56 -0.64 -0.58 0.84 0.02 -0.72 0.20 -0.06 0.38 -0.20 1.53 1.40 0.74 1.41 2.68 2.75 2.64 2.29 2.32 2.92 2.79 2.33 1.13 1.92 1.56 2.15 1.99 1.94 1.33 0.49 0.04 -0.03 1.28 1.87 1.43 0.84 0.48 0.05

820

-0.57 -1.57 -0.20 -0.64 -1.19 0.47 -1.43 -0.36 0.28 0.92 -2.03 -2.72 -1.95 -0.76 -1.92 0.39 -0.58 -0.08 -0.24 -0.08 0.50 -0.20 1.88 1.71 2.03 1.45 2.77 2.05 2.63 1.84 1.38 3.39 2.59 2.54 1.68 1.98 1.55 1.73 1.13 1.92 0.99 0.54 0.20 -0.10 0.54 1.42 0.32 0.90 0.88 0.47

840

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F3

300

C. Interaction of pain status with quadratic habituation 880

1.25 0.04 0.93 0.91 1.40 1.16 0.54 0.04 0.25 0.08 0.94 2.87 2.18 2.44 1.52 0.70 0.46 0.17 0.28 0.34 -0.60 -1.08 -1.18 -0.79 1.11 -0.48 -1.21 -0.90 0.18 0.12 -1.36 -1.58 -1.59 -0.97 -1.48 -2.21 -1.76 -0.62 -0.46 -0.52 -0.32 0.31 -0.08 -0.53 0.58 -0.08 1.71 0.24 -0.53 0.71

900

0.79 0.17 0.77 2.19 2.28 1.76 0.64 0.91 0.38 -0.01 0.89 2.67 2.83 2.00 1.54 1.28 0.49 0.82 0.43 0.67 -1.21 -0.51 -0.94 -0.81 0.34 -1.08 -2.43 -1.07 -0.04 -0.07 -0.76 -2.23 -1.60 -0.70 0.15 -1.40 -0.83 -1.03 -0.92 -0.31 0.31 -0.13 0.65 -0.10 1.92 0.56 2.00 0.16 0.94 1.20

920

1.76 0.18 0.91 3.23 2.07 0.84 0.42 0.73 0.82 0.59 1.01 2.22 2.20 2.51 0.99 0.33 0.36 -0.22 -0.14 0.52 -0.24 -0.92 -0.63 -0.79 0.76 -0.48 -1.40 -0.69 -0.18 0.75 -0.84 -0.78 -0.70 -0.41 -0.40 -1.55 -1.70 -1.81 -1.70 -1.39 -0.27 0.29 0.31 0.10 0.64 -0.87 1.21 0.51 -0.27 0.88

940

Oz

960

O1

980

O2

3.21 1.20 0.89 2.62 2.00 1.72 1.49 1.26 0.30 -0.23 1.86 3.48 2.36 0.99 0.70 0.24 0.20 0.61 -0.01 0.11 -0.18 -0.71 -0.67 -1.67 -0.98 -1.32 -1.82 -1.13 -1.32 -0.96 -2.12 -2.22 -1.09 -1.26 -1.50 -1.44 -1.09 -1.53 -1.51 -1.91 -0.50 0.09 -1.01 -0.30 -0.35 -1.47 0.82 0.20 -0.09 1.04

1.36 1.10 1.06 1.14 1.94 2.01 1.85 -0.20 -1.03 0.25 2.42 2.96 2.99 0.97 2.57 2.49 0.36 0.21 -0.11 1.08 0.57 -0.05 -0.69 -1.10 0.10 0.00 -2.64 -1.02 -1.19 -1.07 -1.49 -1.76 -1.57 -1.25 1.04 -1.10 -0.54 0.24 -0.08 -0.52 -0.43 -0.24 1.13 0.58 0.50 -1.07 0.31 1.98 1.17 1.15

1.16 0.94 1.01 2.22 2.68 1.12 0.80 2.00 0.34 0.97 2.07 3.25 2.46 0.88 0.64 0.16 -0.74 0.14 -0.78 -0.01 -0.67 -0.07 -1.01 -1.09 -0.81 -1.51 -2.92 -0.75 -2.34 -1.28 -1.47 -1.38 -0.55 -1.21 -1.01 -1.32 -1.42 -0.64 -1.22 -0.55 -1.50 -0.86 -0.25 -1.17 -1.36 -1.23 -0.64 0.07 -0.26 0.48

P4

T3

T4

1.91 1.30 1.47 1.42 2.21 2.03 1.45 0.89 0.15 -0.12 1.92 3.83 3.61 1.85 1.13 0.83 1.02 0.90 0.81 0.76 0.17 0.26 -0.72 -1.14 -0.45 -0.39 -1.53 -1.07 -1.13 -0.56 -2.22 -2.60 -1.87 -1.34 -1.29 -2.26 -1.27 -0.81 -0.54 -1.30 -0.09 0.06 -0.53 0.29 0.24 -0.74 1.45 0.19 0.31 1.36

1.62 1.70 0.88 1.45 2.16 1.52 1.63 0.84 0.16 -0.80 1.88 4.09 4.03 2.24 0.87 1.22 1.01 1.01 1.41 1.13 0.34 0.67 -0.80 -1.26 -0.67 -0.74 -2.28 -1.17 -1.15 -0.87 -1.12 -2.56 -1.82 -1.50 -0.19 -1.32 -0.36 -0.46 -0.09 -0.75 0.31 -0.51 -0.44 0.26 0.42 -0.37 1.50 0.42 0.85 1.88

Pz

P3

2.46 0.95 1.20 2.13 2.07 1.27 2.24 1.50 0.04 0.28 1.99 3.46 1.85 0.87 0.71 -0.71 -0.13 -0.40 -0.22 -0.69 -0.80 -0.83 -2.20 -3.01 -2.15 -1.68 -2.22 -2.79 -2.07 -1.94 -2.10 -2.42 -1.92 -2.04 -1.65 -1.92 -1.62 -1.48 -2.12 -1.83 -0.77 -0.40 -0.61 -0.60 -0.24 -1.98 -0.08 0.12 0.11 0.57

1.52 1.51 1.63 0.83 1.75 1.46 1.66 1.31 -0.12 -0.86 2.15 3.93 2.93 1.61 1.36 0.40 1.96 0.13 0.82 1.05 0.57 0.65 -1.61 -0.88 -0.98 -0.49 -2.30 -1.30 -1.36 -1.11 -1.08 -3.49 -2.08 -2.25 -0.71 -1.96 -0.91 -0.91 -0.55 -1.59 -0.47 -0.22 -0.08 0.19 -0.81 -1.28 0.40 0.25 0.90 1.20

1.91 1.66 1.55 1.58 2.32 1.81 1.68 1.41 0.00 -0.07 2.04 3.63 2.75 1.41 0.50 0.39 0.55 0.16 0.56 1.10 -0.04 0.57 -1.54 -1.93 -0.90 -1.35 -2.13 -2.08 -1.70 -1.12 -1.38 -3.06 -2.49 -2.58 -1.36 -1.84 -1.01 -0.96 -0.89 -1.35 -0.37 0.14 0.36 0.46 -0.67 -1.73 0.22 0.46 0.58 1.36

0.04 1.20 0.31 0.48 1.71 1.45 2.21 0.78 0.38 0.42 2.25 2.96 1.12 0.64 0.42 -1.25 0.20 0.55 -0.27 -0.47 0.06 -0.09 -1.09 -1.65 -0.66 -1.42 -2.35 -2.14 -3.28 -1.97 -1.84 -2.30 -2.21 -1.28 -1.45 -1.97 -1.79 -1.32 -1.57 -1.07 -0.31 -0.36 -0.19 -0.34 -0.50 -1.14 0.19 0.28 0.28 0.45

0.72 1.73 0.84 0.90 1.55 0.20 1.88 0.61 0.14 -0.59 2.13 2.90 2.04 1.12 1.87 -0.41 0.69 -0.02 0.15 0.11 -0.53 0.14 -1.94 -2.04 -2.33 -1.65 -2.81 -2.22 -2.68 -1.90 -1.37 -3.14 -2.54 -2.08 -1.24 -1.36 -0.87 -1.13 -0.55 -1.34 -0.72 -0.38 -0.15 0.05 -0.42 -1.46 -0.14 -0.53 -0.29 0.02

0.20 0.40 0.58 0.52 1.28 0.93 1.93 1.25 -0.66 -0.57 1.67 2.34 1.82 1.01 0.53 -0.98 -0.04 0.91 0.12 0.32 -0.31 0.15 -1.38 -1.72 -1.01 -1.64 -2.68 -2.69 -2.59 -2.21 -2.23 -2.67 -2.68 -2.04 -0.92 -1.74 -1.10 -1.71 -1.58 -1.54 -1.05 -0.31 -0.07 -0.01 -1.31 -1.77 -1.21 -0.61 -0.18 0.23

960

-0.23 0.56 1.65 1.13 -1.63 -0.59 0.76 0.54 0.55 2.03 1.77 -0.12 -1.15 -1.38 -0.86 -0.83 0.11 -0.16 -0.94 -1.20 -1.88 -2.61 -2.35 -1.83 -1.41 -0.82 -1.79 -1.59 -1.34 -0.48 -0.51 -0.80 -0.28 0.53 0.00 -0.61 -0.86 -0.04 0.50 0.54 0.14 0.93 1.39 1.81 2.38 0.93 0.63 0.76 0.40 1.70

-1.01 0.74 2.10 0.62 -0.92 -0.25 0.87 0.84 0.60 0.98 -0.16 -1.32 -1.55 -1.74 -1.17 -0.87 -0.73 -0.52 -0.79 -1.67 -1.88 -1.81 -1.00 -0.67 -0.88 -0.80 -0.84 -0.91 -0.95 -0.53 -0.86 -0.50 0.07 -0.32 -0.22 -0.61 -0.80 -0.48 -0.22 -0.61 -0.33 0.54 0.56 1.13 1.51 0.63 0.90 0.39 0.00 1.21

-0.38 0.11 1.61 2.02 -0.35 -0.43 1.31 1.03 1.07 1.24 0.52 -0.69 -1.36 -1.66 -1.03 -0.88 -0.08 0.08 -0.59 -1.26 -1.75 -2.38 -1.88 -1.23 -0.35 -0.35 -0.83 -0.87 -0.98 -0.63 -0.63 -0.47 -0.19 0.17 0.21 -0.79 -0.93 -0.18 0.54 0.40 -0.07 0.96 0.91 2.23 1.89 0.95 1.16 1.31 0.66 1.98

-0.10 0.18 -0.64 1.68 1.39 1.65 1.26 0.07 0.68 0.70 1.18 1.03 0.86 0.31 0.23 0.37 0.33 0.76 -0.58 -0.17 -0.49 -0.84 -0.41 -0.74 -0.04 0.97 -0.04 -0.39 0.21 -0.06 0.93 0.70 0.97 0.71 1.11 0.58 1.10 1.64 1.42 1.74 1.98 1.58 2.63 2.32 2.78 2.13 1.53 2.13 2.19 1.96

-0.62 1.48 1.30 0.76 1.38 0.84 1.93 0.75 0.93 0.57 0.22 0.16 -0.24 -0.79 0.25 -0.26 0.67 0.43 0.24 -0.53 -0.19 0.36 0.79 0.64 0.63 0.25 0.28 0.03 -0.58 -0.31 0.11 0.20 -0.14 0.13 -0.18 0.62 0.11 0.07 -0.32 -0.27 0.68 0.62 0.75 0.97 0.40 0.52 0.76 0.43 0.45 0.71

-0.02 0.26 0.79 1.21 -0.69 0.40 0.91 0.35 0.57 1.71 1.40 0.55 -0.48 -0.66 -0.64 -0.59 0.05 0.23 -0.04 -0.83 -1.53 -1.82 -1.37 -0.91 -0.34 0.12 -0.58 -0.68 -0.72 0.03 0.17 0.15 0.91 1.22 0.41 0.12 -0.21 1.00 1.30 0.89 0.74 1.36 2.10 2.32 2.17 1.44 1.68 1.96 1.47 2.11

-0.78 0.88 1.77 0.75 0.23 0.90 1.48 0.50 0.19 0.44 0.77 -0.02 -0.46 -0.95 -0.88 -0.25 0.31 0.70 0.50 -0.60 -0.84 -0.47 -0.15 -0.04 0.26 0.24 0.42 0.10 -0.08 0.74 0.58 0.64 1.05 0.55 0.10 0.08 -0.15 0.46 0.65 0.09 0.71 0.83 1.17 1.88 1.86 1.70 1.37 1.56 1.03 1.99

-0.31 0.05 1.33 0.81 -0.64 0.70 1.18 0.51 0.51 1.28 1.03 0.35 -0.58 -1.24 -1.09 -0.94 -0.08 0.15 -0.60 -1.54 -1.98 -1.91 -1.36 -1.35 -0.63 -0.41 -0.91 -0.80 -0.96 -0.10 -0.11 0.17 0.19 0.53 -0.13 -0.57 -0.65 0.32 0.67 0.29 0.73 1.16 1.43 1.92 2.02 1.60 1.53 1.58 1.09 1.71

-0.59 0.19 0.27 1.70 -0.04 1.08 1.22 -0.05 0.11 0.86 1.05 0.92 -0.15 -0.74 -0.42 -0.49 -0.41 0.34 0.33 -0.25 -0.81 -1.26 -0.73 -0.28 -0.03 0.17 -0.09 -0.44 -0.40 0.32 0.45 0.94 1.54 1.17 0.67 0.26 0.23 0.91 1.06 0.52 0.94 1.18 1.75 2.04 2.08 1.64 1.61 2.52 1.64 1.91

-0.93 0.46 1.04 1.04 0.60 1.52 1.75 0.36 -0.12 0.37 0.68 0.16 -0.38 -0.68 -1.00 -0.92 -0.30 0.32 -0.09 -0.83 -0.68 -0.71 -0.18 -0.30 -0.02 0.11 0.51 0.05 -0.13 0.44 0.52 0.69 1.05 0.14 -0.11 0.10 -0.48 0.22 0.27 -0.56 0.43 0.62 0.66 1.33 1.12 0.94 0.56 0.59 0.18 0.50

-1.35 -0.23 0.94 1.18 0.56 1.53 1.46 0.05 -0.16 0.83 0.96 0.82 0.14 -0.12 -0.26 -0.72 -0.28 0.27 0.01 -0.90 -1.28 -1.47 -0.78 -0.92 -0.51 -0.38 -0.31 -0.44 -0.53 0.08 -0.14 0.31 1.03 0.56 0.47 -0.13 -0.30 0.27 0.50 0.25 0.18 0.62 1.09 1.29 1.41 1.28 1.26 1.41 0.69 1.09

-1.78 0.03 0.30 1.16 -0.54 0.05 0.69 -0.30 -0.51 0.08 0.54 0.47 0.40 -0.20 -0.18 -0.73 -0.34 0.11 0.01 -0.65 -0.65 -1.08 -0.15 -0.50 -0.33 0.27 -0.01 -0.06 0.10 0.26 0.55 1.04 1.44 1.21 1.15 0.86 1.84 1.63 1.38 1.48 0.99 1.30 1.63 1.86 1.20 2.06 1.71 1.38 1.28 1.20

-1.11 0.37 1.46 0.75 0.35 0.21 0.35 -0.09 -0.32 0.31 0.05 0.34 0.17 -0.20 -0.49 -1.17 -0.78 0.46 0.32 0.19 0.12 0.21 0.70 0.34 0.67 0.55 0.76 0.45 0.58 0.77 0.79 0.82 0.29 0.71 0.54 0.31 -0.08 0.36 0.06 0.28 0.82 0.48 0.70 1.11 0.56 0.67 0.48 0.34 0.06 0.17

280

B. Interaction of pain status with linear habituation

Fz F3 F4 Cz C3 C4

960

-1.63 -0.16 0.78 0.65 0.24 0.44 0.87 0.05 -0.15 0.48 0.22 0.46 0.22 -0.05 -0.46 -0.97 -0.94 0.02 0.32 -0.06 -0.53 -0.55 -0.34 -0.24 -0.05 -0.50 -0.14 -0.08 -0.11 0.50 0.36 0.67 0.97 0.80 0.96 0.60 0.65 1.00 0.47 0.48 0.78 0.81 1.33 1.50 1.55 1.63 1.33 1.53 0.93 1.09

980 980

Fz F3 F4 Cz C3 C4 Pz P3 P4 T3 T4 Oz O1 O2

1000 1000 1000

130

220

A. Interaction of PVAQ with pain status

Chapter 6

940

920

900

880

860

840

820

800

780

760

740

720

700

680

660

640

620

600

580

560

540

520

500

480

460

440

420

400

380

360

340

320

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280

240

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200

180

160

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100

80

60

40

20

-0.35 -0.86 -1.55 -1.03 -1.65 -0.99 -0.62 -0.51 1.32 -0.51 -0.41 -0.43 -1.24 -0.45 -0.23 -0.52 -0.40 -1.16 -0.13 -1.41 -0.98 -1.32 -1.17 -2.12 -2.07 -2.58 -2.71 -2.71 -2.64 -3.02 -2.58 -2.39 -2.06 -2.81 -3.02 -2.85 -1.78 -2.32 -3.08 -2.33 -2.20 -0.45 -0.47 -0.80 -1.54 -0.55 -0.71 0.23 0.61 -0.26

820

800

780

760

740

720

700

680

660

640

620

600

580

560

540

520

500

480

460

440

420

400

380

360

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60

40

20

Figure 4. ERFIA predictor blots of the five interactions (A to E). Columns represent consecutive 20-ms ERFIAs, and rows display cranial locations. Cells with significant results are colored (p < 0.05), and the plus or minus sign expresses the direction of the relationship. Cells in red reflect a positive association, and blue cells denote a negative association

0.15 2.50 1.18 -0.81 -0.04 0.19 0.58 0.93 -1.09 0.40 1.95 2.50 1.88 0.87 0.37 0.65 1.57 0.38 0.29 0.52 0.19 0.68 1.70 2.16 3.10 1.69 2.39 2.13 2.63 1.77 1.69 2.20 1.79 2.21 2.06 2.09 3.06 1.87 1.68 0.69 2.01 0.28 1.08 1.61 -0.52 -0.23 0.29 1.29 1.16 0.56

O2

0.06 1.41 0.43 0.80 2.00 1.21 1.25 1.24 1.06 2.03 1.37 2.38 1.96 1.46 1.56 -0.13 0.40 0.39 -0.01 0.43 0.62 1.07 2.69 2.59 2.74 2.16 2.45 2.65 3.52 2.76 3.04 2.15 2.77 3.16 3.22 1.73 2.20 2.09 2.85 1.46 1.82 0.67 0.93 1.09 2.26 1.48 1.27 0.73 1.27 1.61

0.43 2.79 2.55 0.65 -0.16 -0.70 0.73 1.41 -0.91 0.47 1.82 1.54 1.51 0.17 1.15 0.49 1.06 0.37 -0.07 0.65 -0.14 0.06 0.74 1.38 2.53 1.64 1.79 1.93 2.35 1.90 1.65 1.14 1.15 1.42 1.34 1.48 1.21 0.44 0.37 0.22 2.00 0.75 1.21 1.24 -0.54 -1.55 -0.08 1.32 0.67 0.73

0.62 2.18 2.46 1.88 1.23 0.01 0.40 0.29 -0.14 0.72 2.16 2.02 2.02 0.42 1.00 1.19 0.60 0.43 0.24 0.70 -0.86 -0.08 1.16 1.72 3.37 2.38 1.97 2.72 1.74 1.66 1.27 0.73 1.38 1.02 1.84 1.07 1.85 0.80 1.00 0.13 1.58 1.26 1.31 1.38 0.14 -1.14 0.27 0.36 0.29 0.98

Oz

O1

T4

0.12 2.90 1.75 0.75 0.76 1.24 0.84 1.68 0.49 1.36 1.38 1.19 2.39 0.85 0.02 -0.04 0.59 0.08 0.26 0.46 0.28 1.67 1.99 2.46 3.17 2.33 2.66 2.80 3.36 2.37 1.95 1.59 1.97 2.37 2.24 1.77 1.10 1.40 2.64 1.11 2.46 0.55 1.22 1.76 0.72 0.12 0.61 1.39 1.16 0.96

2.18 0.91 0.75 1.93 0.95 -0.19 -0.12 -0.43 0.63 1.30 0.96 1.08 1.52 0.09 -0.44 0.02 -0.24 0.07 -1.49 -0.25 -1.60 -0.53 -0.35 1.48 1.57 0.30 1.89 1.04 1.04 -0.62 0.83 -0.22 0.19 -0.65 1.74 0.70 0.83 0.12 0.80 -0.06 -0.38 -0.95 -0.18 -0.34 -0.55 -1.64 -0.57 -0.63 -0.04 1.17

P4

T3

1.05 2.27 1.84 1.40 1.37 1.10 1.23 1.80 0.01 1.85 1.12 1.57 1.89 0.76 0.82 0.31 0.59 1.18 0.59 1.35 0.77 1.22 1.76 2.58 2.55 3.68 3.48 3.06 3.19 2.57 2.20 1.49 2.22 2.44 2.32 1.97 1.49 2.07 2.63 2.02 2.34 1.17 1.15 1.79 1.46 0.35 0.37 0.64 0.28 0.70

1.25 3.28 2.50 1.94 0.65 0.73 0.71 1.71 1.10 1.24 1.46 2.12 2.18 0.73 1.44 0.95 1.22 0.73 0.93 0.76 0.57 1.18 1.59 2.81 2.99 2.20 2.52 2.50 2.88 2.22 1.71 1.02 1.59 2.30 2.16 1.71 1.35 1.51 2.41 1.10 2.19 1.32 1.69 1.70 0.64 0.86 0.89 1.20 1.31 1.40

1.15 3.56 2.69 1.85 0.62 0.00 0.08 0.43 0.28 1.13 1.86 2.08 2.18 0.96 1.39 1.26 1.42 1.21 0.74 0.47 0.49 1.13 1.27 2.57 3.05 2.52 2.12 2.67 2.09 1.99 1.33 0.03 1.51 1.54 2.00 2.26 2.49 1.57 2.20 0.75 1.60 1.04 1.93 1.31 1.22 0.41 0.26 0.55 1.22 1.17

Pz

P3

C4

0.12 0.63 1.26 0.99 1.19 0.66 0.39 0.80 -1.06 0.55 0.32 0.76 1.62 0.57 0.64 0.59 0.53 1.19 -0.08 1.54 0.74 1.12 0.85 1.75 1.70 2.39 2.43 2.64 2.77 3.01 2.52 2.21 1.83 2.62 2.91 2.84 1.69 2.34 3.03 2.33 2.04 0.47 0.50 0.91 1.41 0.35 0.61 -0.19 -0.40 0.65

1.29 2.54 2.32 1.81 1.31 1.02 0.80 1.97 1.23 2.23 1.64 1.82 1.84 0.81 1.28 0.84 1.18 1.33 0.79 0.60 0.13 1.00 1.41 3.01 2.93 3.54 2.54 3.41 3.38 2.61 2.47 1.65 2.41 2.38 2.69 3.00 2.19 2.32 3.18 2.36 2.65 1.49 1.52 1.65 1.59 1.31 1.01 0.50 1.03 1.07

1.31 2.51 3.23 1.10 0.38 0.03 0.31 0.91 1.12 1.44 2.53 0.94 2.16 0.42 1.35 0.93 1.76 1.52 1.01 0.28 0.01 0.56 0.88 2.14 1.77 2.10 2.23 1.83 1.81 1.07 1.08 0.55 1.69 0.72 1.76 1.63 1.83 1.51 2.35 1.17 1.33 0.77 0.68 0.71 0.98 0.41 -0.53 -0.79 0.18 -0.28

Cz

C3

F4

0.42 1.93 0.81 0.86 1.19 0.20 -0.18 0.24 -0.66 0.25 0.28 -0.04 0.95 0.80 0.95 1.05 1.36 0.91 -0.12 0.55 0.02 1.39 0.58 1.85 1.23 1.47 2.00 1.92 2.00 1.46 1.31 1.31 1.36 1.64 1.61 1.92 1.34 2.57 3.87 2.51 1.17 0.43 0.14 0.47 0.22 -0.34 -0.16 -0.36 -0.47 -0.11

840

1.06 1.56 1.25 0.57 0.35 -0.51 -0.31 -0.34 -0.57 0.30 0.50 -0.39 0.20 -0.29 0.64 0.50 0.93 0.15 -0.29 -0.58 0.18 -0.04 -0.80 0.68 0.34 0.53 0.66 0.65 1.11 1.30 0.74 0.63 0.87 0.71 1.48 1.40 1.85 1.47 2.26 1.25 -0.69 -1.09 -0.36 -0.31 -0.42 -1.14 -1.79 -1.68 -0.51 -0.60

860

Fz

880

F3

280

E. Interaction of PVAQ with quadratic habituation 900

-0.57 -2.24 -1.14 0.65 -0.37 -0.19 0.09 -0.67 0.96 -0.64 -2.19 -2.33 -1.49 -0.80 0.22 -0.06 -1.52 -0.42 -0.18 -0.37 -0.12 -0.55 -1.80 -1.91 -3.16 -1.60 -2.05 -2.03 -2.50 -1.75 -1.54 -2.32 -1.78 -2.31 -2.04 -2.37 -3.27 -2.03 -1.95 -0.86 -2.04 -0.33 -1.12 -1.34 0.44 0.02 -0.25 -1.13 -1.32 -0.22

920

O2

-0.05 -1.57 -0.63 -0.87 -2.18 -1.49 -1.39 -1.01 -1.14 -2.30 -1.66 -2.30 -1.89 -1.45 -1.26 0.40 -0.46 -0.34 -0.24 -0.54 -1.04 -1.53 -2.91 -2.84 -3.29 -2.50 -2.80 -2.81 -3.64 -2.83 -3.01 -2.43 -2.86 -3.40 -3.38 -1.81 -2.40 -2.27 -3.03 -1.42 -2.08 -0.69 -0.96 -1.03 -2.44 -1.71 -1.55 -0.87 -1.19 -1.46

940

-0.69 -2.51 -2.39 -0.80 -0.16 0.58 -0.19 -1.27 0.65 -0.50 -1.67 -1.05 -1.19 0.09 -0.51 0.23 -0.78 -0.46 0.07 -0.50 0.23 0.24 -0.87 -1.28 -2.63 -1.34 -1.27 -1.78 -2.16 -1.80 -1.54 -1.16 -1.14 -1.45 -1.25 -1.75 -1.23 -0.57 -0.35 -0.34 -2.20 -0.72 -1.18 -1.15 0.55 1.50 0.25 -0.95 -0.74 -0.58

960

-0.77 -2.03 -2.46 -2.12 -1.67 -0.04 -0.11 -0.28 -0.39 -0.77 -2.29 -1.82 -1.97 -0.34 -0.59 -0.59 -0.40 -0.43 -0.29 -0.54 0.92 0.31 -1.25 -1.40 -3.24 -1.97 -1.72 -2.43 -1.68 -1.55 -1.19 -0.74 -1.21 -1.02 -1.83 -1.17 -1.69 -0.75 -0.85 -0.28 -1.82 -1.22 -1.39 -1.52 -0.22 1.13 -0.24 0.00 -0.07 -0.80

980

Oz

1000

O1

T4

-1.23 -3.26 -3.00 -2.00 -0.80 -0.15 0.01 -0.24 -0.52 -1.22 -1.85 -1.69 -1.74 -0.57 -0.87 -0.91 -1.38 -1.33 -0.81 -0.42 -0.35 -0.91 -1.45 -2.48 -2.93 -2.36 -1.87 -2.49 -1.91 -1.97 -1.21 -0.12 -1.57 -1.51 -1.89 -2.36 -2.40 -1.57 -2.17 -0.95 -1.90 -0.97 -2.00 -1.09 -1.20 -0.43 -0.10 -0.19 -0.83 -0.97

-0.27 -2.62 -1.79 -1.03 -1.21 -1.58 -0.48 -1.21 -0.43 -1.66 -1.43 -0.72 -2.04 -0.41 0.59 0.58 -0.19 0.05 -0.11 -0.29 -0.41 -1.61 -2.08 -2.39 -3.28 -2.31 -2.49 -2.63 -2.99 -2.16 -1.78 -1.66 -2.02 -2.44 -2.18 -1.89 -1.27 -1.44 -2.72 -1.21 -2.49 -0.54 -1.15 -1.60 -0.65 -0.29 -0.56 -1.17 -1.07 -0.54

-2.18 -0.93 -1.31 -2.13 -1.26 0.03 0.14 0.22 -0.74 -1.41 -1.01 -1.05 -1.22 0.12 0.49 0.12 0.37 -0.57 1.60 0.32 1.76 0.77 0.67 -1.63 -1.57 -0.29 -1.47 -1.19 -0.76 0.97 -0.58 0.52 -0.06 0.82 -1.34 -0.74 -0.82 0.06 -0.67 0.04 0.28 1.20 0.10 0.60 0.86 1.98 0.99 1.27 0.48 -0.82

P3

P4

T3

-1.25 -1.97 -2.04 -1.65 -1.68 -1.47 -1.19 -1.40 0.18 -1.96 -1.07 -1.14 -1.42 -0.23 -0.29 0.04 -0.20 -1.21 -0.63 -1.15 -0.97 -1.00 -1.84 -2.78 -2.79 -3.78 -3.31 -2.76 -2.73 -2.23 -1.92 -1.68 -2.28 -2.38 -2.20 -1.97 -1.61 -2.04 -2.63 -1.97 -2.27 -1.04 -0.87 -1.52 -1.59 -0.57 -0.28 -0.40 0.00 -0.35

-1.31 -2.81 -2.47 -2.08 -0.81 -0.82 -0.53 -1.38 -1.08 -1.33 -1.30 -1.59 -1.65 -0.23 -0.88 -0.42 -0.93 -0.67 -0.88 -0.62 -0.53 -0.99 -1.61 -2.72 -2.88 -1.95 -2.23 -2.21 -2.47 -1.97 -1.56 -1.18 -1.68 -2.23 -1.98 -1.85 -1.33 -1.53 -2.37 -1.05 -2.48 -1.32 -1.60 -1.47 -0.47 -0.84 -0.63 -0.84 -1.06 -0.93

C4

Pz

-1.39 -2.30 -2.40 -2.00 -1.39 -1.23 -0.85 -1.74 -1.11 -2.38 -1.41 -1.24 -1.17 -0.19 -0.61 -0.40 -0.78 -1.24 -0.65 -0.35 0.07 -0.67 -1.26 -3.04 -2.85 -3.25 -2.29 -2.93 -2.84 -2.31 -2.31 -1.65 -2.40 -2.28 -2.48 -3.13 -2.14 -2.22 -3.08 -2.31 -2.65 -1.41 -1.29 -1.27 -1.58 -1.33 -0.87 -0.06 -0.67 -0.66

-1.41 -2.38 -3.58 -1.36 -0.44 -0.16 -0.36 -0.59 -1.32 -1.52 -2.37 -0.47 -1.59 0.07 -0.92 -0.61 -1.69 -1.53 -1.01 -0.19 0.22 -0.16 -0.82 -2.03 -1.69 -1.99 -1.93 -1.36 -1.47 -0.89 -0.83 -0.57 -1.67 -0.69 -1.62 -1.65 -1.72 -1.45 -2.26 -1.17 -1.37 -0.86 -0.73 -0.51 -1.00 -0.34 0.65 1.24 0.21 0.46

Cz

C3

F4

-0.77 -2.19 -1.17 -1.19 -1.46 -0.41 0.03 0.01 0.97 -0.20 0.02 0.51 -0.43 -0.45 -0.65 -1.10 -1.28 -1.07 -0.08 -0.56 -0.11 -1.34 -0.62 -2.11 -1.37 -1.51 -1.86 -1.68 -1.72 -1.37 -1.39 -1.34 -1.44 -1.65 -1.63 -2.00 -1.30 -2.55 -3.84 -2.48 -1.46 -0.47 0.10 -0.31 -0.38 0.19 -0.04 0.41 0.52 0.33

960

-1.18 -1.75 -1.63 -0.71 -0.29 0.45 0.38 0.64 0.83 -0.30 -0.27 0.67 0.25 0.60 -0.26 -0.58 -0.74 -0.22 0.25 0.42 -0.19 0.29 0.82 -0.70 -0.54 -0.46 -0.39 -0.59 -0.86 -1.19 -0.71 -0.46 -0.73 -0.65 -1.49 -1.67 -1.82 -1.59 -2.32 -1.40 0.36 1.05 0.46 0.64 0.44 1.16 1.93 2.00 0.73 0.97

980

Fz

1000

F3

260

D. Interaction of PVAQ with linear habituation

Does pain hypervigilance further impact the lack of habituation to pain?

131

Chapter 6 There are several explanations for this disparity between studies. For example, the pain rating protocols differed, and habituation courses over 150 stimuli might be dissimilar compared with 25 stimuli. Also, 5 stimulus intensities were used in the previous study instead of 1, as in our report. In the former, the previous stimulus intensity (prior to the current stimulus) affected the cortical processing of the current stimulus intensity tremendously in the region from 400 to 680 ms, implying that habituation courses are affected by intensity processes. Consequently, the advantage of the use of a single stimulus intensity over 25 trials is that habituation can be assessed without the interference of stimulus processes. Conversely, in daily practice, individuals are generally confronted with mixed intensities. Thus, the current model is not as generalizable to daily life as a model with mixed intensities.

The effect of pain hypervigilance on habituation was observed primarily from 480 to 820 ms poststimulus for electrodes on the right side (F4, C4, P4, T4, O2) and centrally (Cz and Pz), whereas the effect of pain status on habituation was seen mainly in the frontal areas, indicating that disparate cortical pathways influence habituation (Figure 4). Although EEG has excellent temporal resolution, its spatial resolution is limited. To confirm that these effects have different pathways, more research is needed in which EEG is combined with other modalities with good spatial resolution, such as fMRI. In our study, however, the interaction effects of pain hypervigilance on habituation were more pronounced and occurred in a broader latency window, compared with a study of only healthy participants (Vossen 2017, submitted). In the current model, the influence of pain hypervigilance on habituation was independent of pain status, requiring other explanations. Because our study consisted of 66 participants versus the 46 participants in the other report, the statistical power might be an issue. Also, unknown factors, which have not been incorporated into the model, might have function. For example, psychological factors, such as anxiety and depression, might influence the association between pain hypervigilance and habituation. High levels of catastrophic thinking about pain are related to a greater fear of pain and attentional bias.32,33 A meta-analysis demonstrated that threat-related attention biases exist in several popula34 tions with high anxiety levels. In addition, negative affect can amplify pain-related fear 17,35 in chronic pain sufferers.

Future research in larger populations is required to determine how these psychological factors alter the mechanism of habituation in the cortical processing of pain.

NRS courses over 25 stimuli NRS scores decreased significantly over 25 trials, but this decline was not modified by pain hypervigilance or pain status. In the present study, pain hypervigilance and pain status influenced the habituation to cortical responses of painful stimuli, but not their subsequent behavioral responses, as expressed by the NRS-scores. One argument is

132

Does pain hypervigilance further impact the lack of habituation to pain?

32,36–38

that the pain experience is also affected by the threat value of the stimulus.

It is conceivable that the threat value of stimuli in an experimental setting is low, because the participant is able to stop the protocol at any time. Further, the NRS merely measures the intensity aspect of pain stimuli—affective and social components are not taken into account. Discrepancies between neurophysiological effects and measures of overt behavior, such as the NRS, are common. With regard to the multidimensionality of the perception of pain, future research should address the challenge that remains in translating the effects at the cortical level to those on behavioral responses.

Limitations One of the limitations of this study is the heterogeneity of the chronic pain group, which consisted of a sample that was derived from the general population. Although the causes and locations of pain differed, all participants had suffered from pain for at least 6 months. Nevertheless, this study should be replicated for specific pain populations. In the context of external validity, our results might be more generalizable to the general population compared with specific pain populations. Also, chronic pain is increasingly considered a distinct nosological category. Thus, central mechanisms, such as habitua39–42 tion, might be comparable between chronic pain populations.

Another issue pertains to the limited number of cranial locations in this study (14). Ideally, more locations should be examined.

The habituation protocol consisted of 25 stimuli that were 25% above the pain threshold. Additional habituation sessions with pain-free stimuli and stimuli that exceed the pain threshold by 25% should be implemented to assess whether the influence of pain status and psychological factors on habituation is associated with stimulus intensity.

Further, the existence of a dose-response relationship and the factors that primarily or solely influence habituation to pain should be determined. In general, habituation is 43,44 more robust with weaker (and thus non-painful) stimuli.

Thus, the effect of pain status and pain hypervigilance on habituation might be less pronounced at lower pain intensities.

This study evaluated the association between 3 variables—pain status, pain hypervigilance, and habituation using a cross-sectional approach. To determine whether and how such factors as pain hypervigilance and the degree of habituation contribute to the development of chronic pain, trials with a longitudinal design are required. The current cross-sectional approach could not address these questions. However, our analyses shed light on the relationship between pain status, pain hypervigilance, and habituation.

New opportunities The ERFIA multilevel method has engendered new opportunities to study the influence of psychological factors on the mechanism of habituation to pain. This study provides

133

Chapter 6 evidence of the effects of pain hypervigilance, a psychological construct, in nonpeakrelated areas. Based on our findings, the range of 480 to 820 ms is notable with regard to the study of other psychological factors that are linked to habituation in pain. Preferably, such research should be accompanied by fMRI to obtain a greater understanding of temporal and spatial information with regard to pain processing.

For example, Smith and colleagues found that resilience, optimism, and purpose in life 11 facilitated habituation of heat pain and cold pressor pain. Because habituation can be seen as a protective mechanism and is believed to be mediated by higher cognitive factors, more insight into ‘positive’ and ‘negative’ psychological factors that facilitate or inhibit habituation can guide the development of psychological treatments, tailored to specific needs.

Conclusions Pain hypervigilance and chronic pain independently impact habituation to painful stimuli—not synergistically. The range of 480 to 820 ms poststimulus may be of importance in studying the influence of psychological factors on the cortical processing of pain.

Acknowledgments We are grateful to Marga Schnitzeler for recruiting participants and performing the EEG measurements and data management and to Dr. Wolfgang Viechtbauer for providing statistical advice (both from the Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre). The authors have no conflicts of interest to disclose.

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Chapter

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General discussion Summary Samenvatting Valorisation Epiloog/Dankwoord Curriculum Vitae List of publications

Waarheid is het leven zelf (Krishnamurti)

139

General discussion The purpose of this thesis was to study habituation in the cortical processing of experimental pain and the relationship of chronic pain and pain hypervigilance with habituation. To examine these links, a paradigm in which event-related EEGs to (non)painful stimuli were measured was used. A new analysis technique was developed to gain greater insight into various habituation courses in relation to chronic pain and pain hypervigilance. In the paragraphs below, the research questions are discussed point by point, and future directions are proposed.

1. Is it possible to develop an alternative event-related EEG method that analyzes non-peak-related poststimulus information?

2. In which poststimulus areas do stimulus intensity and habituation influence the cortical processing of pain in pain-free controls?

3. Does habituation in the cortical processing of pain differs between individuals with chronic pain and pain-free controls?

4. Does pain hypervigilance impact the cortical processing of painful stimuli and its habituation in pain-free controls?

5. Is the association between chronic pain and habituation moderated by pain hypervigilance?

Research question 1: Is it possible to develop an alternative eventrelated EEG method that analyzes non-peak-related poststimulus information?

Traditionally, ERP research focuses on peaks and their latencies. In this thesis, the underlying premise to develop a novel analysis technique was based on the hypothesis that each poststimulus point on the ERP waveform contains potentially meaningful information, regardless of whether it is peak-related. Ideally, the entire variability of an ERP signal is explainedâ&#x20AC;&#x201D;i.e. the variability of all amplitudes of all latency points after every stimulusâ&#x20AC;&#x201D;using a set of variables that modify the amplitude. This model resulted in the ERFIA multilevel analysis method. Next, its validity, reliability, methodological considerations, and context in relation to other analysis techniques are discussed.

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Chapter 7

Validity Validity refers to whether a tool measures what it is intended to measure. The ERFIA multilevel method was compared with other analysis methods in ERP research, such as peak analysis using ANOVA or multilevel analysis. A direct comparison is not possible, because the computation of peaks (ÂľV at a defined latency point) and ERFIAs (the product of ÂľV x width of a latency interval) is fundamentally different. Peaks are based on a series of averaged trials, after which a latency window is defined to establish peaks at single-trial level. Conversely, ERFIAs are computed directly from the raw EEG data. Nevertheless, we can compare the influence of predictor variables between ERFIAS and peaks, if the ERFIAs are within the peak latency windows.

In Chapter 3, it was demonstrated that compared with peak analyses, the ERFIA multilevel method generates comparable results with respect to habituation and stimulus intensity. ERFIAs in the N2 and P2 ranges were significantly related to stimulus intensity in the same direction as in the peak analyses. Moreover, the ERFIA multilevel method was reproducible, yielding comparable results with those of a previous study in which the same dataset was used. For example, the effects of linear habituation were similar to those of the multilevel peak analyses of H.G.M. Vossen, who demonstrated significant effects of linear habituation on the N2 and P2 peaks at all electrodes (Fz, Cz, Pz, C3, C4, T3, T4).1 In addition to across-trial averaging, other ERP analysis techniques, such as continuous wavelet transform and independent component analysis (ICA), consider the phenome2,3 non of latency jitter. Latency jitter is a phenomenon that is related to peaks. The latency of peaks can vary across trials, especially in brain responses that are elicited by the stimulation of afferent nerve fibers with variable conduction velocities, such as 4 small myelinated Aδ and unmyelinated C fibers. This latency jitter of peaks may confound the findings of the ERFIA multilevel method, because it can affect the size of ERFIAs in a latency range. However, in Chapter 3, in an initial attempt, post hoc analyses demonstrated that when the latency variability of P2 peaks at the single-trial level is incorporated into the ERFIA multilevel model, the findings of other variables remain unchanged.

In conclusion, the results in Chapter 3 indicate that the ERFIA multilevel method is valid compared with the standard of peak analyses. Moreover, significant effects were found over a broader latency range than only peaks. Thus, ERFIA multilevel analysis allows one to perform a more detailed investigation of the pain-ERP. This issue will be discussed in greater detail below.

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Reliability A method is reliable when its results can be reproduced consistently. Compared to peak-bases analysis, the ERFIA multilevel method merely differs with regard to the analysis method of the data. Compared with conventional ERP analysis methods, the EEG data that are gathered for further analysis are essentially the same. In principle, the reliability of ERFIAs is superior to that of peaks. Calculations of ERFIAs are well defined and straightforward, in contrast to those of individual N2/P2 peaks. With the latter, a subjective element exists in the calculationsâ&#x20AC;&#x201D;namely, the determination of the latency window and the issue of how to address maximum values at the limits of the window, because peaks that lie outside of the selected window might yield inaccurate peak values at such borders. Despite the advantages in the reliability of the ERFIA analysis method versus peak-based analysis, future studies should determine the reproducibility of the ERFIA multilevel method.

The ERFIA multilevel method in the context of other ERP analysis techniques Although more research is needed on the reproducibility and validity of the ERFIA multilevel method, it is an important addition to conventional ERP analysis methods. The main benefit of ERFIA multilevel methods is that it is based on straightforward computations. Raw EEG data are less transformed with the ERFIA multilevel method compared with peak analysis, which uses maximization procedures within a defined window (see Table 1). A second and perhaps more important advantage is that it allows one to study the whole epoch and, thus, non-peak-related information. The length of the poststimulus epoch can be selected flexibly (for instance, 1000 ms or 1500 ms, depending on the research question) by modeling the variability of each ERFIA interval to a set of stimulus-related and psychological predictor variables. A third advantage of the ERFIA method is that multilevel analysis considers the nested data structure of ERP data (single trials nested within subjects) and uses all available and non-missing single-trial information, without the problem of list-wise deletion of an entire subject. Fourth, the ERFIA multilevel method can model within-session habituation in a flexible manner. Fifth, random person effects were found in all of our studies, implying that intercepts and slopes differ between individuals. This finding supports the use of multilevel analyses.

Sixth, the ERFIA multilevel method is generally applicable to all event-related EEGs that are elicited by various stimuli. Based on these advantages, the ERFIA multilevel method adds value to peak-based ERP analysis.

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Chapter 7 Table 1. Comparison of characteristics of peak-based ERP analyses and ERFIA analyses.

Peaks

ERFIAs

Time

Specific points in time (N1, N2, P2, etc.)

20-ms fixed-interval areas

Transformation

Maximization procedure within a specified window, based on a grand average

Individually calculated areas of consecutive 20-ms intervals, not dependent on an averaging procedure

Dependents

Defined peaks

Multiple dependents, depending on poststimulus range (1500 ms = 75 ERFIAs)

EOG

Correction/rejection applicable

Correction/rejection applicable

Baseline

Baseline correction applicable

Baseline correction applicable

Methodological considerations Despite its advantages, the ERFIA multilevel method requires further development and refinement. The first issue concerns multiple testing. This thesis was explorative in nature, because the region in the entire ERP signal in which certain effects of predictor variables could be found was unknown, resulting in a possible problem with multiple testing. For example, in an epoch of 1500 ms, 75 20-ms ERFIAs were tested as dependents. Consequently, multiple testing depends on the size of the epoch size that is selected. Applying Bonferroni correction in this case, only p-values that were less than 0.0007 were considered to be statistically significant. This correction for multiple testing, however, appears to be too rigid.

To this end, we proposed a pragmatic solution: combining strict Bonferroni correction for single ERFIAs with a more tolerant, less stringent level of significance (< 0.05) for three or more consecutive ERFIAs. We searched for more adequate statistical solutions, such as bootstrapping, but given the explorative nature of the analyses, such approaches would have been too computationally intensive (taking weeks to perform on a fast computer). With future innovations in computer processors, it might be more realistic to perform these tests. Future research should determine whether other methods, such as permutation testing and bootstrapping, are more suitable than the Bonferroni method for correcting multiple testing of ERFIAs. Nevertheless, multiple testing of ERFIAs can be reduced significantly if it is clear in which latency region an effect of a predictor variable is expected to be found. Consequently, a smaller range of the epoch can be selected to test the dependent ERFIAs. The second issue concerns the optimal ERFIA width. In practice, it is nearly impossible to study every latency point. In this thesis, for practical reasons, a width of a 20 milliseconds was chosen to calculate area under the curve values. The optimal width of the intervals should be examined further. The optimal width might be related to the research questionâ&#x20AC;&#x201D;the width of ERFIA intervals should be narrow enough to obtain sufficient resolution but should not be too small, because the use of small ERFIAs will increase the total number of tests and exacerbate the multiple testing issue. In explora-

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General discussion tive research, it could make sense to observe the robustness of a predictor value and the course of t-values over time—i.e. when the effect becomes more pronounced and when it wanes. If the region of the effect is known beforehand, the use of smaller-width ERFIA intervals could be considered, permitting a more detailed investigation of the predictor variables in such a specific window. Another approach for reducing multiple testing is the aggregation of ERFIA segments. For example, we performed this procedure in Chapter 5. However, in such a procedure, the resolution is lost.

A third issue is that a standardized model for all ERFIA segments was used, implying that the same model was used at 220 ms poststimulus and 840 ms poststimulus, whereas the influences of the variables might differ between these latency ranges. It is conceivable that not every variable will have a comparably large influence throughout the entire poststimulus epoch. Thus, ideally, the model should be optimized for every 20-ms poststimulus period. Optimizing models will be a challenge for future studies. The improvement of a model, by adding or excluding a variable, can be tested by comparing the -2 5 log likelihoods of the involved models. Further, the same argument could be made to optimize the model in terms of random effects. It is likely that slopes and intercepts vary statistically between latencies.

A fourth issue relates to the rejection of ERFIAs due to eye movements. A ±25-μV EOG rejection criterion for each 20-ms ERFIA range was used in Chapters 3 and 4, and left and right EOG activity was included per 20-ms ERFIA in the analysis as covariates in Chapters 5 and 6. Further research is needed to test which of these EOG correction methods is optimal. In addition, one could ponder whether the EOG criterion should be extended to surrounding ERFIAs—for example, with the analysis of an ERFIA at 140 ms, EOG rejection could be extended to 100-180 ms. Nevertheless, the use of multilevel analyses is particularly advantageous in handling confounded EOG segments, because all ‘valid’ analyzable segments are included, whereas in analysis of variance, all observations that pertain to a given subject are excluded if there are too many invalid segments.

Research question 2: In which poststimulus areas do stimulus intensity and habituation influence the cortical processing of pain in pain-free controls?

Stimulus intensity in healthy subjects In peak-based ERP analyses, the N2 and P2 peaks are significantly related to stimulus intensity. The N2 peak becomes more negative with greater stimulus intensity, whereas 1,6–9 the P2 peak becomes increasingly positive.

In Chapter 3, the significant effects of stimulus intensity could be replicated in these peak regions with the ERFIA multilevel

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Chapter 7 method. Further, the added value of the ERFIA method became apparent, because stimulus intensity effects could also be demonstrated in non-peak-related latency ranges—for example, 100 to 160 ms, 220 to 360 ms, and 1120 to 1400 ms.

In addition, our study (Chapter 3) revealed a highly significant interaction between the actual and previous stimulus intensity, persisting from 380 to 660 ms poststimulus.

Thus, we postulate that the brain makes a “comparison” with information on previous stimulus intensity, possibly reflecting stimulus-related memory processes. There appears to be a cortical tendency to adjust the actual stimulus intensity in the opposite direction, depending on the level of the previous intensity. More research is required to study the influence of previous stimuli on the processing of the actual stimulus. In this respect, it is unknown whether only the previous stimulus influences the processing of the current stimulus or whether the preceding stimuli do as well, and, if so, the extent to which they do.

Habituation and cortical processing in healthy pain-free subjects One of the major goals of this thesis was to examine the mechanism of habituation to pain, because impaired habituation has been proposed as a mechanism that is involved in the chronification of pain. First, we investigated habituation of the cortical processing of pain in healthy pain-free controls.

Previous research has shown that habituation after repetitive painful stimuli can be 10–14 found by peak-based ERP analysis in the N2-P2 amplitudes.

With the ERFIA multilevel method, three forms of habituation were also noted in the N2-P2 regions (Chapter 3). Habituation was studied in 76 healthy subjects who were subjected to a pain rating protocol of 150 stimuli of 5 intensities. Significant linear habituation was demonstrated from 100 to 160 ms and from 180 to 560 ms for all electrodes. The effect of fast habituation (inverse function) occurred from 340 to 480 ms, primarily on Pz and, to a lesser extent, Cz and C4. The effect of dishabituation was seen at all electrodes predominantly between 100 to 140 ms and 200 and 260 ms. In Chapter 5, habituation was also evaluated in healthy subjects but using a shorter pain-rating protocol of 25 identical painful stimuli. Overall, linear habituation and quadratic habituation were observed from 140 to 480 ms.

This thesis established that studying habituation as a proposed mechanism of the chronification of pain with the ERFIA multilevel method has advantages. First, it was demonstrated that habituation is not a phenomenon limited to peak regions but occurs over a broader poststimulus range. Further, the ERFIA multilevel methods allowed three forms of habituation to be studied in greater detail over the whole poststimulus epoch.

All three forms of habituation took place in several segments of the pain-ERP, and the phenomenon of habituation in the cortical processing of pain was not restricted to peak regions.

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General discussion

Research question 3: Does habituation differ in chronic pain sufferers compared with pain-free controls?

Chronic pain and habituation Research question 3—‘Does habituation in the cortical processing of pain differs between individuals with chronic pain compared and pain-free controls?’—can be answered ‘yes.’ Again, habituation was not limited to peak regions. In Chapter 4, the same analyses that were used and described in Chapter 3 in 76 pain-free subjects were expanded to 65 individuals with chronic low back pain (CLBP). CLBP subjects had a significantly decreased linear habituation at 340 to 460 ms in the midline electrodes and C3 and showed significantly more pronounced dishabituation from 400 to 460 ms and 800 to 820 ms for all electrodes, except the temporal electrode. In Chapter 6, similar analyses with regard to habituation and chronic pain were performed using a pain-rating protocol of 25 identical painful stimuli. Linear habituation and dishabituation were influenced by chronic pain from 220 to 260 ms at all electrodes and from 540 to 680 ms primarily for the frontal and central electrodes.

The different ERP regions that were involved in the habituation of cortical processing in chronic pain between the two studies can be explained by the disparate numbers of stimuli (150 versus 25) and, consequently, the difference in the lengths of the experiments. It is conceivable that habituation also differs within and between sessions. Further, the administration of five stimulus intensities in the first protocol could have had a major impact. These aspects—the number of trials and stimulus intensity—and their relationship with habituation require further research and replication.

Particularly in the context of chronic pain the quadratic function of habituation (dishabituation) in the cortical processing of pain is notable. The initial decrease in response reverses to an increase, reflecting stronger processing of the applied stimulus. Also, a slower decline in linear decrease—i.e. the slope of the linear function becoming less steep—is also important, because it might represent reduced acclimation to painful stimuli in individuals with chronic pain versus healthy subjects.

Dishabituation is commonly defined as the recovery of a habituated response after another presented (usually strong) stimulus.15 In this thesis, the term ‘dishabituation’ was used when habituation was modeled as a quadratic function. In this regard, the term ‘dishabituation’ might be confusing and perhaps should be changed to ‘reversed habituation’ or ‘quadratic habituation,’ relating to the quadratic function modeling of this time course.

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Stimulus intensity and chronic pain In conventional ERP analyses, higher amplitudes (N2â&#x20AC;&#x201C;P2 component) are reported for 13,16,17 several chronic pain populations compared with pain-free controls, whereas other 10,18 studies have not found a statistical difference in amplitudes.

In Chapter 4, no difference between groups with respect to the cortical processing of 5 stimulus intensities was observed, nor was a statistical difference observed between the chronic pain and pain-free groups regarding the interaction of the previous stimulus intensity with the actual intensity.

One explanation is that in our study, the results were corrected for within-session habituation, which is the strength of multilevel analysis over conventio-nal ANOVA techniques.

Another explanation concerns the population selection. In our study, we recruited people from the general population, whereas a clinical population might have rendered a different result, because homogeneity might be greater in a specific clinical cohort.

Research Question 4: Does pain hypervigilance influence the cortical processing of painful stimuli in pain-free controls?

This research question can also be answered â&#x20AC;&#x2DC;yes.â&#x20AC;&#x2122; Pain hypervigilance seems to impact the cortical processing of painful stimuli in general, and high PVAQ scores are associated with more positive ERFIAs for the 440-580 ms region compared with low scores. In addition, pain hypervigilance impacted habituation in the region of 480 to 600 ms. In the high-PVAQ-score group, cortical processing was stronger after an initial decline, thus promoting dishabituation, indicating that healthy subjects with high hypervigilance display an impaired habituation, which in turn might predispose them to chronic pain.

Longitudinal studies are needed to establish the relationship between high hypervigilance and impaired habituation. Nevertheless, in our experiment, compared with peakbased ERP analysis, the ERFIA multilevel method again demonstrated its merit, given that the results were generated over a broad poststimulus latency range and not limited to peaks.

Because hypervigilance, as a psychological factor, influences habituation, other psychological factors might also affect habituation. It is conceivable that anxiety confounds the relationship with habituation. In chronic pain, it is believed that catastrophic cognitions (pain catastrophizing) and elevated fear of pain lead to elevated pain perception and 19 chronic disability, as conceptualized in the fear-avoidance model. In addition, anxiety 20 may also mediate the development of postoperative chronic pain. It is possible that anxiety interacts with habituation in the development of chronic pain, but longitudinal research is needed to test this hypothesis.

Expectation is another factor that might interact in the relationship between pain hypervigilance and habituation. Expectation can heighten (nocebo effect) or decrease

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General discussion (placebo effect) the perception of pain. Pazzaglia and colleagues reported that habituation in a laser pain rating paradigm was disrupted by the suggestion of feeling more pain. The decrease in N2-P2 LEP(Laser Evoked Potential) peak amplitude was significant21 ly less extensive—i.e. less habituation—under the nocebo condition.

In conclusion, the environmental and psychological context in which an individual encounters pain is crucial for cortical pain processing, resulting in a pain experience. More research is required to determine the relationship between these factors. Based on our ERFIA multilevel method, we propose that the range between 440 to 800 ms poststimulus of the pain-ERP is especially important for psychologically pain-related variables.

Research question 5: Is the association between chronic pain and habituation moderated by pain hypervigilance?

In Chapter 6, greater pain hypervigilance in chronic pain was hypothesized to further impair habituation of event-related EEGs compared with pain-free hypervigilant subjects. The results showed that pain hypervigilance does not impact the relationship between pain status and habituation, indicating that pain hypervigilance and chronic pain independently impact habituation to painful stimuli, rather than synergistically.

However, at the behavioral level, habituation was not apparent in the pain ratings of the stimuli.

One explanation is the context in which the experiment was performed. In the experiment, the participant was able to stop the experiment at any time. Thus, the threat value might have been perceived as low by the subjects. The threat value of the experimental setting could be increased dramatically by removing the subjects’ controllability or predictability—for example, by delivering varying stimulus intensities at unpredicta22 ble time intervals.

Another notable explanation is that electrical stimulation activates Aβ and Aδ fibers, which is believed to add ‘noise’ to the signal, whereas laser-evoked potentials only 23 stimulate the latter. In a study by Hird and colleagues, laser-evoked potentials correlated with stimulus intensity, whereas electrical-evoked potentials did not. However, both laser-evoked potentials and electrical-evoked potentials showed habituation and 24 were modulated by expectation. Significant limitations of laser-evoked potentials are that it is a costly technique and can cause skin lesions. Thus, the number of trials is limited. The relationship between ‘pure’ nociception (only Aδ stimulation), intensity, and intensity ratings, corrected for habituation, could be examined in a study that combines LEPS and ERFIA multilevel analysis.

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Future Research Many reported associations that link biological and psychometric variables have not been replicated; thus, the findings in this thesis must be recapitulated to confirm the conclusions to the questions that have been posed. With the introduction of the ERFIA multilevel method, we are just beginning to decipher the entire pain-ERP with respect to the cortical processing of pain signals and their psychological and contextual aspects.

Several pivotal questions in pain processing and pain experience can be answered in greater detail with the ERFIA multilevel analysis method, compared with peak-based analyses: o What factors promote pain perception and what factors are anti-nociceptive? o Where in the ERP do these factors interact and which factors are most important? o What combination of factors skew pain processing toward greater perceived pain?

Translation of ERPs to the experience of pain is not straightforward and remains a challenge. Several studies show correlations between subjective pain ratings and certain 8,25–27 ERP peaks.

In the articles of this thesis, we could not find a distinct association between ERP segments and the accompanying NRS scores of the administered stimuli.

Because pain perception is colored by factors such as previous experiences, mood, expectance, and context, further studies are essential. The experience sampling method (ESM), a reliable structural diary that provides insights into how people function in normal daily life, might be a valuable research tool for a greater understanding of the influence of contextual interactions on pain. In searching for a neurophysiological ‘pain pattern,’ experimental EEG pain data (using the ERFIA multilevel method) could be combined with ESM data to study the relationship between mood and context in pain perception.

Further, large longitudinal EEG studies in which the transition from acute to chronic pain and the role of habituation can be examined within the same subjects must be designed. For example, in light of other mechanisms in pain, such as sensitization, habituation is likely to be protective, by preventing an overflow of afferent input to senso28 ry cortices. Habituation might be a mechanism through which top-down pain can be modulated. Understanding the factors that affect habituation and simultaneously identifying the component in the ERP in the cortical processing of pain is essential in guiding the development of interventions that target habituation. One possibility could be to select a study population that undergoes surgery that is associated with a risk of developing chronic pain. A study population of orthopedic surgery patients—particularly 29,30 those who are undergoing total knee replacement—might be of interest.

In such a

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General discussion longitudinal study, it is strongly advised that ESM be incorporated to examine the influence of mood and context on the development of chronic pain.

The ultimate goal would be to predict those individuals who will develop chronic pain after an acute pain event. Only then can tailored therapeutic perioperative interventions be developed.

Conclusions The core idea that every poststimulus latency point in theory might contain meaningful information is supported by the newly developed ERFIA multilevel analysis method. It has been demonstrated that stimulus intensity, habituation, and the psychological factor pain hypervigilance have influence over broad poststimulus ranges of the pain-ERP. In conclusion, this thesis is the foundation for an examination of the cortical processing of pain in a defined poststimulus epoch. The ERFIA multilevel analysis technique is valuable in studying habituation of the pain-ERP. The results indicate that both chronic pain and pain hypervigilance impact habituation processes in the cortical processing of pain.

The present findings, however, must be replicated. A combination of the ERFIA multilevel analysis method with other modalities, such as MRI and ESM, will undoubtedly provide greater insights into the localization of pain processing, its time effects, and its context.

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Summary A painful event cannot be translated through a one-to-one relationship into a pain experience. A pain experience is influenced by many factors, such as genetics, cognition, attention, prior pain experience, and mood. Thus, pain is a multidimensional experience, and thus, wide variability exists between individuals.

Objective measurement of pain is a challenge. Compared with questionnaires, pain event-related potentials (pain-ERPs), brain potentials that are related to such events as painful stimuli, might be a more objective method of measuring pain. On administration of several stimuli, the corresponding brain potentials diminish, due to the phenomenon of habituation. Habituation is a mechanism that is believed to be involved in the chronification of pain and is impaired in many chronic pain populations. Habituation to pain has been proposed as a protective mechanism in the transition from acute to chronic pain.

The primary goal of this thesis was to examine the relationship between habituation and chronic pain. A secondary objective was to determine the function of pain hypervigilanceâ&#x20AC;&#x201D;heightened attention to pain sensationsâ&#x20AC;&#x201D;in habituation and chronic pain.

Heightened attention to pain can increase the pain intensity report and as such may impair habituation. To these ends, an alternative method was developed to analyze event-related EEGs, called the event-related fixed-interval (ERFIA) multilevel method.

This approach allows one to study not only peaks but the entire defined poststimulus period.

Chapter 1 introduces the phenomenon of pain and its multidimensional nature and interindividual variability. Then, the neurophysiological pathway from nociception to pain experience and several methods to measure pain are discussed. Finally, the research questions that constitute the backbone of this thesis are presented.

Chapter 2 provides a general background on the pain-ERP and its structure. Next, the factors that influence the pain-ERP, such as stimulus intensity, habituation, neuroticism, and gene polymorphisms, and the predictive value of the pain-ERP in clinical pain are discussed. The second part of this chapter deliberates on several methods and their methodological aspects in analyzing the pain-ERP. Peaks are important in the analysis of the ERP, but in theory, each latency point can contain meaningful information. Based on this idea, an alternative analysis method is introduced that combines small areas under the curve (AUCs) of the EEG with multilevel regression, a statistical analysis method, allowing the entire poststimulus epoch to be studied. With this method, ERP information can be examined at the single-trial level, thus enabling an analysis of the habitu-

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Chapter 7 ation of a series of stimuli. At the end of this chapter, pilot results with regard to stimulus intensity, previous stimulus intensity, and habituation are discussed.

Chapter 3 elaborates further on the alternative analysis method. In this thesis, the proposed analysis method is called the event-related fixed-interval areas (ERFIA) multilevel method. The primary goal is to determine whether the ERFIA multilevel method renders similar results as conventional peak analysis. The ERFIA multilevel method is tested on an existing EEG dataset of 84 subjects who underwent a pain rating protocol of 150 electrical stimuli of 5 stimulus intensities. The period after the stimulus, the so-called epoch of 1500 ms, was partitioned into areas of 20 ms. The variance in ERP signal was examined by modeling consecutive 20-ms ERFIAs as dependents, explained by several variables, including stimulus intensity , previous stimulus intensity, and habituation (trial number). Not only did the ERFIA multilevel method generate similar results compared with peak-based analysis, it also demonstrated that the influences of stimulus intensity and ha-bituation could be observed over a much broader poststimulus range. In addition, our study detected a highly significant interaction between actual and previous stimulus intensity, persisting from 380 to 660 ms poststimulus. This finding implicates a process in which the brain makes a â&#x20AC;&#x153;comparisonâ&#x20AC;? with information on previous stimulus intensity, possibly reflecting stimulus-related memory processes.

Chapter 4 evaluates the differences in cortical processing of (non-)painful stimuli between chronic pain and pain-free subjects, with respect to intensity and habituation.

Three forms of habituation are studied: a linear, inverse, and quadratic function of stimulus number. In this study, no differences in cortical processing of stimulus intensity were noted between the chronic pain and pain-free groups. However, subjects with chronic pain showed a decreased habituation in all three forms of habituation. At the cortical level, it appears that individuals with chronic low back pain acclimate to a series of painful and non-painful stimuli compared with pain-free subjects. Habituation to pain might be a key factor in the chronification of pain. However, testing this hypothesis requires longitudinal studies.

Chapter 5 studies the relationship between pain hypervigilance and habituation in the cortical processing of painful stimuli. Pain hypervigilance is defined as heightened attention to painful sensations. Pain hypervigilance may result in a higher pain intensity report. Hypervigilance can already be apparent in pain-free individuals and as such might be a predisposing factor in the onset of chronic pain disorders. This chapter examined whether hypervigilant healthy subjects displayed a disparate cortical processing of pain compared with non-hypervigilant healthy subjects. Also, the influence of hypervigilance on habituation was studied. The results show that pain hypervigilance directly influenced the pain-ERP from 440 to 580 ms poststimulus, independent of habituation. In addition, pain hypervigilance affected linear and quadratic habituation. Thus, pain hy-

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Summary pervigilance impacts the cortical processing of painful stimuli, suggesting that pain hypervigilance modulates the pain experience through altered cortical habituation.

Chapter 6 analyzes whether pain hypervigilance further impairs habituation in chronic pain subjects. The relationship between pain status and habituation was not moderated by pain hypervigilance. Chronic pain status affected linear habituation and dishabituation (quadratic function) from 220 to 260 ms for nearly all electrodes and from 580 to 640 ms for frontal electrodes. The effect of pain hypervigilance on habituation was observed primarily from 480 to 820 ms poststimulus for right-side and central electrodes. Thus, pain hypervigilance and chronic pain independently influence habituation to painful stimuliâ&#x20AC;&#x201D;not synergistically. Further research is required to confirm that these effects are mediated by separate pathways.

Chapter 7 provides an overview and a general discussion of the studies that have been included in this thesis. The advantages of the ERFIA multilevel method versus peakbased methods are described. In addition, methodological considerations are discussed, and aspects for development and refinement of the multilevel method, and suggestions for future research are proposed. With the ERFIA multilevel analysis method, it was demonstrated that stimulus intensity, habituation, and the psychological factor pain hypervigilance had influence over broad poststimulus ranges of the pain-ERP. Replication of the present findings, however, is critical. A combination of the ERFIA multilevel analysis method with other modalities, such as MRI and ESM, will undoubtedly increase insights into the localization of pain processing and its time effects and context.

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Samenvatting Een pijnlijke prikkel kan niet één op één vertaald worden naar een pijnervaring, omdat pijn een subjectieve ervaring is. De pijnervaring wordt onder andere beïnvloed door genetica, cognities, aandacht, vroegere pijnervaringen, stemming. Pijn is dus een multidimensionale ervaring, waardoor er een grote variatie tussen individuen bestaat. Dit alles maakt dat pijn moeilijk objectief te meten is.

In vergelijking tot vragenlijsten, worden Pijn ‘Event-Related Potentials’ (pijn-ERP) oftewel, hersenpotentialen gerelateerd aan een gebeurtenis, bijvoorbeeld een pijnprikkel, gezien als een meer objectieve maat om pijn te meten. Bij meerdere keren toedienen van een pijnprikkel treedt er gewenning op, ook wel habituatie genoemd, en de corresponderende pijnpotentialen worden kleiner. Habituatie aan pijn wordt gezien als een van de mechanismen die beschermend werken in de transitie van acute naar chronische pijn. Uit eerder onderzoek blijkt dat habituatie verminderd is in mensen met chronische pijn. Dit proefschrift onderzoekt de rol van habituatie in de corticale verwerking van pijn bij gezonde mensen en bij individuen met chronische pijn. Verhoogde aandacht voor pijn, zogenaamde pijn hypervigilantie, kan een pijnervaring versterken.

Daarom wordt ook onderzocht of pijn hypervigilantie de habituatie van de corticale pijnverwerking remt en of er een verschil bestaat tussen gezonden en individuen met chronische pijn. Voor dit doeleinde werd een alternatieve methode ontwikkeld voor de analyse van Event-Related Potentials. Deze methode heet de Event-Related FixedInterval Areas (ERFIA) multilevel methode. Met deze methode kan men het hele ERP signaal analyseren en niet alleen de pieken.

Hoofdstuk 1 betreft een introductie in het fenomeen pijn. Het multidimensionale karakter en de variatie tussen individuen wordt besproken, als ook het neurofysiologische proces van pijnprikkel naar de corticale verwerking pijn. Daarnaast worden verschillende methoden om pijn te meten uiteen gezet, zoals vragenlijsten en neuro imaging technieken, waaronder het elektro-encefalogram (EEG). Tenslotte worden de onderzoeksvragen die de basis vormen voor dit proefschrift gepresenteerd.

Hoofdstuk 2 geeft algemene achtergrondinformatie over het pijn-ERP en de structuur van het ERP. Tevens worden factoren die van invloed zijn op het pijn-ERP beschreven, zoals de stimulusintensiteit, habituatie, neuroticisme en genpolymorfismen. Verder wordt de predictieve waarde van het pijn-ERP in relatie tot klinische pijn besproken. Het tweede gedeelte van hoofdstuk 2 gaat in op veel gebruikte analyse methoden voor het pijn-ERP en de methodologische aspecten hiervan. Niet alleen pieken van het pijn-ERP zijn van belang in de analyse, in theorie zou elk punt van het pijn potentiaal na een stimulus relevante informatie kunnen bevatten. Op basis van deze opvatting wordt een

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Chapter 7 alternatieve methode geïntroduceerd die kleine oppervlaktes, zogenoemde ‘areas under the curve’ (AUCs) van het EEG, combineert met de multilevel regressie analyse, een statistische analyse methode. Door AUCs te gebruiken in plaats van pieken kan de gehele periode na een stimulus, ‘de epoch’, bestudeerd worden. Daarnaast kan op ‘single trial’ niveau, dus op het niveau van een individuele pijnprikkel, ERP informatie geanalyseerd worden en daardoor ook habituatie van een serie prikkels beter onderzocht worden. Aan het einde van dit hoofdstuk worden de eerste resultaten van een analyse met betrekking tot de invloed van stimulus intensiteit, de intensiteit van de voorgaande prikkel en habituatie bediscussieerd.

Hoofdstuk 3 werkt de nieuwe analyse methode verder uit. Deze methode wordt verder in het proefschrift de Event-Related Fixed-Interval Areas (ERFIA) multilevel methode genoemd. Het primaire doel is om te onderzoeken of de ERFIA multilevel methode vergelijkbare resultaten met betrekking tot het pijn ERP oplevert ten opzichte van de conventionele piekanalyses. De ERFIA multilevel methode wordt getest op een bestaande EEG dataset die bestaat uit 84 proefpersonen, die 150 elektrische stimuli kregen toegediend van 5 verschillende intensiteiten. De periode na een stimulus die 1500 ms bedraagt, werd opgedeeld in 75 oppervlaktes van 20 milliseconden. Het hele ERP signaal werd onderzocht door de variantie van opeenvolgende 20-ms ‘ERFIAs’ te modelleren voor onder andere de volgende factoren: stimulus intensiteit, voorgaande stimulus intensiteit en habituatie. Ten opzichte van piekanalyses, laat de ERFIA multilevel methode vergelijkbare resultaten zien wat betreft de invloed van stimulus intensiteit en habituatie. Daarnaast blijkt dat de invloed van stimulus intensiteit en het fenomeen habituatie zich beiden niet alleen beperken tot pieken maar met een veel groter deel van het ERP signaal verband hebben. Vervolgens werd aangetoond dat de corticale verwerking van de huidige stimulus intensiteit afhankelijk is van de intensiteit van de voorgaande prikkel in een groot deel van het ERP signaal. Deze bevinding impliceert dat het brein een vergelijk maakt van de voorgaande prikkel met de huidige, mogelijk als een soort stimulus geheugen proces.

Hoofdstuk 4 onderzoekt of de corticale verwerking van pijnprikkels verschillen tussen gezonde proefpersonen en individuen met chronische lage rugpijn met betrekking tot intensiteitsverwerking en habituatie. Er worden 3 vormen van habituatie onderzocht, namelijk een lineaire afname, een inverse afname en een kwadratische vorm (parabool). In deze studie werd er geen verschil in verwerking van de verschillende intensiteiten van de toegediende prikkels gevonden tussen gezond en chronische pijn. Echter, er werd aangetoond dat er bij proefpersonen met chronische lage rug pijn minder habituatie optrad voor alle 3 de vormen ten opzichte van de gezonde proefpersonen. Individuen met chronische lage rugpijn lijken op corticaal niveau dus ‘minder te wennen’ aan een serie pijnlijke prikkels dan gezonden. Habituatie aan pijn kan dus een belangrijke sleutelfactor zijn in het chronisch worden van pijn. Om dit definitief aan te tonen zijn echter longitudinale studies van belang.

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Samenvatting Hoofdstuk 5 bestudeert de relatie tussen pijn hypervigilantie, habituatie en corticale verwerking van pijn in een gezonde individuen. Pijn hypervigilantie wordt gedefinieerd als verhoogde waakzaamheid (vigilantie) toegespitst op pijnlijke sensaties. Pijn hypervigilantie wordt gezien als een van de mechanismen waardoor een hogere pijnintensiteit wordt gerapporteerd. Hypervigilantie kan al bij gezonde mensen aanwezig zijn en zou als predisponerende factor een rol kunnen spelen in het ontstaan van chronische pijn. In dit hoofdstuk werd onderzocht of gezonde hypervigilante personen een andere corticale verwerking van pijn tonen in vergelijking tot niet hypervigilante individuen. Ten tweede werd onderzocht of de habituatie van pijn verschilt tussen hypervigilante en niet-hypervigilante proefpersonen. De resultaten toonden dat pijn hypervigilantie direct verband heeft met het pijn-ERP van 440 tot 580ms poststimulus, onafhankelijk van gewenning. Daarnaast werd aangetoond dat pijn hypervigilantie effect heeft op de habituatie van pijnlijke prikkels, zowel op de lineaire als ook de kwadratische habituatie.

Bij een hoge hypervigilantie score werd de prikkelverwerking na een initiĂŤle afname sterker. Deze resultaten wijzen erop dat hypervigilantie, middels een veranderde corticale verwerking, de beleving van pijn zou kunnen beĂŻnvloeden.

Hoofdstuk 6 gaat in op de vraag of de remming in gewenning aan pijn bij chronische pijn verder wordt geremd door hypervigilantie. Dit komt niet naar voren uit het onderzoek.

Het blijkt namelijk dat de factoren chronische pijn en hypervigilantie beiden onafhankelijk effect hebben op habituatie, en niet in een synergistisch verband. De associatie van chronische pijn met habituatie werd gezien van 220-260 ms in bijna alle EEG-electroden en van 580 tot 640 ms poststimulus voor de frontale electroden. Pijn hypervigilantie heeft effect op habituatie van 480 tot 820ms poststimulus voor de rechts en centraal gelokaliseerde electroden. Toekomstig onderzoek is noodzakelijk om vast te stellen of de invloed van pijn hypervigilantie en chronische pijn op habituatie via verschillende corticale routes loopt.

Hoofdstuk 7 bespreekt de resultaten van de studies in de voorgaande hoofdstukken. De voordelen van de ERFIA multilevel methode ten opzichte van de gangbare analyse technieken worden besproken, maar ook de aspecten die nog verdere ontwikkeling en aanpassing behoeven. Daarnaast worden toekomstige perspectieven voor onderzoek besproken. De ERFIA multilevel methode is een veelbelovende methode om het hele poststimulus periode en specifieke tijd gerelateerde fenomenen zoals habituatie te onderzoeken. Met deze methode werd aangetoond dat invloeden van stimulus intensiteit, habituatie en pijn hypervigilantie in een groot deel van het ERP invloed hadden en niet alleen op pieken. Replicatie van deze bevindingen is echter belangrijk. Daarnaast zal een combinatie van de ERFIA multilevel methode met andere technieken zoals MRI en ESM het inzicht verder kunnen vergroten in de lokalisatie van corticale pijnprocessen, hun tijdseffecten en context.

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Valorisation Below, the research presented in this thesis is placed in the context of social relevance, in other words valorisation. Valorisation is defined as â&#x20AC;&#x153;the process of creating value from knowledge, by making knowledge suitable and/or available for social and/or economic use and by making knowledge suitable for translation into competitive products, services, processes and new commercial activities.â&#x20AC;? In the next paragraphs, the relevance of the topic, possible target groups and future directions will be addressed.

Relevance of the topic Many individuals suffer from chronic pain. According to a large European study, almost 20% of adults experience moderate to severe chronic pain, and in approximately 50% of 1 individuals pain was managed inadequately. Another important issue is acute postoperative pain. Forty to 75% of patients suffer from moderate to severe pain during the 2 first couple of days after surgery, many of whom develop chronic pain. Remarkably, recent research suggests that patients who experience severe pain during the acute 3 postoperative phase are at risk of developing chronic pain. So, despite the availability of sophisticated pain management options, physicians fail to reduce pain to acceptable levels in a substantial number of patients. In the Netherlands, on average 13 workdays are lost annually due to chronic pain, decreasing productivity and costing billions of euros. Thus, chronic pain is a major health care problem and places a great burden on society and economy.

One of the difficulties in the treatment of (chronic) pain is its multidimensionality. Pain is a subjective experience, and many factors, such as genetic, psychological, and contextual factors, are involved in the pain experience. This explains why a large variability exists between individuals.

Because of its subjective nature, objective measurement of pain is a challenge. Compared to questionnaires, pain Event-Related Potentials (pain-ERPs) is considered to be a more objective way to measure pain. Pain-ERPs are time-locked EEG responses to painful stimuli. After receiving several pain stimuli, the corresponding brain potentials usually decrease, due to a phenomenon called habituation. In pain research, habituation is viewed as one of the key top-down processes that may prevent the chronification of pain. Several experimental studies have demonstrated an impaired habituation to pain4â&#x20AC;&#x201C;6 ful stimuli in various chronic pain populations. More knowledge about these factors and mechanisms, which explain the wide variability in pain experiences, will aid the

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Chapter 7 development of new diagnostic and tailored therapeutic options that predict and prevent the transition from acute to chronic pain.

In this thesis, an analysis method was developed, called Event-Related Fixed-Interval Area (ERFIA) multilevel method, enabling an in-depth investigation of the phenomenon of habituation to pain. Not only did the ERFIA multilevel method show similar results compared to conventional peak-based analysis, it also demonstrated that influences of stimulus intensity and habituation could be found in a much broader range of the painERP. Furthermore, this thesis replicated earlier findings that habituation is impaired in chronic pain and again, that habituation was not limited to peak regions in the pain-ERP. In addition, it was demonstrated that pain hypervigilance, a psychological variable, independently of chronic pain, influenced the cortical processing of pain and habituation.

Target groups A logical target group is the surgical population. More insight in the chronification of postoperative pain will aid in reducing suffering from chronic pain. First, with more knowledge, prediction models to assess the risk of chronic postsurgical pain can be improved. Good prediction models aid physicians and their patients in decision-making concerning surgery. Next, insight in the chronification process will provide new targets for treatment and prevention. Besides surgical patients, patients with chronic pain will also benefit in several ways from more knowledge about factors and mechanisms, such as habituation, involved in chronic pain. For example, this knowledge could be used in the development of new treatment options and tools to measure the effectiveness of treatments.

Future directions This thesis provides evidence that habituation plays a role in chronic pain and that the psychological factor pain hypervigilance may modify habituation. Future research is of importance in several ways. First, longitudinal studies are undoubtedly needed to establish whether the degree of habituation to pain can (partially) predict chronification of pain. Based on such knowledge prediction models can be developed for the assessment of risk of developing chronic pain. Currently, these prediction models contain variables such as patient demographics, perioperative data, and psychological factors. Adding a neurophysiological measure such as cortical habituation to pain, may help to improve these prediction models. Good prediction models will aid in decision making regarding surgery. Second, research into the identification of factors that influence habituation,

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Valorisation positively or negatively, is also of great importance. Understanding the factors that affect habituation and simultaneously identifying the component in the pain-ERP is essential in guiding the development of interventions that target habituation. This thesis suggests that cognitions such as pain hypervigilance might be a target in the prevention of chronification of pain. Third, the identification of important components of the pain-ERP might lead to the development of an objective tool to assess whether treatments are effective.

In conclusion, this thesis, provides further evidence that habituation to pain and factors related to habituation may be fruitful targets for further development in prediction tools for chronification of pain and management of pain. If we could reduce suffering from chronic pain it will decrease the burden on society and economy, through increase in work participation, social participation and a decreased health care demand.

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Breivik H, Collett B, Ventafridda V, Cohen R, Gallacher D. Survey of chronic pain in Europe: Prevalence, impact on daily life, and treatment. Eur J Pain. 2006;10(4):287-333. doi:10.1016/j.ejpain.2005.06.009.

Gramke H-F, de Rijke JM, van Kleef M, et al. The prevalence of postoperative pain in a cross-sectional group of patients after day-case surgery in a university hospital. Clin J Pain. 2007;23(6):543-548. doi:10.1097/AJP.0b013e318074c970.

Hoofwijk DMN, Fiddelers A a. a., Peters ML, et al. Prevalence and Predictive Factors of Chronic Postsurgical Pain and Poor Global Recovery One Year after Outpatient Surgery. Clin J Pain. 2015;31(12):10171025. doi:10.1097/AJP.0000000000000207.

Flor H, Diers M, Birbaumer N. Peripheral and electrocortical responses to painful and non-painful stimulation in chronic pain patients, tension headache patients and healthy controls. Neurosci Lett.

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Peters ML, Schmidt AJM, Van den Hout MA. Chronic low back pain and the reaction to repeated acute pain stimulation. Pain. 1989;39(1):69-76. doi:10.1016/0304-3959(89)90176-0.

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Epiloog/Dankwoord Een aantal jaren geleden begon ik vol inspiratie aan een wetenschappelijke reis, waarvan dit proefschrift het eindresultaat is. Als beginnend onderzoeker was ik in de overtuiging dat ik door een goed design, een deugdelijke dataverzameling en goede analyses een gedeelte van de ‘waarheid’ over pijn mocht ontdekken. Nu, aan het eind van de reis gekomen, ben ik zeer dankbaar voor de inzichten die ik heb mogen verkrijgen tijdens deze periode.

Hoewel men met het paradigma van de huidige wetenschap vele facetten van wereld en mensheid goed kan onderzoeken, kwam ik gaandeweg tot de ontdekking dat de ‘volledige waarheid’ misschien niet te vinden is. Want: Hoe kun je de volledige waarheid omvatten met je bewustzijn? Hoe kun je de werking van je brein ontrafelen met je eigen brein? Dan zou je ‘boven’ je brein moeten uitstijgen om het geheel te kunnen omvatten. Dit deed mij denken aan een citaat van een Vietnamese monnik Thich Nhat Hanh:

“Er drijft een wolk in dit vel papier Als je een dichter bent, dan zie je duidelijk dat er een wolk drijft in dit vel papier. Zonder wolk is er geen regen; zonder regen kunnen de bomen niet groeien; en zonder bomen kunnen we geen papier maken. De wolk is nodig voor het bestaan van het papier. Als de wolk er niet is, kan dit vel papier er ook niet zijn... Als we nog dieper in dit papier kijken, kunnen we er zonneschijn in zien. Als de zon er niet zou zijn, kan het bos niet groeien...

En zo weten we dat er ook zonneschijn in dit vel papier is. Als we nog langer kijken, kunnen we de houthakker zien die de boom velde en hem naar de fabriek bracht waar hij omgevormd werd tot papier. En we zien de tarwe. We weten dat de houthakker niet kan leven zonder zijn dagelijks brood en daarom is de tarwe die zijn brood werd, eveneens in dit vel papier. En ook zijn vader en zijn moeder zijn in dit papier... Als we nog dieper kijken, zien we dat we er ook zelf in zijn. Dat is niet zo moeilijk in te zien: wanneer we naar een vel papier kijken, wordt het vel een deel van onze waarneming... We kunnen dus zeggen dat alles hier is, in en met dit vel papier. Er is niets aan te wijzen dat niet hier is - tijd, ruimte, de aarde, de regen, de mineralen in de bodem, de zon, de wolk, de rivier, de warmte... Dit vel papier bestaat omdat al het andere bestaat.”

Thich Nhat Hanh, citaat in: De natuur als beeld in religie, filosofie en kunst, Matthijs.

G.C. Schouten, KNNV Uitgeverij, Utrecht, 2001.

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Chapter 7 Dit filosofische citaat deed mij realiseren dat ik onmogelijk iedereen en elke omstandigheid kan bedanken. Dus bij deze doe ik een bescheiden poging en verontschuldig ik me bij voorbaat aan alle mensen die hier niet bij naam worden genoemd. Aan hen ook mijn oprechte dank!

Allereerst wil ik Richel Lousberg bedanken, want per slot van rekening is het idee van dit proefschrift in jouw EEG-lab ontstaan. Graag wil ik je danken voor alle inspirerende gesprekken en spannende analysemomenten. Ik kon altijd bij je terecht en dan stond er weer een lekkere pot thee klaar. Jij hebt me geleerd dat het soms efficiënter is om een uur te brainstormen en vrij te denken, dan te blijven ploeteren achter de computer om een letter op papier te krijgen. Dit gezamenlijke pionierswerk heb ik als zeer waardevol ervaren.

Ook mijn grote dank aan mijn twee promotoren Jim van Os en Bert Joosten.

Beste Jim, je bent een groot wetenschappelijk voorbeeld voor mij en ik ben je dankbaar dat je ‘die anesthesioloog’ een kans gaf om te promoveren op dit interdisciplinaire onderwerp. Dank voor alle steun en ik bewonder je grondige revisie van manuscripten en je buitengewoon snelle reactietijd op mails.

Beste Bert, je bent een wetenschapper pur sang. Door jouw biologische invalshoek waren onze discussies zeer waardevol. Ik dank je voor de coaching en de vrijheid die je me hebt gegeven om nieuwe onderzoeksideeën uit te werken. Ik hoop dat we de komende jaren nog veel mogen samenwerken.

De wetenschappelijke beoordelingscommissie dank ik hartelijk voor de waardevolle tijd en de beoordeling en van dit proefschrift.

Marco Marcus, bedankt voor de kans die je me gaf om een wetenschapsstage te lopen aan het einde van mijn opleiding. Jij was het die me aanspoorde het management rond acute pijn op me te nemen en dat bleek achteraf precies het onderwerp dat bij mij past.

Beste Wolfgang Buhre, ons afdelingshoofd, dank voor het beschikbaar stellen van researchtijd in de afgelopen paar jaar. Je hebt de gave om mensen te laten ontdekken waar ze goed in zijn en hen hierin de vrijheid te geven. Je bent voor mij een groot voorbeeld op het gebied van organisatie en management en hoop nog veel van je te mogen leren.

Beste Lonneke Bodar, Rosan Luijcks, Suzanne Roggeveen en Marga Schnitzeler, dank jullie wel voor de mooie RILOCA reis. Zonder onze samenwerking konden al die onderzoeksprojecten nooit kwalitatief zo goed worden afgerond. Dank voor de inclusie, metingen en gezelligheid. Lonneke, Rosan en Suzanne, ik wens jullie een mooie carrière in het artsen vak. Marga dankjewel voor al je werk en ik ben er van overtuigd dat we elkaar nog regelmatig in het lab zien!

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Epiloog/Dankwoord Ook zou ik graag mijn directe collega’s anesthesiologen en AIOS op de werkvloer willen bedanken voor de fijne werksfeer. Ik weet dat ik op jullie kan bouwen.

In het bijzonder dank ik Andrea Balthasar voor alle steun die je me als vriendin en collega gaf. Ik waardeer enorm je wetenschappelijke gedrevenheid, je energie en organisatietalent. Ik denk dat wij een goed onderzoeksteam vormen en elkaar aanvullen. Ik hoop nog jaren met je te mogen samenwerken.

Boukje Hermans en Ankie Hamaekers, bedankt voor jullie vriendschap, waardevolle adviezen en alle leuke spontane koffiemomenten. Ik weet dat ik altijd bij jullie terecht kan.

Bas Timmerman, dank voor al je inspanningen om mijn researchtijd in te plannen. Je bent een belangrijke spil binnen onze afdeling.

Vivianne en Hermina, bedankt voor de secretariële ondersteuning tijdens dit proefschrift.

Brigitte Bazuin, dank voor je mooie ontwerp van de kaft en de prettige samenwerking.

Het was meteen een spijker op zijn kop. Bijzonder om te ervaren dat kunst zich zonder woorden kan verbinden met wetenschap.

Beste Geraldine, dankjewel dat je al die jaren mijn vriendin bent. Ik bewonder je doorzettingsvermogen en pragmatisme; je staat altijd voor me klaar. Ik waardeer alle leuke spontane koffiemomenten en gesprekken zeer. Ik hoop dat we weer meer tijd zullen gaan krijgen voor een lekkere high tea of een saunadagje. Dank dat je mijn paranimf wilt zijn.

Roos, ook al woon je helemaal in Italië, als we elkaar weer zien, dan is het net alsof je nooit bent weggeweest. Ik dank je voor alle mooie en spannende belevenissen in de buitensport. Jij weet het gevoel van vrijheid en leven in me naar boven te halen. In de toekomst hoop ik weer spannende avonturen met je te mogen beleven, zoals een via ferrata tocht in de Dolomieten.

Marja, je bent niet alleen mijn nicht, maar ook een zeer speciale vriendin. Je kent me letterlijk van haver tot gort. Ook al zien we elkaar de laatste jaren niet heel veel, je bent er altijd voor me met raak advies. Zo ook je citaat: “Als je je niet lekker en niet fit voelt, is het niet het moment om je leven te gaan analyseren”. Dit heeft me menig piekeren bespaard. Ik hoop dat ons contact weer intensiever gaat worden nu dit proefschrift klaar is.

Lieve Helen en Christine, ik ben blij dat ik zo’n lieve zussen mag hebben. Ook al wonen we ver uit elkaar, elke keer is het samenzijn weer als een thuiskomen. Het is ook zo leuk dat onze familie zich nu zo uitbreidt en ik kijk uit naar alle weekenden met de kinderen.

Helen, dank dat ik op jouw wetenschappelijke werk mocht voortborduren en de nuttige adviezen die je me daarbij gaf. Heel jammer dat je door omstandigheden niet op de

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Chapter 7 promotie aanwezig kunt zijn, maar sommige dingen zijn gewoon veel belangrijker.

Christine, dank dat je mijn paranimf wilt zijn. Jouw nuchterheid als psycholoog zal me helpen bij de voorbereiding voor de verdediging.

Mijn lieve ouders, jullie hebben ons drietjes zo’n mooie basis gegeven en daar ben ik jullie zeer dankbaar voor. Jullie hebben ons alle kansen gegeven om te kunnen worden tot wie we nu zijn. Dank jullie wel dat jullie altijd voor ons klaar staan en ik hoop dat jullie nog lang van al jullie kleinkinderen mogen genieten.

Sergej, liefde van mijn leven, jij bent mijn rots en ik weet dat ik altijd op je kan bouwen.

Dank dat je me in ons hectische leven de ruimte gaf om dit proefschrift af te ronden.

Lieve Sophie, we zijn dankbaar dat jij in ons gezinnetje kwam. Met jouw vrolijke lach ben je ons lief zonnestraaltje.

Ten slotte, dank ik de uitdagingen, tegenslagen, hindernissen en weerstanden op mijn weg, want uiteindelijk leer je veel van de contrasten in je leven. De dankbaarheid voor iedereen om me heen, brengt mij weer terug naar het bovenstaande citaat van Thich Nhat Hanh: “Ook in dit vel papier is alles met alles verbonden”. Waarheid kan immers alleen in die verbondenheid worden gevonden.

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Curriculum Vitae Catherine (Carine) Jeanne Vossen was born in Geleen on January 25th of 1979 and grew up in Neerbeek (The Netherlands). In 1997, she graduated high school at Scholengemeenschap Sint Michiel in Geleen. She began her study in Environmental Health Science at Maastricht University in 1997, after which she decided to also study Medicine in 1999, finishing both studies in 2003 and 2005 respectively.

Her first job was as a resident (ANIOS) Intensive Care in Catharina Hospital in Eindhoven in 2005. In 2006, she began her residency in Anesthesiology at the Maastricht University Hospital. During her residency, she was secretary and member of the National Committee for Trainees in Anesthesiology.

In the last year of her residency, she met Richel Lousberg, also copromotor of her sister Helen Vossen. Together, they developed the ERFIA multilevel method, which is the basis of this thesis. In 2012, she became staff member in the department of Anesthesiology in the Maastricht University Medical Centre. She has a special interest in locoregional anesthesia and for several years she was medical coordinator for Acute Pain Management. In addition, she enjoys teaching and is instructor for simulation-based learning and Advanced Life Support.

In research, her focus is in psychological aspects of pain and its context. Presently, she is performing a pilot study, together with her colleague Andrea Balthasar, to implement a digital application to measure pain, mood and context in the postoperative period.

Carine is married to Sergej de Vries and has a daughter, Sophie.

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List of publications Articles 1. Vossen CJ, Vossen HGM, Marcus MAE, Van Os J, Lousberg R. Introducing the event related fixed interval area (ERFIA) multilevel technique: A method to analyze the complete epoch of event-related potentials at single trial level. PLoS One. 2013;8(11). doi:10.1371/journal.pone.0079905.

2. Luijcks R, Hermens HJ, Bodar L, Vossen CJ, Os J van, Lousberg R. Experimentally Induced Stress Validated by EMG Activity. PLoS One. 2014;9(4):e95215. doi:10.1371/journal.pone.0095215.

3. Vossen CJ, Vossen HGM, Joosten EA, van Os J, Lousberg R. Does habituation differ in chronic low back pain subjects compared to pain-free controls? A cross-sectional pain rating ERP study reanalyzed with the ERFIA multilevel method. Medicine (Baltimore). 2015;94(19):e865. doi:10.1097/MD.0000000000000865.

4. Luijcks R, Vossen CJ, Hermens HJ, van Os J, Lousberg R. The Influence of Perceived Stress on Cortical Reactivity: A Proof-Of-Principle Study. Schalk G, ed. PLoS One. 2015;10(6):e0129220. doi:10.1371/journal.pone.0129220.

5. Luijcks R, Vossen CJ, Roggeveen S, van Os J, Hermens HJ, Lousberg R. Impact of early life adversity on EMG stress reactivity of the trapezius muscle. Medicine (Baltimore).

2016;95(39):e4745. doi:10.1097/MD.0000000000004745.

6. Vossen CJ, Luijcks R, Van Os J, Joosten EA, Lousberg R. Does pain hypervigilance further impact the lack of habituation to pain in individuals with chronic pain? A cross-sectional pain ERP study. J Pain Res. 2018;11. doi:10.2147/JPR.S146916.

Book chapters 1. Vossen CJ, Vossen HGM, Van de Wetering W, Marcus MAE, Van Os J, Lousberg R. The use of event-related potentials in chronic back pain patients. Norasteh A, ed.

Low Back Pain . 2012:1-22.

2. Vossen CJ, Roggeveen S, Lousberg R. Ik moet geopereerd worden, wat een catastrofe! Siemonsma M, ed. ProbleemgeoriĂŤnteerd denken in de pijngeneeskunde 2017: 43-48.

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Abstracts 1. Vossen CJ, Theunissen M, Marcus MAE. Dings J. Postoperative care for elective neurosurgical patients: Is PACU/NMC recovery a safe alternative for postoperative ICU monitoring? Presentation at the Dutch National Anesthesiology Meeting, 2009.

2. Vossen CJ, Marcus MAE, van Os J, Lousberg R. Cortical processing of pain is modulated by state and trait depression. Presentation in best 4 category; Dutch National Anesthesiology Meeting, 2012.

3. Vossen CJ, van Os J, Joosten EA, Lousberg R. Structural changes in resting-state EEG activity in chronic pain? 7th World Congress World Institute of Pain, Maastricht, May 7-10, 2014. Best Oral Abstract $500,4. Vossen CJ, Joosten EA, van Os J, Lousberg R. Does hypervigilance influence the cortical processing of experimentally induced pain? EFIC Congress: Pain in Europe IX, Vienna, September 2-5, 2015.

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