Jop 2016 30 issue 1

Volume 30 / Number 1 / 2016

Volume 30 / Number 1 / 2016

Journal of

Psychophysiology

Journal of Psychophysiology

Editor-in-Chief Michael Falkenstein

An International Journal

Editorial Board Monika Althaus Markus Breimhorst Tavis Campbell Istvan Czigler Patrick Gajewski Edward Golob Sien Hu Julian Koenig Cristina Ottaviani Patrick Papart Walter Sannita Henrique Sequeira Franck Vidal Jin-Chen Yang Juliana Yordanova

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17.12.2015 09:15:49

Contents Articles

Journal of Psychophysiology (2016), 30(1)

Antidepressant Medication May Moderate the Effect of Depression Duration on Hippocampus Volume Mark A. Rogers, Hidenori Yamasue, and Kiyoto Kasai

1

An Arousal Effect of Colors Saturation: A Study of Self-Reported Ratings and Electrodermal Responses Piotr Zielin´ski

9

Empathy, Approach Attitude, and rTMs on Left DLPFC Affect Emotional Face Recognition and Facial Feedback (EMG) Michela Balconi and Ylenia Canavesio

17

Connectivity of Superior Temporal Sulcus During Target Detection Martin Pail, Petra Dufkova´, Radek Marecˇek, Jana Zelinkova´, Michal Mikl, Daniel Joel Shaw, and Milan Bra´zdil

29

Cold Face Test-Induced Increases in Heart Rate Variability Are Abolished

by Engagement in a Social Cognition Task Frank Iorfino, Gail A. Alvares, Adam J. Guastella, and Daniel S. Quintana

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Ó 2016 Hogrefe Publishing

Article

Antidepressant Medication May Moderate the Effect of Depression Duration on Hippocampus Volume Mark A. Rogers,1 Hidenori Yamasue,2 and Kiyoto Kasai2 1

Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, Victoria, Australia, 2Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, Hongo, Bunkyo-ku, Japan

Abstract. Hippocampus volume has been frequently, but not universally reported to be reduced in people with major depression relative to agematched healthy controls. Among the potential reasons for this discrepancy in finding across studies is the effect of antidepressant medication.

Hippocampus volume was determined by MRI (1.5 Tesla) for 10 people diagnosed with major depression for who detailed history of depression and antidepressant treatment history were known, and 10 age-matched healthy controls with no history of depression. Left, but not right, hippocampus volumes were significantly smaller in the patient group compared to the controls. Furthermore, there was a significant correlation such that left hippocampus volume was smaller with increasing lifetime duration of depression. However, this relationship was moderated by a significant correlation such that greater lifetime duration of antidepressant medication was associated with larger left hippocampus volume.

The findings support the contention that antidepressant medication may act to normalize hippocampus volume.

Keywords: major depression, hippocampus volume, antidepressant

In addition to the salient effects on mood, major depression may have negative effects on cognition. Indeed, it is problems with cognition and consequent impaired performance at work or other daily activities that are the most common cause for people to seek relief from depression (Dew, Bromet, Schulberg, Parkinson, & Curtis, 1991; Coryell et al., 1995).

Extensive findings from both functional and structural imaging studies have provided evidence of abnormalities in the brains of some people with depression (Harrison, 2002). To a considerable extent, the regions identified in such studies correspond to those regions likely to be involved in the various mood and cognitive disturbances that tend to occur in depression. Thus it might be concluded that the behavioral manifestations of depression are, at least in part, a consequence of underlying neurophysiological changes. In particular, these findings of neurophysiological abnormalities in depression provide evidence that the cognitive deficits in depression are not simply a secondary effect of impaired mood and motivation.

The literature on brain structural abnormalities in depression identifies a number of structures affected in the illness, including the amygdala, and orbitofrontal cortex. Probably the most commonly reported finding, however, is of reduced volume of the hippocampus, medial prefrontal cortex, and the basal ganglia (Kanner, 2004).

The findings related to reduced hippocampus volume are 2015 Hogrefe Publishing

of particular interest because the hippocampus is known to be crucial to normal memory function, and impaired memory is the most commonly reported cognitive problem in people with depression (Campbell & MacQueen, 2004).

Frodl et al. (2006) reported that cognitive deficits were more severe in depressed patients with reduced hippocampus volume. Furthermore, while it is uncertain whether small hippocampi are a result of depression or, conversely, if small hippocampi represent a biological predisposition to depression, there is evidence that hippocampus volume may be normal relative to healthy controls in remitted patients (Caetano et al., 2004; Zhao, Deng, & Gage, 2008) so that reduced hippocampus volume may be specific to the depressed state. Stockmeier et al. (2004) compared postmortem major depression and control brains and identified increased packing density of glia and pyramidal and granule cell neurons in the hippocampus in conjunction with decreased size of pyramidal cell soma in the depression brains relative to control brains. It was concluded that these cellular changes may account for reports of reduced hippocampus volume.

Although numerous studies have reported smaller hippocampus volume in depression this is far from universal.

Some studies with sample size and methodology theoretically capable of detecting such differences have failed to do so (Rusch, Abercrombie, Oakes, Schaefer, & Davidson, 2001; Vakili et al., 2000). One likely reason for this Journal of Psychophysiology 2016; Vol. 30(1):1–8 DOI: 10.1027/0269-8803/a000148

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M. A. Rogers et al.: Antidepressants and Hippocampus Volume

inconstancy across studies is the heterogeneous nature of depression. There is considerable variability of symptomatology and life history among sufferers of depression and it may be that reduced hippocampus volume tends to be associated with particular symptom profiles and/or life experiences. Greenberg, Payne, MacFall, Steffens, and Krishnan (2008), however, found no effect of depression subtype on hippocampus volume.

A potential causal mechanism that might link depression with reduced hippocampus volume lies in the involvement of stress. Depression is known to be associated with stress (Anda et al., 2002) and stress is also implicated as an agent that may cause reduced hippocampus volume.

Animal models have shown strong effects of stress in reducing hippocampus volume (McEwen, 2000) and stress-related disorders in human beings have also demonstrated an association with reduced hippocampus volume (Andersen & Teicher, 2004). It has been proposed that the often reported disturbance of the hypothalamicpituitary-adrenal axis (HPA axis) may be the mechanism, and there is evidence that HPA disturbance in response to external stressors may result in reduced proliferation and reduced survival of adult-born neurons (Paizanis, Kelaı, Renoir, Hamon, & Lanfumey, 2007).

Some studies have suggested that reduced hippocampus volume in depression becomes more pronounced the longer the duration of depression (Videbech & Ravnkilde, 2004).

As the time spent in an anxious state will likely tend to increase in proportion with time spent in a depressed state, such findings are in line with the suggestion that stress mediates the relationship between depression and reduced hippocampus volume. However, these findings have been difficult to replicate (Drevets, 2004). It may be that additional factors are involved in moderating how stress operates in depression and in moderating the effects of stress in depression. For example, Vythilingam et al. (2002) suggested that experience of trauma in childhood may be an important indicator of the presence of reduced hippocampus volume in depression.

Another possible factor is the administration of antidepressant medication. It has been reported that antidepressants may promote neurogenesis in the hippocampus (Paizanis et al., 2007; Sahay & Hen, 2007) and therefore might help to promote normalization of hippocampal volume in depressed participants. In addition, there is some evidence that the positive effects of antidepressants on neurogenesis may be a factor in their antidepressive action (Paizanis et al., 2007). Sheline, Gado, and Kraemer (2003) have reported that while hippocampus volume decreased with increasing duration of depression, this was true only for duration of untreated depression. Time spent on antidepressant medication while depressed did not contribute to the observed reduction in hippocampus volume. This would suggest that variation in time spent on antidepressant medication may indeed be an important moderator of any tendency for hippocampus volume to reduce with increasing duration of depression.

The present study sought to further examine firstly the possibility that reduced hippocampus volume may be related to duration of depression, and secondly that this Journal of Psychophysiology 2016; Vol. 30(1):1–8

relationship may be moderated by antidepressant agents.

This is important because the relationship between reduced hippocampus volume and depression duration has been reported only in three studies with overlapping participant samples (Sheline et al., 2003; Sheline, Sanghavi, Mintun, & Gado, 1999; Sheline, Wang, Gado, Csernansky, & Vannier, 1996). Thus it is necessary to test for this effect in an independent sample. Secondly, the finding that antidepressant agents may moderate any negative correlation between duration of depression and hippocampus volume has to date been reported only in a single study (Sheline et al., 2003) and only in female participants. The present study therefore sought to replicate these findings and extend them to male participants.

Method Participants Twenty people (10 diagnosed with major depression and 10 with no history of the illness) participated. Participants in the depression group were diagnosed by a trained psychiatrist (H.Y.) according to DSM-IV criteria for major depressive illness and rated for symptom severity using the Hamilton 17-item rating scale for depression. Exclusion criteria were any Axis I disorder other than major depression (for the depression group) and history of any psychiatric disorder in themselves or first-degree relatives (for the control group). Exclusion criteria common to all participants were any neurological history, history of substance abuse, or an episode of unconsciousness longer than 2 min. Control participants were screened for neuropsychiatric disorder using the mini-international neuropsychiatric interview.

MRI Acquisition As in our previous research (Yamasue et al., 2003; 2004) 1.5 mm-slice high spatial resolution MRI acquisition was employed. Briefly, the MRI data were obtained using a 1.5-Tesla scanner (General Electric Signa Horizon Lx version 8.2, GE Medical Systems, Milwaukee, WI).

Three-dimensional Fourier-transform spoiled gradient recalled acquisition with steady state was used because it affords excellent contrast between gray matter and white matter in the evaluation of brain structures. The repetition time was 35 ms, the echo time 7 ms with one repetition, the nutation angle 30 , the field of view 24 cm, and the matrix 256 · 256 (192) · 124. A trained neuroradiologist (Ha.Ya. or O.A.) evaluated the MRI scans and found no gross abnormalities in any of the subjects.

Manual Tracing for Hippocampus and Amygdala Volumetry The amygdala and hippocampus gray-matter regions of interest (ROIs) were outlined manually by one rater 2015 Hogrefe Publishing

M. A. Rogers et al.: Antidepressants and Hippocampus Volume

(M.A.R.) blind to diagnostic status. For the manual tracing, we used a software package for medical image analysis (3D Slicer; software available at http://www.slicer.org), which enables a simultaneous view of orthogonal planes. The landmarks to delineate the ROIs in the present study were refined from those in our previous studies (Yamasue et al., 2004). As described in detail below, to accurately measure the volume of these structures in the present study, we developed an additional protocol for delineating the anterior boundary of the amygdala and the boundary between amygdala and hippocampus similar to those described in previous literature (Schumann et al., 2001).

Dentate gyrus, CA fields, subiculum, presubiculum, and parasubiculum were referred to as the hippocampus, while the fornix, fimbria, and alveus were not included in the volumetric measurements. Tracing of the hippocampus was mainly performed in the sagittal plane; however, they were also edited in the axial and coronal planes in every slice to delineate the boundary of the ROIs as precisely as possible.

Once drawn, hippocampal ROIs could be viewed in any plane and as a three-dimensional object, for any further editing. Tracing began on the slice in the most lateral extent of the ventricular temporal horn on which the hippocampus was first visible. The outline represents the inferior border (determined by drawing a line through the white matter separating the hippocampus from the parahippocampal and fusiform gyri), superior border (determined by drawing a line through the alveus separating the hippocampus from the lateral ventricles), and the posterior border (determined by following the structure to its furthest posterior extent).

More medially, the anterior hippocampus was separated from amygdala by a thin line of white matter between the two structures (alveus).

For the tracing of amygdala boundaries, the initial tracing process involved defining the borders in coronal sections starting with the most caudal level in which the amygdala was visible. At its caudal extent, the amygdala is bordered dorsally by the substantia innominata, laterally by the putamen, and ventrally by the temporal horn of the lateral ventricle. The medial surface of the amygdala abuts the optic tract. Proceeding rostrally, it is bordered dorsally by fibers of the anterior commissure as well as the substantia innominata. The lateral border is formed by white matter of the temporal lobe. The ventral surface is formed by the temporal horn of the lateral ventricle. However, because the hippocampus often appears to be fused with the ventral surface of the amygdala, a more reliable boundary is the alveus, the white matter that forms the dorsal surface of the hippocampus. In more rostral sections, the hippocampus decreases in size and the entorhinal cortex begins to form part of the medial surface of the amygdala. At this point, a thin band of white matter separates the amygdala from the entorhinal cortex. In most rostral sections, the dorsomedial surface of the amygdala forms a portion of the medial surface of the brain. The amygdala is bordered laterally by white matter of the temporal lobe, ventrally by the temporal horn of the lateral ventricle and by subamygdaloid white matter, and ventromedially by the entorhinal cortex.

At the rostral pole of the amygdala, the outlining rules

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are very similar to what has just been described above.

However, the gray-matter-white-matter boundaries are more difficult to delineate. Therefore, it was necessary to confirm the rostral boundary of the amygdala by reviewing the outlines in sagittal images.

For interrater reliability, two raters (H.I. and M.R.) blind to group membership, independently traced ROIs. Ten cases were selected at random, and the raters traced ROIs on every slice. The intraclass correlation coefficient was 0.87/0.85 for left/right amygdala, 0.93/0.94 for left/right hippocampus, respectively. Intrarater reliability, computed by using all of the slices from one randomly selected brain and measured by one rater (M.A.R) at two separate times (approximately 2 months apart), was > 0.92 for all structures.

Total gray matter, white matter, and cerebrospinal fluid volumes were calculated from the voxel-based morphometry (VBM) procedure using SPM2 (Good et al., 2001).

Intracranial content (ICC) was then calculated by summing up total gray matter, white matter, and cerebrospinal fluid volume. To validate this method, the ICCs of an independent sample of 50 adult subjects were measured by both the VBM and intensity-based semiautomated segmentation procedure using ANAYZE PC 3.0 (Yamasue et al., 2004). We then confirmed that the calculated intraclass correlation coefficient for ICCs was acceptable (0.96).

Image Processing for VBM Image analysis was performed using SPM2 software (Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK) running in MATLAB 6.5 (Mathworks, Sherborn, MA). Briefly, images were first spatially normalized into the standard space of Talairach and Tournoux (1988). Normalized images were then segmented into the gray matter, white matter, cerebrospinal fluid, and skull/scalp compartments using an automated and operator-independent process. The segmentation step incorporates an image density nonuniformity correction to address image density variations caused by different positions of cranial structures within the MRI head coil.

The spatially normalized segments of the gray and white matter were smoothed with a 12 mm full-width at halfmaximum isotropic Gaussian kernel to accommodate individual variability in the sulcal and gyral anatomy.

Results Demographics The patient group and control group did not differ on any of the demographic variables measured: Age t(1, 38) = 1.463, p = .152, Parental SES t(1, 38) = .218, p = .828, Self SES t(1, 36) = .123, p = .903 (see Table 1).

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M. A. Rogers et al.: Antidepressants and Hippocampus Volume

Table 1. Mean and standard error of demographic variables for Controls and Depression group participants and t-test comparison across group Age Self SES Parental SES

Controls

Depression

t-test

43.1 (SE 1.76) 2.25 (SE 0.099) 2.89 (SE 0.179)

48.2 (SE 3.01) 2.30 (SE 0.207) 2.85 (SE 0.254)

t(1, 38) = 1.463, p = .152 t(1, 36) = 0.123, p = .903 t(1, 38) = 0.218, p = .828

Table 2. Mean and standard deviation values for the Control and Depression group for the relative volume (as a percentage of total intracranial contents) of the four regions of interest. Mann-Whitney U-test values are given for the Group comparison at each ROI. All p values are two-tailed ROI

Control group

Left hippocampus mean relative volume (% TICC) (SD) Right hippocampus mean relative volume (% TICC) (SD) Left amygdala mean relative volume (% TICC) (SD) Right amygdala mean relative volume (% TICC) (SD)

.1708 (0.025) .1755 (0.028) .0798 (0.013) .083 (0.015)

Region of Interest (ROI) Volumes Due to deviation from normalcy, the relative volumes (expressed as a percentage of total intracranial volume) of ROIs (left and right hippocampus and left and right amygdale) were analyzed by Mann-Whitney U-test separately for each ROI between the patient and control groups.

Left hemisphere hippocampus volumes differed significantly between the two groups with the depression group having smaller left hippocampus volumes an average than did the control group U(36) = 2.35, u = .019 (two-tailed).

No other ROI showed any significant between group differences. See Table 2.

Correlations Between Regions of Interest (ROIs) and Demographic Variables of Interest Spearman’s Rho was used to correlate ROI relative volumes with variables of interest. Significant negative correlations were found between Days depressed with left hemisphere hippocampus volume r = 0.542 and Age and right hemisphere hippocampus volume rs = 0.482, n = 19, p < .05, two-tailed. The correlation between relative left hemisphere hippocampus volume and days spent on medication showed an interesting trend but did not reach significance rs = 0.407, n = 19, p = .087, two-tailed. Neither HAM-D severity of depression nor any other demographic values produced significant findings (see Table 3).

The scatterplot for the correlation of left hippocampus volume and Days depressed is given below (Figure 1).

In addition to showing a significant negative correlation between left hippocampus volume and lifetime days depressed, there was a near significant logarithmic relationship between the two variables F(1, 17) = 3.79, p = .068.

If the two highest values for Total lifetime days depressed Journal of Psychophysiology 2016; Vol. 30(1):1–8

Depression group .1549 .1662 .0826 .081

(0.021) (0.037) (0.010) (0.016)

Mann-Whitney U-test U(36) = 2.35, p = .019 U(38) = 1.00, p = .317 U(37) = 0.450, p = .653 U(38) = 0.162, p = .871

are removed, the resulting correlation is close to significant, p = .054.

Relationship Between Days Depressed and Days Medicated and Left Hippocampus Volume Although the correlation did not achieve significance, there was a trend toward increasing number of days medicated being associated with larger left hippocampus volume.

This, in conjunction with the observation that relative left hippocampus volume does not seem to reduce linearly with increasing number of days depressed, raises the question of whether antidepressant medication might be acting to oppose the association between Total lifetime days depressed and smaller hippocampus volume. As sample size (n = 19) was insufficient to carry out a moderated regression to directly assess for any interaction between Total lifetime days depressed and Total lifetime days medicated, an alternative derived measure was analyzed.

The value of the ratio (Total lifetime days medicated/Total lifetime days depressed) was assessed against relative left hippocampus volume.

Spearman’s correlation of Relative hippocampus volume by ratio (days medicated/days depressed) was significant, rs = 0.512, p = .025, n = 19, two-tailed. If the two highest ratio values are removed, the correlation remains significant, p = .008. There was therefore a significant tendency for a higher ratio of days medicated to days depressed to be associated with larger relative left hippocampus volumes. In addition, there was a significant logarithmic relationship between these variables as shown in the scatterplot (Figure 2).

Figure 2 suggests that while increasing time spent medicated relative to time spent depressed may be associated with more normal left hippocampus volume, it may be that 2015 Hogrefe Publishing

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Table 3. Spearmanâ&#x20AC;&#x2122;s Rho values for correlations between regions of interest and demographic and clinical variables (all p values are two-tailed). Bolded entries are significant correlations ( p < .05) Left hippocampus Controls Age (years)

Self SES

Parental SES

rs p n rs p n rs p n

= = = = = = = = =

Days depressed (lifetime) Antidepressant medication (total days) Antipsychotic medication (total days) Depression severity (HAM-D)

.200, .423 18 .203, .419 18 .160, .540 17 NA

NA

NA

NA

Right hippocampus

Patients

Controls

Patients

rs = .375, p = .113 n = 19 rs = .271, p = .261 n = 190 rs = .177, p = .461 n = 19 rs = .488, p = .034* n = 19 rs = .404, p = .087 n = 19 rs = .236, p = .330 n = 19 rs = .138, p = .573 n = 19

rs = .271, p = .247 n = 20 rs = .511, p = .021* n = 20 rs = .177, p = .481 n = 18 NA

rs = .570, p = .009* n = 20 rs = .285, p = .223 n = 20 rs = .312, p = .180 n = 20 rs = .107, p = .654 n = 20 rs = .375, p = .104 n = 20 rs = .321, p = .168 n = 20 rs = .078, p = .742 n = 20

NA

NA

NA

Left amygdala Controls rs p n rs p n rs p n

= = = = = = = = =

.155, .515 20 .130, .584 20 .235, .348 18 NA

NA

NA

NA

Right amygdala

Patients rs p n rs p n rs p n rs p n rs p n rs p n rs p n

= = = = = = = = = = = = = = = = = = = = =

.025, .918 19 .167, .494 19 .188, .440 19 .186, .445 19 .252, .298 19 .026, .917 19 .070, .742 19

Controls rs p n rs p n rs p n

= = = = = = = = =

.192, .418 20 .130, .584 20 .020, .937 18 NA

NA

NA

NA

Patients rs p n rs p n rs p n rs p n rs p n rs p n rs p n

= = = = = = = = = = = = = = = = = = = = =

.205, .387 20 .041, .863 20 .040, .867 20 .119, .617 20 .002, .995 20 .063, .793 20 .072, .762 20

Furthermore, while ratio < 3 patients had relative left hippocampus volumes significantly smaller than those of controls (mean = .175) U(28) = 3.387, p = .001 (twotailed); ratio > 3 patients did not show a significant difference in relative left hippocampus volume compared to the control group, U(23) = .605, p = .545 (two-tailed).

In order to check that the values of ratio (Total lifetime days medicated/Total lifetime days depressed) were independent of Total lifetime days depressed, the two variables were assessed by Pearsonâ&#x20AC;&#x2122;s correlation. The result confirmed that there was no association between these variables, rp = .367, p = .111, n = 20, two-tailed.

Discussion

Figure 1. Scatterplot of Left hippocampus volume by Total lifetime days depressed. the greatest effect is evident at ratio values less than approximately 5. Figure 3, below presents relative left hippocampus volume data plotted separately for patients with ratio values of < 3 and ratio values > 3.

Mann-Whitney U-tests confirm a significant difference in relative left hippocampus volume between ratio < 3 patents (mean = .146) and ratio > 3 patients (mean = .170), U(17) = 2.366, p = .018 (two-tailed).

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The relative effects of total lifetime days of major depression and lifetime days of antidepressant medication on hippocampus volume were examined. The depression group showed significantly smaller mean relative left hippocampus volume compared to the control group, while no group differences were observed in the right hippocampus or bilateral amygdala. In addition, there was a significant negative correlation such that increasing lifetime duration of depression was associated with smaller left hippocampus volumes. This finding supports those of Sheline et al.

(1999) who originally reported an association between total lifetime days time spent in a depressed state and reduction in hippocampus volume.

In addition, the relationship between lifetime duration of depression and left hippocampus volume appeared to be Journal of Psychophysiology 2016; Vol. 30(1):1â&#x20AC;&#x201C;8

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M. A. Rogers et al.: Antidepressants and Hippocampus Volume

Figure 2. Scatterplot of Left hippocampus volume by ratio (Total lifetime days medicated/Total lifetime days depressed). modified by lifetime duration of antidepressant medication such that increasing ratio of lifetime days of antidepressant medication to lifetime days of depression appeared to ameliorate the deleterious effects of prolonged exposure to depression on left hippocampus volume. This is consistent with the findings of Sheline et al. (2003) who reported that time spent in medicated depression did not contribute to decreased hippocampus volume. In the present study, increasing ratio of time spent on antidepressant medication relative to total time depressed was significantly associated with a tendency toward normalization of left hippocampus volume. In fact, patients with depression who had a ratio of days medicated to days depressed greater than 3, showed hippocampus volumes that were both significantly larger than those with a ratio less than 3, and not significantly different from those of the comparison control group.

The present findings may therefore suggest that prolonged exposure to antidepressant medication may act to reverse reduction in hippocampus volume due to major depression.

However, longitudinal studies are required to further examine this possibility. In addition, if this finding is confirmed in subsequent studies, it would be of great interest to determine the duration of antidepressant medication to restore hippocampus volume.

The finding of increasing reduction in hippocampus volume with increasing duration of depression has been difficult to replicate (Drevets, 2004). There are a number of likely factors that may account for this. The first point to bear in mind is that the basic observation of the presence or absence of hippocampus volume reduction itself has not been universally replicated. Likely reasons for this inconsistency range from variation in the methodologies of scanning and volumetric measurement employed, to the heterogeneous nature of depressive illness itself (Saylam, Ucerler, Kitis, Ozand, & Gonul, 2006). In addition, Journal of Psychophysiology 2016; Vol. 30(1):1–8

Figure 3. Relative Left hippocampus volume plotted independently for patients with ratio (Total lifetime days medicated/Total lifetime days depressed) values of < 3 and > 3. demographic differences, such as gender balance, may be important (Frodl et al., 2002). It is also possible that the number of episodes of depression may be important (Sheline et al., 1996). However, Frodl et al. (2002) reported significantly smaller hippocampus volume in male first episode depression patients compared to controls suggesting that this may not be the case. Another important point is that the definition of duration of depression has been inconstant and imprecise. A number of studies (e.g., Caetano et al., 2004) refer to ‘‘duration of depression’’ but do not define the term. A strict definition of duration of depression in terms of the total number of days spent depressed across the lifetime has been employed in three studies (Sheline et al., 1999; McQueen et al., 2003, the 3rd being the present study) and each has reported a significant relationship between total lifetime days depressed and hippocampus volume. The fact that the present study found no significant relationship between duration of depression (time since onset) and hippocampus volume points up the importance of the definition employed.

A further possible explanation for the difficulty in replicating the relationship between lifetime time spent depressed and hippocampus volume might be the proportion of time spent depressed for which antidepressants were being taken. If antidepressant medications do indeed act to reverse or inhibit the reduction of hippocampus volume in depression then, variation in the medication status and history of patients across studies would introduce variation in the extent to which the relationship was apparent.

While, as noted above, numerous structural imaging studies have reported reduction of hippocampus volume in depression, little investigation has been made of the physical manifestations of this reduction at the cellular level. Stockmeier et al. (2004) compared hippocampus from postmortem brains of persons with depression and controls. Compared to the control brains, the brains of 2015 Hogrefe Publishing

M. A. Rogers et al.: Antidepressants and Hippocampus Volume

depressed patients showed significantly higher cell density (granule cells, pyramidal neurons, and glia) and decreased soma of pyramidal cells. It was concluded that the observed reduction of hippocampus volume in depression may be due to reduced neuropil and it was proposed that this may result from reduced availability of neurotrophic factors.

As the present findings are consistent with the possibility that antidepressant medication might act to counter the association between depression and hippocampus volume reduction it is important to consider by what mechanism this might occur. Firstly, reduction of dendritic field due to stress associated with depression would tend to reverse upon the remission of that stress (Sapolsky, 2001). To the extent that antidepressants alleviate depression and associated stress, they may be expected to lead to a reversal in reduction of hippocampus volume. A further possibility that has received attention recently is that of neurogenesis.

The hippocampus is a known site of active neurogenesis that persists into adulthood in the mammalian, including human, central nervous system (Eriksson et al., 1998), so it is possible that neurogenesis may be responsible for any medication-mediated reversal of hippocampus atrophy in depression. In this context, Sahay and Hen (2007) reviewed findings very suggestive of a role in antidepressant agents (pharmacological and behavioral) in promoting neurogenesis in the hippocampus. Indeed, it was suggested that the promotion of hippocampal neurogenesis may be an important mechanism by which antidepressants exert their therapeutic effect.

There are a number of further points to consider with regard to the current findings. At least one previous study has reported increased asymmetry of left and right hippocampus volume in depression (Frodl et al., 2002). Furthermore, while most studies have reported reduction in the left hippocampus, a number of them have instead found the right hippocampus to be reduced in depression (Janssen et al., 2004; Lekwauwa, McQuoid, & Steffens, 2005; Videbech & Ravnkilde, 2004). This raises the possibility that it is abnormal asymmetry of hippocampus volume rather than reduced hippocampus volume itself that might be involved in depression. The present findings did not support this idea, however, as there was no significant or near significant intergroup difference in hippocampus asymmetry.

The present paper found no significant intergroup difference in bilateral amygdale volume. The literature on amygdale volume in depression is very inconsistent. In a meta-analysis Campbell et al. (2004) reported that of six studies to have treated the amygdale as a discreet structure (independent of the hippocampus) half reported smaller volumes in depression while the others reported either greater volumes in depression, or reported no difference.

None of five studies to have compared the hippocampusamygdale as a combined structure reported a significant intergroup difference.

The finding of a negative correlation between right hippocampus volume and age in the depressed patients is somewhat puzzling. While reduction in hippocampus volume with age has been reported as beginning from the 2015 Hogrefe Publishing

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third decade of life (Pruessner, Collins, Pruessner, & Evans, 2001), this finding was present only in males and was found in bilateral hippocampus. In their meta-analysis of MRI studies of hippocampus volume in depression, Videbech and Ravnkilde (2004) note that neither age nor gender has had a detectable effect on hippocampus volume.

The present findings represent an important replication of the finding of Sheline et al. that total lifetime days of depression show a significant negative correlation with hippocampus volume. The inconsistency in the literature with respect to the relationship between duration of depression and hippocampus volume might, therefore, be attributed to inconsistent definition of the former. Further studies employing total lifetime days depression as the variable of interest should demonstrate this one way or the other.

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Accepted for publication: March 3, 2015 Published online: September 15, 2015

Mark A. Rogers Cognitive Neuroscience Unit School of Psychology Faculty of Health, Deakin University 221 Burwood Highway Burwood, Victoria, 3125 Australia Tel. +61 3 92446479 E-mail mark.rogers@deakin.edu.au

2015 Hogrefe Publishing

Article

An Arousal Effect of Colors Saturation A Study of Self-Reported Ratings and Electrodermal Responses Piotr Zielin´ski Department of Aviation Psychology, Military Institute of Aviation Medicine, Warsaw, Poland Abstract. The purpose of the experiment was to test the relationship between attributes of color, self-rated arousal, and autonomic reactions to color stimuli. Sixteen colored backgrounds of different hue, saturation, and brightness were each viewed by 64 subjects (females, Mage = 22.48) while skin conductance responses (SCRs) were recorded. Subjective judgments relating to pleasantness (valence) and arousal were also measured. Results show that among color attributes only saturation had an effect on SCR magnitude, F(1, 63) = 6.31, p < .05, gG2 = .01. There was also significant correlation, r(14) = .64, p < .01, between aggregated SCR magnitude and arousal ratings. It confirms that SCR could be used as a marker of phasic arousal even in response to the abstract, devoid of content stimuli. Saturation seems to be the main property connected with color’s ability to elicit orienting response. More saturated stimuli are better in capturing attention regardless of hue, thus suggesting that at the first stage of color perception, color intensity is more important than qualitative properties. Such results clarify some incoherent findings known from previous studies on psychophysiological responses to color stimuli.

Keywords: arousal, color perception, skin conductance response, color saturation

Color and Physiological Arousal: An Effect of Saturation Affective responses can be evoked by stimuli of different modalities. Valence (pleasantness), arousal, and dominance have been identified as the principal dimensions of such responses, and studies have shown that they could be measured with the use of semantic differential scales (e.g., Osgood, 1952) or nonverbal techniques (Bradley & Lang, 1994). Besides self-report, during the perception of emotional stimuli affective responses can also be observed in autonomic activity. It has been shown that, for example, skin conductance responses (SCRs) covary with ratings of arousal, while heart rate variation is correlated mainly with valence (for review see Bradley & Lang, 2007). Similar pattern has been found for different modalities, with stimuli such as pictures (Lang, Greenwald, Bradley, & Hamm, 1993), sounds (Bradley & Lang, 2000), or odors (Bensafi et al., 2002).

Another type of stimulus that is assumed to be well described by two- or three-dimensional affective space is color. Pleasantness of colors has been a subject of psychological research since the end of XIX century (e.g., Major, 1895), although those early studies lacked scientific

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precision: colors were often described simply as ‘‘red,’’ ‘‘blue,’’ ‘‘green,’’ and so forth (see Whitfield & Wiltshire, 1990 for broader discussion of the problem). Guilford (1934) was one of the first to criticize this qualitative approach. He managed to show that valence of colors cannot be considered only in terms of hue, since not only hue, but also saturation (the perceived color intensity; saturated colors are pure and vivid, while less saturated colors are closer to gray) and brightness (how light or how dark the color is) are connected with affective reactions evoked by color stimuli. Colors should then be described precisely with the use of one of the color systems (e.g., Munsell color system; Munsell, 1912) and all standard dimensions (commonly named hue, saturation, and brightness) should be taken into consideration. While some studies relied only on verbal description of colors (e.g., Adams & Osgood, 1973; Terwogt & Hoeksma, 1995), which significantly lowers validity of the results, most of the recent work is done with the use of color systems that allow to describe and classify presented stimuli within a standardized, quantitative color model.

Comprehensive review of the research on the association of color attributes and affective judgments can be found in Whitfield and Wiltshire (1990). Majority of the

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P. Zielin´ski: An Arousal Effect of Colors Saturation

standardized studies (e.g., Hogg, Goodman, Porter, Mikellides, & Preddy, 1979; Wright & Rainwater, 1962) consistently showed that ratings of arousal covary mainly with saturation of colors, while valence is also strongly connected with brightness. Hue, the third of color attributes, seems to play inferior role and its relation to affective judgments is relatively weak when compared with saturation and brightness.

Similar results can be found in more recent studies.

For example, Valdez and Mehrabian (1994) used 76 color samples from Munsell color system and found Pleasure dimension (from the PAD emotion model, which is composed of three main factors: pleasure-displeasure, degree of arousal, and dominance-submissiveness; see Russell & Mehrabian, 1977) to be a joint positive function of brightness and saturation. Arousal increased linearly and strongly with saturation. Dominance decreased strongly with increases in color brightness and increased moderately with saturation. Effects of hue were analyzed separately, so no interactions of hue and other color attributes were assessed.

Nevertheless, relation of hue to Pleasure, Arousal, and Dominance was relatively weak.

Although most of above studies rely only on subjective responses, there is often an assumption that affective judgments directly reflect color-evoked physiological reactions (e.g., Ou, Luo, Woodcock, & Wright, 2004; Valdez & Mehrabian, 1994). Although not stated directly, similar suggestion could be read also in Elliot and Maier’s (2007) model of color and psychological functioning: the authors describe color influence as automatic, nonconscious and producing motivated behavior of approach or avoidance, so this influence should also have some psychophysiological correlates. The effect of color on physiological responses is also a common belief in pseudoscientific publications. There is, however, not much empirical evidence on that subject.

In psychophysiological research colors were usually characterized only by qualitative attributes such as hue, without controlling or sometimes even without reporting brightness and saturation. Additionally, most of the known studies were conducted with the use of only a few arbitrarily selected color stimuli. For example, Gerard (1958, as cited in Robinson, 2004) used three colored lights, ‘‘red,’’ ‘‘blue,’’ and ‘‘white,’’ while Nourse and Welch (1971) used only ‘‘green’’ and ‘‘violet.’’ Wilson (1966) presented ‘‘red’’ and ‘‘green’’ slides, Jacobs and Hustmyer (1974) used ‘‘red,’’ ‘‘green,’’ ‘‘blue,’’ and ‘‘yellow’’ in a similar manner, although it is unclear whether ‘‘red’’ and ‘‘green’’ were similar or different in these two experiments. Mikellides (1990) placed his subjects in a room painted half in ‘‘red,’’ half in ‘‘blue’’ hue. Except for Gerard (1958, as cited in Robinson, 2004) and Mikellides (1990), no other researcher related physiological data to affective judgments.

Although there were a few different measures used in mentioned studies, most significant effects were reported for electrodermal activity (EDA). Findings, however, show little consistency. For example, Wilson (1966) noted stronger responses during ‘‘red’’ presentation than during

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‘‘green,’’ while Jacobs and Hustmyer (1974) found no such difference. They nonetheless found ‘‘red’’ to elicit stronger responses than ‘‘blue,’’ but this effect, in turn, was not replicated by Mikellides (1990).

Kaiser (1984) in his review argues that observed physiological effects were probably connected not with reported hue, but with other color attributes, hence the lack of coherence of the results. Similar conclusion is reached by Valdez and Mehrabian (1994) in their study on emotional perception of colors. They, however, did not employ any psychophysiological measures in their own research. In fact, while in recent years there are many solid publications on subjective or behavioral aspects of color perception (e.g., Elliot & Maier, 2007; Hill & Barton, 2005; Palmer & Schloss, 2010), published studies on psychophysiological responses to color are very rare and do not provide any conclusive results.

If the suggestions of Kaiser (1984) and Valdez and Mehrabian (1994) are correct, physiological arousal should relate not only (or maybe not at all) to hue, but to other color attributes, in a similar way that color attributes relate to subjective judgments. As cited authors never provided an empirical evidence, the main purpose of the current study was to check, in the standardized color sample, to what extent all perceptual attributes (hue, brightness, and saturation) are connected with physiological reactions evoked by color stimuli. The studies on self-reported judgments of color stimuli consistently show that arousal dimension of affective responses is connected mostly with saturation, so it could be hypothesized that saturation should be the main factor affecting physiological responses.

Electrodermal activity was chosen as a measure of physiologial arousal, as there is a vast amount of data showing relation of EDA and self-assessed arousal elicited by presented stimuli (see Bradley & Lang, 2007). However, stimuli used in such studies usually have clearly defined content (i.e., picture of a snake, sound of a crying baby), while plain colored background is an abstract stimulus with only formal qualities, devoid of content. So it raises an additional question, if the relationship between subjective judgments of color stimuli and electrodermal responses is similar to the relationship observed with more specific material. So far, there is a lack of published studies exploring the relationship of color attributes and arousal simultaneously on the level of self-report and on the level of autonomic activity.

Only recently an experiment by Rajae-Joordens (2011) connected self-report and psychopysiological measures in wellcontrolled design (12 colored lights were used: blue, red, and green hue, each in two levels of lightness and saturation). Clear (and coherent with previous studies) differences in affective judgments were found, but there was no significant relation of colorimetric properties and physiological (electrodermal and cardiovascular) measures. However, it is important that only indicators of tonic arousal (skin conductance level and the number of SCRs during color presentation) were recorded, while in experiments reported

by Bradley and Lang (2007) the phasic activity is often a

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Table 1. Color attributes (NCS Codes and approximate RGB Values from the NSC 1750 edition) and mean affective responses for 16 presented backgrounds RGB values NCS code

Red

Green

Blue

NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S NCS-S

225 220 147 155 77 143 35 86 76 139 31 84 224 216 147 153

104 149 60 95 170 189 108 125 146 177 90 115 176 185 109 125

104 145 61 93 122 157 74 101 186 196 125 133 70 118 44 76

2040-R 2020-R 4040-R 4020-R 2040-G 2020-G 4040-G 4020-G 2040-B 2020-B 4040-B 4020-B 2040-Y 2020-Y 4040-Y 4020-Y

log SCR

Arousal ratings

Valence ratings

M (SD)

M (SD)

M (SD)

0.01 0.11 0.17 0.11 0.14 0.10 0.15 0.10 0.14 0.08 0.12 0.12 0.13 0.09 0.15 0.10

(0.15) (0.25) (0.32) (0.12) (0.30) (0.18) (0.30) (0.15) (0.19) (0.12) (0.22) (0.19) (0.23) (0.14) (0.24) (0.19)

7.47 3.92 7.22 3.72 6.66 3.31 6.22 3.14 5.03 3.13 5.45 3.48 7.05 3.50 5.61 3.08

(1.66) (2.26) (2.40) (1.90) (2.26) (2.33) (2.67) (1.66) (2.83) (2.24) (2.88) (2.09) (2.14) (2.34) (2.16) (1.50)

4.97 4.03 5.36 3.78 5.75 4.36 6.19 4.19 6.84 4.58 6.33 4.80 5.88 3.69 3.89 3.38

(2.52) (2.46) (2.31) (1.97) (2.43) (2.30) (2.38) (2.21) (2.07) (2.32) (2.15) (2.21) (2.24) (2.08) (2.46) (2.27)

Note. In the color notation ‘‘NCS-S XXYY-Z,’’ the ‘‘XX’’ is the value of Blackness, the ‘‘YY’’ is the value of Chromaticness, and ‘‘Z’’ describes the Hue in the NCS color circle.

measure of interest. Based on this, two hypotheses were stated as follows: Hypothesis 1 (H1): Saturation is the main factor influencing magnitude of SCRs elicited by color stimuli.

Hypothesis 2 (H2): Magnitude of SCRs to color correlates with self-reported ratings of arousal.

Method Participants Sixty-seven females (Mage = 22.48, SD = 5.26, age range: 19–42 years) were recruited from Introductory Psychology course at the University of Finance and Management in Warsaw (Poland). Participants received course credit as compensation. They were aware that experiment is about measuring electrodermal activity during computer presentation, although there was no specific mention of colors in the research description and instruction. All participants gave informed consent. After the main experimental task, color vision of participants was assessed by Ishihara test. Three persons showed little color vision deficiency and their results were excluded from further analysis. The remaining sample size was N = 64.

Materials and Design Sixteen colors were chosen as experimental stimuli. Color attributes were standardized with the use of Natural Color 2015 Hogrefe Publishing

System, which is a perceptual system created to describe human color sensations (see Hård & Sivik, 1981). The set contained four elementary hues: red, green, blue, and yellow; each in two variants of Blackness (how dark the color is) and Chromaticness (how colorful and intense the color is). Although even the correctly calibrated monitor screen does not guarantee the accurate display of colors, choice of NCS color space allowed to control relative differences in perceptual properties, and that was the main goal of the study. For the purpose of presentation, NCS was then converted to RGB model typically used in computer display (the conversion was based on approximate RGB values available for NCS 1750 edition 2 color standards). All color codes are gathered in Table 1.

Each color was presented as the plain background of 2200 monitor screen. Every presentation lasted 10 s and between every two colors the gray background (NCS-S 3000-N) was presented (also for 10 s). It allowed to control the effect of stimulus change since every new stimulus appeared after the same achromatic background and was not preceded by another chromatic color. This set was repeated three times, giving 960 s for the task (20 · 16 · 3) and then ending with additional 10 s gray background. During presentation a small black-and-white circle was moving on screen with low, constant speed, while the participant had to keep the mouse pointer inside. This tracking task was easy and continuous in nature, so it would not evoke any additional phasic electrodermal responses (as verified in the pilot study), and its main purpose was to keep participant’s attention constantly within boundaries of the screen. In such experiment any reaction to color stimuli would be connected with attentional shift from object to background and could be interpreted in the context of orienting response.

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Pleasantness and arousal ratings were obtained by displaying the same colored backgrounds as in previous part and interactive (mouse-controlled) 9-point semantic differential scale that participant could use to rate the color.

Scales used were pleasant to unpleasant and calming to arousing.

Physiological Data Collection and Reduction Electrodermal activity was recorded by two circular Ag/AgCl electrodes (diameter: 0.8 cm; filled with Med Associates electrode paste TD-246) attached to the medial phalanges of the forefinger and middle finger of the nondominant hand. Signal was collected at 500 Hz with the QuickAmp72 system (24-bit resolution within a range of 0–100 lS). Skin conductance response (in microSiemens, lS) was defined as the maximum increase of the first response starting within 1–4 s after stimulus onset. Peak changes less than 0.01 lS were considered noise and converted to 0, then individual SCRs were log-transformed (log[SCR + 1]) to reduce skewness. Since each color stimulus was presented three times, collected data were aggregated and for each of 16 colors used in experiment SCR magnitude (mean SCR of three trials, including null responses) was computed. SCRs during gray background presentations were also recorded for control purposes.

Procedure The study took place in a small, dark room (no windows), with the monitor for stimuli presentation as the only light source. Experiment was performed individually with each subject. After informed consent had been obtained, physiological sensors were attached and subject was comfortably seated in a room in front of the dimmed computer screen, and instructed to relax. After 5 min the computer was turned on with the instruction on screen (see Appendix) and participant was given a simple tracking task during which screen background’s color changed every 10 s (as described above). Order of presented backgrounds in 16-color set was different for every participant (four 16 · 16 Latin squares were created and used for this purpose) to control the effect of the novelty of the color stimuli.

After the task each color was presented again and participant was instructed to rate it on the semantic differential scales (firstly all colors on pleasant-unpleasant, then again on calming-arousing scale). Order of presentation was the same as during the tracking task, there was no time pressure to rate each color.

Data Analysis To assess relationship between color attributes and SCR, univariate 4 · 2 · 2 (Hue · Blackness · Chromaticness) repeated-measures analysis of variance was conducted on obtained SCR magnitudes. For the control purpose, to Journal of Psychophysiology 2016; Vol. 30(1):9–16

check if the relationship between color parameters and affective ratings for the color sample used in the experiment is similar to the results obtained by Valdez and Mehrabian (1994), the same 4 · 2 · 2 repeated-measures ANOVA design was used for arousal judgments as a dependent variable. Greenhouse-Geisser e was applied when needed.

Generalized eta squared (gG2) was reported as a recommended effect size statistic (see Olejnik & Algina, 2003), although one should remember that in the case of repeated-measures gG2 can take a relatively low value, because it also takes into account the interpersonal variability.

To assess relationship between subjective judgments and electrodermal responses, same procedure as in Lang et al.

(1993) was conducted. For each subject, colors were ranked along semantic differential scale from low (1) to high (16), based on participant’s ratings. If two or more colors were rated identically on a scale, mean ratings for these colors were used to resolve the tie. This procedure yielded a set of 16 ranked ratings for each subject on each scale. To test the effects of within-subject variables on physiological responses, univariate repeated-measures ANOVA was conducted on obtained SCR magnitudes. Because of explicit hypothesis about rating-SCR relationship, the repeatedmeasure variable (color rank) was orthogonally decomposed to test significance of the linear trend.

Sensitivity of dimensional concordance was examined by testing predicted arousal rating-SCR relationship.

Specificity of this covariation was examined by analyzing alternative, nonpredicted valence rating-SCR relationship.

Relationship between judgments and electrodermal response was tested by correlating the mean affective rating (valence or arousal) with the mean SCR magnitude at each judgment rank.

All the data analysis was performed in statistical software R version 3.1.1 (R Core Team, 2014). The ‘‘ez’’ package (Lawrence, 2013) was used to perform repeatedmeasures analysis of variance. Plots were created with the ‘‘ggplot2’’ package (Wickham, 2009).

Results Color Attributes and Arousal Ratings Descriptive statistics of valence and arousal ratings can be found in Table 1. Analysis of the effects of color attributes on arousal ratings showed significant main effects of Hue, F(3, 189) = 14.06, p < .001, gG2 = .04, e = .87, and Chromaticness, F(1, 63) = 166.54, p < .001, gG2 = .3, as well as significant two-way interactions of Hue · Blackness, F(3, 189) = 8.93, p < .001, gG2 = .01, e = .96, and Hue · Chromaticness, F(3, 189) = 8.63, p < .001, gG2 = .02, e = .91. The main effect of Blackness and remaining two- and three-way interactions did not reach the .05 level of statistical significance.

Significant Hue · Blackness interaction appeared to reflect the fact that within the yellow Hue, brighter color 2015 Hogrefe Publishing

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Figure 1. Mean arousal ratings depending on the hue and chromaticness of presented colors. Error bars represent 95% CI.

Figure 2. Mean electrodermal response magnitude depending on the chromaticness of presented colors.

Error bars represent 95% CI.

was perceived as more arousing than the color with higher Blackness level, F(1, 63) = 10.27, p < .05, gG2 = .08.

There was no significant Blackness effect within remaining red, green, or blue Hue. Analysis of simple effects of Hue · Chromaticness interaction (see Figure 1) showed that more saturated colors were rated as significantly more arousing within red, F(1, 63) = 158.8, p < .001, gG2 = .56, green, F(1, 63) = 88.56, p < .001, gG2 = .44, blue, F(1, 63) = 37.17, p < .001, gG2 = .18, and yellow, F(1, 63) = 121.84, p < .001, gG2 = .51. Hue, so the interaction effect was based only on the size of the differences (the difference was considerably weaker within the blue Hue).

Generally, judging from the direction of the differences and from their effect sizes, results are similar to those reported in previous literature, for example by Valdez and Mehrabian (1994), thus providing validity of color sample and measurement method applied in the current study.

Affective Judgments and Skin Conductance Response

Color Attributes and Skin Conductance Responses Descriptive statistics of SCR can be found in Table 1.

Among color attributes only the main effect of Chromaticness was significant, F(1, 63) = 6.31, p = .015, gG2 = .01, with greater SCR magnitude for more saturated colors (see Figure 2). Other main effects (Hue and Blackness) and interactions did not reach the .05 level of statistical significance.

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Skin conductance response increased monotonically with ranked arousal. Only the linear trend was significant, F(1, 63) = 4.57, p = .036, gG2 = .07. Linear correlation of the mean arousal rating and the mean SCR magnitude of 16 color samples was r(14) = .64, p = .004 (see Figure 3).

There was not significant relationship between SCR and ranked valence.

Discussion The main goal of the study was to examine the effects of attributes of color stimuli on SCRs. In accordance with the hypothesis, saturation (which had also strong effect on self-rated arousal) was the only significant factor connected with SCR magnitude, although strength of this relationship was rather modest. It is possible that such effect size is connected with little variability on saturation dimension, with only two rather similar values used among stimuli.

Nonetheless, result suggests that previously reported findings concerning psychophysiological reactions to color could be indeed effects of saturation, not hue, hence the inconsistencies. In most cases it is not possible to reanalyze previous data, as authors have not provided numerical descriptions of color stimuli, but, for example, Jacobs and Hustmyer (1974) reported Munsell notation of the four hues

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Figure 3. Linear correlation of arousal judgments with electrodermal response magnitude.

used by them. The greatest significant difference in electrodermal activity they found was for ‘‘red’’ and ‘‘blue’’ and it is clear that it was a pair with the greatest difference in saturation (chroma = 15.4 for ‘‘red’’ and chroma = 10.4 for ‘‘blue,’’ while chroma for ‘‘green’’ and ‘‘yellow’’ was between those two values; there were also significant differences in brightness). It is a strong argument that in further studies – preferably with the use of more varied color samples – it is important to fully control all color attributes, not only the qualitative hue attribute.

Such findings also strengthen the assertion that color could be a source of physiological responses other than physiological processes directly connected with color vision. It is worth noting that this conclusion seems true for the phasic reactions, while data collected for the tonic activity (Rajae-Joordens, 2011) show otherwise. Interpretation of SCR should always depend on experimental paradigm (Dawson, Schell, & Filion, 2007) and in the case of current experiment different arousal levels reflected by SCR magnitudes (assuming that novelty of stimuli is controlled, as in the present study) could be interpreted in the context of orienting response (for review see Boucsein, 2012, Chapter 3.1.1), showing more saturated colors as more likely to capture the attention of the perceiver than less saturated ones.

A notion that this orienting response and physiological arousal is related mostly to saturation, regardless of hue, can at first seem counterintuitive and at odds with the common knowledge. For example, green is often treated as peaceful and calming, while red is supposed to be more stimulating. There are also data confirming the significance of certain hues as a signal for mobilization, for example red as a cue for avoidance behavior (Elliot, Maier, Binser, Friedman, & Pekrun, 2009) or enhancing performance in Journal of Psychophysiology 2016; Vol. 30(1):9–16

sport competitions (Hill & Barton, 2005). Those studies however analyzed color in a specific situation (e.g., red outfits of sport contestants), while in the current study color stimuli were abstract and not connected with particular context. So current results are, in some way, complementary – saturation (regardless of hue) could be seen as a main factor in capturing attention and being a signal for a general mobilization, while at the later stage (not necessarily conscious, as shown by Elliot et al., 2009) the hue, depending on context, is the main factor directing the behavior. Similar stages of color influence were in less direct way shown, for example, in the studies of Fernandez and Rosen (2000) and Lohse and Rosen (2001), where color advertisements (in opposition to noncolor ads in Yellow Pages) were better noticed and initially chosen as worth consideration, but their final, overall evaluation was depending on whether the choice of colors was adequate to the advertisements’ content. Current research shows that this first stage (better attention catching by more vivid colors) could be observed even on the level of psychophysiological activity and that it is more than a simple distinction of chromatic and achromatic cues, because difference in responses’ strength is observed even between two chromatic colors of different saturation.

There is of course risk of misinterpretation of the reported results. With experiment designed as in the study described here, one can argue that higher SCR magnitudes were not the effect of color attribute itself but an effect connected with stimulus change. Every stimulus was preceded by the gray background, and the saturation is the main difference between chromatic and achromatic colors. There is greater difference between gray and highly saturated hue than between gray and low saturated hue, and this difference in chromatic contrast could be a true cause of reported SCR differences. In order to check if this were really the case, similar analysis as for color stimuli was performed with SCRs recorded during gray backgrounds’ presentation.

Gray stimuli were classified according to preceding background (i.e., gray after bright, low saturated red, gray after dark, highly saturated blue, etc.). If this ‘‘chromatic contrast hypothesis’’ was true, gray backgrounds preceded by highly saturated colors should elicit stronger responses than gray backgrounds preceded by low saturated ones. However, no significant effects or interactions were found, thus proving that effect of saturation on SCR magnitude was really an effect of the color property and not of the magnitude of change between presented backgrounds.

Such findings have practical implications for fields such as advertising, where an ability to capture attention is an important factor in choosing the ‘‘right’’ color. However, one has to remember that findings reported here do not give direct explanation as to why an effect of saturation occurs, or at least is more evident than more intuitive (due to cultural associations) effect of hue. It is possible that more saturated colors could stimulate receptors more strongly and/ or have other properties making them more effective in capturing one’s attention (which results in higher SCR).

Another explanation could lie in typical color-objects associations. For the valence dimension, an ecological valence theory formulated by Palmer and Schloss (2010) suggests 2015 Hogrefe Publishing

P. Zielin´ski: An Arousal Effect of Colors Saturation

that people like colors typically associated with objects they like and dislike colors associated with objects described as unpleasant. It is possible that similar mechanism exists also for the arousal dimension. If it would be true, it would mean that a common trait between objects and signals requiring greater attention and mobilization is their intensity, not hue (it seems true e.g., for traffic lights, with both red and green being important, significant signals). But at this moment these are only speculations requiring further research.

Second research hypothesis stated that subject’s ratings of arousal should correlate with magnitude of SCR, as has been previously observed for stimuli other than color.

Statistical analysis confirmed that hypothesis, showing color as another kind of stimulus for which self-reported ratings covary with autonomic activity. It is an interesting result, since color, in opposition to other tested stimuli (pictures, etc.), is rather an abstract one, with no apparent meaning. Elliot and Maier (2007) in their model of color and psychological functioning suggest two possible sources of such relationship: learned associations and biological proclivities. While the former source suggests mostly huerelated associations (and, what is important, contextual ones, so a given color has different implications for affect and behavior in different contexts), there is the possibility that the latter is connected mostly with other color attributes. In that case additional studies, concerning main affective dimensions (valence and arousal) and various psychophysiological and behavioral measures, are worth considering. Such studies could help to better understand the affective value of color. They should, however, not be based on ‘‘common knowledge’’ (e.g., ‘‘red’’ treated as more arousing than ‘‘green’’), but on careful positioning of colors in affective space, with all color attributes taken into consideration.

In conclusion, it could be said that there is a correspondence between the subjective judgments and phasic activity of sympathetic nervous system, and in both cases responses are related to attributes of color stimuli, namely to saturation. There is of course a question about possibility of generalization of this statement, since only female subjects took part in the current study, and there could be possible sex differences both in electrodermal activity (see Boucsein, 2012) and affective judgements of color stimuli (see Whitfield & Wiltshire, 1990). These differences, however, are usually mostly seen in strength of the responses (e.g., Valdez & Mehrabian, 1994), so one can expect that the overall pattern stays the same for both sexes, but this of course needs empirical verification. Further studies should also provide better control of sex-specific (like the menstrual cycle) and unspecific factors (like caffeine or nicotine intake) that could affect electrodermal activity.

Lack of such control in the current study could result in increased error variance and in the reduction of statistical power.

Nevertheless, results suggest that ‘‘effect of color on emotions,’’ as Valdez and Mehrabian (1994) titled their article, is not about general arousal and intensity of emotional states connected with color environment, but rather about

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the general ability to capture attention and contextual meaning of certain colors. Such conclusion clarifies some incoherent findings known from previous studies and sets ground for further research, since many new questions arise from current study’s result, for example to what extent individual or cultural color associations could mediate this relationship, or is it still present in more complex environment (which is important in the case of practical application as in advertising or color coding).

Acknowledgments The views, opinions, and findings contained in this article are author’s own and should not be construed as an official Polish Air Force position, policy, or decision, unless so designated by other official documentation.

Ethics and Disclosure Statements Informed consent was obtained from all participants.

The author discloses no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) his work.

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Boucsein, W. (2012). Electrodermal activity (2nd ed.).

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In J. T. Cacioppo, L. G. Tassinary, & G. Berntson (Eds.), Handbook of Psychophysiology (3rd ed., pp. 581–607).

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Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The electrodermal system. In J. T. Cacioppo, L. G. Tassinary, & G. Berntson (Eds.), Handbook of Psychophysiology (3rd ed., pp. 159–181). New York, NY: Cambridge University Press.

Elliot, A. J., & Maier, M. A. (2007). Color and psychological functioning. Current Directions in Psychological Science, 16, 250–254.

Elliot, A. J., Maier, M. A., Binser, M. J., Friedman, R., & Pekrun, R. (2009). The effect of red on avoidance behavior in achievement contexts. Personality and Social Psychology Bulletin, 35, 365–375.

Fernandez, K. V., & Rosen, D. L. (2000). The effectiveness of information and color in Yellow Pages advertising. Journal of Advertising, 29, 59–73.

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Gerard, R. (1958). Differential effects of colored lights on psychophysiological functions (Unpublished doctoral dissertation). Los Angeles, CA: University of California.

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Appendix Instructions for Participants of the Study ‘‘The goal of this study is to measure psychophysiological activity during monotonous task. You will see a blank screen with a slowly moving circle. Your task is to follow the circle with the mouse pointer, that is,

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R Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

Rajae-Joordens, R. J. E. (2011). The effects of colored light on valence and arousal. In J. Westerink, M. Krans, & M. Ouwerkerk (Eds.), Sensing emotions. Philips research book series (vol. 12, pp. 65–84). The Netherlands: Springer.

Robinson, W. S. (2004). Colors, arousal, functionalism, and individual differences. Psyche, 10. Retrieved from http:// theassc.org/files/assc/2598.pdf Russell, J. A., & Mehrabian, A. (1977). Evidence for a threefactor theory of emotions. Journal of Research in Personality, 11, 273–294.

Terwogt, M. M., & Hoeksma, J. B. (1995). Colors and emotions: Preferences and combinations. The Journal of General Psychology, 122, 5–17.

Valdez, P., & Mehrabian, A. (1994). Effects of color on emotions. Journal of Experimental Psychology: General, 123, 394–409.

Whitfield, T. W. A., & Wiltshire, T. J. (1990). Color psychology: A critical review. Genetic, Social & General Psychology Monographs, 116, 387–411.

Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York, NY: Springer.

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Accepted for publication: March 18, 2015 Published online: September 15, 2015

Piotr Zielin´ski Military Institute of Aviation Medicine 54/56 Krasin´skiego Street 01-755 Warsaw Poland Tel. +48 26 185-2664 Fax +48 26 133-4154 E-mail pzielins@wiml.waw.pl

for the duration of the study you have to keep the mouse pointer inside the circle.

Background color will change from time to time.

This serves to minimize eyestrain and has no impact on the tracking task.

After the test, you will find out how good you were in following the circle.

When you are ready, press ‘‘OK’’ to start the test.

Good luck!’’

2015 Hogrefe Publishing

Article

Empathy, Approach Attitude, and rTMs on Left DLPFC Affect Emotional Face Recognition and Facial Feedback (EMG) Michela Balconi1,2 and Ylenia Canavesio2 1

Research Unit in Affective and Social Neuroscience, Catholic University of Milan, Italy, 2Laboratory of Cognitive Psychology, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy Abstract. Empathic trait (Balanced Emotional Empathy Scale [BEES]) and emotional attitude (Behavior Activation System [BAS]) were supposed to modulate emotional face recognition, based on left dorsolateral prefrontal (DLPFC) cortex contribution. High-empathic trait (highBEES) was compared with low-empathic trait (low-BEES), when detection performance (Accuracy Index; Response Times [RTs]) and facial activity (electromyogram, EMG, i.e., zygomatic and corrugators muscle activity) were analyzed. Moreover, the implication of the left DLPFC was tested by using low-frequency rTMS (repeated Transcranial Magnetic Stimulation) to induce a decreased response to facial expression of emotions when subjects (N = 46) were required to empathize with the emotional stimuli. EMG and behavioral responses were found to be modulated by BEES and BAS, with a decreased performance and a reduced facial responsiveness in response to happiness for high-BEES and high-BAS in the case of TMS on left DLPFC. Secondly, an emotion-specific effect was found: the DLPFC effect was observed for the positive emotion (happiness) more than for the negative emotions (anger and fear) with a decreased performance (lower Accuracy Index [AI] and higher RTs) and a decreased zygomatic muscle activity. Finally, a direct correlation was found between BEES and BAS and the latter was revealed to be predictive (regression analysis) of the behavioral and EMG modulation induced by TMS. These results suggest significant effect by empathic and emotional attitude component on both EMG and behavioral level in emotional face recognition. This mechanism appears to be supported and regulated by DLPFC. The lateralization (left) effect was discussed in light of the valence model of emotions.

Keywords: emotional facial expression, empathy, BEES, BAS, TMS, EMG

Empathy refers to both cognitive and emotional processes that allow us to mentally represent other people’s mental and affective processes and to produce an actual reaction coherent with others’ behaviors (Spinella, 2005). Previous evidence suggests a close relationship between the experience of emotional empathy and the ability to recognize facial emotions. Indeed we generally know what emotional states others are experiencing by reading their facial expressions (Balconi & Bortolotti, 2012b; Balconi & Lucchiari, 2005; Balconi & Pozzoli, 2009; Hofelich & Preston, 2012), since emotional cue detection may guarantee an adequate empathic response to that emotional condition.

Recent research has examined whether people with higher dispositional empathy are better at recognizing facial expressions of emotion (Andréasson & Dimberg, 2008; Balconi & Bortolotti, 2012b; Balconi & Canavesio, 2013). Indeed empathic personality measures have been considered valid criteria with which to evaluate the presence of structural differences in emotional behavior (Besel, 2007; de Wied, van Boxtel, Zaalberg, Goudena, & Matthys, 2006). Moreover, there is evidence demonstrating the 2015 Hogrefe Publishing

existence of interindividual differences in emphatic cerebral activations (Hein & Singer, 2008; Jabbi, Swart, & Keysers, 2007). These differences in neural activity appear to correlate with measures of behavioral trait empathy assessed through questionnaires like the Balanced Emotional Empathy Scale (BEES; Mehrabian, 1996; Mehrabian & Epstein, 1972), which is a measure of the vicarious emotional qualities of empathy that examines the emotional ‘‘primitive’’ level of interpersonal interactions, and a measure of one’s tendency to empathize with the emotional experiences of others. According to several results, higher scores obtained by subjects in this questionnaire were associated with higher activation levels of the anterior insula and anterior cingulate cortex (Hein & Singer, 2008; Jabbi et al., 2007). Saarela et al. (2007) found that the activation of the anterior insula and inferior frontal gyrus region in subjects who viewed provoked pain faces was positively correlated with the BEES. Therefore, a central key to explore the relationship existing between empathy and emotional face recognition was the neural mechanisms able to support these processes, Journal of Psychophysiology 2016; Vol. 30(1):17–28 DOI: 10.1027/0269-8803/a000150

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focusing on cortical areas which influence emotional detection based on an empathic factor. The main contribution of the prefrontal cortex has been suggested (Lev-Ran, Shamay-Tsoory, Zangen, & Levkovitz, 2012). In fact, the ability to correctly evaluate one’s own inner affective response to facial emotional cues appears to be partially compromised in the case of a deficit in prefrontal cortex functioning (Shamay-Tsoory, Tomer, Berger, Goldsher, & Aharon-Peretz, 2005). Moreover, neuroimaging studies of emotion detection with relevance to empathy have revealed a very wide range of areas activated in response to emotional cues, including the medial prefrontal cortex (MPFC) and more specifically the dorsolateral prefrontal cortex (DLPFC; Seitz, Nickel, & Azari 2006; Shamay-Tsoory, 2007). Significant impairment of angry face recognition was observed when DLPFC activity was disrupted by adopting a Transcranial Magnetic Stimulation (TMS) paradigm (Harmer, Thilo, Rothwell, & Goodwin, 2001). In addition, DLPFC was shown to be implicated in face processing and emotional memories (Balconi & Ferrari, 2012a, 2012b) or when an empathic task was included (Balconi & Bortolotti, 2012b; Balconi, Bortolotti, & Gonzaga, 2011; Rameson & Lieberman, 2009). Rameson, Morelli, and Lieberman (2012) found that higher levels of self-reported experienced empathy were associated with greater activity in the prefrontal area, and activity in empathy-related areas was higher in the empathize condition.

Additionally, a significant relationship was found between empathic response and Gray’s model of behavioral motivational system (Gray, 1981). Gray suggested that the two behavioral systems, the behavioral activation system (BAS) and the behavioral inhibition system (BIS), have their own specific emotional quality: the latter for positive affects (conditioned reinforcement stimuli, rewarding stimuli), with greater left frontal cortical activation, and the former for negative affect (fear, anxiety, negative stimuli), with greater right frontal cortical activation. Moreover, higherBAS subjects were found to be more attentive to positive conditions, where they can reinforce their positive attitude toward appetitive external cues. Contrarily, higher-BIS subjects were found to be more responsive to negative, threatening situations, with significant attention focused on emotionally negative cues, like conflict (Balconi, Falbo, & Conte, 2012; Everhart & Harrison, 2000; Heller, 1993; Mardaga, Laloyaux, & Hansenne, 2006). Previous data also suggest that specific patterns of temperamental reactivity (approach and avoidance, respectively) can be predictive of empathic responding (Balconi & Cobelli, 2014; Balconi et al., 2012). Therefore subjects with higher levels of BAS empathically respond more consistently to positive emotions, showing a specific left lateralization effect in concomitance with positive stimuli processing (Balconi & Mazza, 2010; Balconi et al., 2012; Harmon-Jones & Allen, 1997). Indeed previous EEG studies revealed that such motivational systems are linked to asymmetry of frontal activity, in particular the BAS system (Balconi & Lucchiari, 2005; Stewart, Coan, Towers, & Allen, 2014), and neurophysiological correlates of the approach-withdrawal continuum were associated, respectively, with more left and right prefrontal activity (Balconi & Mazza, 2010). Therefore a Journal of Psychophysiology 2016; Vol. 30(1):17–28

specific lateralization effect was found, with increased left PFC activity for BAS in response to positive emotions, and increased right PFC activity for BIS in response to negative emotions.

In addition, individuals with greater tendency to reciprocate emotional facial expressions scored higher on an empathy questionnaire (Krause, Enticott, Zangen, & Fitzgerald, 2012; Lee, Dolan, & Critchley, 2008; SonnbyBorgström, 2002), suggesting that personality aspects of emotional empathy are coupled with autonomic processes (Balconi et al., 2011). Indeed, some emotional reactions are supposed to be the starting point of the empathic processes (Moore, Gorodnitsky, & Pineda, 2012). Between the others, facial muscle reactions are assumed to be related to emotional responses and hence the electrical activity of the facial muscle (electromyography [EMG]) could be hypothesized to be related to emotional empathy. Specifically, activity of corrugator supercilii muscle (muscle above the eyes responsible for frowning), and that of zygomaticus major muscle (mouth muscle responsible for smiling), are useful measures of empathic emotional response. Activity of corrugator muscle is generally related to negativelyvalenced stimuli, while activity of zygomatic muscle is related to positively-valenced stimuli (Bradley, Codispoti, Cuthbert, & Lang, 2001). Concerning empathy, it was found that low trait empathy subjects showed less corrugator EMG activity than moderate and high empathy subjects (Balconi & Bortolotti, 2012b; Westbury & Neumann, 2008).

Thus, taking into account these results, we suggest an integration between EMG activity that has been shown to be able to signal the presence of an empathic tuning between subjects, and the empathic trait as revealed by BEES measures on the one hand, and emotional attitude indexed by BAS on the other hand. In addition, a more direct exploration was conducted to verify the contribution by empathic and emotional attitude in emotional face recognition, and how it is mediated by the prefrontal (DLPFC) system. A possible left DLPFC effect was considered in relationship with the stimulus valence (i.e., positive stimuli).

Therefore emotional empathic responsiveness was adduced in the present research to explain face recognition: first the ability to detect facial emotional cues; secondly to activate our own resonance mechanism, that is, the facial mimic behavior produced (EMG) as a response to the observed emotional facial cues; thirdly the relationship between empathic trait and other emotional attitude (BAS construct) which may facilitate facial cue detection. Indeed previous studies did not systematically analyze the role of personality traits related to empathy (BEES) or emotional attitude (BAS system). Specifically, we supposed the existence of a strong relationship between facial expression detection in response to different facial emotional patterns, personal response to empathic scale (BEES), BAS attitude, and EMG activity.

Moreover, neural prefrontal contribution to support the facial detection task for empathic behavior was considered.

Specifically, we intended to better explore the role of DLPFC in empathic behavior. We supposed that the ability to recognize facial expression of emotion (emotional cue 2015 Hogrefe Publishing

M. Balconi & Y. Canavesio: DLPFC, Empathy and Approach-Attitude

detection) and facial mimicry process may be modulated by prefrontal functioning. Previous studies did not explore the direct contribution by prefrontal areas for facial expression recognition, and facial mimicry responsiveness in the case of empathic behavior. In this regard, a specific prefrontal lateralization effect was also analyzed, considering the left DLPFC activity. Indeed, based on Gray’s model, we supposed a more significant implication of the left DLPFC for positive more than negative patterns.

In the present research rTMS method was used to produce perturbation of the left dorsolateral portion of the prefrontal cortex. In this way we intended firstly to examine the role this area has in emotion detection in response to different emotional patterns (in terms of valence). Some previous studies focused on the effect induced by DLPFC inhibition in empathic behavior, taking into account the possible ‘‘disruption’’ effect of TMS (Balconi & Bortolotti, 2012a; Balconi et al., 2011; Krause et al., 2012). The TMS method was used to produce a temporary ‘‘inhibition’’ of specific cortical sites and the inhibition of this circuit was found to produce an effective impairment in emotional facial detection (Balconi & Bortolotti, 2012a, 2013). However, in this case no specific effect of approach versus avoidance attitude was tested. Moreover, in the other research the lateralization effect was not considered, taking into account a more generic prefrontal effect (Balconi & Canavesio, 2013). Finally, in other cases, the behavioral and facial EMG relationship with prefrontal activation have not been explored together (Balconi & Bortolotti, 2013; Balconi et al., 2011).

Therefore a lateralization effect was expected for higher-BAS and higher-BEES in response to positive emotions: prefrontal perturbation may induce a more significant effect in high-BEES subjects, since the reduced ability on face recognition may be more important for people who are more responsive to these emotional markers. A significant relationship was also attended between BEES and BAS measures, since high-BEES scores are potentially related to the subjective high sensitivity to emotional relevant situations. Additionally, in light of Gray’s model, general more empathic responses were attended for high-BAS subjects in the case of positive conditions, such as positive expressions (happiness). Therefore, the systematic left DLPFC ‘‘perturbation’’ should induce a higher impairment in high-BAS and high-BEES subjects, since the TMS effect should be more relevant for ‘‘approach’’ conditions and for subjects who are more responsive to these conditions.

Finally, based on the simulation mechanism, we supposed that the psychophysiological subjective response could vary in relationship with the cortical stimulation. This modulation was expected to be valence-related, as a specific ‘‘depotentiation’’ of some EMG responses could be revealed.

Thus, corrugator and zygomatic facial muscle activity were monitored to test the effect, respectively, of negative and positive stimuli on the subjects’ facial responses when left DLPFC was modulated. This facial response was also expected to be related to BEES and BAS measures, with a more significant effect of the left DLPFC ‘‘depotentiation’’ on the EMG measure for high-BEES and high-BAS subjects in response to positive patterns.

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Methods Subjects Twenty females and 26 males (age M = 26.77; SD = 0.17; range = 22–29; mean education level M = 11.98; SD = 0.28) participated in the experiment. We avoided submitting the female participants to the experimental session during the ovulation and follicular phase. We included young subclinical subjects who were all right-handed (Edinburgh Handedness Inventory [EHI]; Oldfield, 1971) and with normal or corrected-to-normal visual acuity.

Exclusion criteria were a history of psychopathology (Beck Depression Inventory [BDI-II]; Beck, Steer, & Brown, 1996) for the subjects or immediate family. Moreover, specific psychiatric examinations (two expert clinicians applied a semi-structured interview) evaluated the general psychopathological profiles of the subjects and their direct family members, in a preliminary phase of the research. No neurological or psychiatric pathologies were observed. No payment was provided for their participation. They gave informed written consent for participating in the study and the research was approved by the Ethical Committee of the institution where the work was carried out.

Stimuli Stimuli materials were taken from the set of pictures used

by Ekman and Friesen (1976). They were black and white pictures of the same male/female actor who presented either a happy, angry, fearful, or neutral face (expressions were not repeated for each condition; level C intensity). After viewing the images, the subjects were asked to analyze the stimuli they viewed and evaluate their emotional significance (‘‘What type of emotion you can see here?’’) and the valence (‘‘Do you think it is a positive or a negative emotion?’’) attributed to each face. The successive data analysis reported the evaluation results.

Procedure The subjects were seated comfortably in a moderately lit room with the monitor screen positioned approximately 50 cm in front of their eyes. Pictures were presented in a randomized order in the center of a computer monitor, with a horizontal angle of 9 and a vertical angle of 11.6 (Stim2 software). The subjects were required to empathize with the situation by placing themselves in the other person’s situation (‘‘Try to enter into the other’s feelings by observing the facial stimulus represented’’).

To allow for clear sympathizing with the reproduced scene, the actor in the picture was similar in age to the experimental subjects (a young adolescent, age eighteen).

In-group similarity (a Caucasian subject) was maintained as suggested in previous studies (Brown, Bradley, & Lang, 2006). Successively, subjects were instructed to make a two-alternative forced-choice response (emotion or no Journal of Psychophysiology 2016; Vol. 30(1):17–28

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emotion) by pressing a left/right button to indicate their judgment (with a stimpad, Stim2 software). Accuracy and speed of the response were stressed (see type of indexes in the Analysis section).

After the experimental phase, each subject was asked to evaluate the degree of empathy that he/she experienced in viewing the facial stimuli (‘‘How much did you enter into the actor’s feelings and situations?’’). Specifically, the subjects rated their degree of empathy on a 7-point Likert scale. Differences in response ratings were assessed using a separate repeated-measures Analysis of Variance (ANOVA) (independent factor: stimulus type, 4). The main effect of condition was significant, F(3, 45) = 9.04, p .001, g2 = 0.39. The subjective empathic response was considered consistently high for each emotional type, whereas the neutral face did not produce high empathic behavior. The gender effect was not significant, as preliminarily demonstrated by a specific analysis. Moreover, they described their experience as emotional empathy due to the level of involvement they reported with the experience.

Balanced Emotional Empathy Scale (BEES) Scores Trait empathy was assessed by a questionnaire for empathy, BEES (Mehrabian, 1996), which tests the vicarious experience of another’s emotional experience (Mehrabian & Epstein, 1972). The BEES questionnaire consists of 30 items, all ranging from 4 (very strong disagreement) to +4 (very strong agreement). Higher scores represent higher levels of emotional empathy. Two different groups were created using a median split on BEES scores: high BEES (M = 66.03; SD = 2.13) and low-BEES (M = 10.05; SD = 1.22). For the overall sample, M = 42.63; SD = 5.09; and range = 15/91. Interitem Cronbach’s alpha was calculated for BEES measure (total 0.92). The two groups did not differ in terms of gender, age, and education level (respectively, for gender: high-BEES N = 22; low-BEES N = 24; for age: high-BEES M = 26.44; SD = 0.13; lowBEES age M = 26.90; SD = 0.19; education level: highBEES M = 11.80; SD = 0.21; low-BEES M = 12.07; SD = 0.25).

Seeking, four items, e.g., ‘‘I’m always willing to try something new if I think it will be fun’’). The questionnaire was presented to the subject after completing the experimental phase (3 days later). Based on these measures, two total scores (BIS and BAS total) were calculated. The mean values and standard deviations for each scale were, respectively, for BIS: 19.06 (3.03); BAS: 39.16 (2.90). Finally, Cronbach’s alphas were calculated for BIS (0.89) and BAS (0.85). Only BAS rating was used for the successive data analysis.

Transcranial Magnetic Stimulation (TMS) We used a transcranial magnetic stimulator (Magstim Rapid2) with a 70 mm figure-of-eight coil (maximum output 1.2 T). The center of the coil, that produced the maximum electric field, was positioned perpendicularly to the cortical site to be stimulated. The approximate location of the DLPFC was automatically identified on the subject’s scalp using the SofTaxic navigator system (Brainsight Magstim, SofTaxic Optic 2.0), which uses a set of digitized skull landmarks (nasion, inion, and two preauricular points), and about 50 scalp points entered with a Fastrack Polhemus digitizer system and an averaged stereotaxic MRI atlas brain in Talairach space. The average Talairach coordinates in the SofTaxic navigator system were transformed through a linear transformation to each subject’s scalp. Talairach coordinates of cortical sites underlying coil locations were estimated on the basis of an MRI-constructed stereotaxic template (accuracy of about 1 mm). This scan procedure suggested that TMS was applied over the left DLPFC (Talairach coordinates 10, 40, 25, medial frontal gyrus) (Figure 1). TMS pulses were set at an intensity of 120% of the motor threshold, defined as the TMS intensity that caused a visible twitch in the muscle of the right hand in 80% of the delivered pulse (two series of ten pulses) over left M1 (passive condition) (for the paradigm see Balconi & Bortolotti, 2012a, 2013). Two control conditions were included into the experimental design in order to control

Behavior Activation System (BAS) Scores Behavioral inhibition system (BIS) and behavioral activation system (BAS) scores were calculated for each subject by using the Italian version of Carver and White’s Questionnaire (1994) (Leone, Pierro, & Mannetti, 2002). It included 24 items (20 score-items and four fillers, each measured on a 4-point Likert scale), and two total scores for BIS (e.g., ‘‘Even if something bad is about to happen to me, I rarely experience fear or nervousness’’ (range = 7–28; 7 items) and BAS (range = 13–52; 13 items). BAS also includes three subscales (Reward, five items, e.g., ‘‘When I’m doing well at something I love to keep at it’’; Drive, four items, e.g., ‘‘I go out of my way to get things I want’’; and Fun Journal of Psychophysiology 2016; Vol. 30(1):17–28

Figure 1. Coil position on the scalp: brain stimulation site (Talairach coordinates: 10, 40, 25).

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for the simple stimulation effect (sham condition: absence of TMS stimulation) and the location effect (control site condition: Pz stimulation). The first effect was checked for the acoustic noise induced by the TMS procedure. For the latter, it is important to demonstrate not just that the TMS effect is specific to a particular cognitive task, or a particular type of trial within a cognitive task, but it is also necessary to show that the effect of the TMS is specific to a specific region of the cortex.

During the sham condition, the same intensity and timing of stimulation was used, but the coil was held in such a manner that no magnetic stimulation reached the brain (i.e., the TMS coil was placed at a 45 angle to the head, and the point of maximal activation was superficial as compared with active stimulation) (George et al., 1997; Kimbrell et al., 1999; Wassermann, Wedegaertner, Ziemann, George, & Chen, 1998). The subjective sensation of coil-scalp contact and the discharge noise in the sham condition were similar to the sensations in the real stimulation phase.

The sequence of the three conditions (TMS; control site; sham stimulation) was counterbalanced across-subjects to prevent order effects and to reduce possible carryover effects, in line with the normal standard TMS stimulation for an on-line paradigm (Miniussi et al., 2008). A 2-hr time interval was provided between each condition. A 5-s rTMS stimulation (1 Hz, five pulses) was time locked to the stimulus. A trial started with the rTMS stimulation (5,000 ms before the stimulus presentation, simultaneously with the presentation of a fixation point), the facial stimulus (2,500 ms), and a blank screen (500 ms), according to the recommendation for repetitive stimulation (Rossi, Hallett, Rossini, & Pascual-Leone, 2009). Subjects could respond starting from the onset of the stimulus (Figure 2).

Each emotional expression (anger, fear, happiness, neutral) was shown 20 times in random order in all stimulation conditions (TMS, Pz, sham) for a total of 240 trials. A different block for each stimulation condition was randomly run. Each block was divided into two subblocks separated by a brief pause. A training phase preceded the experimental phase, in order to allow the subjects to become familiar with the overall procedure.

Electromyography (EMG) Bio-feedback (Biofeedback 2000, version 7.01) connected to a PC was used to record the facial activity. Facial EMG activity in the zygomaticus major and corrugator supercilii muscle regions was studied in experimental subjects during exposure to facial stimuli. The electrodes (4 mm diameter Ag/AgCl electrodes), filled with Surgicon electrolyte paste, were positioned over the corrugator and zygomatic muscles in accordance with guidelines for psychophysiological recording. The set of electrodes was connected to the Bio-feedback Amplifier with a sampling rate of 1,000 Hz. Trials with artifacts were excluded from analysis (2%). Activity of the corrugator and zygomaticus muscle was recorded with a bandpass 20–1,000 Hz and rectified and integrated with a contour following integrator.

Frequencies of interest generally ranged from 20 to 400 Hz. 2015 Hogrefe Publishing

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Figure 2. Experimental procedure for emotion detection task. Five-second rTMS stimulation (1 Hz) was time locked to the stimulus (facial expression of emotions).

Corrugator and zygomatic EMG responses were successively scored as the difference between the mean rectified corrugator/zygomatic signals observed during the presentation of the stimuli and the mean rectified signals in the 2 s prior to stimulus presentation (baseline measure), and it was estimated before the experimental stimulation, in order to prevent the overlapping of EMG baseline registration with the TMS effect. Baseline correction was applied to the data and no differences were found between high-BEES and low-BEES. A positive value indicates that the corrugator/ zygomatic measures were greater during the face viewing phase than during the baseline phase.

Results Subject Ratings of Facial Stimuli Subjects correctly recognized the emotional categories portrayed in each image (96% correctly identified the happy face, 96% anger, 98% fear, and 93 % the neutral face). In the case of a neutral stimulus, the response was ‘‘no emotion.’’ Moreover, no significant differences were found among the four categories, v2(3, N = 46) = 1.32, p = .29.

The subjects were also asked to judge the valence of each stimulus and rank that parameter on a 5-point Likert scale.

Valence attribution differed across emotions, F(3, 45) = 9.87, p .001, g2 = 0.31. Post hoc analysis showed that anger and fear were rated as more negative than happiness, whereas happiness was considered the most Journal of Psychophysiology 2016; Vol. 30(1):17–28

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positive (for all comparisons, p .001). The neutral stimulus was considered to be non-valenced.

0.90

Two factorial repeated-measures ANOVAs with three independent factors (BEES, low/high-BEES subjects; Condition, stimulation/control site/sham; Emotion, four emotions) were applied on the dependent measures of accuracy (total of correct response/total occurrence, AI: Accuracy Index) and RTs (msec after the stimulus onset) measures. A third repeated-measure ANOVA was applied to the EMG. Type I errors associated with inhomogeneity of variance were controlled by decreasing the degrees of freedom using the Greenhouse-Geiser epsilon. The normal distribution of the data was assessed by using skewness and curtosis test in a preliminary statistical phase. Contrast analysis for repeated-measure ANOVA was used to analyze post hoc comparisons.

0.80 0.70 0.60 0.50

TMS

Response Times (RTs) Significant effects were found for Condition, F(2, 45) = 9.87, p .001, g2 = 0.31, Condition · Emotion, F(6, 45) = 10.06, p .001, g2 = 0.31, and BEES · Condition · Emotion, F(6, 45) = 12.31, p .001, g2 = 0.35. Increased RTs were found for stimulation than sham, F(1, 45) = 9.16, p .001, g2 = 0.30, and control, F(1, 45) = 7.89, p .001, g2 = 0.27, condition. In addition, post hoc comparisons showed increased RTs for stimulation than sham and control in response to happiness more than the other emotions (for all comparisons, p .001). Finally Journal of Psychophysiology 2016; Vol. 30(1):17–28

Control

Sham

Figure 3. Accuracy Index (AI, reported M and SD values) modulation as a function of BEES groups, the three conditions of stimulation and facial expression of emotions.

540

Accuracy Index (AI)

520

*

high-BEES low-BEES

500 480

RTs

The first ANOVA applied to AI showed significant effect for BEES, F(1, 45) = 10.53, p .001, g2 = 0.32, Condition, F(2, 45) = 9.51, p .001, g2 = 0.31, Condition · Emotion, F(6, 45) = 11.33, p .001, g2 = 0.34, and BEES · Condition · Emotion, F(6, 45) = 9.12, p .001, g2 = 0.30. Specifically, high-BEES revealed a higher AI than low-BEES. Moreover, as shown by posthoc comparisons (contrast analyses for planned comparisons), general decreased AI was observed for stimulation than sham, F(1, 45) = 8.16, p .001, g2 = 0.29, and control, F(1, 45) = 7.88, p .001, g2 = 0.28, condition. In addition, AI decreased in the case of stimulation than sham and control condition in response to happiness than the other emotions (for all comparisons, p .001). Moreover, a decreased AI was observed for high-BEES in the case of stimulation than sham, F(1, 45) = 9.80, p .001, g2 = 0.32, and control, F(1, 45) = 8.80, p .001, g2 = 0.31, condition in response to happiness (Figure 3).

On the contrary, low-BEES did not show these significant differences.

low-BEES

*

AI

Behavioral and Electromyography (EMG) Measures

high-BEES

1.00

460 440 420 400 380

TMS

Control

Sham

Figure 4. Response Times (RTs, reported M and SD values) modulation as a function of BEES groups, the three conditions of stimulation and facial expression of emotions.

high-BEES showed increased RTs in response to happiness more in case of stimulation than sham, F(1, 45) = 10.11, p .001, g2 = 0.32, and control, F(1, 45) = 9.55, p .001, g2 = 0.30, condition. In contrast, no significant effect was found for low-BEES (Figure 4).

Electromyography (EMG) (Corrugators and Zygomatic) Measure Repeated-measures ANOVAs (BEES · Condition · Emotion) were applied to EMG (zygomatic and corrugators) dependent measures (Figure 5). About the zygomatic 2015 Hogrefe Publishing

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Figure 5. EMG display for mean corrugators and zygomatic activity (raw data). The baseline condition (registered before the experimental session) was reproduced.

Correlational and Regression Analyses A correlational analysis (Pearson correlational values) was applied to BEES and BAS measures.

Significant correlation was found (r2 = .754; p = .001), with a significant direct relationship between empathic trait (BEES) and emotional attitude (BAS) (Figure 7).

Successive Distinct regression analyses were performed for each behavioral and EMG measure in each condition and for each emotion. Predictor variable was BAS rating, and predicted variables were, respectively, AI, RT, zygomatic, and corrugators measures. We reported in Table 1 the correlations between predictor and predicted variables (R), the explained variance (R2), and the regression weights (b). Due to multiple independent analyses and comparisons, we applied Bonferroni test for inequality.

As shown, BAS accounted for the modulation of AI and RT in response to happiness in the case of TMS condition.

No other effect was significant as regards the behavioral measures. Similarly, BAS explained the amplitude of the zygomatic muscle activity in response to happiness in the 2015 Hogrefe Publishing

(A)

high-BEES

Zygomatic muscle (Mv)

1.60

low-BEES

*

1.40

*

1.20

*

1.00 0.80 0.60 0.40 0.20 0.00

TMS

Control

(B)

Sham high-BEES

1.60

Corrugators muscle (Mv)

muscle, ANOVA showed significant effects for Emotion, F(4, 45) = 10.65, p < .01, g2 = 0.35, BEES · Emotion, F(3, 45) = 9.96, p < .01, g2 = 0.32, and BEES · Condition · Emotion, F(6, 45) = 12.65, p < .01, g2 = 0.36, (Figure 6). As shown by contrast effects, happiness produced an increased zygomatic response as compared with other emotions (for all comparisons p .001). Moreover, high-BEES was facially more responsive to happiness than low-BEES F(1, 45) = 14.98, p < .01, g2 = 0.37). Finally high-BEES showed a decreased consistent zygomatic activity in response to happiness in the case of stimulation than sham, F(1, 45) = 11.85, p < .01, g2 = 0.36, and control condition, F(1, 45) = 10.05, p < .01, g2 = 0.32. No other effect was statistically significant.

About the corrugators activity, Emotion, F(3, 45) = 10.12, p < .01, g2 = 0.32, and BEES · Emotion, F(3, 45) = 13.76, p < .01, g2 = 0.35, were statistically significant. Corrugators response was higher in case of anger, and fear than happiness and neutral stimuli (for all comparisons p .001). Moreover, high-BEES showed a general increased corrugators response to anger and fear than the other facial expressions (for all comparisons p .001). No other effect was statistically significant.

low-BEES

1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00

TMC

Control

Sham

Figure 6. Zygomatic (A) and corrugators (B) muscle activity (reported M and SD values) as a function of BEES groups, the three conditions of stimulation and facial expression of emotions. case of TMS. In contrast, sham and control condition did not show significant effects.

Finally a correlational analysis (Pearson correlational values) was applied to AI and RTs measures and zygomatic/corrugators activity for each condition and each emotion. Significant correlation was found for both AI and RTs, Journal of Psychophysiology 2016; Vol. 30(1):17–28

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Discussion

70 60

BEES

50 40 30 20 10 0

0

10

20

30

40

50

60

BAS

Figure 7. Scatterplot of BEES and BAS relationship. A significant positive correlation was found between the two measures.

respectively, with zygomatic activity in TMS condition in response to happiness, with a significant positive relationship between AI and zygomatic activity (r2 = .698; p = .001) and a significant negative relationship between RTs and zygomatic activity (r2 = .711; p = .001). No other effect was statistically significant. Therefore, a general decreased performance (lower AI, higher RTs) in concomitance with a decreased zygomatic activity was revealed in response to happiness in case of TMS condition.

The present research highlighted three main significant results. We firstly revealed a consistent impact of the empathy trait (BEES) in emotional face processing. Indeed highBEES subjects were more responsive to the emotional facial expressions. Secondly left DLPFC stimulation was able to induce a significant modification in face recognition (AI and RTs) and EMG modulation, with a partial higher difficulty in face recognition in the case of happiness, more accentuated for high-BEES. Thirdly approach attitude (BAS) was found to be relevant in explaining the TMS effect, with a significant impact of BAS in modulating the behavioral and EMG results in relationship with the empathy trait.

Firstly trait empathy seems to affect the degree of subjective responsiveness to facial cues, that is, high-BEES subjects were more accurate in responding to facial expressions, whereas an increased impairment was observed for these subjects in the case of left DLPFC perturbation (TMS effect). Indeed the subjective responsiveness to empathy, as marked by BEES measure, was able to affect the detection performance in an emotional condition. In fact, a clear generally better competence in emotional cue detection task was found for high-BEES subjects. They were generally better able to recognize facial patterns than low-BEES subjects, as evidenced by the general increased correctness. This fact may imply the presence of specific competences to attribute an emotional value to facial mimic

Table 1. Regression analysis. BAS as predictor variable, behavioral and EMG as predicted variables TMS

Sham

Control

Happiness

Anger

Fear

Neutral

Happiness

Anger

Fear

Neutral

Happiness

Anger

Fear

Neutral

AI R b Stderror t

0.71 0.50 0.20 2.85**

0.32 0.45 0.39 1.02

0.30 0.29 0.20 0.98

0.27 0.45 0.39 0.87

0.30 0.37 0.23 0.93

0.33 0.60 0.30 0.45

0.28 0.23 0.33 1.06

0.22 0.18 0.22 0.75

0.28 0.39 0.30 0.70

0.25 0.33 0.22 0.28

0.22 0.28 0.20 0.31

0.20 0.33 0.31 0.74

RTs R b Stderror t

0.55 0.44 0.28 2.33**

0.32 0.56 0.34 0.87

0.31 0.55 0.31 0.78

0.28 0.20 0.29 0.90

0.27 0.36 0.22 0.84

0.23 0.23 0.28 0.54

0.37 0.20 0.35 1.01

0.20 0.23 0.33 0.62

0.30 0.36 0.31 1.06

0.19 0.29 0.36 0.58

0.18 0.39 0.36 0.61

0.25 0.33 0.23 0.89

Zygomatic R b Stderror t

0.53 0.70 0.28 2.12**

0.30 0.62 0.29 0.88

0.25 0.43 0.22 0.80

0.30 0.20 0.29 0.98

0.24 0.34 0.39 0.85

0.29 0.23 0.35 0.54

0.33 0.20 0.30 1.01

0.20 0.25 0.32 0.70

0.21 0.30 0.29 0.77

0.18 0.22 0.46 0.65

0.22 0.31 0.33 0.73

0.11 0.29 0.44 0.52

Corrugators R b Stderror t

0.33 0.22 0.28 0.93

0.20 0.36 0.30 0.73

0.28 0.30 0.49 0.88

0.20 0.20 0.29 0.71

0.32 0.24 0.39 1.03

0.26 0.29 0.23 0.84

0.28 0.20 0.30 0.80

0.21 0.28 0.33 0.70

0.27 0.37 0.30 0.90

0.12 0.32 0.28 0.68

0.15 0.32 0.22 0.74

0.18 0.33 0.3 0.81

Notes. Bold values = significant effect; **p = .01.

Journal of Psychophysiology 2016; Vol. 30(1):17â&#x20AC;&#x201C;28

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by high-trait-empathy subjects. Previous research pointed out the contribution of emotional appraisal in empathic conditions as a functional mechanism able to activate better recognition (behavioral measures) and a mirroring function (EMG measures) of the emotional behavior displayed by other people, where sharing similar emotional responses allows for a direct form of understanding other people via a simulation process (Preston & de Waal, 2002; Seitz et al., 2008). More specifically, emotionally involving and significant context may ingenerate a consonant shared response by a higher empathic observer, who firstly recognizes and secondly ‘‘mimics’’ the somatic markers related to the experienced emotions (Preston, 2007).

Contrarily, virtual impairment induced by rTMS on left DLPFC area produced a ‘‘deficit’’ in the emotional appraisal processes, which was more consistent in high-BEES. Regarding recognition of facial cues, we observed an increase of difficulty to detect emotional expressions of happiness when this cortical area was modulated. This result was confirmed by the simultaneous increasing of RTs and decreasing of AI when left DLPFC cortical site was temporarily ‘‘perturbated’’ by rTMS stimulation.

Therefore a main conclusion might be that the left DLPFC was found to support positive facial cue detection task in the case of a higher empathic response, as an impaired performance in happiness recognition in the case of TMS stimulation (inhibition) of left prefrontal area was observed.

Taking into account the present results, the effect of DLPFC activity on the ability to detect emotional facial expression was explored. However, it has to be noted that this effect was not generalized in response to all the emotion types. Indeed we found a significant emotion-specificity impact, related to the positive valence of the facial expression (happiness). This cortical modulation produced an effective incidence on the emotional cue detection: behavioral performance was worse in TMS condition for high empathic subjects, reflecting the increased cognitive difficulty to check for emotional content of faces in the case of prefrontal inhibition. In that condition, the ability to monitor and detect facial cues could be partially compromised, and this area seems to be related to facial expression monitoring, as also shown in previous experiments (Krause et al., 2012; Seitz et al., 2008). In fact, the suggested interpretation of these results is supported by the fact that the prefrontal area includes specific processing modules for emotional information comprehension. Specifically, in the present study the role of left DLPFC for empathy-related response was elucidated, with a possible circular effect on both monitoring ability (cue detection) and empathy responsiveness (trait empathy).

We observed an analogous trend induced by TMS stimulation also for EMG measures. In general, it was found that the EMG activity was modulated by the stimulus valence. Indeed, in the absence of TMS modulation, subjects showed a significant increased zygomatic activity for happiness and corrugator activity for anger and fear.

These results were supported by previous research: facial actions during picture viewing were generally associated with reports of positive vs negative value, where more corrugator tension was found when viewing negative pictures 2015 Hogrefe Publishing

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(Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 2000; Ribeiro, Teixeira-Silva, Pompéia, & Bueno, 2007). This fact confirmed that greater corrugator activity on the facial EMG was found in response to negative patterns compared to positive stimuli, which in contrast induced higher zygomatic activity (Lang, Greenwald, Bradley, & Hamm, 1993).

A related consideration produced by the present results was that the DLPFC plays a relevant role in regulating the facial response, since it was demonstrated that facial mimicry was modulated by prefrontal activity, and secondly, that it was related to the emotional valence of facial stimuli.

More specifically, left DLPFC was found to regulate these facial mechanisms in response to specific valenced-stimuli (i.e., happiness) in relationship with the BEES measure (Preston, Bechara, Grabowski, Damasio, & Damasio, 2007). In fact, it was previously stated that sharing similar emotional responses allows for a direct form of understanding other people via a simulation process (Preston, 2007).

Prefrontal activity appeared to ingenerate a consonant shared emotional response to the facial displays by the observer. More generally, we may conclude that EMG measures were shown to be implicated in empathy and they may function as a biological marker of an underlying ‘‘resonance mechanism’’ when emotions are shared. The facial display was interpretable as a functional mechanism of mirroring the emotional condition displayed by other people, where sharing similar emotional responses allows for a direct form of understanding other people by simulating their emotions.

We observed that, in the case of TMS, the EMG responsivity was reduced. Subjects showed a decreased zygomatic (more for positive expressions) activity and no specific effect for the corrugator. As shown in the case of behavioral measures, the facial activity was limited by left DLPFC ‘‘perturbation,’’ more for happiness than negative stimuli and for high-BEES than low-BEES. To explain this emotion-specific effect, the localization site we stimulated should be considered. The valence model of emotional cue comprehension suggested that the left versus right side of the prefrontal cortex allows for modulating positive more than negative emotions (Balconi & Mazza, 2009, 2010).

A relevant result was also related to the significant relationship between behavioral performance and autonomic activity in the case of TMS stimulation. As underlined by the correlational analyses, the decreased performance (higher RTs and lower AI) was concomitant to the decreased zygomatic response to happiness when left DLPFC was ‘‘inhibited.’’

However, the valence model and empathic component were unable to completely explain the present results, since the BAS component was also found to modulate the left DLPFC ‘‘impairment effect’’ for higher-BEES. That is, based on these results we may consider a second potential explicative factor: the BAS approach-mechanism contribution. Personal attitudes toward emotions, represented by BAS motivational systems, can better explain previous results by pointing out why subjects are more or less oriented to the positive versus negative emotional domain (Balconi, Falbo, & Brambilla, 2009). A consistent effect was observed in relationship with BAS measures, which were found to be effective in modulating subjects’ mimic Journal of Psychophysiology 2016; Vol. 30(1):17–28

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responses, as well as behavioral performance. Indeed not only was BAS found to be related to the empathic trait but it was also observed to modulate the behavioral and EMG measures in relationship with happiness.

Indeed, first a direct relationship was revealed between high-BEES and high-BAS measures, as reported by the correlational values. Secondly the BAS component accounted for the variations in accuracy and RTs in the case of DLPFC modulation by TMS, as shown by regression analysis. In parallel, a significant effect of BAS was found for the EMG measures (the zygomatic activity) in response to happiness. Moreover, a lateralization effect was adduced, since the BAS contribution was found in response to left DLPFC ‘‘perturbation.’’ High-BAS subjects showed, in fact, a significantly more intense ‘‘impairment effect’’ to positive than negative and neutral faces. Thus, higher-BAS subjects may be more responsive to and empathic with positive stimuli, which potentially induce an approach attitude toward the emotional condition (Balconi & Canavesio, 2013).

More generally, we may suppose that emotional attitudes are implicated in empathy and that the proactive attitude stated by BAS may be reflected in a higher empathic profile. In fact, a similar contribution by BAS and BEES inducing a coherent response profile was shown with positive cues. They are both necessary to explore the incidence of trait empathy and motivational attitude to emotional faces in different emotional contexts. Therefore we may suppose that the individual differences in emotional reactivity, stemming from the motivational tendencies for approach (BAS) versus withdrawal (BIS) continuum, may predispose with different degrees an empathic response, and they may affect the subjective empathic behavior toward external positive and negative situations respectively (Krause et al., 2012).

In summary, recognition of emotional facial expressions was mediated by the left DLPFC, with a specific significant increased effect for positive (happiness) emotions. TMS modulation was shown to be more effective for high-BEES in response to positive stimulation. More empathic subjects may have paid greater attention to the detection task, as a consequence of this cortical perturbation, with a significantly worse performance in cue recognition. It should be due to the main function the prefrontal cortex has in appraisal processing when high empathic subjects are required to empathize with the emotional situation: whereas they are more prompt in responding to emotional cues thanks to DLPFC contribution, they are also more impaired when this cortical area is perturbed, with a significant lateralization (more left, positive-related emotions) effect.

Taken together with the subjective trait measure of empathy, the present results suggest that the high empathy group was consistently better able to detect emotions from the face than the low empathy group. Moreover, the relationship between trait empathy and left DLPFC perturbation, with lower performance in high-BEES, suggests that this brain area significantly contributes to the emotional detection in the case of positive emotional cues, especially for subjects who show an increased ability to recognize

Journal of Psychophysiology 2016; Vol. 30(1):17–28

emotions in empathic situations (high-BEES) by using facial expressions as a valid cue to detect emotions.

A significant factor able to integrate this explanation was found to be the motivational trait, as marked by BAS. Indeed this construct was directly related to BEES modulation on the one hand, and was able to explain the decreased performance and facial responsiveness in subjects in response to positive emotions in the case of left DLPFC perturbation on the other hand.

Firstly, future research should better explain the relationship between empathic trait and emotional attitude, also taking into account the ‘‘cognitive’’ components of empathy in addition to the more emotional ones. Secondly, a more direct comparison between left and right DLPFC could better elucidate, on the one hand, the role of the left-approach (BAS) versus right-avoidance (BIS) attitude and, on the other hand, the contribution from the two hemispheres to the empathic responsiveness to emotions. Indeed, in the present research we may clearly state a valenced cortical effect (left DLPFC for positive emotions) for empathic behavior. A deeper exploration is recommended to better verify the contribution of the right DLPFC in relationship with the negative emotions. Moreover, some methodological caveats should be considered, that is: the possible ‘‘ceiling effect’’ due to the high subjective performance.

However, the significance of the present results for some specific categories (of subjects and experimental conditions) may argue in favor of the relevance of these categories. A second methodological limitation may be related to the significant interpersonal differences related to the TMS effect on the PFC (Brune et al., 2012; Krause & Cohen Kadosh, 2014). Finally the relationship between the EMG measure and the empathic trait should be considered taking into account the mediation by the prefrontal network.

Indeed the facial display modulation could be explained by considering an indirect effect by the DLPFC, that primarily affects the empathic responsiveness, which in turn produces the EMG activity reduction; or we may suppose a more direct effect of the DLPFC on the facial feedback, which is ‘‘perturbated’’ by TMS, in parallel with the empathic behavior modulation.

Acknowledgments This study was supported by Catholic University Research Found D 1.1. 2013.

Ethics and Disclosure Statements All participants of the study provided written informed consent and the study was approved by an Ethics Committee.

The author disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

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Accepted for publication: March 23, 2015 Published online: September 15, 2015 Michela Balconi Department of Psychology Catholic University of the Sacred Heart Largo Gemelli 20123 Milano Italy Tel. +39 2 7234-2233 Fax +39 2 7234-2280 E-mail michela.balconi@unicatt.it

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Article

Connectivity of Superior Temporal Sulcus During Target Detection Martin Pail,1,2 Petra Dufková,2 Radek Marecˇek,2,3 Jana Zelinková,1,2 Michal Mikl,2,3 Daniel Joel Shaw,1 and Milan Brázdil1,2 1

Behavioural and Social Neuroscience Research Group, CEITEC – Central European Institute of Technology, Masaryk University, Czech Republic, 2First Department of Neurology, Masaryk University, School of Medicine and St. Anne’s University Hospital, Brno, Czech Republic, 3 Molecular and Functional Neuroimaging Research Group, CEITEC – Central European Institute of Technology, Masaryk University, Czech Republic Abstract. The aim of the current research was to study functional connectivity (FC) of the right superior temporal sulcus (rSTS) during visual target stimulus processing. This structure is presumed to be crucial in social cognition, but evidently participates in target detection as well.

Twenty subjects participated in functional magnetic resonance examination for studying FC. We used psychophysiological interaction (PPI) analysis of data acquired during the visual oddball task. During the visual oddball task rSTS had increased connectivity bilaterally with structures involved in memory operations (mesiotemporal cortices and basal ganglia) and evaluative processing related to decision making (left anterior cingulate cortex). Moreover, we revealed decreased connectivity of rSTS with structures involved in attentional processes (right dorsolateral prefrontal cortex (DLPFC) and the posterior area with bilateral parietal cortex). Based on our results we hypothesize that in the detection of rare events, during visual information processing, rSTS is involved within neuronal networks related to attention, but also at later stages of stimuli processing.

Keywords: fMRI, oddball task, psychophysiological interaction, connectivity, superior temporal sulcus

Every organism is surrounded by a rapidly changing environment that requires continual adaptive behavior. It is necessary to be able to select biologically relevant stimuli from among the varied, unending, and multimodal sensory flow of stimuli of extra personal space. In light of the quantity of sensory stimulation the brain requires reliable mechanisms for directed attention, correct selection of relevant stimuli, and then their selective processing (Corbetta, Patel, & Shulman, 2008). Together this establishes the basis for adaptive behavior and so enables the initiation of appropriate reactions (Corbetta & Shulman, 2002; Lee, 2013; Mesulam, 1990; Norman & Shallice, 1986).

Attention allocation is dependent on two different aspects, each with its own relatively independent neuronal substrate. One aspect is top-down processing (Hopfinger, Buonocore, & Mangun, 2000; Hopfinger, Woldorff, Fletcher, & Mangun, 2001; Miller & D’Esposito, 2005), which is endogenous and goal-directed, relying on previous experience rather than sensory stimulation (Corbetta & Shulman, 2002). This system involves bilaterally mainly the inferior frontal junction, which is functionally and structurally distinguishable from the dorsolateral prefrontal cortex (DLPFC), superior parietal lobule, intraparietal sulci (IPS), and motion-sensitive middle temporal area (it is also

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referred to as the dorsal frontoparietal network; Corbetta & Shulman, 2002; Kim, 2014; Sestieri, Shulman, & Corbetta, 2012). The other aspect is bottom-up, stimulus-driven processing (Corbetta & Shulman, 2002; Corbetta et al., 2008; Nardo, Santangelo, & Macaluso, 2011; Sestieri et al., 2012). It is activated by unexpected, potentially relevant environmental stimuli. Unlike the first aspect, stimulusdriven processing seems to comprise areas bilaterally especially around the temporo-parietal junction (TPJ), with right-sided predominance, supplementary motor area, supramarginal area, anterior cingulate cortex (ACC), and anterior insula with the frontal operculum (the ventral frontoparietal network; Corbetta & Shulman, 2002; Kim, 2014; Nardo et al., 2011; Sestieri et al., 2012). Both systems mutually interact (Corbetta & Shulman, 2002; Kim, 2014; Miller & D’Esposito, 2005).

One of the most widely utilized experimental paradigms in cognitive neuroscience is the oddball task, which has been used extensively to identify and study neuronal correlates of cognitive response; namely target (task-relevant) stimulus processing in the human brain. The goal of the task is detection and reaction to rare target stimuli, which are interspersed randomly and unexpectedly among frequent ones. The task requires not only early attentional

Journal of Psychophysiology 2016; Vol. 30(1):29–37 DOI: 10.1027/0269-8803/a000151

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cognitive processes, but also later evaluative and executive processes (Brázdil, Roman, Daniel, & Rektor, 2003; Polich, 2007; Verleger, 1997). These processes consist of, for example, evaluation of stimulus representation in working memory, recognition of its novelty, its further categorization, and initiation of adequate executive response, all modulated by levels of arousal (Paller, Kutas, & Mayes, 1987; Polich, 2007; Squires, Squires, & Hillyard, 1975; Verleger, 1988). From the point of view of neural networks, the dorsal network is involved in both frequent and target stimulus processing (only a modest association of the dorsal network with oddball effects), whereas the ventral network is more strongly involved in target than frequent stimulus processing (Kim, 2014). In this sense, the oddball task can be used to investigate particularly bottom-up, but partly also top-down processing.

Oddball paradigms are used in electrophysiological cognitive neuroscience research to evoke a specific eventrelated potential (ERP) response (Donchin, Ritter, & McCallum, 1978; Sutton, Braren, Zubin, & John, 1965).

Especially P300/P3 potentials (with main P3a and P3b parts) are specific neuronal responses elicited by conscious target stimulus detection (Donchin et al., 1978; Polich, 2007; Pritchard, 1981). P3a part (elicited mainly in DLPFC, cingulate cortex, and supramarginal gyrus) represents primarily the reorientation of attention to new and unexpected stimuli, regardless of whether they are the target stimuli.

P3a is believed to index both exogenous (bottom-up) and endogenous/goal-directed (top-down) attention (Halgren, Marinkovic, & Chauvel, 1998; Polich, 2007). The P3b part has been proposed to represent different cognitive functions related to the conscious processing of an event, the adaptation of working memory to changing data in the environment in response to task-relevant unexpected events, evaluative processes, and to response preparation (Friedman, Cycowicz, & Gaeta, 2001; Halgren et al., 1998; Polich, 2007; Verleger, Jaskowski, & Wascher, 2005). It is generated mainly in the hippocampus, the ventrolateral prefrontal cortex, and the superior temporal sulcus (STS; Friedman et al., 2001; Halgren et al., 1998; Polich, 2007; Verleger et al., 2005).

In recent years, functional magnetic resonance imaging (fMRI) has been used widely to study the results of the oddball paradigm and to investigate what brain regions are specialized in target stimulus processing. This includes simple auditory (Brázdil et al., 2005; Czisch et al., 2009; Kiehl, Laurens, Duty, Forster, & Liddle, 2001; Kiehl et al., 2005; Mulert et al., 2004; Opitz, Mecklinger, Friederici, & von Cramon, 1999; Petit et al., 2007; Stevens, Calhoun, & Kiehl, 2005; Stevens, Pearlson, & Kiehl, 2007) and visual oddball tasks (Ardekani et al., 2002; Bledowski, Prvulovic, Goebel, Zanella, & Linden, 2004; Bledowski, Prvulovic, Hoechstetter, et al., 2004; Brázdil, Mikl, Marecek, Krupa, & Rektor, 2007; Clark, Fannon, Lai, Benson, & Bauer, 2000; Linden et al., 1999; McCarthy, Luby, Gore, & Goldman-Rakic, 1997), as well as more complex oddball designs (Melcher & Gruber, 2006; Ngan et al., 2003; Yomogida et al., 2010). Functional MRI produces results with superior spatial resolution compared to

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ERPs, nevertheless, fMRI is temporally insensitive compared to electrophysiological techniques. Significant discrepancies exist between the fMRI data and the results of previously published intracranial ERP studies of oddball task. Regions with evident hemodynamic and electrophysiological responses overlap only partially. Both methods should thus be viewed as mutually complementary in investigations of the spatial distribution of cortical and subcortical activation during oddball task (Brázdil et al., 2005).

Indeed, previous fMRI studies with the oddball paradigm confirm the activation of a widely distributed fronto-temporo-parietal cognitive neuronal network in response to target stimuli, with clear lateralization to the right hemisphere (Corbetta & Shulman, 2002). A more precise functional differentiation of the areas involved and their connectivity is currently a source of ongoing debate, however. To the best of our knowledge, data about functional or effective connectivity of structures participating in visual target detection are very limited and oriented to ACC, DLPFC, and parietal cortex around IPS – that is, attention-related structures – that show the most significant signal increases in General Linear Modeling analyses (e.g., Brázdil et al., 2007, 2009). These three structures have been also investigated intensively in terms of their participation in attentional processes in general, and it is very likely that during the oddball task they participate in attentional capture. There are, however, other structures that contribute to visual target detection. One is the STS, a brain structure involved in many cognitive functions and is plausibly one of the key structures in social behavior (Hein & Knight, 2008; Zelinková et al., 2013). Even though the activation of STS during target detection has been frequently reported (Brázdil et al., 2007; Czisch et al., 2009; Linden et al., 1999; Melcher & Gruber, 2006; Ngan et al., 2003; Opitz et al., 1999; Petit et al., 2007; Stevens et al., 2007; Yomogida et al., 2010), this interesting and functionally relevant area has not yet been fully explored. To our knowledge, no fMRI study has been published that deals directly with the STS in terms of its activation within the frame of neurocognitive networks related to target detection. However, a recent study confirmed its widespread functional connectivity in the resting state (Habas, Guillevin, & Abanou, 2011).

For this reason, a closer look at this structure was a goal of our study. The aim of present study, therefore, was to restate the previous findings of Brázdil et al. (2007) with a focus on STS and its functional connectivity, and so advance our knowledge about this brain structure in the context of visual target detection. To achieve this we investigated functional connectivity of the STS during task-relevant/ goal-directed stimulus processing in the context of target detection, attentional, evaluative, and executive processes.

The functional connectivity enables the testing of specific hypotheses about indifferentiable directional/nondirectional interregional interactions during specific cognitive tasks (Rehme, Eickhoff, & Grefkes, 2013). This involved the analysis of dataset acquired with functional magnetic resonance imaging. We studied functional connectivity using psychophysiological interaction analysis of data acquired during the visual oddball task.

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Materials and Methods Subjects and Study Design Functional connectivity analyses were performed on data collected during a visual oddball task, involving 20 healthy volunteers (13 females, 7 males) ranging in age from 18 to 33 years (mean = 23 years; median = 22; SD = 3.9).

All subjects were right-handed and had normal vision.

Informed consent was obtained from each subject prior to the experiment. The study received the approval of the St. Anne’s Hospital Ethics Committee.

In the visual oddball task a series of visual stimuli were presented. There were two types of stimuli: the frequent stimuli and the target stimuli. The frequent stimuli were presented more frequently than the targets. The frequent visual stimulus (presented for 93.75% of the trials) was an image of a string of white characters OOOOO on a dark background, while the target image (6.25% of the trials) was a string of white characters XXXXX on the same dark background. The target frequency varies widely in fMRI oddball studies, from 5% (e.g., Bledowski, Prvulovic, Goebel, et al., 2004) to 20% (e.g., Mulert et al., 2004).

We presumed that using a relatively low frequency would lead to a greater contrast between frequent and target stimuli, and therefore greater power.

The visual stimuli were presented by Presentation software (Version 0.70, http://www.neurobs.com; Neurobehavioral Systems) and were presented centrally on the projection screen via a data projector. They were seen by the subjects through a mirror that was mounted on the MRI radio frequency head coil. Stimuli subtended approximately 15 of visual angle. A total of 1,024 images were shown to the subjects (64 target and 960 frequent stimuli) in four experimental runs of 256 images each. The interstimulus interval (the time between the initiation of each stimulation) was fixed at 1,600 ms. The duration of stimuli exposure was constant at 500 ms. During the time in which stimuli were not shown (1,100 ms), the screen was dark (Figure 1). Each run contained a fixed amount of 16 targets.

The position of targets within the run was randomized.

The subjects were instructed to count silently the target stimuli and to report the total number of target stimuli at the end of each run. They were instructed to pay attention to the letters presented on the screen. Of the 20 subjects, 13 reported the correct amount of targets, four subjects missed one target, two missed two targets, and one reported one target more than the correct amount. Overall, the task execution was sufficient and subjects kept their attention on the task until it was completed.

Image Acquisition Imaging was performed on a 1.5 T Siemens Symphony scanner equipped with Numaris 4 System (MRease). Functional images during the oddball task were acquired using a gradient echo, echoplanar imaging (EPI) sequence: time to repeat (TR) = 1,600 ms, echo time (TE) = 45 ms, field of 2015 Hogrefe Publishing

Figure 1. The oddball paradigm contains two types of stimuli – frequent visual stimulus (white O characters on a black background) and target visual stimuli (white X characters on a black background). There was a total of 64 target and 960 frequent stimuli in four experimental runs (4 · 256 stimuli in each run). Each run contains 16 targets randomly intermixed with frequents. The interstimulus interval is fixed at 1,100 ms. The stimuli duration is 500 ms. The duration of each trial is 1,600 ms (500 ms stimulus presentation plus 1,100 ms dark time). view (FOV) = 250 · 250 mm, flip angle (FA) = 90 , matrix size 64 · 64, in plane resolution 3.9 · 3.9 mm, slice thickness = 6 mm, 15 transverse slices per scan acquired in sequential order from bottom to top. The image volume covered most of the brain including the upper cerebellum and excluding the vertex, temporal poles, amygdales, and anterior part of the hippocampi. Four runs were acquired for each subject; each run consisted of 256 scans (total 1,024 scans per subject). These runs were separated by only a few seconds to obtain the number of counted targets from the subjects. The subjects were instructed to remain still during these breaks and to report only the number of counted targets. Following these functional measurements, high-resolution anatomical T1-weighted images were acquired for each participant using a MPRAGE 3D sequence with 160 sagittal slices matrix size 256 · 256 resampled to 512 · 512, slice thickness = 1.17 mm, in plane resolution 0.96 · 0.96 mm, TR = 1,700 ms, TE = 3.96 ms, FOV = 246 · 246 mm, FA = 15 .

Image Preprocessing Functional MRI data were analyzed using SPM5 (Functional Imaging Laboratory, the Wellcome Department of Journal of Psychophysiology 2016; Vol. 30(1):29–37

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Figure 2. Results of fMRI analysis applied to group data acquired during oddball task. These images illustrate regions with significantly greater activity in response to target relative to frequent stimuli. Activity in anterior and posterior part of STS is indicated with arrows ( p < .05, FWE corrected for multiple comparisons).

Imaging Neuroscience, Institute of Neurology at University College London, UK) running under Matlab 7.6.0 (Mathworks Inc., USA). The following preprocessing was applied to each participant’s time-series of fMRI scans: realignment to correct any motion artifacts by rigid body spatial transformation; normalization to fit into a standard anatomical space (MNI); spatial smoothing using a Gaussian filter with FWHM of 8 mm and high-pass filtered to remove cycles longer than 128 s which contain predominantly physiological noise.

The voxel size generated from the above acquisition parameter was oversampled to 3 · 3 · 3 mm.

Results of Our Previous fMRI Analysis of Oddball Data In our previous fMRI study using an oddball task (Brázdil et al., 2007) we observed two distinct cortical regions that exhibited significant activity: The anterior (MNI Journal of Psychophysiology 2016; Vol. 30(1):29–37

coordinates: x = 54, y = 27, z = 6; T value: 8.56) and the posterior (MNI coordinates: x = 45, y = 48, z = 6; T value: 9.84) STS. Importantly, both regions of the STS expressed higher t-statistics and Z-scores in the right compared with the left hemisphere. Figure 2 presents composite maps that indicate increased activity in the STS. To define the seed coordinates, we used these group average maxima.

Due to the multifunctionality of STS, we considered each as a functionally distinct region (Beauchamp, Lee, Argall, & Martin, 2004; Bledowski, Prvulovic, Goebel, et al., 2004; Bledowski, Prvulovic, Hoechstetter, et al., 2004; Brázdil et al., 2005; Czisch et al., 2009; Haxby, Hoffman, & Gobbini, 2000; Ishai, Schmidt, & Boesiger, 2005; Materna, Dicke, & Thier, 2008; Melcher & Gruber, 2006; Mulert et al., 2004; Pelphrey & Carter, 2008; Petit et al., 2007; Thompson, Clarke, Stewart, & Puce, 2005).

Right-hemisphere predominance in activations during target detection is in concordance with fMRI studies published previously using an oddball paradigm and detecting neuronal activity after target stimuli (Ardekani et al., 2002; Clark et al., 2000; Kiehl et al., 2001, 2005; Linden et al., 1999; 2015 Hogrefe Publishing

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Table 1. Brain areas with significant functional connectivity with the anterior (x = 54, y = 27, z = 6) and posterior (x = 45, y = 48, z = 6) parts of the right-sided STS. The presented results were significant at p < .05 (FWE corrected) using cluster level inference; initial cut T = 3.58 ( p = .001 uncorrected); critical FWE corrected cluster size of 18 adjacent voxels; estimated isotropic smoothness FWHM = 13 mm; 398 resells in whole volume. STS = Superior Temporal Sulcus; R = right; L = left Seed region Anterior STS

Positive interaction Negative interaction

Posterior STS

Positive interaction Negative interaction

Target region

MNI coordinates [mm]

Number of voxels

T value in cluster maximum

R hippocampus L putamen R dorsolateral prefrontal cortex

30 30 6 30 15 9 36 6 42

97 40 19

6.50 5.66 5.12

L anterior cingulate R superior parietal lobule L inferior parietal lobule

9 39 9 30 57 51 36 42 54

67 24 26

6.70 4.91 4.46

McCarthy et al., 1997; Menon, Ford, Lim, Glover, & Pfefferbaum, 1997; Ngan et al., 2003; Opitz et al., 1999; Stevens et al., 2005, 2007; Yomogida et al., 2010). Hemispheric asymmetry in activations during target detection with right-hemisphere predominance was demonstrated also by Shulman et al. (2010), for example, using direct voxelwise comparisons. For this reason, our seeds were located in the right STS. Specifically, set spheres (6 mm diameter) centered on the peak coordinates in right anterior and posterior STS from our previous study (Brázdil et al., 2007).

frequent stimuli, convolved with the HRF, and (d) nuisance regressors. The individual maps of interaction effects were then entered to the one-sample t-test to evaluate randomeffect group results. We used a significance threshold of p < .05 FWE corrected for multiple comparisons at the cluster level. The initial cut for cluster definition was set to t = 3.58 ( p = .001, uncorrected).

Functional Analysis of Oddball Data

Functional Connectivity Analysis of Oddball Data

We applied psychophysiological (PPI) analysis to the preprocessed data of first group of subjects and to assess the functional connectivity of anterior/posterior part of STS during the oddball task (Friston et al., 1997; Gitelman, Penny, Ashburner, & Friston, 2003). Fluctuations of the BOLD signal are contaminated by various types of artifacts and noise, including scanner artifacts and physiological noise. Moreover, many of these signals are spread over the full frequency range due to aliasing errors (Lund, Madsen, Sidaros, Luo, & Nichols, 2006), and therefore cannot be removed completely by high-pass filtering. Therefore, as a first step we performed a voxelwise regression of 15 nuisance regressors from all voxels time-series to filter out these artificial signals. We used signals from cerebrospinal fluid and white matter as well as time-series of movement parameters estimated by the step of realignment (four regions in the white matter from the anterior and posterior parts of each hemisphere and five regions in the lateral ventricles, left and right dorsal parts, left and right posterior horns, and medially around the septum pellucidum were defined for each subject individually in approximately similar locations). The signal from our seeds was computed as the first eigenvariate of the time-series of all voxels included in a given sphere. Two separate PPI models were set for each subject with right anterior or posterior STS as a seed. The design matrix for each subject contained (a) a seed time-series, (b) a time-series of interactions between the seed time-series and stimulus presentation convolved with the HRF, (c) the time-series for the target and 2015 Hogrefe Publishing

Results

Using PPI we discovered, when compared target to frequent stimuli, the anterior area of STS had increased connectivity with mesiotemporal structures bilaterally with its maximum in right hippocampus ( p = .001, FWE corrected) and leftsided putamen ( p = .008, FWE corrected), and the posterior section with left anterior cingulate cortex ( p = .004, FWE corrected). Moreover, we have found decreased connectivity of the anterior area of STS with right dorsolateral prefrontal cortex ( p = .043, FWE corrected) and the posterior area with region encompassing intraparietal sulcus bilaterally – with its maximum in right superior ( p = .046, FWE corrected) and left inferior ( p = .047, FWE corrected) parietal lobule (Table 1). These results are depicted in Figure 3.

Discussion Superior Temporal Sulcus (STS) and Neurocognitive Networks Many ERP and fMRI studies using oddball tasks have provided information about the probable anatomical substrates of visual target detection. In performing a simple oddball task, at least two distinct neurocognitive networks are activated – the network for directed attention (or less Journal of Psychophysiology 2016; Vol. 30(1):29–37

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Figure 3. Functional connectivity. Group t-statistical maps illustrating regions with significant difference between target and frequent stimuli in functional connectivity with (A) anterior and (B) posterior section of STS. Seed located in anterior part of STS (A). Hot and cold colors show regions with, respectively, increased and decreased connectivity with STS when target stimulus occurs. The results are significant at p < .05, FWE corrected for multiple comparisons at the cluster level.

The initial cut for cluster definition was set to T = 3.58. specifically top-down attentional-control processes) and the network for selective processing of target stimuli (Corbetta & Shulman, 2002; Kim, 2014; Shulman et al., 2010).

Results of our previous fMRI analysis during an oddball task confirm the activation of a widely distributed network that includes the STS. This confirms the involvement and important role of STS in a neurocognitive network activated during the oddball task. As such, our results from PPI analyses extend knowledge about this brain structure by providing evidence of its functional connectivity.

Functional connectivity (connectivity analyses of taskbased fMRI data) distinguishes between different patterns of connectivity associated within different cognitive states (Rehme et al., 2013). We performed PPI analyses, revealing a pattern of connectivity that includes the STS, demonstrating the role of this brain structure during the selection of relevant/target stimuli – a task that is necessary for adaptive behavior.

Functional Connectivity We turn now to our observation of the involvement of STS during the oddball task. Specifically, we ask whether this reflects attentional cognitive processes or later evaluative and executive processes.

Attentional Processes Regarding the significant contribution of attentional processes in an oddball task, we should consider the hypothesis Journal of Psychophysiology 2016; Vol. 30(1):29–37

by Corbetta and Shulman (2002) of dorsal frontoparietal network involvement. According to these authors, a dorsal network that includes the IPS and inferior frontal junction/DLPFC is top-down mediated and participates in the voluntary reorientation of attention to new stimuli. Our previous study (Brázdil et al., 2007) confirms DLPFC, IPS, and ACC (part of ventral frontoparietal network) as the crucial structures for target processing. The results revealed strong target-related activity in these structures, predominantly in the right hemisphere. Functional connectivity among these structures, with a leading role of ACC during target detection, has been revealed previously (Brázdil et al., 2007, 2009). A crucial condition for target detection is directed attention that is organized at the level of a distributed large-scale network revolving around these three hub nodes (or local networks). All these brain structures very likely provide slightly different but interactive and complementary types of attentional function (Fan, McCandliss, Fossella, Flombaum, & Posner, 2005; Hopfinger et al., 2000; Mesulam, 1990, 1999; Nebel et al., 2005).

Interestingly, the cooperation of the anterior STS with right DLPFC and the posterior section with regions around IPS was seen to decrease when target stimuli were presented. Our data proved decrease in direct or indirect communication between STS and the structures involved in directed attention (top-down processing involved in both frequent and oddball stimulus processing). The decreased cooperation of STS with right DLPFC and regions around IPS might modulate (in our case decrease) sensitivity of target structures (right DLPFC and bilateral IPS) for new upcoming target stimuli. Nevertheless, direction of connectivity was not tested and so by contrast DLPFC and bilateral IPS might decrease activation of STS during attentional processes. Interestingly we did not detect the influence of STS over a right-sided bottom-up network (more strongly involved in oddball than frequent stimulus processing in comparison with the top-down network).

In addition to fMRI, electrophysiological ERP studies using oddball tasks also confirm large-scale attentional networks (the DLPFC, cingulate cortex, and supramarginal gyrus) that generate P3a potential, evoked by unexpected stimuli and representing orienting response (Halgren et al., 1998; Polich, 2007; Verleger et al., 2005). In this instance STS is not referred as we can see subsequently.

Later Stages of Target Stimulus Processing Aside from the system generating the P3a potential, a second system associated with a later P3b has been proposed as representing cognitive functions related to later stages of target stimulus processing – for example, stimulus recognition, executive functions related to response initiation, and components of attention (Halgren et al., 1998; Polich, 2007; Verleger et al., 2005). The STS, together with the hippocampus, posterior parietal cortex and ventrolateral prefrontal cortex, seem to be the main generators of this P3b potential (Friedman et al., 2001; Halgren et al., 1998; Polich, 2007; Verleger et al., 2005). In agreement with electrophysiological data, our data are in accordance with the 2015 Hogrefe Publishing

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claim about the involvement of anterior STS in later stages of target stimulus processing, through the increased functional connectivity to the bilateral mesiotemporal structures when compared to frequent stimuli. This is also supported

by Blatt, Panday, and Rosene’s (2003) study, in which anatomical connectivity between the upper bank of the STS and the parahippocampal cortex was presented. The functional connectivity of the STS with the parahippocampal gyrus suggests its relation to memory operations.

Noteworthy is our observation of increased functional connectivity of the right posterior part of STS with left ACC when comparing target with frequent stimuli. Importantly, we did not detect a modulation of right-sided ACC

by STS. This surprising interhemispheric modulation might be explained by Elliot and Dolan’s (1998) work. These authors suggest many possible reasons for the left ACC activation, including its involvement in the evaluation of emotion-related aspects of choice. The ventral ACC seems to be relevant for making choices, but dorsal areas of ACC appear to play a role in hypothesis testing and complex executive function (Elliott & Dolan, 1998). If we compare standardized MNI coordinates of the ACC detected in our study, it is in close proximity to the ventral region of ACC associated with choice reported by Elliot and Dolan.

The ventral region is interconnected mainly with other classical limbic structures, including the amygdala (Kunishio & Haber, 1994; Vogt & Pandya, 1987), which is involved typically in emotional processing. In this context it seems that posterior STS activation in part reflects evaluative processing related to the emotional consequences of making a choice. The role of STS in socio-emotional cognition was confirmed in recent study (Jabbi et al., 2015).

Functional Differentiation of the Anterior and Posterior STS Regions Our results demonstrate a differentiation of the anterior and posterior STS regions according to functional connectivity.

This converges with the results of previous studies that demonstrate a differentiation of the STS region in an anterior portion, involved mainly in speech processing, and a posterior portion recruited by cognitive processes like theory of mind, audiovisual integration, motion, and face processing (Beauchamp et al., 2004; Haxby et al., 2000; Ishai et al., 2005; Materna et al., 2008; Pelphrey & Carter, 2008; Thompson et al., 2005). These are different activities that occupy presumably distinct groups of neurons within the STS with their own connectivity patterns. The STS is a large anatomical area, spreading from the temporal pole to temporo-parietal junction. Although its microscopic architecture has been described in detail (Seltzer & Pandya, 1978), in line with anatomical evidence from tracer studies, Hein and Knight (2008) propose that the function of the STS varies depending on the nature of network coactivations with different brain regions. It was suggested that brain activation in response to targets reflects not only areas that are necessary for performing the task correctly, but many other potentially useful regions important for other 2015 Hogrefe Publishing

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cognitive functions; for example, adaptive behavior (Kiehl et al., 2005), incidental learning, and performance monitoring (Halgren et al., 1998). Social cognition can be viewed as a comparison of internal predictions with actual external events (Decety & Lamm, 2007). It is likely that dealing successfully with unexpected events (which is simplified in oddball tasks) includes considering their behavioral and social impact.

Our study revealed a neuronal network in which right STS becomes involved during the visual oddball task, involving a comparison of target to frequent stimuli. Functional connectivity was observed among right STS and only a few select brain areas. These results indicate and support the claim of the multifunctionality of right STS, with connectivity profiles varying among a variety of different tasks.

We can hypothesize that right STS during the detection of events (during oddball) is involved within neuronal networks related to attention, but also to later stages of stimulus processing – for example, memory operations and evaluative processing related to decision making. This hypothesis is supported by the association of STS and mesiotemporal areas or left ACC (increase functional connectivity) and its decreased functional connectivity with structures involved in attentional processes.

Acknowledgments This work was supported by the ‘‘CEITEC – Central European Institute of Technology’’ project (CZ.1.05/1.1.00/ 02.0068) from the European Regional Development Fund.

Ethics and Disclosure Statements Informed consent was obtained from all participants.

The study was approved by the Ethics Committee of the St. Anne’s University’s Hospital.

All authors disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

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Accepted for publication: May 3, 2015 Published online: September 15, 2015

Martin Pail First Department of Neurology Masaryk University School of Medicine and St. Anne’s University Hospital Pekarˇská 53 656 91 Brno Czech Republic Fax +420 543 182-624 E-mail martin.pail@fnusa.cz

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Article

Cold Face Test-Induced Increases in Heart Rate Variability Are Abolished

by Engagement in a Social Cognition Task Frank Iorfino, Gail A. Alvares, Adam J. Guastella, and Daniel S. Quintana Autism Clinic for Translational Research, Brain & Mind Research Institute, University of Sydney, Camperdown, NSW, Australia Abstract. The vagus nerve is a major constituent in the bidirectional relationship between the heart and the prefrontal cortex. This study investigated the role of the vagus in social cognition using the cold face test (facial cooling) to stimulate the vagus nerve and increase prefrontal inhibitory control. Heart Rate Variability (HRV) was measured to index parasympathetic outflow while social cognition ability was tested using the Reading the Mind in the Eyes Test (RMET). Healthy males (n = 25) completed the RMET under two conditions: with and without facial cooling. Results indicated that although facial cooling increased HRV at rest, there was no improvement in the RMET during the facial cooling condition. Interestingly, completing the RMET with facial cooling abolished this increase in HRV, suggesting interference along the vagal reflex arc. These results are consistent with the involvement of a common cortico-subcortical circuit in autonomic and cognitive processes, important for emotion recognition.

Keywords: heart rate variability, social cognition, vagal function, cold face test

Optimal social behavior is characterized by accurate social cognition ability, defined as the interpretation and perception of the intentions and behaviors of others (Fiske & Taylor, 2013). This process involves both central (‘‘brain’’) and autonomic (‘‘heart’’) integration (Thayer & Lane, 2009). While the medial prefrontal cortex (Amodio & Frith, 2006) and amygdala (Davis & Whalen, 2001) are key central brain areas involved in these processes, the role of the Autonomic Nervous System (ANS) in social cognition has increasingly become a focus of investigation (Appelhans & Luecken, 2006; Quintana, Kemp, Alvares, & Guastella, 2013).

The neurovisceral integration model provides an important framework for understanding the relationship between these complex systems in social cognition (Thayer & Lane, 2009). The model proposes that an adaptive neural network – including ‘‘the Central Autonomic Network’’ – is involved in the self-regulation of behavioral, emotional, and cognitive processes (Benarroch, 1993; Thayer & Lane, 2000). Optimal functioning within this network promotes flexible adaptation to changing environmental demands that reflects good emotional regulation, crucial for social cognition. Specifically, the prefrontal cortex exerts inhibitory control over subcortical activity to regulate these processes

Journal of Psychophysiology 2016; Vol. 30(1):38–46 DOI: 10.1027/0269-8803/a000152

in a cortico-subcortical circuit (Thayer & Lane, 2009). This bidirectional pathway includes the myelineated vagus nerve, which exerts parasympathetic control over the sinoatrial node, the heart’s pacemaker, along with the stellate ganglion, which exerts sympathetic control over the sinoatrial node (Benarroch, 1993; Porges, 2003; Thayer & Lane, 2000).

The autonomic output of this network can be indexed via Heart Rate Variability (HRV), a measure of beat-to-beat variation in the heart over time (Berntson et al., 1997).

High-frequency (HF; 0.15–0.4 Hz) heart rate oscillations are strongly associated with cardiovagal activity (Akselrod et al., 1981; Berntson et al., 1997; Camm et al., 1996), and provide an index of parasympathetic cardiac input to the sinoatrial node and efficient cardiac vagal control from subcortical regions (Berntson, Cacioppo, & Quigley, 1993).

Consistent with the neurovisceral integration model, neuroimaging data supports a link between cortico-subcortical areas involved in social and emotional regulation, and HRV. For example, Lane et al. (2009) found that increased regional cerebral blood flow to the ventromedial prefrontal cortex and insula during emotion perception and recollection tasks correlated with increased HF-HRV. Additionally, an association between the dorsomedial prefrontal cortex,

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involved in emotion recognition, and greater autonomic cardiac control was identified by a recent meta-analysis (Thayer, Åhs, Fredrikson, Sollers, & Wager, 2012).

Together, these findings support the relationship between prefrontal cortical activity and parasympathetic cardiac regulation in social cognition, as indexed by HRV. Characterizing the role of the neural circuits involved in social cognition and autonomic function is of further interest given that reduced HRV has been linked to a number of psychiatric disorders with notable social cognition or social functioning impairments (Alvares et al., 2013; Ieda et al., 2014; Kemp et al., 2010; Malpas, Whiteside, & Maling, 1991; Quintana, McGregor, Guastella, Malhi, & Kemp, 2013). Interestingly, HRV changes during social interactions may provide a useful marker for effective social interaction skill in children (Shahrestani, Stewart, Quintana, Hickie, & Guastella, 2014). We recently showed that HRV was positively associated with social cognition performance in a healthy population, whereby those with higher HF-HRV performed better on the Reading the Mind in the Eyes Task (RMET; Quintana, Guastella, Outhred, Hickie, & Kemp, 2012), an established test of ‘‘theory of mind’’ (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001). The RMET requires participants to indicate what emotion an individual is displaying based on viewing photographs of the eye region alone. It has been used to test emotion recognition ability of individuals with autism (Baron-Cohen et al., 2001) and depression (Lee, Harkness, Sabbagh, & Jacobson, 2005). This finding is consistent with the observed social cognition impairments in those psychiatric disorders associated with reduced HRV. However, it is yet to be determined whether autonomic activity, as indexed by HRV, is causally related to poor social cognition.

Previous studies have demonstrated that HRV can be increased experimentally via noninvasive vagal stimulation using the ‘‘cold face test’’ (Khurana & Wu, 2006; La Marca et al., 2011). The cold face test (from here referred to as ‘‘facial cooling’’) involves applying a cold stimulus to the face. This invokes the diving reflex (Butler & Jones, 1997) and then subsequent bradycardia via the trigeminal system. Facial cooling is used to index the integrity of the trigeminal-brainstem-vagal pathways, and interference at any point of this reflex arc can reduce or abolish the characteristic cardiovascular effects (Khurana, Watabiki, Hebel, Toro, & Nelson, 1980). For example, patients with brainstem lesions or peripheral vagus nerve dysfunction exhibit impaired cardiovascular responses to facial cooling (Bannister & Oppenheimer, 1972; Baumert & Sacre, 2013; Benarroch & Parisi, 2000; Kristensson, Olsson, & Sourander, 1971). Importantly, stimulation of the diving reflex does not appear to significantly influence blood pressure (Allen, Shelley, & Boquet, 1992) or respiration rate (Hayashi, Ishihara, Tanaka, Osumi, & Yoshida, 1997), which are important considerations when experimentally manipulating the cardiorespiratory system given their relationships with heart rate (Elstad, Walløe, Chon, & Toska, 2011; Hirsch & Bishop, 1981; Quintana & Heathers, 2014).

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with better executive function, supporting the hypothesized association between HRV and prefrontal activity (Hansen, Johnsen, Sollers, Stenvik, & Thayer, 2004).

Specifically to facial cooling, stimulating the trigeminalbrainstem-vagal system acts on the cortico-subcortical circuit that is involved in cognitive and affective processes (Thayer, Hansen, Saus-Rose, & Johnsen, 2009). However, the influence that experimentally increased cardiovagal activity has on social cognition, and in particular, emotion perception, is yet to be fully examined. We have previously proposed (Guastella et al., 2013) that this pathway may be important for mediating the effect of social-cognitive interventions, such as oxytocin nasal spray, to enhance social cognition and behavior. More direct evidence would provide a clearer understanding of autonomic and central functions mediating optimal social behavior, a crucial association given the functional impact of social cognition impairments in psychiatric illness (Bauminger, 2002; Couture, Penn, & Roberts, 2006).

Thus, we aimed to examine whether increasing Parasympathetic Nervous System (PNS) activity improves social cognition performance in healthy males. We have selected a restricted population group to maintain high homogeneity (i.e., all male, within a narrow age range, exhibiting average physical and mental health), to investigate these psychophysiological phenomenon, free of known confounds on autonomic physiology. Firstly, we hypothesized that facial cooling would increase PNS outflow to the heart, indexed by greater HRV, in comparison to no facial cooling. Secondly, we predicted that this increase in PNS function during facial cooling would result in better performance on the social cognition task, relative to no facial cooling.

Material and Methods Participants Twenty-five male volunteers were recruited from advertisements placed in local classifieds. The University of Sydney Human Research Ethics Committee provided approval (Project Number: 2013/641). Exclusion criteria included: current or history of psychiatric illness determined by the Structured Clinical Interview for DSM-IV (First, Spitzer, Gibbon, & Williams, 2007), current use of any medication, and self-reported major physical health or heart problems.

Participants were asked to refrain from alcohol and illicit substances 24 hr prior to testing, and from smoking, food, and drink (including caffeine) 3 hr before testing, to reduce the influence of such substances on autonomic physiology.

Instruments Heart Rate Variability All resting baseline heart rate measurements were recorded when participants were seated in a relaxed position with Journal of Psychophysiology 2016; Vol. 30(1):38–46

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their eye gaze directed at a stationary dot stimulus presented on a computer monitor. The Polar RS800CX (Polar Electro Oy, Kempele, Finland) heart rate monitoring system was used to measure Inter-Beat Intervals (IBI) for the entire experiment (approximately 45 min) at 1,000 Hz. The monitor wirelessly collected HRV data via a two-lead chest strap worn by the participants. The validity of Polar monitors to measure R-R intervals has been reported as comparable to electrocardiogram (ECG) with intraclass correlations (95% confidence interval values were > 0.75) and BlandAltman limits of agreement demonstrating exceptional agreement between the two instruments (Weippert et al., 2010).

Reading the Mind in the Eyes Test (RMET) Participants completed a split version of the original 36-item RMET (Baron-Cohen et al., 2001). This split version, created for this study to reduce the impact of practice effects with repeated administrations, contains the original 36 images of the eye region of different faces with the corresponding four response options for each image (e.g., panicked). These were split into two versions with 18 items per set that took approximately 4 min to complete. Items in each set were matched according to the gender of face and weighted difficulty of the item as calculated from normative data from the original test (Quintana et al., 2012).

Split half reliability was moderate at .563, comparable to internal consistency values reported for the RMET (Vellante et al., 2013).

Facial Cooling (FC) A FC headband, made of thin cotton, was strapped to the participant’s head with a pouch (24 · 11 cm) containing a gel ice pack (‘‘Freeza Pak’’, Jackeroo, KMART Australia) covering the entire forehead. The ice packs (23 · 10.5 cm) were kept at 0–1 C for the FC (La Marca et al., 2011) condition and at room temperature (22–23 C) for the ‘‘No Facial Cooling’’ (NFC) condition. Both conditions used the same headband. The time course for facial cooling effects on heart rate occurs relatively quickly, approximately 5–10 s, with maximum effects occurring at approximately 35–50 s and a slow decline in effect following the stimulus removal (Khurana & Wu, 2006). Since these effects occur quickly and the effects are sustained, the length of HRV recordings (2 min) was deemed sufficient to assess changes in HRV.

Factors Influencing Heart Rate Variability (HRV) To assess factors that influence HRV, including smoking (Barutcu et al., 2005), depression (Carney et al., 2001;

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Kemp et al., 2010), anxiety (Thayer, Friedman, & Borkovec, 1996), stress (Dishman et al., 2000), and alcohol consumption (Quintana, McGregor, et al., 2013; Thayer, Hall, Sollers, & Fischer, 2006), a separate battery of questionnaires were completed. This included the Depression Anxiety and Stress Scale (DASS-21; Lovibond & Lovibond, 1995), Alcohol Use Disorders Identification Test (AUDIT; Babor, Korner, Wilber, & Good, 1987), and the State-Trait Anxiety Inventory (STAI-Y2; Spielberger, Gorsuch, Lushene, & Vagg, 1983). Participants also selfreported smoking habits. Body Mass Index (BMI) was calculated using the standard equation of mass (kg) divided by their height (m) squared.

The Visual Analogue Scale-pain (VAS; Duncan, Bushnell, & Lavigne, 1989) and the State-Trait Anxiety Inventory 6 (STAI-6; Marteau & Bekker, 1992) were used before and after every HRV recording, as brief measures to assess any changes in state anxiety and/or head pain due to facial cooling. The STAI-6 has good reliability (a = .82) and concurrent validity with the 6-item mean comparable to the full 20-item STAI-Y1 (Marteau & Bekker, 1992).

Moreover, the validity of the shortened STAI has been reviewed and deemed specifically appropriate for research settings (Kruyen, Emons, & Sijtsma, 2013).

Procedure All participants were tested in the same room between the hours of 12:00 and 19:00. A room temperature of 22–23 C was consistently maintained for all experiments.

See Figure 1 for a timeline of the experiment’s procedures.

Upon completion of the questionnaires, participants’ height and weight measurements were taken to calculate BMI. Participants were also asked to empty their bladder (Mehnert, Knapp, Mueller, Reitz, & Schurch, 2009) before being fitted with the Polar chest strap, which remained on the participant for the duration of testing. Baseline HRV measurements were recorded for 10 min in a seated rest position consisting of 2-min rest with no IBI recording, 2-min baseline recording, 1-min break, 2-min NFC recording, 1 min break, and 2-min FC recording. The break periods were utilized to apply the relevant facial cooling headband to the forehead of the participant. Participants then completed the RMET under both conditions (FC and NFC), presented in a computer-generated randomized order. Participants completed the STAI-6 and VAS before and after every HRV recording.

Data Analysis Raw IBI data from the Polar device was extracted into Kubios (version 2.0, 2008, Biosignal Analysis and Medical Imaging Group, University of Kuopio, Finland, MATLAB).

All raw data was filtered using a low automatic filter and visually inspected for artifacts by the investigator (FI),

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Questionnaires & height and weight measurement

Baseline HRV measurements

RMET FC

Consisting of (in order): 1. 2-min rest 2. 2-min BL 3. 1-min break 4. 2-min NFC 5. 1-min break 6. 2-min FC

before calculating average HR from R-R intervals and HF-ab (0.15–0.4 Hz; absolute units) using the Fast Fourier transform. Preliminary analyses were conducted to ensure the assumptions of normality, linearity, multicollinearity, and homoscedasticity were not violated. According to the Kolmogorov-Smirnov statistic, HRV was positively skewed ( p = .02). Thus, a logarithmic transformation to base-10 was then applied after which no significant skew was evident ( p = .20). Pearson’s product moment correlations were used to examine the relationships between RMET scores without facial cooling, HRV, age, BMI, depression, anxiety, stress, alcohol use, and trait anxiety. A 2 (FC and NFC) · 2 (rest and RMET) repeated-measures ANOVA was employed to investigate the main effect and interaction of facial cooling and task conditions, on HRV. A follow-up test was conducted to investigate the effect of task condition on HRV during FC. Partial eta Squared (g2p) was used as a measure of effect size (.01 = small, .06 = medium, and .14 = large) (Cohen, 1973). A paired samples t-test was used to compare RMET performance during testing for both conditions. Two separate 2 (FC and NFC) · 2 (rest and RMET) repeated-measures ANOVAs were employed to assess the changes in the VAS and STAI-6 scores after facial cooling and task conditions.

Results Participants Participant characteristics, including heart rate values, are presented in Table 1. In addition to the SCID, which ruled out the presence of psychiatric illness, the mean scores of the DASS, STAI, and AUDIT were below clinical cutoffs.

Pearson’s bivariate correlations indicate that all covariates had no significant correlation with HF-HRV, while significant correlations were identified between RMET during NFC and DASS-A scores (Table 2).

Effect of Facial Cooling and Task Conditions on Heart Rate Variability A significant overall main effect of facial cooling on HRV was found. HRV during the FC condition was significantly 2015 Hogrefe Publishing

RMET NFC

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Figure 1. A timeline depicting the different phases of the experiment.

BL = Baseline, HRV = Heart Rate Variability, FC = Facial Cooling condition, NFC = No Facial Cooling condition.

Randomized order

Table 1. Participant characteristics (N = 25) Variable Age (range) BMI Depression Anxiety Stress Alcohol use Trait anxiety Smoker (yes/no) HR FC HR NFC HR RMET FC HR RMET NFC

Mean

SD

23.96 (20–30 years) 24.18 1.84 1.20 3.76 8.92 33.48 (1/24) 63.91 66.48 65.08 65.56

2.19 3.28 1.93 1.35 2.48 4.09 6.63 5.87 5.82 5.42 5.13

Notes. BMI = Body Mass Index, HR FC = Heart Rate during Facial Cooling at rest, HR NFC = Heart Rate during No Facial Cooling at rest, HR RMET FC = Heart Rate during Facial Cooling while completing RMET, HR RMET NFC = Heart Rate during No Facial Cooling while completing RMET.

higher than that of the NFC, F(1, 24) = 13.88, p = .001, g2p = .37, averaged across task condition. There was also a significant interaction effect between the facial cooling condition and task condition, F(1, 24) = 6.49, p = .018, g2p = .21. At baseline, HRV under FC (M = 3.06, SD = 0.36) was significantly higher than NFC (M = 2.82, SD = 0.44). However, during the RMET, HRV under FC (M = 2.91, SD = 0.36) was not significantly different to NFC (M = 2.88, SD = 0.36). HRV under FC at baseline was significantly greater than during the RMET, F(1, 24) = 7.217, p = .01, g2p = .23. Thus, at rest FC significantly increased HRV when compared to NFC; this effect was absent during the RMET condition (Figure 2).

Effect of Facial Cooling on Social Cognition Performance No significant differences in RMET scores between FC (M = 14.20, SD = 2.20) and NFC (M = 14.24, SD = 2.19) conditions, t(24) = 0.08, p = .94, were found.

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Table 2. Correlation coefficients among participant measures (N = 25) Age BMI Depression Anxiety Stress Alcohol use Trait Anxiety HF-HRV BL RMET NFC score

.150 .189 .236 .025 .246 .042 .221 .192

BMI .118 .227 .049 .016 .051 .096 .097

Depression

.411* .516** .252 .569** .029 .357

Anxiety

Stress

.152 .326 .160 .028 .521**

.216 .244 .038 .029

Alcohol use

.092 .372 .027

Trait anxiety

.245 .034

HF-HRV BL

.112

Notes. BMI = Body Mass Index, HF-HRV BL = High-frequency HRV at baseline, RMET NFC = RMET score during No Facial Cooling. *p < .05. **p < .01.

Effect of Facial Cooling on State Anxiety and Pain In regard to STAI-6 scores, there was a significant overall main effect of facial cooling, F(1, 24) = 12.68, p < .00, g2p = .35, and task condition, F(1, 24) = 5.49, p = .28, g2p = .19, on state anxiety. This indicated state anxiety was lower following FC, and was lower following rest compared to the RMET. However, a significant interaction effect, F(1, 24) = 4.36, p = .05, g2p = .15, indicated that the decreases in state anxiety during facial cooling were greatest at baseline (M = 0.84, SD = 1.41), with no change observed following the RMET (M = 0.00, SD = 0.76). In regard to the pain scores, a significant main effect was found in the FC condition, F(1, 24) = 17.71, p < .00, g2p = .43, indicating that there was a significantly greater increase in pain ratings after FC compared to NFC.

Discussion This study demonstrates that HRV can be increased experimentally by facial cooling. While there was no evidence to suggest that facial cooling subsequently altered social cognition ability, the results suggest that the effect of facial cooling on HRV is moderated by engagement in the social cognition task. When participants in the current study were completing the RMET, the hypothesized increases in HRV under facial cooling were not observed. This cannot be explained by a decrease in HRV while completing the task, as HRV during task completion was comparable to HRV during no facial cooling at rest. These results are important for understanding the role of HRV in the relationship between ANS function and central processes integral to optimal social cognition.

Increased vagal activity is primarily responsible for the increases in HRV observed during facial cooling at rest (Ryan, Hollenberg, Harvey, & Gwynn, 1976). The application of the cold stimulus stimulates trigeminal afferent pathways to central areas, which exert greater inhibitory control over vagal efferent pathways from the brainstem to the heart (Khurana et al., 1980). The increases in HRV observed in Journal of Psychophysiology 2016; Vol. 30(1):38â&#x20AC;&#x201C;46

Figure 2. The effect of facial cooling on HRV; at baseline and during the RMET. Error bars depict standard error of the mean. RMET = Reading the mind in the eyes task; HF = High frequency. For heart rate values for each condition see Table 1. *p < .05. ***p < .001.

this study are consistent with the expected intact trigeminal-brainstem-vagal function of the participants recruited for the present study. Since facial cooling increased vagally mediated HRV, our previous work (Quintana et al., 2012) linking high HRV with better social cognition performance would suggest that participants should have improved on the RMET during facial cooling. Despite this expectation, no such effect was observed in the present study as the facial cooling manipulation failed to successfully increase HRV during the social cognition tasks. Thus, we were unable to test whether increases in HRV during the social cognition test causally result in improved social cognition performance.

While facial cooling increased HRV at rest, no effect of facial cooling was observed during the social cognition task. An abolished cardiovascular effect during facial cooling could imply interference along the trigeminalbrainstem-vagal pathway has occurred (Khurana et al., 1980). In the present study, any interference is unlikely to be attributed to lesions along afferent and/or efferent pathways as these deficits were absent at rest, thus interference may have occurred at another point along this reflex arc.

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This explanation is consistent with the inhibitory hypothesis of the neurovisceral integration model, which proposes that a common cortico-subcortical circuit is involved in psychological and physiological regulation (Thayer et al., 2009).

In this view, facial cooling at rest increased the vagal activity in this inhibitory circuit; however, completing the RMET abolished this effect, and the subsequent increases in vagally mediated HRV. This explanation implicates common resources involved in autonomic control and cognitive ability, which is consistent with the positive relationship observed between vagally mediated HRV and cognitive function (Hansen et al., 2004). Further investigations should characterize the specific effect that the social element of the task is having on HRV during facial cooling by employing a range of cognition tasks. This would delineate whether the effect of facial cooling on the hypothesized corticosubcortical circuit is specific to social cognition or more generally to broader cognitive processes.

An alternative explanation for the lack of an increase in HRV during facial cooling while completing the RMET may be habituation to the cold face test. Some evidence has shown that repeated facial immersion procedures or cold pressor tasks, similar to facial cooling used in this experiment, lead to attenuated responses (Zbro_zyna & Westwood, 1992), but not uniformly (Durel et al., 1993).

While, this may account for the differences in HF-HRV observed in the second application of the facial cooling mask, the habituation described previously occurred after repeated facial immersion and warmer facial cooling temperatures. Since the present study applied a more intense stimulus, and for shortened period of time, any potential habituation effects would be more limited.

The effect of cognitive load may have prevented an increase in HRV while completing the RMET. Evidence suggests that performing advanced neuropsychological tests for a prolonged period (30 min or greater) to induce mental fatigue can reduce parasympathetic activity (Mizuno et al., 2011; Tanaka, Mizuno, Tajima, Sasabe, & Watanabe, 2009). However, it is unlikely that the use of the RMET in this present study would induce the same level of mental fatigue as there was no time limit imposed on task completion, and we used a shortened version of the RMET. Nevertheless, these findings provide further insight into the complex cortico-subcortical processes involved in autonomic regulation, which is critical to understanding the relationship between these neural mechanisms and social dysfunction in psychiatric illnesses.

This study has some limitations. Firstly, we did not measure respiration rate, a factor identified as having an indirect effect on HRV (Quintana & Heathers, 2014), and a potential confound since breathing patterns may have been affected by facial cooling (Brick, 1966). However, others have shown that the dive reflex does not affect respiration (Hayashi et al., 1997; Stemper, Hilz, Rauhut, & Neundörfer, 2002), and that heart rate during facial cooling with or without breath holding does not differ (Kawakami, Natelson, & DuBois, 1967). It will be important for future research to monitor and analyze respiratory frequency to clarify the influence of the dive reflex on respiration and its

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relationship with HRV. Secondly, we identified significant effects of facial cooling on pain and state anxiety, sources of potential confound. Small decreases in subjective state anxiety ratings were observed following facial cooling.

Such an effect is expected considering that increases in HRV are commonly associated with ‘‘rest and digest,’’ low arousal, and feeling calm (Goldie, McGregor, & Murphy, 2010). Given that this effect is a result of the facial cooling manipulation, we could conclude that facial cooling is responsible for these observed state anxiety changes.

While the alternate relationship could be argued (i.e., reduced state anxiety leads to increased HRV), significant increases in head pain during facial cooling suggest otherwise. As previous research has suggested that increased pain actually leads to reductions in HRV (Appelhans & Luecken, 2008), we suggest that the state anxiety effects are a result of the facial cooling and increased HRV. While the effects of anxiety and pain could have been controlled for in the analysis, the small size of this study is a notable limitation, and was the primary reason for excluding pain and anxiety covariates in the analysis of HRV during the RMET task (Van Breukelen & Van Dijk, 2007).

A number of methodological strengths add to the significance of the present study. The use of a repeated-measures design, high homogeneity of the included sample (i.e., participants were male, within a narrow age range, exhibiting good physical and mental health), and strict control of a number of known influences on human physiology (i.e., emptied bladder, no food, drink, or substances prior to study, testing during a similar time of day), increase our confidence in the observed effects of facial cooling on HRV and subsequent discussion. By implementing such control, we have demonstrated the reliable use of a noninvasive cold face test to increase HRV at rest, and provided further insight into the important cortico-subcortical mechanisms responsible for autonomic cardiac control and socio-cognitive processes.

In summary, this study demonstrates that facial cooling increases HRV. The observed increase was abolished by completing a social-cognitive task, providing indirect evidence for the involvement of a common cortico-subcortical circuit involved in autonomic regulation and psychological processes. Although further investigations are warranted to clarify this finding in other samples and with other tests of social cognition, this preliminary evidence emphasizes the importance of considering the mutual action of the heart and brain in an individual’s ability to respond to changing environmental demands (Thayer & Lane, 2009). Understanding these mechanisms is crucial for determining the underlying causes of social cognition deficits that may be important for directing targeted interventions to the affected areas.

Acknowledgments Daniel S. Quintana is now at the Institute of Clinical Medicine, University of Oslo, Oslo, Norway. Frank Iorfino is now supported by the NHMRC CRE Optymise scholarship.

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Ethics and Disclosure Statements All participants of the study provided written informed consent and the study was approved by The University of Sydney Human Research Ethics Committee.

All authors/disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

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Accepted for publication: May 1, 2015 Published online: September 15, 2015

Adam J. Guastella Brain & Mind Research Institute University of Sydney 100 Mallet street Camperdown, Sydney, NSW 2050 Australia Tel. +61 2 9351-0539 Fax +61 2 9351-0731 E-mail adam.guastella@sydney.edu.au

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