Journal of Psychophysiology 2020 by Hogrefe

Volume 34 / Number 1 / 2020

Journal of

Psychophysiology

Editor-in-Chief Michael Falkenstein Editorial Board Markus Breimhorst Tavis Campbell Ritobrato Datta Nicola Ferdinand Patrick Gajewski Edward Golob David R. Herring Sien Hu Julian Koenig Cristina Ottaviani Patrick Papart Daniel S. Quintana Walter Sannita Henrique Sequeira Juliana Yordanova

An International Journal

Contents Editorial

Circadian Rhythms, Sleep, and the Autonomic Nervous System: A Position Paper Sergio Garbarino, Paola Lanteri, Nicole R. Feeling, Marc N. Jarczok, Daniel S. Quintana, Julian Koenig, and Walter G. Sannita

Articles

Fatty Fish Intervention and Psychophysiological Responses to Mental Workload in Forensic Inpatients: A Randomized Control Trial Anita L. Hansen, Gina Ambroziak, David Thornton, Lisbeth Dahl, Helge Molde, and Bjørn Grung

The Evaluation of Creative Ideas in Older and Younger Adults: A View From sLORETA Study Evgeniya Yu. Privodnova, Nina V. Volf, and Gennady G. Knyazev

Changes in the Electroencephalographic Activity in Response to Odors Produced by Organic Compounds Minju Kim, Jieun Song, Kosuke Nishi, Kandhasamy Sowndhararajan, and Songmun Kim

Some Compliments (and Insults) Are More Heartfelt: High Cardiac Awareness Increases P2 Amplitudes to Emotional Verbal Stimuli That Involve the Body Erik M. Benau and Ruth Ann Atchley

The Effect of the Menstrual Cycle on Daily Measures of Heart Rate Variability in Athletic Women Renée L. Kokts-Porietis, Nathaniel R. Minichiello, and Patricia K. Doyle-Baker

Journal of Psychophysiology (2020), 34(1)

Ó 2020 Hogrefe Publishing

Editorial Circadian Rhythms, Sleep, and the Autonomic Nervous System A Position Paper Sergio Garbarino1, Paola Lanteri2, Nicole R. Feeling3, Marc N. Jarczok4, Daniel S. Quintana5,6, Julian Koenig7,8, and Walter G. Sannita1 1

Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences, Polyclinic Hospital San Martino IRCCS, University of Genova, Italy

Department of Medical and Surgery, Neuroscience, Rehabilitation – Continuity of Care, Neurophysiology Center, Institute G. Gaslini, Genova, Italy

Department of Psychology, The Ohio State University, Columbus, OH, USA

Clinic for Psychosomatic Medicine and Psychotherapy, Ulm University, Germany

Brain and Mind Centre, Central Clinical School, Sydney Medical School, University of Sydney, Camperdown, Australia

NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Norway

University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland

Section for Translational Psychobiology in Child and Adolescent Psychiatry, Department of Child and Adolescent Psychiatry, Centre for Psychosocial Medicine, University of Heidelberg, Germany

Circadian rhythms play an essential role in the homeostatic regulation and functional balance of temperature, cardiovascular and metabolic adaptation, feeding, reproduction, development and maturation, hormonal status/ neurohormonal interaction, and the sleep-wakefulness cycle that guarantee survival, adaptation, efficient action in everyday’s life, and well-being. In this framework, the autonomic nervous system (ANS) mediates a complex, highly differentiated network of distributed organs and biological sensors and compensates for internal and external needs, thus contributing in the processes that collectively sustain the internal environment constancy. Neuroimaging indicates direct/indirect functional interactions between autonomic control and activation in brain structures that are involved in higher brain functions, including conscious processes; a model network (the Central Autonomic Network) has been proposed to describe these interactions (Berntson & Cacioppo, 2004; Friedman, 2007; Hagemann, Waldstein, & Thayer, 2003; Riganello, Dolce, & Sannita, 2012; Thayer & Lane, 2009; Thayer & Sternberg, 2006). Disorders at the central or cellular level and peculiar lifestyles can impair (or disarrange) the respective rhythms with respect to the circadian organization and cause medical, subjective, professional, or behavioral changes ranging Ó 2019 Hogrefe Publishing

from functional to medical relevance (Garbarino, Lanteri, Durando, Magnavita, & Sannita, 2016).

Circadian Rhythms and the Brain In human beings, the suprachiasmatic nuclei (SCN) and clock genes (intrinsically cyclic and regulated by inner and environmental factors) cooperate in modulating the dorsal nucleus of the hypothalamus, which in turn modulates the lateral hypothalamic (wakefulness, feeding) and paraventricular (corticosteroids) nuclei, and the middle preoptic area (temperature); the ventrolateral preoptic nucleus triggers sleep. Time regulation is achieved by genetic, cellular, and neural mechanisms that act directly on neuro-secretory neurons or set phase to the clock genes that regulate circadian functions at cellular levels. Thermoregulation entrains circadian rhythms of clock gene expression and changes in transcription processes setting the clock. The light-dark cycle contributes to homeostatic processes via retinohypothalamic innervation to the SCN and by regulating factors such as the dopamine/melatonin/vitamin B12 secretion and interaction, and the serotonin concentration. Circadian rhythms mediate the activity of various brain structures, Journal of Psychophysiology (2020), 34(1), 1–9 https://doi.org/10.1027/0269-8803/a000236

including the frontal cortex, thalamic, and hypothalamic regions, and the locus coeruleus. The SCN clockwork also influences peripheral, non-neural tissues by tissue-specific cycles of gene expression that underpin circadian organization (Dunlap, 2006; Hastings, Reddy, & Maywood, 2003; Sannita, 2006; Saper, Scammell, & Lu, 2005; Silver & Kriegsfeld, 2014; Touitou, Reinberg, & Touitou, 2017). Sleep is a major expression of the physiological cyclic arrangement and is crucial for strategies of adaptation. Sleep is an active process resulting of a functional reorganization of brain functions and involvement of the brainstem nuclei projecting diffusely to the cortex with cholinergic (pedunculopontine and laterodorsal nuclei), histaminergic (tuberomamillary nucleus), dopaminergic (locus ceruleus), and serotoninergic (median raphe) neuromediation. These key components of the ascending brainstem system sustain wakefulness and are counter-modulated by the preoptic ventrolateral nucleus, with inhibitory galaninergic and GABAergic neurotransmission inducing sleep. As a result of this complex organization and its adaptation to inner or outer changes, brain function varies during daytime also depending on sleep habits, patterns, deprivation or debt, as well as on metabolism, hormone delivery and neurohormonal interaction, and neurotransmission that are also cyclic or rhythmic over 24 hr (De Valck, Cluydts, & Pirrera, 2004; Sannita, 2006; Saper et al., 2005; Siegel, 2005).

Autonomic Nervous System Function and Sleep The sympathetic (SNS) and parasympathetic (PNS) nervous systems innervate all organs of the body. The vagus nerve exerts mainly an inhibitory control (e.g., decelerates the heart rate) and is crucial in rest and recovery as well as in the sleep-wakefulness organization. Vagal activity can be readily quantified by measuring heart rate variability (HRV) (Table 1; Riganello, Garbarino, & Sannita, 2012, 2014). HRV is a non-invasive measure of variations in the heart rate (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996) easily recorded via electrocardiogram, which is routinely included in overnight polysomnography measures of sleep. As acetylcholine (ACh) – the main neurotransmitter underlying vagal activity – has a quicker action than its sympathetic antagonists (norepinephrine), fast changes in the HRV time series reflect PNS activity in regulating heart rate (Levy, 1990). Typically, greater vagally mediated HRV is associated with preferable health outcomes. The PNS is theorized to be broadly involved in regulating circadian rhythms in association with sleep (Meerlo, Sgoifo, Journal of Psychophysiology (2020), 34(1), 1–9

Editorial

& Suchecki, 2008). Vagal activity fluctuates in a pattern of diurnal variation, approximating the solar day, with peak levels at nighttime (Jarczok, Li, Mauss, Fischer, & Thayer, 2013; Li et al., 2011). A blunted nighttime increase in vagal activity is associated with unfavorable health outcomes such as a hyperglycemic state, elevated pro-inflammatory cytokines, and depressive symptoms (Jarczok, König, Mauss, & Thayer, 2014; Jarczok et al., 2013, 2018), all of which have been shown to be associated with poor sleep (Karatsoreos & McEwen, 2014). The circadian variation pattern (CVP) of vagal activity (Figure 1) can be easily outlined using cosine regression analysis given the three major parameters of CVP: the rhythm adjusted 24 mean (MESOR), the amplitude with lower values indicating diminished day/night variability as well as the timing of the peak (acrophase) usually present in the early morning (Li et al., 2011; Refinetti, Lissen, & Halberg, 2007). In general, the MESOR and amplitude diminish by age and morbidity (Jarczok et al., 2013; Li et al., 2011) and show overall sex differences comparable to measures of resting state HRV (Koenig & Thayer, 2016). Non-REM sleep is classified as light sleep (stages 1 and 2) and slow-wave sleep (SWS, stages 3 and 4). In normal sleepers, vagal activity indexed by HRV increases during sleep, peaks prior to awakening in the morning, and quickly decreases after waking (Huikuri et al., 1990; Vanoli et al., 1995). It reportedly decreases during non-REM and increases during REM sleep (Tobaldini et al., 2013; Vanoli et al., 1995). Chemical blocking of the PNS by dosing laboratory rodents with atropine subcutaneously for 14 days altered the circadian rhythms of blood pressure and heart rate, leading to increased heart rate and blood pressure during daylight (Makino et al., 1997). In humans, psychological stress has been associated with decreased PNS modulation during sleep, which in turn has been associated with increased waking during sleep and less deep sleep (Hall et al., 2004). Two main reviews on HRV and insomnia, based mostly on studies such as those by Bonnet and Arand (1998), Spiegelhalder and colleagues (Spiegelhalder et al. (2011), and Yang and colleagues (2011) showing impaired autonomic cardiac modulation in insomnia, resulted in partly conflicting conclusions (Stein & Pu, 2012; Tobaldini et al., 2013). Tobaldini and colleagues drew evidence of altered ANS patterns in insomniacs, specifically a predominance of SNS activity during sleep and wakefulness. Stein and Pu (2012), however, also acknowledged a study by Fang, Huang, Yang, and Tsai (2008) reporting no differences on HRV between insomniacs and healthy controls. The authors ultimately concluded that there is mixed evidence on differences in cardiovascular activity in insomniacs and controls, with some evidence for altered HRV in insomniacs. Finally, Dodds, Miller, Kyle, Marshall, and Gordon Ó 2019 Hogrefe Publishing

Editorial

Table 1. Heart rate variability, methodologies, and application (adapted from Riganello et al., 2012, 2014) Heart rate variability (HRV) Methods of analysis

HRV spectral profile main frequency components (linear analyses)

HRV spectral profile (nonlinear analyses) Applications

HRV measures reflect the action of physiological factors modulating the heart rhythm and its adaptation to sympathetic/parasympathetic conditions or changes. In the absence of cardiac disorders, stimulus- or condition-related changes are in the range of physiological variability and are seldom detectable without appropriate analyses of the heart tachogram in the time or frequency domains or by geometrical or nonlinear methods. HRV changes are measured in the time domain by calculating descriptors derived from statistical operations on the RR intervals; fast Fourier transform (FFT) or autoregressive models are of common use in the frequency analyses. – High: 0.15–0.5 Hz, mainly associated with activation of the parasympathetic nervous system; – Low: 0.04–0.15 Hz, reflecting contributions from both the parasympathetic and sympathetic system as well as baroreflex activity; and – Very low: < 0.04 Hz, reflecting temperature, vasomotor, hormonal, and metabolic regulation. Provide tools for analysis in the entropy domain (such as the simple or approximate entropy) which are thought to describe the complexity, irregularity or randomness of HRV and its changes. – Disorders of the cardiovascular and metabolic systems; – Sleep disorders; – Neuroendocrine modulation; – Epilepsy; – Brain damage; – Psychiatry (e.g., anxiety, depression), impaired emotion-specific processing, personality or communication disorders; – Sport medicine; – Brain injury, severe disorders of consciousness.

far that approximately 24 hr of sleep deprivation alter HRV compared to controls (Burgess, Trinder, Kim, & Luke, 1997; Pagani et al., 2009; Quintana et al., 2017); longer periods of deprivation (e.g., 36–60 hr) appear to reduce HRV (Glos, Fietze, Blau, Baumann, & Penzel, 2014; Sauvet et al., 2010; Vaara, Kyröläinen, Koivu, Tulppo, & Finni, 2009), suggesting that longer periods of sleep deprivation are needed to influence PNS activity. Altogether, these results suggest that alterations of HRV that are associated with insomnia might be better explained by secondary factors. Figure 1. Circadian variation pattern in vagal activity indexed by heart rate variability. RMSSD = root mean square of successive difference in milliseconds, a commonly used time-domain measure of vagally mediated heart rate variability.

(2017) concluded that the current research does not point to a relation of insomnia and HRV (Dodds et al., 2017). This conclusion is based on the insufficient replication of older studies (e.g., by Bonnet & Arand, 1998) and the considerable methodological differences between studies assessing HRV in sleep (specifically regarding their inclusion criteria, study protocols, HRV measures and sleep assessment, HRV processing techniques, and outcome reporting). Longitudinal cohort studies with 24-hour recordings have been recommended. Insomnia is a possible model to understand the role of the PNS in sleep deprivation, but teasing apart its secondary effects (e.g., psychological distress) on HRV can be difficult. An alternative approach is to experimentally induce sleep deprivation in healthy participants. Research has shown thus Ó 2019 Hogrefe Publishing

Sleep Patterns and Aging Sleep patterns change ontogenetically and with age. The SCN clock develops early during gestation and is responsive to light at very premature stages, but ultradian rhythms predominate at birth, and the sleep-wakefulness cycle develops later in childhood (Heraghty, Hilliard, Henderson, & Fleming, 2008). During adolescence, sleep onset is delayed from around 8.00–8.30 p.m. (in preadolescents) to about 10.30–11.00 p.m. (Crowley, Acebo, & Carskadon, 2007), conceivably as a result of developmental changes in the circadian timing system (Hagenauer, Perryman, Lee, & Carskadon, 2009). Neuronal connectivity remodeling (including sleep, cognition, emotion, and social skills, Maywood, Mrosovsky, Field, & Hastings, 1999) then follows, and aberrant remodeling may result in cognitive and behavioral defects only detectable at full maturation. The chronotype changes progressively during adulthood, with the sleep quality worsening from around 55–65 years Journal of Psychophysiology (2020), 34(1), 1–9

of age. In elderly without sleep disorders, the circadian indicators of the sleep-wakefulness rhythm (sleep onset and offset), melatonin (onset), core body temperature (acrophase), and cortisol (acrophase) appear active earlier during the day (Hagenauer et al., 2009). Circadian abnormalities increase with age, are common in patients with neurodegenerative disorders such as Alzheimer’s disease, and are related to the disease severity (Kondratova & Kondratov, 2012). Changes related to the circadian cycle have been observed in the occurrence of neurological indices of responsiveness relied upon in the classification, monitoring, and prediction of outcome of severe disorders of consciousness (Candelieri, Cortese, Dolce, Riganello, & Sannita, 2011; Giacino et al., 2002; Laureys et al., 2010; Riganello et al., 2015). Similar, ANS activity changes with age. HRV decreases with increasing age in adolescents (de Zambotti et al., 2017) and adults; adolescents typically show greater HRV compared to adults (Antelmi et al., 2004). A study on the CVP of cardiac autonomic modulation in a population aged over 45 years showed an average decrement in the MESOR of 2.6 ms, based on the root mean square of successive differences (RMSSD) – a popular time-domain measure of vagally mediated HRV – per age decade (Li et al., 2011). Unpublished data by our group in a sample of healthy employees (N = 5,538) show a sex-adjusted decrement of 7.7 ms (MESOR) and 2.8 ms (amplitude) per decade, using multivariate regression models. The average decline per 5-year age group became smaller in older age groups. It has been suggested that decreased HRV in the elderly is associated with decreased SWS duration (Brandenberger, Viola, Ehrhart, & Charloux, 2003). However, further studies addressing the association between age-related changes in ANS function and sleep outcomes are warranted.

Autonomic Function, Innate Immune Responses, and Inflammation The ANS is further involved in the regulation of innate immune responses and inflammation through neuroendocrine mechanisms (Maier, Goehler, Fleshner, & Watkins, 1998) and qualifies as a centrally integrated neural reflex (Pavlov & Tracey, 2012). The so-called cholinergic antiinflammatory reflex is based on an afferent vagal signaling pathway activated by pathogens or cytokines as well as an efferent vagally mediated pathway that regulates inflammation and pro-inflammatory cytokine release from acetylcholine-synthesizing T-cells such as interleukin 6 (IL-6). Accordingly, plasma levels of pro-inflammatory cytokines increase in cervical or subdiaphragmatic vagotomy, while vagal stimulation and acetylcholine decrease these Journal of Psychophysiology (2020), 34(1), 1–9

Editorial

cytokines (Maier et al., 1998; Pavlov & Tracey, 2012). This neural reflex has much shorter response times compared to humoral anti-inflammatory pathways such as cortisol release via the hypothalamic–pituitary–adrenal axis, as shown in a vagus nerve stimulation study (Borovikova et al., 2000). A recent meta-analysis reports IL-6 to also demonstrate a diurnal variation pattern with dual zenits around 1–2 a.m. and 5 p.m. (Nilsonne, Lekander, Åkerstedt, Axelsson, & Ingre, 2016). Interestingly, this pleiotropic cytokine is described as a mediator of sleepiness (Vgontzas et al., 2005). It is produced in a variety of immune and nonimmune cells and peaks during the day following sleep deprivation. However, behavioral results are moderated by high or low cortisol levels, feelings of tiredness, fatigues and poor sleep (high cortisol), or inducing sleepiness and deep sleep (low cortisol) (Vgontzas et al., 2005). Moreover, sex differences in monocyte expression of IL-6, as well as in measures of sympathovagal balance have been reported. Autonomic mechanisms were reported to have a differential influence on IL-6 production (using LPS-stimulated monocytes) in women (increased production of IL-6 across the circadian period) as compared to men (O’Connor, Motivala, Valladares, Olmstead, & Irwin, 2007). Complex interactions exist between sleep-wakefulness rhythms, CVP of immune and autonomic markers, HPA axis, and sex.

Effects of Altered Sleep-Wakefulness Rhythms and Patterns Sleep disorders with medical relevance are common in Western societies ( 18–23%), are a major risk factor for co-morbidity and psychiatric, cardiovascular, metabolic, or hormonal diseases, and affect everyday’s life. Subjects with sleep disorders complain of reduced vigilance and increased drowsiness during the day, impaired cognition, mood changes and anxiety, and fatigue more often than good sleepers according to meta-analyses and populationbased studies. They seek medical assistance or anticipate retirement more frequently, use more prescribed medication, and rely on self-medication more frequently (Barclay & Gregory, 2013; Garbarino, Lanteri, et al., 2016; Lavie, 2001). Insufficient or otherwise inadequate sleeping is today widespread and appears unavoidable in our 24/24-hour societies (Garbarino & Sannita, 2017). Today’s numerous and common stressors (working at night or in shifts, social preferred rhythms, professional obligations, family commitments, insufficient sunlight, late-night eating and prolonged nocturnal exposure to artificial light, jet traveling, etc.) cause misalignment with the physiological circadian rhythms and contribute to SCN dysfunction. Evidence indicates that the health risks and effects on life quality of the Ó 2019 Hogrefe Publishing

Editorial

Table 2. Association of sleep disorders/poor sleeping with medical disorders, psychological/behavioral conditions, professional inadequacies or risks Medical disorders

Cardiovascular disorders

Metabolic disorders

Hormonal disorders

Psychological and behavioral conditions

Cognition

Psychopathological symptoms

Occupational/professional problems

Absenteeism

Productivity impairment

Occupational accidents

Sleep disorders (e.g., primary insomnia) and inadequate sleeping are associated with higher risk of cardiovascular disorders and stroke. OSA causes hypertension and cardiovascular disorders and increases the risk of diabetes, metabolic disorders, and insulin resistance. Cardiovascular disorders are more frequent among workers at night or in shifts. Inadequate sleep is associated with metabolic disorders (insulin resistance, T2 diabetes, obesity, unbalanced cholesterol metabolism, and inflammatory response). Shift work is associated with body weight gain and overweight, impaired glucose tolerance, metabolic syndromes, and T2 diabetes. Insomnia is associated with overactivity of the hypothalamus–pituitary– adrenal/thyroid axes and high. GnRH morning levels. Low levels of testosterone are more frequent in OSA than in controls. Sleep deprivation is associated with hypothalamic–pituitary–adrenal and hypothalamic–pituitary–gonadal axes unbalancement. Testosterone and prolactin concentrations are reduced in males after one night of total sleep deprivation. The evening/night cortisol response of workers is lower in night shifts workers. Chronic insomnia impairs sustained attention and episodic memory; information processing is slower in OSA than in controls, with a correlation between speed reduction and severity of disorder. Chronic insomnia impairs the ability to focus, memorize, and perform at work or school, with alterations in prefrontal cognitive tasks and executive functions; cognitive impairment, reduced alertness, poor emotional adaptation, excessive drowsiness, social problems are often associated. Several executive functions are impaired in subjects with untreated OSA. OSA is associated with depression and anxiety. Non-depressed insomniacs have a twofold risk of developing depression, compared to subjects normal sleepers. Sleep deprivation impairs subjective well-being and increases fatigue and depression in adults and adolescents. Shift work is often associated with anxiety and mental distress, but there is no evidence of higher incidence of psychiatric disorders. Insomnia is a risk factor for sick leave and absenteeism. Patients with OSA take more days of sick leave than controls. A strong association between working in the evening and sick leave is documented in females. Subjects at risk for OSA report difficulties in concentrating and getting organized more often than controls and apply for disability pensions more often than controls also after normalizing for other factors; their productivity is lower. Chronic insomniacs and poor sleepers have worse productivity, performance, and safety outcomes than controls. Insomnia and short sleep duration are independently associated with poor work ability. Poor sleep quality decreases human productivity and performance. Shift work disease is often associated with decreased work performance in shift working nurses on rapid-rotation schedules. Subjects that report poor sleep quality or short sleep present a significantly increased risk of occupational accident. Insomniacs have a significant odds ratio with workplace accidents and errors. The risk of occupational accidents is almost double in OSA subjects than controls. Overtime and irregular work scheduling have an adverse effect on worker safety; shift work including nights carries a substantial increased risk of accidents; risk of occupational accidents is at least 60% higher for non-day shift workers.

Note. OSA = obstructive sleep apnea; GnRH = gonadotropin-releasing hormone.

circadian rhythms disruption resulting of shift work or voluntary sleep deprivation are equivalent to those of sleep disorders with medical relevance. Desynchronization Ó 2019 Hogrefe Publishing

between the SCN and oscillators in peripheral tissues (liver, stomach, adipose tissue, gut) and mistimed eating (voluntary or due to night eating syndrome [NES]) often Journal of Psychophysiology (2020), 34(1), 1–9

result in metabolic disorders such as impaired insulin responsiveness, T2 diabetes, pancreatic dysfunction, and abnormal adipogenesis. The electrical status epilepticus during slow-wave sleep (ESES) and the vulnerability to psychiatric disorders have been linked to several genes also responsible for the circadian cyclic organization (Garbarino, Guglielmi, Sanna, Mancardi, & Magnavita, 2016; Mahowald & Schenck, 2005) (Table 2). Accuracy of action at any given time of the day depends on the interaction among the length of preceding wake episode (homeostatic factor), the chronic sleep debt carried by the individual, and the circadian phase at which performance is assessed. Shift work and reduced or otherwise poor sleeping have been identified as an occupational health and safety hazard. A significant association between impaired cognitive functions due to disordered or inadequate sleeping and professional efficiency, increased error rates and reduced safety at work or when driving is documented. Human errors (mostly due to fatigue and sleepiness) are blamed in 90% of road accidents. About 16% of insomniacs because of a restless limbs syndrome or periodic limb movement have been involved in traffic accidents. Approximately 50% of drivers surviving a collision reported at least one sleep-related risk factor and sleep disorders in about 16.9% of cases (OSA: 5.2%; insomnia: 9.3%). About 8.9% highway drivers reported experiencing at least once a month episodes of sleepiness severe enough to require an immediate stop. One-third of interviewed drivers (31.1%) reported near-miss accidents (50% of which were sleep-related), 7.2% reported a driving accident in the past year, and 5.8% of accidents were deemed sleep-related (Garbarino, Guglielmi, Sannita, Magnavita, & Lanteri, 2018). Collectively, inadequate sleeping and its effects stand as a medical, welfare, and social problem. Direct and indirect costs are high. The estimated costs of the three most common sleep disorders (obstructive sleep apneas [OSA], primary insomnia, and restless legs syndrome) are in the range of billions (US dollars) per year in developed countries (more than 50% for associated conditions). The indirect financial and non-financial costs of reduced quality of life hover just as high (Garbarino, Guglielmi, et al., 2016; Garbarino & Sannita, 2017; Mahowald & Schenck, 2005), and supporting these costs in the long term is probably unrealistic. Sleep disorders with medical relevance (e.g., OSA) have obtained attention by health regulatory agencies, and a sleep risk management system has been proposed (Costa, Accattoli, Garbarino, & Roscelli, 2013). A systematic medical interest on and comprehensive strategies against the negative effects of sleep deprivation (particularly if voluntary) are still lacking in the medical practice (Garbarino & Sannita, 2017). Focused scientific investigation and protocols/procedures to identify and monitor particularly vulnerable subjects or categories of subjects at Journal of Psychophysiology (2020), 34(1), 1–9

Editorial

risk should be enforced. To this end, impaired performance, poorer quality of life, and risks associated with inadequate sleeping need to be monitored and assessed by means of independent neurophysiological or psychophysiological indicators.

Concluding Remarks The sleep–wakefulness cycle is the most relevant circadian event in humans, its cyclic organization is as readily experienced as are the effects of its misalignment, and abnormalities of any degree can affect everyday life, often quite severely. It nevertheless results of a larger, complex, and pervasive functional arrangement governed by circadian mechanisms. In this respect, circadian balance depends on a variety of internal/external interacting factors and on individual variability and can be disarranged by pathologies or adaptation to unsuitable conditions. Major sleep disorders, inadequate sleeping, and misalignment of circadian rhythms compare as to detrimental effects and are today a major healthcare, welfare, and social problem. Sleep disorders with medical relevance (OSA) have recently obtained attention by the healthcare authorities; the misalignment or disarrangement of other circadian rhythms and the resulting pathophysiological conditions have not yet achieved the deserved focus. In particular, the functional interaction and interplay between the CSN and the ANS have not yet obtained proper attention by the scientific community and healthcare authorities, and investigation in this field should be more systematic. HRV gained a peculiar position in this framework, and HRV measures are being regarded with increasing interest. The available tests of sympathetic and parasympathetic functions and responsiveness are in fact invasive or indirect and difficult to apply to investigate the interaction between the ANS and CNS in quasi-physiological conditions (Berne, Fagius, Pollare, & Hjemdahl, 1992; Eckberg, 1983, 2003; Esler, 1993; Grossman & Taylor, 2007; Katona & Jih, 1975; Wallin & Charkoudian, 2007). The extent to which HRV changes can be specific descriptors of the CNS/ANS interplay and simple metrics qualify as reliable measures remains to be validated in each specific application. Systematic research would give neuroscientists a novel applicative domain and help setting guidelines in the approach.

References Antelmi, I., de Paula, R. S., Shinzato, A. R., Shinzato, A. R., Peres, C. A., Mansur, A. J., & Grupi, C. J. (2004). Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. The American Journal of Cardiology, 93, 381–385. https://doi.org/ 10.1016/j.amjcard.2003.09.065

Ó 2019 Hogrefe Publishing

Editorial

Barclay, N. L., & Gregory, A. M. (2013). Quantitative genetic research on sleep: A review of normal sleep, sleep disturbances and associated emotional, behavioural, and health-related difficulties. Sleep Medicine Reviews, 17, 29–40. https://doi.org/ 10.1016/j.smrv.2012.01.008 Berne, C., Fagius, J., Pollare, T., & Hjemdahl, P. (1992). The sympathetic response to euglycaemic hyperinsulinaemia. Diabetologia, 35, 873–879. https://doi.org/10.1007/BF00399935 Berntson, G. G., & Cacioppo, J. T. (2004). Heart rate variability: Stress and psychiatric conditions. In M. Malik & A. J. Camm (Eds.), Dynamic electrocardiography (pp. 56–63). Elmsford, NY: Futura. Bonnet, M. H., & Arand, D. L. (1998). Heart rate variability in insomniacs and matched normal sleepers. Psychosomatic Medicine, 60, 610–615. Borovikova, L. V., Ivanova, S., Zhang, M., Yang, H., Botchkina, G. I., Watkins, L. R., . . . Tracey, K. J. (2000). Vagus nerve stimulation attenuates the systemic inflammatory response to endotoxin. Nature, 405, 458–462. https://doi.org/10.1038/35013070 Brandenberger, G., Viola, A. U., Ehrhart, J., & Charloux, A. (2003). Age-related changes in cardiac autonomic control during sleep. Journal of Sleep Research, 12, 173–180. https://doi.org/ 10.1046/j.1365-2869.2003.00353.x Burgess, H. J., Trinder, J., Kim, Y., & Luke, D. (1997). Sleep and circadian influences on cardiac autonomic nervous system activity. American Journal of Physiology, 273, H1761–H1768. https://doi.org/10.1152/ajpheart.1997.273.4.H1761 Candelieri, A., Cortese, M. D., Dolce, G., Riganello, F., & Sannita, W. G. (2011). Visual pursuit: Within-day variability in the severe disorder of consciousness. Journal of Neurotrauma, 28, 2013–2017. https://doi.org/10.1089/neu.2011.1885 Costa, G., Accattoli, M. P., Garbarino, S., & Roscelli, F. (2013). Sleep disorders and work: Guidelines for health surveillance, risk management and prevention. La Medicina del Lavoro, 104, 251–266. Crowley, S. J., Acebo, C., & Carskadon, M. A. (2007). Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Medicine, 8, 602–612. https://doi.org/10.1016/j.sleep.2006.12.002 De Valck, E., Cluydts, R., & Pirrera, S. (2004). Effect of cognitive arousal on sleep latency, somatic and cortical arousal following partial sleep deprivation. Journal of Sleep Research, 13, 295–304. https://doi.org/10.1111/j.1365-2869.2004.00424.x de Zambotti, M., Javitz, H., Franzen, P. L., Brumback, T., Clark, D. B., Colrain, I. M., & Baker, F. C. (2017). Sex- and agedependent differences in autonomic nervous system functioning in adolescents. Journal of Adolescent Health, 62, 184–190. https://doi.org/10.1016/j.jadohealth.2017.09.010 Dodds, K. L., Miller, C. B., Kyle, S. D., Marshall, N. S., & Gordon, C. J. (2017). Heart rate variability in insomnia patients: A critical review of the literature. Sleep Medicine Reviews, 33, 88–100. https://doi.org/10.1016/j.smrv.2016.06.004 Dunlap, J. C. (2006). Physiology: Running a clock requires quality time together. Science, 311, 184–186. https://doi.org/10.1126/ science.1122839 Eckberg, D. L. (1983). Human sinus arrhythmia as an index of vagal cardiac outflow. Journal of Applied Physiology, 54, 961–966. Eckberg, D. L. (2003). The human respiratory gate. The Journal of Physiology, 548, 339–352. https://doi.org/10.1113/ jphysiol.2002.037192 Esler, M. (1993). Clinical application of noradrenaline spillover methodology: Delineation of regional human sympathetic nervous responses. Basic and Clinical Pharmacology and Toxicology, 73, 243–253. https://doi.org/10.1111/j.1600-0773.1993. tb00579.x Fang, S.-C., Huang, C.-J., Yang, T.-T., & Tsai, P.-S. (2008). Heart rate variability and daytime functioning in insomniacs and

Ó 2019 Hogrefe Publishing

normal sleepers: Preliminary results. Journal of Psychosomatic Research, 65, 23–30. https://doi.org/10.1016/j.jpsychores. 2008.02.003 Friedman, B. H. (2007). An autonomic flexibility–neurovisceral integration model of anxiety and cardiac vagal tone. Biological Psychology, 74, 185–199. https://doi.org/10.1016/j.biopsycho. 2005.08.009 Garbarino, S., Guglielmi, O., Sanna, A., Mancardi, G. L., & Magnavita, N. (2016). Risk of occupational accidents in workers with obstructive sleep apnea: Systematic review and metaanalysis. Sleep, 39, 1211–1218. https://doi.org/10.5665/sleep. 5834 Garbarino, S., Guglielmi, O., Sannita, W. G., Magnavita, N., & Lanteri, P. (2018). Sleep and mental health in truck drivers: Descriptive review of the current evidence and proposal of strategies for primary prevention. International Journal of Environmental Research and Public Health, 15, 1852. https://doi.org/10.3390/ijerph15091852 Garbarino, S., Lanteri, P., Durando, P., Magnavita, N., & Sannita, W. G. (2016). Co-morbidity, mortality, quality of life and the healthcare/welfare/social costs of disordered sleep: A rapid review. International Journal of Environmental Research and Public Health, 13, 831. https://doi.org/10.3390/ijerph13080831 Garbarino, S., & Sannita, W. G. (2017). Poor sleeping has underrepresented medical, healthcare, and social costs?. European Journal of Internal Medicine, 38, e15–e16. https://doi.org/ 10.1016/j.ejim.2016.10.020 Giacino, J. T., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D. I., . . . Zasler, N. D. (2002). The minimally conscious state: Definition and diagnostic criteria. Neurology, 58, 349–353. Glos, M., Fietze, I., Blau, A., Baumann, G., & Penzel, T. (2014). Cardiac autonomic modulation and sleepiness: Physiological consequences of sleep deprivation due to 40 h of prolonged wakefulness. Physiology & Behavior, 125, 45–53. https://doi. org/10.1016/j.physbeh.2013.11.011 Grossman, P., & Taylor, E. W. (2007). Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions. Biological Psychology, 74, 263–285. https://doi.org/10.1016/j.biopsycho.2005.11.014 Hagemann, D., Waldstein, S. R., & Thayer, J. F. (2003). Central and autonomic nervous system integration in emotion. Brain & Cognition, 52, 79–87. https://doi.org/10.1016/S0278-2626(03) 00011-3 Hagenauer, M. H., Perryman, J. I., Lee, T. M., & Carskadon, M. A. (2009). Adolescent changes in the homeostatic and circadian regulation of sleep. Developmental Neuroscience, 31, 276–284. https://doi.org/10.1159/000216538 Hall, M., Vasko, R., Buysse, D., Ombao, H., Chen, Q., Cashmere, J. D., . . . Thayer, J. F. (2004). Acute stress affects heart rate variability during sleep. Psychosomatic Medicine, 66, 56–62. Hastings, M. H., Reddy, A. B., & Maywood, E. S. (2003). A clockwork web: Circadian timing in brain and periphery, in health and disease. Nature Reviews Neuroscience, 4, 649–661. https://doi.org/10.1038/nrn1177 Heraghty, J. L., Hilliard, T. N., Henderson, A. J., & Fleming, P. J. (2008). The physiology of sleep in infants. Archives of Disease in Childhood, 93, 982–985. https://doi.org/10.1136/adc.2006. 113290 Huikuri, H. V., Kessler, K. M., Terracall, E., Castellanos, A., Linnaluoto, M. K., & Myerburg, R. J. (1990). Reproducibility and circadian rhythm of heart rate variability in healthy subjects. The American Journal of Cardiology, 65, 391–393. Jarczok, M. N., Aguilar-Raab, C., Koenig, J., Kaess, M., Borniger, J. C., Nelson, R. J., . . . Fischer, J. E. (2018). The Heart’s rhythm “n” blues: Sex differences in circadian variation patterns of vagal activity vary by depressive symptoms in predominantly

Journal of Psychophysiology (2020), 34(1), 1–9

healthy employees. Chronobiology International, 35, 896–909. https://doi.org/10.1080/07420528.2018.1439499 Jarczok, M. N., Koenig, J., Mauss, D., & Thayer, J. F. (2014). Lower heart rate variability predicts increased level of C-reactive protein 4 years later in healthy, nonsmoking adults. Journal of Internal Medicine, 276, 667–671. https://doi.org/10.1111/ joim.12295 Jarczok, M. N., Li, J., Mauss, D., Fischer, J. E., & Thayer, J. F. (2013). Heart rate variability is associated with glycemic status after controlling for components of the metabolic syndrome. International Journal of Cardiology, 167, 855–861. https://doi. org/10.1016/j.ijcard.2012.02.002 Karatsoreos, I. N., & McEwen, B. S. (2014). Timing is everything: A collection on how clocks affect resilience in biological systems. F1000Research, 3, 273. https://doi.org/10.12688/ f1000research.5756.1 Katona, P. G., & Jih, F. (1975). Respiratory sinus arrhythmia: Noninvasive measure of parasympathetic cardiac control. Journal of Applied Physiology, 39, 801–805. Koenig, J., & Thayer, J. F. (2016). Sex differences in healthy human heart rate variability: A meta-analysis. Neuroscience & Biobehavioral Reviews, 64, 288–310. https://doi.org/10.1016/j. neubiorev.2016.03.007 Kondratova, A. A., & Kondratov, R. V. (2012). The circadian clock and pathology of the ageing brain. Nature Reviews Neuroscience, 13, 325–335. https://doi.org/10.1038/nrn3208 Laureys, S., Celesia, G. G., Cohadon, F., Lavrijsen, J., LeónCarrión, J., Sannita, W. G., . . . Dolce, G. (2010). Unresponsive wakefulness syndrome: A new name for the vegetative state or apallic syndrome. BMC Medicine, 8, 68. https://doi.org/ 10.1186/1741-7015-8-68 Lavie, P. (2001). Sleep disturbances in the wake of traumatic events. The New England Journal of Medicine, 345, 1825–1832. https://doi.org/10.1056/NEJMra012893 Levy, M. N. (1990). Autonomic interactions in cardiac control. Annals of the New York Academy of Sciences, 601, 209–221. Li, X., Shaffer, M. L., Rodriguez-Colon, S., He, F., Wolbrette, D. L., Alagona, P. Jr., . . . Liao, D. (2011). The circadian pattern of cardiac autonomic modulation in a middle-aged population. Clinical Autonomic Research, 21, 143–150. https://doi.org/ 10.1007/s10286-010-0112-4 Mahowald, M. W., & Schenck, C. H. (2005). Insights from studying human sleep disorders. Nature, 437, 1279–1285. https://doi. org/10.1038/nature04287 Maier, S. F., Goehler, L. E., Fleshner, M., & Watkins, L. R. (1998). The role of the vagus nerve in cytokine-to-brain communication. Annals of the New York Academy of Sciences, 840, 289–300. Makino, M., Hayashi, H., Takezawa, H., Hirai, M., Saito, H., & Ebihara, S. (1997). Circadian rhythms of cardiovascular functions are modulated by the baroreflex and the autonomic nervous system in the rat. Circulation, 96, 1667–1674. Maywood, E. S., Mrosovsky, N., Field, M. D., & Hastings, M. H. (1999). Rapid down-regulation of mammalian Period genes during behavioral resetting of the circadian clock. Proceedings of the National Academy of Sciences of the USA, 96, 15211–15216. Meerlo, P., Sgoifo, A., & Suchecki, D. (2008). Restricted and disrupted sleep: Effects on autonomic function, neuroendocrine stress systems and stress responsivity. Sleep Medicine Reviews, 12, 197–210. https://doi.org/10.1016/j.smrv.2007.07.007 Nilsonne, G., Lekander, M., Åkerstedt, T., Axelsson, J., & Ingre, M. (2016). Diurnal variation of circulating interleukin-6 in humans: a meta-analysis. PLoS One, 11, e0165799. https://doi.org/ 10.1371/journal.pone.0165799 O’Connor, M.-F., Motivala, S. J., Valladares, E. M., Olmstead, R., & Irwin, M. R. (2007). Sex differences in monocyte expression of IL-6: Role of autonomic mechanisms. American Journal of

Journal of Psychophysiology (2020), 34(1), 1–9

Editorial

Physiology-Regulatory, Integrative and Comparative Physiology, 293, R145–151. https://doi.org/10.1152/ajpregu.00752.2006 Pagani, M., Pizzinelli, P., Traon, A. P.-L., Ferreri, C., Beltrami, S., Bareille, M.-P., & . . .Philip, P. (2009) Hemodynamic, autonomic and baroreflex changes after one night sleep deprivation in healthy volunteers. Autonomic Neuroscience: Basic & Clinical, 145, 76–80. https://doi.org/10.1016/j.autneu.2008.10.009 Pavlov, V. A. & Tracey, K. J. (2012). The vagus nerve and the inflammatory reflex-linking immunity and metabolism. Nature Reviews Endocrinology, 8, 743–754. https://doi.org/10.1038/ nrendo.2012.189 Quintana, D. S., Elvsåshagen, T., Zak, N., Norbom, L. B., Pedersen, P. Ø., Quraishi, S. H. . . . Westlye, L. T. (2017). Diurnal variation and twenty-four hour sleep deprivation do not alter supine heart rate variability in healthy male young adults. PLoS One, 12, e0170921. https://doi.org/10.1371/journal.pone. 0170921 Refinetti, R., Lissen, G. C., Halberg, F. (2007). Procedures for numerical analysis of circadian rhythms. Biological Rhythm Research, 38, 275–325. https://doi.org/10.1080/ 09291010600903692 Riganello, F., Cortese, M. D., Arcuri, F., Dolce, G., Lucca, L., & Sannita, W. G. (2015). Autonomic nervous system and outcome after neuro-rehabiliation in disorders of consciousness. Journal of Neurotrauma, 33, 423–424. https://doi.org/10.1089/neu. 2015.3906 Riganello, F., Dolce, G., & Sannita, W. (2012). Heart rate variability and the central autonomic network in the severe disorder of consciousness. Journal of Rehabilitation Medicine, 44, 495–501. https://doi.org/10.2340/16501977-0975 Riganello, F., Garbarino, S., & Sannita, W. G. (2012). Heart rate variability, homeostasis, and brain function. Journal of Psychophysiology, 26, 178–203. https://doi.org/10.1027/02698803/a000080 Riganello, F., Garbarino, S., & Sannita, W. (2014). Heart rate variability and the two-way interaction between CNS and the central autonomic network. Journal of Experimental & Clinical Cardiology, 9, 5584–5595. Sannita, W. G. (2006). Individual variability, end-point effects and possible biases in electrophysiological research. Clinical Neurophysiology, 117, 2569–2583. https://doi.org/10.1016/j.clinph. 2006.04.026 Saper, C. B., Scammell, T. E., & Lu, J. (2005). Hypothalamic regulation of sleep and circadian rhythms. Nature, 437, 1257–1263. https://doi.org/10.1038/nature04284 Sauvet, F., Leftheriotis, G., Gomez-Merino, D., Langrume, C., Drogou, C., Van Beers, P. . . . Chennaoui, M. (2010). Effect of acute sleep deprivation on vascular function in healthy subjects. Journal of Applied Physiology, 108, 68–75. https://doi.org/10.1152/japplphysiol.00851.2009 Siege, J. M. (2005). Clues to the functions of mammalian sleep. Nature, 437, 1264–1271. https://doi.org/10.1038/nature04285 Silver, R., Kriegsfeld, L. J. (2014). Circadian rhythms have broad implications for understanding brain and behavior. European Journal of Neuroscience, 39, 1866–1880. https://doi.org/ 10.1111/ejn.12593 Spiegelhalder, K., Fuchs, L., Ladwig, J., Kyle, S. D., Nissen, C., Voderholzer, U. . . . Riemann, D. (2011). Heart rate and heart rate variability in subjectively reported insomnia. Journal of Sleep Research, 20, 137–145. https://doi.org/10.1111/j.13652869.2010.00863.x Stein, P. K. & Pu, Y. (2012). Heart rate variability, sleep and sleep disorders. Sleep Medicine Reviews, 16, 47–66. https://doi.org/ 10.1016/j.smrv.2011.02.005 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996).

Ó 2019 Hogrefe Publishing

Editorial

Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation, 93, 1043–1065. Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart– brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience & Biobehavioral Reviews, 33, 81–88. https://doi.org/10.1016/j.neubiorev.2008.08.004 Thayer, J. F., & Sternberg, E. (2006). Beyond Heart Rate Variability. Annals of the New York Academy of Sciences, 1088, 361–372. https://doi.org/10.1196/annals.1366.014 Tobaldini, E., Nobili, L., Strada, S., Casali, K. R., Braghiroli, A., & Montano, N. (2013). Heart rate variability in normal and pathological sleep. Frontiers in Physiology, 4, 294. https://doi. org/10.3389/fphys.2013.00294 Touitou, Y., Reinberg, A., & Touitou, D. (2017). Association between light at night, melatonin secretion, sleep deprivation, and the internal clock: Health impacts and mechanisms of circadian disruption. Life Sciences, 173, 94–106. https://doi.org/10.1016/ j.lfs.2017.02.008 Vaara, J., Kyröläinen, H., Koivu, M., Tulppo, M., & Finni, T. (2009). The effect of 60-h sleep deprivation on cardiovascular regulation and body temperature. European Journal of Applied Physiology, 105, 439–444. https://doi.org/10.1007/s00421008-0921-5 Vanoli, E., Adamson, P. B., Ba-Lin., Pinna, G. D., Lazzara, R., & Orr, W. C. (1995). Heart rate variability during specific sleep stages. A comparison of healthy subjects with patients after myocardial infarction. Circulation, 91, 1918–1922. Vgontzas, A. N., Bixler, E. O., Lin, H.-M., Prolo, P., Trakada, G., & Chrousos, G. P. (2005). IL-6 and its circadian secretion in humans. Neuroimmunomodulation, 12, 131–140. https://doi. org/10.1159/000084844 Wallin, B. G., & Charkoudian, N. (2007). Sympathetic neural control of integrated cardiovascular function: Insights from

Ó 2019 Hogrefe Publishing

measurement of human sympathetic nerve activity. Muscle Nerve, 36, 595–614. https://doi.org/10.1002/mus.20831 Yang, A. C., Tsai, S.-J., Yang, C.-H., Kuo, C.-H., Chen, T.-J., & Hong, C.-J. (2011). Reduced physiologic complexity is associated with poor sleep in patients with major depression and primary insomnia. Journal of Affective Disorders, 131, 179–185. https://doi.org/10.1016/j.jad.2010.11.030 History Received October 19, 2018 Accepted December 4, 2018 Published online February 28, 2019 Funding No funding has been requested or obtained. Conflict of Interest There is no conflict of interest to be disclosed. Authorship The authors equally contributed in the manuscript preparation.

Walter G. Sannita Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child Health University of Genova 3, Largo p. Daneo 16132 Genova Italy wgs@dism.unige.it

Journal of Psychophysiology (2020), 34(1), 1–9

Article

Fatty Fish Intervention and Psychophysiological Responses to Mental Workload in Forensic Inpatients A Randomized Control Trial Anita L. Hansen,1,2 Gina Ambroziak,3 David Thornton,2,4 Lisbeth Dahl,5 Helge Molde,1 and Bjørn Grung6 1

Department of Clinical Psychology, University of Bergen, Norway

Centre for Research and Education in Forensic Psychiatry, Haukeland University Hospital, Bergen, Norway

Sand Ridge Secure Treatment Center (SRSTC), Mauston, WI, USA

Forensic Assessment, Training, & Research (FAsTR), LLC, Madison, WI, USA Food Security and Nutrition, Institute of Marine Research (IMR), Bergen, Norway

5 6

Department of Chemistry, University of Bergen, Norway

Abstract: The overall aim of this randomized controlled trial was to investigate the effect of a long-term fatty fish intervention during winter time on psychophysiological responses, that is, heart rate variability (HRV), to mental workload. Forty-seven forensic inpatients were randomly assigned into a fish group (FG) or a control group (CG). HRV responses to an experimental test procedure consisting of a resting baseline, mental workload, and a resting recovery were measured pre- and post-intervention. The results revealed that the FG showed attenuated physiological responses to mental workload from pretest to posttest by a significant increase in HRV. Additionally, the FG showed a higher HRV during recovery compared to the baseline and test conditions at both pretest and posttest. The CG showed no changes in psychophysiological responses from pretest to posttest to mental workload. Importantly, the CG showed impaired recovery at posttest, indicating a sustained physiological arousal after the stressor (mental workload) ended. Thus, the results indicate that increased fatty fish intake has the potential to increase resilience to mild cognitive stress in human beings with psychiatric illnesses. Keywords: fatty fish consumption, heart rate variability, mental workload, resilience

Human beings with psychiatric illness have impaired resilience to stress, and effective interventions improving resilience mechanisms should be identified (McEwen, Gay, & Nasca, 2015; Reul et al., 2015). Reul et al. (2015) define resilience as “an individual’s ability to effectively adapt to stress and adversity, resulting in the prevention of physical and or psychological disease” (p. 45). To adapt successfully to everyday demands and environmental challenges, physiological regulation and cognitive functioning are crucial (Thayer, Åhs, Fredrikson, Sollers, & Wager, 2012; Thayer, Hansen, Saus, & Johnsen, 2009). Regular fatty fish consumption has been shown to increase resting heart rate variability (HRV; Mozaffarian, Stein, Prineas, & Siscovick, 2008), which is an important index of physical and mental health (Thayer et al., 2012). The mechanisms responsible for the effect are still not identified. Individuals with Journal of Psychophysiology (2020), 34(1), 10–18 https://doi.org/10.1027/0269-8803/a000231

psychiatric illness compared with nonpsychiatric individuals have been shown to have lower levels of omega-3 fatty acids, which are important for maintaining good health and well-being (Perica & Delas, 2011). Fatty fish is a rich source of these and other important nutrients such as vitamin D (VKM, 2014). Thus, fatty fish interventions may act as an important treatment strategy to improve resilience to stress. To gain more knowledge about this, it is necessary to investigate the effects of regular fatty fish consumption on psychophysiological responses such as HRV to challenging situations (e.g., mental workload). A series of studies conducted in Norway and the US revealed that increased fatty fish consumption improved objective and subjective measures of mental health. The first pilot study in Norway investigating the effects of a six-month fatty fish intervention on HRV in inmates Ó 2018 Hogrefe Publishing

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

showed that fatty fish consumption increased HRV (i.e., the high-frequency [HF] band; 0.15–0.4 Hz). However, this was an average of HRV for both resting and test conditions (Hansen, Dahl, Bakke, Frøyland, & Thayer, 2010). A larger follow-up project involving forensic inpatients in the US expanded the pilot study showing that fatty fish consumption during winter time also influenced daily functioning (Hansen, Dahl, et al., 2014), anxiety (Hansen, Olson, et al., 2014), executive functioning including both planning and decision-making (Hansen et al., 2015), but not working memory as IQ and age were the strongest predictors in this domain (Hansen et al., 2018). Moreover, fatty fish consumption increased resting baseline HF-HRV (cf. Hansen, Dahl, et al., 2014) and HRV measured as the root mean square of successive differences (RMSSD), in addition to reduced resting heart rate (cf. Hansen, Olson, et al., 2014). In contrast, the control group, receiving a diet without fatty fish, showed increased resting heart rate (Hansen, Olson, et al., 2014) as well as increased sleep latency from pretest to posttest (Hansen, Dahl, et al., 2014). In a hypothesis-generating study using principal component analysis and partial least squares (Grung et al., 2015), a question related to the importance of a resting HRV recovery (i.e., a post-stress) measure as an index of physiological cost and mental health was also raised. As this study was a hypothesis-generating study, no conclusions were drawn. In order to follow up, Grung et al.’s (2015) hypothesis-testing is needed. This will generate knowledge about the recovery measure, which may also have important implications with regard to stress resilience. HRV can be regarded as a measure of the time period between successive heartbeats. Higher variability between these time periods indicates higher parasympathetic activity, which is dominant when the organism is in a homeostatic balance (Malik, 1996; Thayer, Hansen, & Johnsen, 2010). There are different parameters of HRV in two domains: the RMSSD in the time domain, and the HF band, low-frequency band (LF; 0.04–0.15 Hz), and the LF/HF ratio (Thayer et al., 2012) in the frequency domain. HF power and RMSSD have been shown to be reliable measures of parasympathetic activity, but there is no consensus in the literature with regard to the LF power as a marker of sympathetic nervous system (SNS) or parasympathetic nervous systems (PNSs) or both (Appelhans & Luecken, 2006; Thayer et al., 2010, 2012). When it comes to the effect of fatty fish consumption on resting HF and RMSSD, Hansen, Dahl, et al.’s (2014) and Hansen, Olson, et al.’s (2014) results are consistent with those of Mozaffarian et al. (2008). However, Erkkilä et al. (2008) did not find any effect of fatty fish consumption on RMSSD. Resting HRV is usually conducted in a sitting or supine position, and participants are instructed to relax. Importantly, HRV is also sensitive to environmental challenges

and it has been argued that HRV can be regarded as a biomarker of stress and resilience as well (Thayer et al., 2012). It is well known that stress causes an increased heart rate because of secretion of neurotransmitters (e.g., norepinephrine and adrenaline) and increased sympathetic activity. However, the parasympathetic part of the autonomic nervous system (ANS) contributes to decreased heart rate, mediated by acetylcholine via the vagus nerve. Importantly, the parasympathetic part of the ANS affects the heart rate more rapidly than the sympathetic part of the ANS. Thus, the PNS is a sensitive measure of changes in the environment (Thayer et al., 2010, 2012). To understand more about the role of dietary fish intake and the influence on resilience, it is necessary to investigate the effect of fish consumption on psychophysiological responses such as HRV to challenges. HRV has shown to be sensitive to both emotional (Johnsen et al., 2003; Thayer & Lane, 2000) and cognitive load (Hansen, Johnsen, & Thayer, 2003; Thayer & Lane, 2000; Thayer et al., 2009). HRV is related to brain functions and structures such as the anterior cingulate cortex (Thayer & Lane, 2000), which has an affective and a cognitive subdivision, both of which are also intricately bounded in cognitive tasks and self-regulation or effortful control (Best, Miller, & Jones, 2009). The Neurovisceral Model developed by Thayer and Lane (2000) illuminates the close relationship between HRV and the brain. Numerous studies have demonstrated a significant reduction in HRV during execution of cognitive tasks taxing attentional control and in particular working memory (WM) compared to a resting condition (e.g., Backs & Seljos, 1994; Hansen et al., 2003). Importantly, WM is essential in order to handle everyday demands and mental challenges (Baddeley, 2003). Thus, in the laboratory, experimental cognitive tasks measuring WM have been widely used to measure HRV responses to mental workload. Notably, HRV has been regarded as a better measure than heart rate because it has better statistical properties (Backs, 2001). An earlier investigation “stress-testing” mental workload in a laboratory setting has shown that resting HRV registered before or after exposure to a cognitive test procedure may make a difference. In a group of healthy military personnel, Hansen et al. (2003) found a higher HRV recovery compared to a baseline and two cognitive test conditions (i.e., WM tasks). In this study, it was argued that a recovery could be regarded as a better measure of resting cardiac activity since there is no anticipation effect confounded in the recording of a resting recovery measure. However, the question is whether this pattern can be observed in another population. In order to “stress-test” mental workload, psychophysiological responses to mental workload should therefore be compared with a resting HRV recovery measure. Importantly, physiological recovery has also been

Ó 2018 Hogrefe Publishing

Journal of Psychophysiology (2020), 34(1), 10–18

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

regarded as the ability to shut off the physiological stress response (Forcier et al., 2006). Thus, a recovery measure may also provide important information about stress resilience. The primary aim of the present study was to experimentally test the effects of a fatty fish intervention on psychophysiological responses measured by HRV to mental workload. We expected that increased fatty fish consumption would cause increased HRV in response to mental workload. In order to gain more knowledge about the recovery measure (cf. Grung et al., 2015), the secondary aim was to compare psychophysiological responses to a resting recovery with the psychophysiological responses to mental workload and a baseline measure in this sample of forensic inpatients. Based on Hansen et al. (2003), one would expect a suppressed HRV during mental workload and baseline conditions compared to the recovery.

electrodes), heart rate signals during the experimental procedure were lost on 39 participants. Missing data were not replaced since there seems to be little consensus on how to address the issue of missing data, and there is always the risk of introducing a bias (Alshurafa et al., 2012).

Methods Participants and Design Forty-seven male forensic inpatients participated in this study. The mean age was 40.5 years (range: 21–55). The majority of the sample (89%) had mental health problems of some kind. Most participants were diagnosed with a personality disorder (79%), while 34% had anxiety disorders, 28% had depressive disorders, and 26% had both a personality disorder and an anxiety/depressive disorder. Moreover, 32% of this sample had a history of alcohol or drug abuse. Participation was voluntary, and those who were interested in participating were welcome. As it has been described elsewhere (Hansen, Dahl, et al., 2014; Hansen, Olson, et al., 2014; Hansen et al., 2015), participants were recruited by both written and oral information about the study. Participants were further matched on age, psychopathy score (Psychopathy Checklist-Revised; Hare, 1991), and IQ (Wechsler Adult Intelligence Scale – Fourth Edition; Wechsler, 2008) before they were randomized into the fish group (FG) or the control group (CG). IQ > 75 was used as an inclusion criterion. For randomization, a computerized random number generator (in Excel) was used to assign each of the matched pairs. The random allocations to the groups were completed after all participants were enrolled and had completed baseline testing (pretest battery; Hansen, Olson, et al., 2014; Hansen et al., 2015). Figure 1, a CONSORT (Consolidated Standards of Reporting Trials) flow diagram, presents the present study progress. Due to technical problems caused by the ECG electrodes (faulty

Journal of Psychophysiology (2020), 34(1), 10–18

Outcome Measures and Experimental Procedure HRV was used as a measure of psychophysiological responses to mental workload. HRV was registered by the Actiheart System (Cambridge Neurotechnology Ltd., Cambridge, UK) (Brage, Brage, Franks, Ekelund, & Wareham, 2005), a compact lightweight device that records heart rate and variability of R-R inter-beat intervals (IBIs). The Actiheart clips onto a single ECG electrode (M-00-S/50 Blue Sensor) with a short ECG lead to another electrode that detects the ECG signal. The Actiheart was placed on the upper chest. HRV was measured as HF power (0.15–0.4 Hz) and derived using fast Fourier transform. Artifacts in IBI were screened for and handled manually in the Actiheart program. The HF data were log 10 transformed. To investigate psychophysiological responses to mental workload and in order to compare these responses with recovery, the participants were exposed to a WM task, that is, n-back (2-back and 3-back), as previous investigation found a higher HRV recovery following this task (cf. Hansen et al., 2003). The task required monitoring of a series of stimuli. The participants were instructed to focus on the task, to detect identical stimuli (i.e., letters) to the one presented n trials previously, and to be as fast and accurate as possible. The task taxes different key processes within the WM such as online monitoring, updating, and manipulation of remembered information (Owen, McMillan, Laird, & Bullmore, 2005). Due to the fact that performance of WM tasks causes greater energy expenditure in the form of increased physical cost compared to a rest condition (e.g., Backs & Seljos, 1994; Hansen et al., 2003), this experimental test procedure can be regarded as a mild cognitive stress regime. Additionally, in cognitive experiments, the participants are instructed to respond as quickly and accurately as possible and the whole test situation is an unfamiliar and strange situation. Thus, in its entirety, exposure to cognitive experimental test situations can be regarded as a challenging situation. Before and after the WM task, a resting baseline and recovery were registered. Thus, physiological responses were registered at the following conditions: baseline (5 min), mental workload, that is, 2-back and 3-back (both 2 min), and recovery (5 min). Recovery was simply the psychophysiological responses during 5 min of rest after

Ó 2018 Hogrefe Publishing

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

Assessed for eligibility (n = 102)

Excluded (n = 7)

Randomized (n = 95)

Allocated to Control group (alternative meal) (n = 47)

Allocated to Fish group (intervention) (n = 48)

Allocation

Lost to follow-up:

- Moved to other institution (n = 3)

- Moved to other institution (n = 4)

- Withdraw from project (n = 2)

Follow-Up

Missing HF-HRV due technical problems (n = 14)

Missing HF-HRV due to technical problems (n = 25)

Participants in present study: (n = 29)

Participants in present study: (n = 18)

Analysis Figure 1. Study progress. HF-HRV = high-frequency heart rate variability.

exposure to the experimental WM task (stress situation; cf. Hansen et al., 2003; see also Linden, Earle, Gerin, & Christenfeld, 1997). Based on a time log for each individual, an average HRV for each condition was calculated by a MATLAB (The MathWorks, Natick, MA) function developed by the authors. As the present study is part of a larger project investigating the effects of a dietary intervention on different mental health variables (e.g., anxiety, sleep, blood parameters), changes in HRV during baseline and recovery have been reported elsewhere (Hansen, Dahl, et al., 2014). The baseline and recovery for the current sample (47 participants) were included in the present study simply for comparison reasons. The participants were exposed to the whole test procedure before and after the dietary intervenÓ 2018 Hogrefe Publishing

tion period of 6 months. All participants were tested individually.

Intervention The project was approved by the Ethics Committee at Sand Ridge (April 10, 2008), and it was in compliance with the Helsinki Declaration. Volunteers were given written and oral information about the study. All participants signed an informed consent form and were instructed about the option to withdraw from the study at any time for any reason without penalty. All participants were subjected to a dietary intervention – either fish or various kinds of meat meals differing from the institutional meals normally Journal of Psychophysiology (2020), 34(1), 10–18

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

served. The FG was given farmed Atlantic salmon (Salmo salar L.) for dinner (portion size 150–300 g) thrice weekly during 23 weeks (September–February). The CG was given an alternative meal (meat) having the same nutritional value as they normally received with the same frequency and duration. Blinding is very difficult when it comes to dietary intervention studies. However, in the present study, both the intervention and the control group received “special treatment”. The salmon was prepared by the kitchen staff at the institution three times a week, and they were not allowed to deep fry the salmon. Therefore, the salmon was baked, prepared in a wok, or cooked as fish burgers. Different side dishes were used to make variations in the menus. The two groups were served the same side dishes (e.g., vegetables, bread, potatoes). A standard portion of salmon was 300 g (three times per week), but during the final four weeks of the study, they were served portion sizes of 150 g of salmon. The intake of EPA + DHA and vitamin D in a standard portion of fatty fish (300 g) was 4.8 g and 15 μg, respectively (Hansen, Dahl, et al., 2014). The meals were repeated over a 12-week cycle. To determine nutrients and energy levels in the diet, double portions of all meals (i.e., breakfast, lunch, dinner) during a week were collected over six consecutive weeks during the intervention. It was found that 100 g of the CG’s diet contained energy 219 ± 22 kcal, protein 9.3 ± 1.0 g, vitamin D 2.9 ± 0.7 μg, sum EPA + DHA < 0.01 mg, and fat 13.3 ± 0.5 g. For 100 g of the FG’s diet, the results were as follows: energy 198 ± 16 kcal, protein 9.1 ± 0.7 g, vitamin D 2.9 ± 0.7 μg, sum EPA + DHA 72 ± 6 mg, and fat 14.3 ± 1.0 g (Hansen et al., 2018). The mean results from the double portions showed that the level of vitamin D in both diets, sampled over 6 weeks, was 2.9 μg/100 g. However, the vitamin D content of the salmon overall ranged from 1.7 to 6.2 μg/100 g. Importantly, the vitamin D content of the FG’s double portions (sampled by the kitchen staff) ranged from 1.9 to 4.1 μg/100 g (the range for the control diet was 1.9–3.7 μg/100 g). Thus, as the double portions were taken from 6 of 23 weeks with intervention, the vitamin D result shows that the fish included in the double portions were among those with the lowest level of vitamin D. The blood samples showed that the vitamin D status measured as 25hydroxyvitamin D (25 OHD) in the FG was 85 ± 36 nmol/L at pretest and 71 ± 27 nmol/L at posttest. For the CG, it was 75 ± 34 (nmol/L) at pretest and 55 ± 23 nmol/L at posttest. Thus, not surprisingly both groups had a drop in vitamin D during winter time, but it should be noted that the CG had a higher drop than the FG. The vitamin D level in the FG was close to the recommended level in the US (i.e., 75 nmol/L) at posttest. The level of sum EPA + DHA (μg/g) in the red blood cells for the CG at pretest was 89 ± 39, and at posttest, it was 104 ± 60. For the FG, the sum EPA + DHA

(μg/g) was 83 ± 32 at pretest and 185 ± 60 at posttest (Hansen, Dahl, et al., 2014). Other details concerning content of several undesirable substances in the fish have been described elsewhere (Hansen, Dahl, et al., 2014).

Journal of Psychophysiology (2020), 34(1), 10–18

Statistical Analyses Statistica 13 was used for data analyses. The assumption of sphericity was checked by the Mauchly’s test, and this was not significant. Changes in psychophysiological responses to mental workload were investigated by a three-way repeated-measures ANOVA, 2 (FG and CG) 2 (pretest and posttest) 4 (baseline, 2-back, 3-back, recovery). Our specific hypotheses were tested regardless of significant F-tests (Rosnow & Rosenthal, 2009; Wilcox, 1987) by a priori planned comparison. The number of preplanned comparisons was 18 (i.e., within-group comparison for both FG and CG from pretest to posttest on mental workload, i.e., 2-back and 3-back (4 comparisons), from recovery to baseline, 2-back and 3-back at both pretest and posttest (12 comparisons), and finally between-group comparisons for 2-back and 3-back at posttest (2 comparisons)). To investigate the magnitude of the differences between the independent means, the effect sizes were calculated as Cohen’s d (1992).

Results Descriptive Results Means and standard deviations for HF-HRV variables are presented in Table 1.

HF-HRV Responses to Mental Workload Analyses of the HF-HRV data showed that the time x group interaction was significant, F(1, 45) = 4.30, p < .044, ηp2 = .087. The FG showed a significant increase in HF-HRV from pretest to posttest (p = .008, d = .50). There was a main effect of conditions, F(3, 135) = 12.08, p < .001, ηp2 = .21. Overall, the HF-HRV was significantly higher at recovery compared to the baseline and two WM tasks (all p’s < .001; d = .45, d = .47, and d = .47, respectively). The Time Condition Group interaction was not significant, F(3, 135) = 1.94, p = .126, ηp2 = .041. However, planned comparisons for the FG revealed a significantly higher HF-HRV during the 2-back, F(1, 45) = 10.19, p < .003; d = .72, and the 3-back, F(1, 45) = 4.85, p < .03; Ó 2018 Hogrefe Publishing

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

Table 1. Means (M) and standard deviations (SD) for high-frequency heart rate variability (HF-HRV) variables Control group (n = 18) Pretest

Fish group (n = 29) Posttest

Pretest

Posttest

Baseline

2.25

0.48

2.25

0.60

2.12

0.54

2.42

0.58

2-back

2.36

0.49

2.18

0.66

1.98

0.54

2.44

0.72

3-back

2.23

0.49

2.35

0.54

2.06

0.55

2.37

0.70

Recovery

2.55

0.46

2.34

0.49

2.49

0.70

2.64

0.64

2.5

Pretest 1.5

Posttest

HF-HRV responses to conditions

HF-HRV

Figure 2. High-frequency heart rate variability (HF-HRV) responses to mental workload for both groups pre- and post-intervention.

2.5

Pretest 1.5

Posttest

Control group

Fish group

d = .49, at posttest compared to the pretest. At pretest, the results also revealed a significantly higher HF-HRV response to the recovery compared to the 2-back, F(1, 45) = 26.49, p < .001, d = .82, 3-back, F(1, 45) = 16.03, p < .002; d = .68, and the baseline, F(1, 45) = 12.84, p < .008; d = .59, for the FG. For the FG, this pattern of results was the same at posttest. HF-HRV during recovery was significantly higher compared to the 2-back, F(1, 45) = 5.63, p = .022; d = .29, 3-back, F(1, 45) = 8.40, p < .006; d = .40, and baseline, F(1, 45) = 7.80, p < .008; d = .36 (see Figure 2). Contrary to the FG, the planned comparisons for the CG showed no changes from pretest to posttest on HF-HRV during 2-back, F(1, 45) = 0.938, p = .339; d = .29, and 3-back tasks, F(1, 45) = 0.416, p = .522; d = .23. However, the CG showed a significantly higher HF-HRV during recovery compared to 3-back, F(1, 45) = 5.37, p < .025; d = .67, and baseline, F(1, 45) = 5.37, p < .025; d = .64, at pretest, but not compared to the 2-back, F(1, 45) = 2.45, p = .124; d = .42. Notably, at posttest, the results for the CG showed no variation in HF-HRV at all. No significant differences between HF-HRV recovery and the other test conditions were found, that is, from recovery to 2-back, F(1, 45) = 2.41, p = .128; d = .28, from recovery to 3-back, F(1, 45) = Ó 2018 Hogrefe Publishing

0.01, p = .920; d = .02, and from recovery to baseline, F(1, 45) = 0.75, p = .390; d = .16. Finally, between-group differences for mental workload at posttest showed that the FG had significantly higher HF-HRV compared to the CG during the 2-back task, F(1, 45) = 7.49, p < .009; d = .72. However, no significant difference was found for the 3-back task, i(1, 45) = 0.73, p = .396; d = .36 (see also Figure 2).

Discussion Regular fatty fish consumption caused increased HF-HRV responses to mental workload. Moreover, the FG showed higher HF-HRV during recovery compared to baseline and mental workload at both pretest and posttest. This pattern was also true for the CG at pretest. However, at posttest, the CG showed sustained stress responses during the recovery phase. By using an experimental design, the present study expands earlier knowledge concerning the beneficial effects of fatty fish consumption. The changes observed in the FG from pretest to posttest indicate that regular fatty fish Journal of Psychophysiology (2020), 34(1), 10–18

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

consumption may be able to improve stress resilience in people with psychiatric illnesses. Importantly, the FG also showed higher HF-HRV to the 2-back task compared to the CG at posttest. People with stress-related disorders, such as psychiatric illnesses, may lack the capacity to cope with everyday demands, because important underlying resilience mechanisms are failing (Lovallo, 2013; McEwen et al., 2015; Reul et al., 2015). Resilience is crucial for maintenance of health, well-being, prevention of diseases, and quality of life throughout the life span (Reul et al., 2015). Psychiatric patients have been shown to have a shorter life expectancy compared to healthy subjects (Nome & Holsten, 2012). This shorter life expectancy may be related to side effects of medications and cardiovascular diseases. A review by Kemp and Quintana (2013) highlights the link between psychiatric illnesses, decreased HRV, side effects of medication on HRV, cardiovascular diseases, and mortality. The need for identification of non-pharmacological treatments able to increase HRV is also discussed. HRV plays a key role in cardiovascular diseases, and low HRV has been shown to be a major risk factor for morbidity and mortality (Thayer & Lane, 2007). Moreover, the present study is an important follow-up study of Grung et al.’s (2015) hypothesis-generating study as it tests HF-HRV responses during recovery against HFHRV responses to a WM task, a measure of pure cognitive functioning shown to cause energy expenditure and stress responses (Backs & Seljos, 1994; Hansen et al., 2003). The results revealed that the present sample overall (i.e., main effects of conditions) had a significantly higher HFHRV recovery compared to mental workload and the baseline. Importantly, these results are in line with Hansen et al. (2003) and illustrate higher stress/energy expenditure to the mental workload and the resting baseline (which may involve anticipatory anxiety), compared to the resting recovery. Looking at the two groups separately, both groups revealed a higher HF-HRV recovery compared to mental workload and baseline at pretest. At posttest, the FG showed higher recovery compared to the other conditions as well (see Figure 2). However, at posttest, the CG did not show higher HF-HRV recovery compared to baseline or mental workload. Rather, they showed a sustained suppression in HF-HRV (i.e., sustained physiological arousal) throughout the whole experimental procedure. In other words, for the CG, the physiological stress responses persisted even when the stressor was terminated. This indicates higher physiological cost, which is associated with less flexibility and adaptability to changing environment and external demands (cf. Porges, 1992), that is, poor stress resilience. Importantly, the present study adds something significant to the existing studies concerning fatty fish con-

sumption (cf. Grung et al., 2015; Hansen, Dahl, et al., 2014; Hansen, Olson, et al., 2014) as it shows that a diet without fatty fish causes poor recovery after a mild stress regime. As there is a relationship between physiological responses to stress and health, both physical and mental health, these findings have important implications. The present pattern of results may be related to different plausible explanations. Importantly, the intervention took place primarily during the winter. Since vitamin D status varies throughout the year because of seasonal changes in sunlight exposure, it is of importance to mention that the CG had a suboptimal vitamin D status (55 nmol/L) at posttest (i.e., February), while the FG was close to the recommended level (for US population: > 75 nmol/L) at posttest (Hansen, Dahl, et al., 2014). One may therefore speculate whether this lack of increased HF-HRV recovery found in the CG at posttest could be caused by the low vitamin D status at posttest. Since fatty fish is a rich source of vitamin D (VKM, 2014), and a relationship between vitamin D and HRV has been observed (Hansen, Olson, et al., 2014), fatty fish consumption may be a powerful strategy to improve resilience to stress, especially during winter time. Fatty fish is also a rich source of omega-3. Interestingly, the present results correspond with Matsumura et al. (2017) who investigated the effects of omega-3 supplementation in accident survivors (post-traumatic stress disorder) and found reduced heart rate during both rest condition (i.e., baseline) and a task condition (script-driven imagery) compared to a placebo group. However, the mechanisms involved in the beneficial effect of fish consumption are not yet elucidated. As argued by Mozaffarian and colleagues (2008), the effects may be direct or indirect. The present study focuses primarily on fish as whole food and PNS in relation to mental workload. Thus, to gain deeper understanding about the effects of fatty fish consumption on stress resilience, future studies should also focus on other types of stress (e.g., social stress), different key nutrients found in fish (e.g., selenium, B12, vitamin D), and other reliable biomarkers of stress (e.g., cortisol responses and neurotransmitters such as serotonin). Future studies should also take into account some of the present study’s limitations. The sample size is small, and it only includes males. Thus, future studies should investigate HF-HRV responses to stress in a larger sample including both males and females as there might be gender differences with regard to stress management and resilience. As the sample in the present study is forensic inpatients, one should also be careful with regard to generalization of the results. Importantly, the present results should be interpreted with some caution. Despite these limitations, the present study has notable strengths. Not many long-term fish interventions

Journal of Psychophysiology (2020), 34(1), 10–18

Ó 2018 Hogrefe Publishing

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

exist. By nature, dietary intervention studies are difficult to carry out successfully. As the study took place in a secure facility, there was a high degree of accuracy concerning frequency, amount, and type of fish consumed. Even if blinding is difficult in dietary intervention studies, both the FG and the CG underwent an intervention as the CG received an alternative meal different from the other meals served as part of the regular menu. The present study also illuminates the complexity and importance of differentiating between different conditions when investigating the effect of fish consumption. As fish consumption increased a resting baseline HF-HRV, but not a resting recovery HF-HRV (Hansen, Dahl, et al., 2014), the question is whether the resting baseline before an experimental condition should be regarded as a measure of stress due to possible performance anxiety, and expectations of the experimental situation itself, rather than rest. However, further investigation is necessary. Future studies should include a delayed resting condition, that is, a resting HRV the day after the experiment, to control for factors such as expectancy anxiety before and relief after the experiment.

Conclusion The current study suggests that fish interventions have a therapeutic effect on HF-HRV and may be particularly important for boosting resilience to stress. What is especially noteworthy in this study is that a diet without fatty fish during winter time may have negative psychophysical impacts, as it caused a sustained suppression in HF-HRV during a post-stress period (recovery). This is of particular importance with regard to stress resilience; therefore, the study also has important implications for health promotion and prevention of stress-related diseases. At the same time, as the mechanisms involved are not clearly understood, more experimental fish intervention studies investigating underlying mechanisms in relation to both rest and stress conditions are of importance.

Acknowledgments The authors wish to thank all participants for their cooperation. Thanks go to Grethe Rosenlund at Skretting for providing the Atlantic salmon. We also wish to thank the kitchen staff for preparing all the meals and the health department for collecting blood samples at the secure forensic inpatient facility in US. Funding: This work was supported by the “Program Board Nutrition, University of Bergen, Norway” (#1516).

Ó 2018 Hogrefe Publishing

Ethics and Disclosure Statements All participants signed an informed consent form, and the project was approved by the Ethics Committee at Sand Ridge. The authors declare no conflicts of interest, including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

References Alshurafa, M., Briel, M., Akl, M., Haines, T., Moayyedi, P., Gentles, S. J., . . . Guyatt, G. H. (2012). Inconsistent definitions for intention-to-treat in relation to missing outcome data: Systematic review of the methods literature. PLoS One, 7, e49163. https://doi.org/10.1371/journal.pone.0049163 Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Review of General Psychology, 10, 229–240. https://doi.org/10.1037/1089-2680. 10.3.229 Backs, R. W. (2001). An autonomic space approach to the psychophysiological assessment of mental workload. In P. A. Hancock & P. A. Desmond (Eds.), Stress, workload, and fatigue (pp. 279–289). London, UK: Erlbaum. Backs, R. W., & Seljos, K. A. (1994). Metabolic and cardiorespiratory measures of mental effort: The effects of level of difficulty in working memory task. International Journal of Psychophysiology, 16, 57–68. https://doi.org/10.1016/0167-8760(94) 90042-6 Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews: Neuroscience, 4, 829–839. https:// doi.org/10.1038/nrn1201 Best, J. R., Miller, P. H., & Jones, L. L. (2009). Executive functions after age 5: Changes and correlates. Developmental Review, 29, 180–200. https://doi.org/10.1016/j.dr.2009.05.002 Brage, S., Brage, N., Franks, P. W., Ekelund, U., & Wareham, N. J. (2005). Reliability and validity of the combined heart rate and movement sensor Actiheart. European Journal of Clinical Nutrition, 59, 561–570. https://doi.org/10.1038/sj.ejcn. 1602118 Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. https://doi.org/10.1037/0033-2909.112.1.155 Erkkilä, A., Schwab, U. S., De Mello, V. D. E., Lappalainen, T., Mussalo, H., Lehto, S., . . . Uustupa, M. I. J. (2008). Effects of fatty and lean fish intake on blood pressure in subjects with coronary heart disease using multiple medications. European Journal of Nutrition, 47, 319–328. https://doi.org/10.1007/ s00394-008-0728-5 Forcier, K., Stroud, L. R., Papandonatos, G. D., Hitsman, B., Reiches, M., Krishnamoorthy, J., & Niaura, R. (2006). Links between physical fitness and cardiovascular reactivity and recovery to psychological stressors: A meta-analysis. Health Psychology, 25, 723–739. https://doi.org/10.1037/02786133.25.6.723 Grung, B., Hansen, A. L., Berg, M., Møen-Knudseth, M. P., Olson, G., Thornton, D., . . . Thayer, J. F. (2015). Exploratory multivariate analysis of the effect of fatty fish consumption and medicinal use on heart rate and heart rate variability data. Frontiers of Psychology, 6, 135. https://doi.org/10.3389/ fpsyg.2015.00135

Journal of Psychophysiology (2020), 34(1), 10–18

A. L. Hansen et al., Fatty Fish Consumption and Stress Resilience

Hansen, A. L., Ambroziak, G., Thornton, D., Dahl, L., & Grung, B. (2018). Age and IQ explained working memory performance in a RCT with fatty fish in a group of forensic inpatients. Nutrition, Health and Aging, 22, 513–518. https://doi.org/10.1007/ s12603-018-0998-4 Hansen, A. L., Dahl, L., Bakke, L., Frøyland, L., & Thayer, J. F. (2010). Fish consumption and heart rate variability. Preliminary results. Journal of Psychophysiology, 24, 41–47. https://doi.org/ 10.1027/0269-8803/a000005 Hansen, A. L., Dahl, L., Olson, G., Thornton, D., Graff, I. E., Frøyland, L., . . . Pallesen, S. (2014). Fish consumption, sleep, daily functioning and heart rate variability. Journal of Clinical Sleep Medicine, 10, 567–575. https://doi.org/10.5664/jcsm.3714 Hansen, A. L., Dahl, L., Olson, G., Thornton, D., Grung, B., & Thayer, J. F. (2015). A long-term fatty fish intervention improved executive function in inpatients with antisocial traits and a history of alcohol and drug abuse. Scandinavian Journal of Psychology, 56, 467–474. https://doi.org/10.1111/sjop.12229 Hansen, A. L., Johnsen, B. H., & Thayer, J. F. (2003). Vagal influence on working memory and attention. International Journal of Psychophysiology, 48, 263–274. https://doi.org/ 10.1016/S0167-8760(03)00073-4 Hansen, A. L., Olson, G., Dahl, L., Thornton, D., Grung, B., Graff, I. E., . . . Thayer, J. F. (2014). Reduced anxiety in forensic inpatients after a long-term intervention with Atlantic salmon. Nutrients, 6, 5392–5404. https://doi.org/10.3390/nu6125405 Hare, R. D. (1991). The Hare Psychopathy Checklist-Revised. Toronto, Canada: Multi-Health Systems. Johnsen, B. H., Thayer, J. F., Laberg, J. C., Wormnes, B., Raadal, M., Skaret, E., . . . Berg, E. (2003). Physiological and attentional characteristics of patients with dental anxiety. Journal of Anxiety Disorders, 40, 1–13. https://doi.org/10.1016/S08876185(02)00178-0 Kemp, A. H., & Quintana, D. S. (2013). The relationship between mental and physical health: Insights from the study of heart rate variability. International Journal of Psychophysiology, 89, 288–296. https://doi.org/10.1016/j.ijpsycho.2013.06.018 Linden, W., Earle, T. L., Gerin, W., & Christenfeld, N. (1997). Physiological stress reactivity and recovery: Conceptual siblings separated at birth? Journal of Psychosomatic Research, 42, 117–135. https://doi.org/10.1016/S0022-399(96)00240-1 Lovallo, W. R. (2013). Early life adversity reduces stress reactivity and enhances impulsive behavior: Implications for health behaviors. International Journal of Psychophysiology, 90, 8–16. https://doi.org/10.1016/j.ijpsycho.2012.10.006 Malik, M. (1996). Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17, 354–381. Matsumura, K., Noguchi, H., Nishi, D., Hamazaki, K., Hamazaki, T., & Matsukuoka, Y. J. (2017). Effects of omega-3 polyunsaturated fatty acids on psychophysiological symptoms of post-traumatic stress disorder in accident survivors: A randomized, doubleblind, placebo-controlled trial. Journal of Affective Disorders, 224, 27–31. https://doi.org/10.1016/j.jad.2016.05.054 McEwen, B. S., Gay, J. G., & Nasca, C. (2015). Recognizing resilience: Learning from the effects of stress on the brain. Neurobiology of Stress, 1, 1–11. https://doi.org/10.1016/ j.ynstr.2014.09.001 Mozaffarian, D., Stein, P. K., Prineas, R. J., & Siscovick, D. S. (2008). Dietary fish ω-3 fatty acid consumption and heart rate variability in US adults. Circulation, 117, 1130–1137. https:// doi.org/10.1161/CIRCULATIONAHA.107.732826 Nome, S., & Holsten, F. (2012). Changes in mortality after first psychiatric admission: A 20-year prospective longitudinal

clinical study. Nordic Journal of Psychiatry, 66, 97–106. https://doi.org/10.3109/08039488.2011.605170 Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: A Meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25, 46–59. https://doi.org/10.1002/hbm.20131 Perica, M. M., & Delas, I. (2011). Essential fatty acids and psychiatric disorders. Nutrition in Clinical Practice, 26, 409– 425. https://doi.org/10.1177/0884533611411306 Porges, S. W. (1992). Autonomic regulation and attention. In B. A. Campbell, H. Hayne, & R. Richardson (Eds.), Attention and information processing in infants and adults (pp. 201–223). Hillside, NJ: Erlbaum. Reul, J. M. H. M., Collins, A., Saliba, R., Mifsud, K. R., Carter, S. D., Gutierrez-Mecinas, M., . . . Linthorst, A. C. E. (2015). Glucocorticoids, epigenetic control and stress resilience. Neurobiology of Stress, 1, 44–59. https://doi.org/10.1016/j.ynstr.2014.10.001 Rosnow, R. L., & Rosenthal, R. (2009). Effect sizes: Why, when, and how to use them. Journal of Psychology, 217, 6–14. https:// doi.org/10.1027/0044-3409.217.1.6 Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., & Wager, R. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a maker of stress and health. Neuroscience & Biobehavioral Reviews, 36, 747–756. https://doi.org/10.1016/j.neubiorev.2011.11.009 Thayer, J. F., Hansen, A. L., & Johnsen, B. H. (2010). The noninvasive assessment of Autonomic influences on the heart using impedance cardiography and heart rate variability. In A. Steptoe (Ed.), Handbook of behavioural medicine – Methods and applications. London, UK: Springer. Thayer, J. F., Hansen, A. L., Saus, E. R., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The Neurovisceral Integration Perspective. Annals of Behavioral Medicine, 37, 141–153. https://doi.org/ 10.1007/s12160-009-9101-z Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion Regulation and dysregulation. Journal of Affective Disorders, 61, 201–216. https://doi.org/10.1016/ S0165-0327(00)00338 Thayer, J. F., & Lane, R. D. (2007). The role of vagal function in the risk for cardiovascular disease and mortality. Biological Psychology, 74, 224–242. https://doi.org/10.1016/j.biopsycho.2005.11.013 VKM. (2014). Benefit-risk assessment of fish and fish products in the Norwegian diet – an update. Scientific Opinion of the Scientific Steering Committee. VKM Report 2014: 15. Oslo, Norway: Author. Retrieved from https://vkm.no/download/18.2994e95b15cc54507161ea1a/1498222018046/0a646edc5e.pdf Wechsler, D. (2008). Wechsler Adult Intelligence Scale – Fourth Edition. San Antonio, TX: Pearson. Wilcox, R. R. (1987). New designs in analysis of variance. Annual Review of Psychology, 38, 29–60. https://doi.org/10.1146/ annurev.ps.38.020187.000333

Journal of Psychophysiology (2020), 34(1), 10–18

Received July 2, 2017 Revision received July 4, 2018 Accepted July 31, 2018 Published online December 14, 2018 Anita L. Hansen Department of Clinical Psychology University of Bergen Christiesgt. 12 5015 Bergen Norway anita.hansen@uib.no

Ó 2018 Hogrefe Publishing

Article

The Evaluation of Creative Ideas in Older and Younger Adults A View From sLORETA Study Evgeniya Yu. Privodnova,1,3 Nina V. Volf,1,2 and Gennady G. Knyazev1 Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine”, Novosibirsk,

Russian Federation Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russian Federation

2 3

Department of Psychology, Novosibirsk State University, Novosibirsk, Russian Federation

Abstract: The ability to solve problems of divergent type is one of the most intact functions in successful aging. However, neurophysiologic mechanisms that support the efficiency of creative thinking remain largely unknown. This study was aimed to investigate age-related difference in localized induced electroencephalogram (EEG) changes during creative idea evaluation stage of divergent problem-solving (Alternate Uses Task), using standardized low-resolution brain electromagnetic tomography. Younger (45 women, 44 men, Mage = 22.1 years, age range: 18–30 years) and older adults (46 women, 43 men, Mage = 64.9 years, age range: 55–75 years) participated in the study. Higher synchronization in individually adjusted theta frequency band [from (individual alpha peak frequency 6 Hz) to (individual alpha peak frequency 4 Hz)] in anterior areas with the maximum values in anterior cingulate gyrus was revealed in older as compared with younger participants by group contrast. Higher desynchronization in wide beta range [from (individual alpha peak frequency +2 Hz) to 30 Hz] was localized in posterior brain regions with the highest values in posterior cingulate gyrus, precuneus, and parietal lobule in older adults. Induced beta 2 synchronization was positively correlated with originality (as measured by the mean frequency of ideas) in younger and years of education in older subjects. Based on the data, it was supposed that controlling the decision-making processes is more important for older adults while maintenance of the internal image of elements’ recombination may play essential role for younger subjects. Keywords: divergent thinking, aging, sLORETA, idea evaluation, theta, beta

Changes in creative abilities with aging have previously been researched from a psychometric standpoint, but the results have been somewhat contradictory. Earlier studies have shown age-related decline in creativity in the late years of life (Alpaugh & Birren, 1977; Alpaugh, Parham, Cole, & Birren, 1982; McCrae, Arenberg, & Costa, 1987; Ruth & Birren, 1985). Recent studies support the idea of long-term preservation of creative functions (Addis, Pan, Musicaro, & Schacter, 2016; Leon, Altmann, Abrams, Gonzalez, & Heilman, 2014; Madore, Jing, & Schacter, 2016; Palmiero, Giacomo, & Passafiume, 2014). At the moment, there is lack of sufficient evidence of neural mechanisms underlying the preservation of the creative potential in older adults. Creative thinking is not a homogeneous cognitive process, and the methodological importance of studying the neurophysiologic basis of creativity subprocesses has been emphasized by some researchers (Dietrich & Kanso, 2010). The majority of psychological concepts of creative cognition are related one way or another to dual model of creativity, which considers this ability as the result of shifting between generative and explanatory/evaluation phases (Basadur, 1995; Finke, Ward, & Smith, 1992; Gabora, 2005; Ó 2018 Hogrefe Publishing

Howard-Jones, 2002). Every stage may consist of several specific subprocesses, each of which is manifested in distinctive patterns of brain activity. The idea generation is the most widely studied process. It is based on retrieval of information stored in long-term memory and includes associative processes (Benedek, Könen, & Neubauer, 2012; Benedek et al., 2014; Radel, Davranche, Fournier, & Dietrich, 2015). Evaluative process is much less explored. It is thought to occur during rejection/approval of preliminary hypotheses or prior to making decision of finding creative response. Evaluative phase of creative thinking has been suggested to be associated with the assessment of significance, applicability, appropriateness, and originality of created ideas (Mayseless, Aharon-Peretz, & ShamayTsoory, 2014; Sowden, Pringle, & Gabora, 2015). We found only a few studies that directly examined idea evaluation process during divergent problem-solving, and all of them have been performed on young subjects (Ellamil, Dobson, Beeman, & Christoff, 2012; Kröger et al., 2012, 2013; Mayseless et al., 2014). In studies where subjects were asked to evaluate somebody else’s creative idea (unusualness or appropriateness

Journal of Psychophysiology (2020), 34(1), 19–34 https://doi.org/10.1027/0269-8803/a000232

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

of presented idea), reaction time of problem-solving was about 650 ms during evaluation of idea of alternate uses (Kröger et al., 2012) and 600–700 ms during evaluation of metaphors’ creativity (Rutter et al., 2012). It has been revealed using the method of event-related potential that unusualness or novelty of someone else’s solution in Alternate Uses Task was estimated in 300–500 ms after its presentation and idea appropriateness was assessed in a later time window (500–900 ms). So the time interval of idea evaluation took place between 300 and 900 ms (i.e., about 600 ms). This was reflected by N 400 amplitude, an eventrelated potential component indexing the processing of semantic information (Kröger et al., 2013). In our previous study in order to investigate age-related difference at the final stage of divergent problem-solving (during 600 ms before preparation of motor response signaling the finding of task solution), we performed the channel-level analysis of electroencephalogram (EEG) power reactivity (i.e., induced EEG power changes) (Privodnova, Volf, & Knyazev, 2017). This method is a direct measure of neuronal activity (Nunez & Silberstein, 2000) and has excellent temporal resolution. Analysis of frequency characteristics makes it possible to gain understanding of cognitive processes, such as memory and attention (Freunberger, Werkle-Bergner, Griesmayr, Lindenberger, & Klimesch, 2011; Klimesch, 1999, 2012). Age difference in event-related synchronization/desynchronization (ERS/ERD) was found in theta and beta frequency ranges. However, spatial resolution of the method is relatively low (5–9 cm; Babiloni, Cincotti, Carducci, Rossini, & Babiloni, 2001; Nunez et al., 1994). Additionally, we had to combine the electrode groups corresponding to eight brain areas due to taking into account a large number of independent variables. Thus, clear spatial pattern of age-related EEG differences between younger and older adults was absent in that research. Methods for localization of the neuronal generators responsible for measured extracranial EEG activity are termed inverse solutions. In cognitive neuroscience, such techniques are used to localize the sources of the different frequency bands, to assess the dynamics of cognitive functions and mental states (Grech et al., 2008). In this regard, the goal of the present study was to investigate age-related difference in the spatial localization of changes of EEG power, induced by last stage of solving divergent problem, in the preliminarily identified time interval within the final stage of Alternate Uses Task implementation. Creative idea evaluation involves such subprocesses as selection of information, emotional evaluation of the decision, maintenance of the internal image of the recombination of elements, and visual representation of the idea. Changes in the theta rhythm can be related to both the selection of information (Cohen & Donner, 2013; Luu, Tucker, & Makeig, 2004; Van Driel, Sligte, Linders, Elport, Journal of Psychophysiology (2020), 34(1), 19–34

& Cohen, 2015) and the emotional evaluation of the decision (Knyazev, 2007). Based on our earlier studies, we assume that the age-related differences in theta rhythm reactivity identified in this study will be more closely related to the features of cognitive control processes (Privodnova & Volf, 2017), that is, older adults tend to enhance top-down control during the decision-making and creative idea evaluation. This hypothesis can be confirmed by differences in the activity of such frontal brain structures as cingulate and dorsolateral prefrontal cortex (Aron, 2007; Dosenbach et al., 2007; Posner, Rothbart, Sheese, & Tang, 2007; Wang, Ulbert, Schomer, Marinkovic, & Halgren, 2005). Experimental findings have brought evidence that beta oscillations most prominent at parieto-occipital sites play essential role in providing visual attention (Gola, Magnuski, Szumska, & Wróbel, 2013; Güntekin, Emek-Savasß, Kurt, Yener, & Basßar, 2013; Kamiński, Brzezicka, Gola, & Wróbel, 2012; Kukleta, Bob, Brázdil, Roman, & Rektor, 2009) and internal focus of attention during meditation (Travis & Shear, 2010). A large number of studies have reported that the beta ERS is associated with information integration. Left temporal localization is shown during lexical integration (Lewis & Bastiaansen, 2015; Lewis, Wang, & Bastiaansen, 2015; Magyari, Bastiaansen, de Ruiter, & Levinson, 2014) while predominant centro-parietal scalp distribution – during integrative perceptual processes (Aissani, Martinerie, Yahia-Cherif, Paradis, & Lorenceau, 2014; Donner & Siegel, 2011; Donner et al., 2007; Göschl, Friese, Daume, König, & Engel, 2015). Beta rhythm ERD is also associated with motor imagery (Neuper, Wörtz, & Pfurtscheller, 2006), which may be important for task, involving use of object (Järveläinen, Schürmann, & Hari, 2004; Pineda & Hecht, 2009). Our previous study has revealed generalized difference in beta rhythm in younger and older subjects at the stage of creative idea evaluation (Privodnova et al., 2017). Experimental findings have brought evidence that the posterior cingulate cortex and precuneus are involved in the realization of such process as the maintenance of the internal image of elements’ recombination (Buckner, Andrews-Hanna, & Schacter, 2008; Chen et al., 2015; Leech & Sharp, 2014; Mason et al., 2007). Visual representation of the idea may be associated with activity of such occipital brain structures as cuneus, lingual, and fusiform gyrus (Binder, Desai, Graves, & Conant, 2009; Grill-Spector & Weiner, 2014; Hall et al., 2014; Machielsen, Rombouts, Barkhof, Scheltens, & Witter, 2000) to the greatest extent. During the imagination of motor activity, as well as during its execution, activation of the premotor, supplementary motor area, parietal cortex is observed (Dechent, Merboldt, & Frahm, 2004; Hanakawa et al., 2003; Park et al., 2015). Against the background of functional meaning of the above-mentioned brain structures, we suppose that Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

localization of areas with maximum age difference will help to interpret the EEG data as associated with special cognitive processes. So, the current work is aimed at analysis of the spatial localization of age-related effects within previously identified in our preliminary study (Privodnova et al., 2017) EEG rhythms, namely, theta, beta 1, beta 2.

Method Participants One hundred seventy-eight younger adults (YA, 45 women, 44 men, Mage = 22.1 years, age range: 18–30 years) and older adults (OA, 46 women, 43 men, Mage = 64.9 years, age range: 55–75 years) participated in our study. All subjects were graduate and postgraduate students or full-time employees – scientific, administrative, and technical staff of research institutes and universities. All participants were right-handed according to the Annett questionnaire (Annett, 1970), had normal or corrected-to-normal vision, and reported no history of neurologic or psychiatric disorder, major medical disorders (cardiac illness, stroke, cancer), head injury. The experiment was conducted in accordance with the Declaration of Helsinki; informed, written consent to the study was obtained from all subjects before the examinations. The present study was divided into 2 parts: EEG session and psychological assessment (included modified version of the Flanker test); each was performed in different days. Procedures were separated from each other by 2–7 days and were counterbalanced across participants.

EEG Recording and Preprocessing The EEG data were recorded from 60 Ag–AgCl electrodes mounted in an elastic cap according to the modified version of the international 10–20 system (American Electroencephalographic Society, 1991) using “Neuroscan 4.4” (Compumedics Neuroscan USA Ltd., Charlotte, NC, USA). Frontocentral electrode was used as the ground, electronically linked mastoid electrodes as reference. Electrode impedances were below 5 kΩ. The EEG was digitized at a rate of 1,000 Hz and amplified using “Neuroscan (USA)” amplifiers with a gain of 250 and a bandpass of 0–50 Hz. Horizontal and vertical electrooculograms were used to remove eye movement artifacts via Automatic Artifact Removal (AAR; Gómez-Herrero, 2007), but they cannot eliminate non-eye activity. Remaining artifacts from EEG data were rejected by independent component analysis

Ó 2018 Hogrefe Publishing

via the EEGLAB toolbox (https://sccn.ucsd.edu/eeglab/) (Jung et al., 2000). EEG was registered under resting condition and during Alternate Uses Task implementation. Participants were asked to be relaxed and to avoid moving and blinking. The EEG session started with recording of two 3-min EEG sequences under resting condition first one with eyes closed and the second one with eyes open. Each experimental trial started from five-second presentation of central fixation point, signaling the onset of the preparation interval. The stimulus word was presented in the center of the monitor for 5 s. Participants were given 15 s to generate the answer (only one solution in each trial), whereupon the word “Answer” appeared on the screen, signaling the need to press a keyboard bimanually and report the answer. If subject had solved the problem earlier, he or she could induce the appearance of word “Answer” by pressing response buttons. In every trial, subjects had to press a button, when he/she found the solution. After button press, solutions had to be verbalized by subjects and were recorded by the experimenter. The word “Answer” was followed by the cross in the center of the monitor, which indicated a rest between trials (10 s). After that, regardless of presence/absence of subject’s answer, the new trial began. Mean number of events with provided answer was 27 of possible 30 in younger and 26.6 in older adults. The course of the experiment is shown in Figure 1.

EEG Analysis Choice of Time Interval Under Analysis The present study focuses on creative idea evaluation. The choice of time period in the current study is based on the fact that every solution in divergent task is preceded by an evaluation of previously generated idea. So creative idea evaluation is reflected in time interval before the button press, signaling about finding the solution. Because subjects spend some time preparing finger movement prior to press of the button, signaling about finding the answer, we took into account reaction time (RT). It is well known that reaction time increases with aging (Der & Deary, 2006; Fozard, Vercryssen, Reynolds, Hancock, & Quilter, 1994; Meijer, de Groot, van Gerven, van Boxtel, & Jolles, 2009; Salthouse, 1996), that is why simple sensorimotor reaction time for each subject was assessed to restrict interval of EEG analysis to processes prior to planning/preparation of motor response. An individual simple sensorimotor reaction time was calculated for each subject based on data of mean reaction time to congruent and neutral stimuli in Attentional Network Test (ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002). This approach allows taking into account

Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

(A)

(B)

Figure 1. Sequence of events within a trial of the Alternate Uses Task for the object “Brick”: (A) Schematic illustration and timeline of temporal intervals taking into consideration. (B) Individual reaction time assessed for each subject corresponds to simple sensorimotor reaction time. The time point that preceded the moment of pressing the buttons by reaction time was taken as zero starting point. Time window from 600 to 300 ms before the motor preparation was considered as test interval (is marked with a circle).

individual (including age-related) difference in RT in order to isolate the cognitive processes of idea evaluation from organization of finger movement. Experimental indicators of mean reaction time are presented in Table 1. The time point that preceded the moment of pressing the keyboard by reaction time was considered as a zero starting point. As it was described in introduction, time of idea evaluation process is restricted to an interval of about 600 ms (Kröger et al., 2013). According to our previous work (Privodnova et al., 2017), time window from 600 ms to 300 ms before a motor preparation was extracted from the time interval of 600 ms, because EEG indicators during

this period were significantly different from those during later time window (from 300 to 0 ms). Based on temporal dynamic of theta and alpha 2,3 ERD common for all participants, we supposed that time window from 600 ms to 300 ms before a motor preparation is more independent of components associated with the organization of the motor response. For each appropriate trial, time window from 600 ms to 300 ms before zero starting point was analyzed (see Figure 1). In order to explore spontaneous voluntary generation of creative solutions, trials in which subjects reported the

Table 1. Demographic characteristics and test results in younger and older subjects (mean and standard deviation) and its age-related difference (level of significance p, effect size Cohen’s d) Measure

Younger

Older

M (SD)

Age difference p

Cohen’s d

Age (years)

22.10 (3.2)

64.90 (6.70)

Years of education*

14.37 (0.79)

15.75 (2.53)

.000

0.741

Individual alpha peak frequency (Hz)*

10.16 (0.78)

9.78 (0.80)

.002

0.479

Reaction time (ms)*

280 (45)

343 (51)

.000

1.320

Executive control (ms)

95 (36)

105 (40)

.100

0.230

Originality of creative ideas (scores)

0.20 (0.09)

0.18 (0.08)

.074

0.246

Speed of creative problem-solving (s)*

8.77 (2.48)

7.05 (2.80)

.000

0.647

Note. *Statistically significant difference.

Journal of Psychophysiology (2020), 34(1), 19–34

Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

solution after presentation of word “Answer” and trials with no response were excluded. Data from subjects who had at least 15 artifact-free epochs were taken in further analysis, and the number of epochs was roughly equal across age groups (mean of epochs = 20.7 in YA and M = 20.8 in OA). Five participants from OA and six from YA were excluded due to the insufficient number of accepted epochs.

Definition of Individually Adjusted Frequency Bands Considering shift of peak alpha power toward lower frequencies in older adults (Klimesch, 1999), bandwidths for the frequency bands were defined using individual alpha peak frequency (IAF) as the anchor point. We identified frequency bandwidths as follows: theta [(IAF 6) to (IAF 4)]; alpha 1 [(IAF 4) to (IAF 2)]; alpha 2 [(IAF 2) to (IAF)]; alpha 3 [(IAF to (IAF +2)]; beta 1 [(IAF +2) to 20 Hz)]; beta 2 (20–30 Hz) (Doppelmayr, Klimesch, Pachinger, & Ripper, 1998). IAF was calculated for posterior recording sites from the resting EEG (eyes closed condition). IAFs for younger and older subjects are presented in Table 1.

sLORETA Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) is a linearly distributed solution that is based on standardized values of the current density estimates given by the minimum norm solution. The sLORETA functions on the assumption that the EEGs measured on the scalp are generated by highly synchronized postsynaptic potentials occurring in large clusters of neurons; that is, neighboring voxels have a maximal similar electrical activity (Pascual-Marqui, 2002). sLORETA inverse solutions are restricted to 6,239 voxels (spatial resolution of 5 mm) within cortical gray matter and hippocampus with zero localization error. For forward modeling, sLORETA uses simulations which were carried out in a three-shell spherical head model registered to the Talairach human brain atlas (Talairach & Tournoux, 1988), available as a digitized MRI from the Brain Imaging Centre, Montreal Neurological Institute (1988). Artifact-free epochs were supplied for time-varying cross-spectrum calculation in sLORETA. The regularization factor was set at 1/100 (Congedo, 2006). We analyzed EEG files 2,617 ms in duration, which include prestimulus time interval 1,500 ms in duration and time period which lasted for 1,117 ms from the time point of 600 ms prior to zero starting point. We calculated power using local fast Fourier transform with a sliding time window (the continuous Gaussian window), with width of 440 ms for each epoch using sLORETA software (Pascual-Marqui, 2002) via option “EEGs to time-varying cross Ó 2018 Hogrefe Publishing

spectrum.” With sample rate of 250 Hz, time resolution was 4 ms. Frequency resolution was 0.24 Hz. We estimated current source density for each of 6,239 voxels via “EEGs to time-varying cross spectrum to Loreta” in 3 individually adjusted theta, beta 1, beta 2 frequency bands. Then, we extracted current source density indicators for time windows 1,500 ms before task presentation (reference interval) and from 600 to 300 ms before zero starting point (test interval). Finally, induced oscillations for each voxel were calculated as follows: the log (test) log (reference) current source density estimates. Increases in current source density estimates relative to a pre-event baseline period are called event-related synchronization, whereas decreases are termed event-related desynchronization. ERS/ERD meanings were submitted for statistical nonparametrical mapping.

Testing of Verbal Creativity Alternate Uses Task, in which a subject has to generate unusual uses for common objects (Guilford, Christensen, Merrifield, & Wilson, 1978), was used to investigate the verbal divergent thinking. Five words (brick, pencil, paper, clip, and tin) were presented pseudorandomly such that no word was presented two times in a row, six times each (Razumnikova, 2016). Problem-solving performance was assessed by such indicators as originality and speed of problem-solving. Originality scores were calculated as 1/N, where N – a number of similar answers in computerized database containing total responses from 178 participants of both age groups. Mean originality was computed for every subject. Speed of problem-solving scored in seconds was measured as a time interval from the stimulus onset to the press of a button signaling the finding of task solution.

Flanker Test Subjects performed modified version of the Flanker test (Kopp, Rist, & Mattler, 1996), namely – ANT (Fan et al., 2002). Test stimuli consisted of a row of five visually presented horizontal black lines, with arrowheads pointing leftward or rightward. The target was a leftward or rightward arrowhead at the center. This target was flanked on either side by two arrows in the same direction (congruent condition), or in the opposite direction (incongruent condition), or by lines (neutral condition). The participants’ task was to identify the direction of the centrally presented arrow by pressing one key for the left direction and a different key for the right direction. First, there was a fixation period for a random variable duration (400–1,600 ms). Then, a Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

warning cue was presented for 100 ms. After a fixed delay of 1,600 ms, the target was presented until the participant responded or 1,700 ms elapsed. A session consisted of a 24trial full-feedback practice block and 3 experimental blocks of trials with no feedback. Each experimental block consisted of 96 trials. The presentation of trials was in a random order. Participants were instructed to respond as fast and accurately as possible. Reaction time to congruent and neutral flanking conditions was used to assess individual sensorimotor reaction time. To assess a level of executive functions in older adults, we also calculated the executive control (conflict effect) by subtracting the mean RT of all congruent flanking conditions, summed across cue types, from the mean RT of incongruent flanking conditions (for more details, see Fan et al., 2002). It is well known that reaction time is significantly greater in incompatible than compatible conditions (the difference termed the Flanker effect; Eriksen & Eriksen, 1974). Thus, executive control indicator, scored in ms, implies the control processes that are involved to manage the conflict of Flanker interference.

For direct contrasts of reactivity, significant level was set at p = .016 due to the consideration of 3 frequency bands. Analysis of variance (ANOVA) in Statistica was used for statistical evaluation of difference in demographic characteristics and test results between younger and older subjects. One-way ANOVAs were performed for originality (measured in scores), speed of problem-solving (measured in ms), executive control (measured in ms), individual alpha peak frequency (measured in Hz), reaction time (measured in ms), and years of education as dependent variables and age group as categorical predictors (two factor levels: younger and older adults). Significant level was set at p = .008 due to the performance of six comparisons. The effect sizes were calculated using Cohen’s d (Soper, 2017).

Results Behavioral Results

Statistical Analysis We carried out the following analysis of data in theta, beta 1, beta 2 rhythms: (1) direct contrasts of reactivity of current source density estimates between the two groups; (2) direct contrasts of prestimulus current source density estimates between the two groups; (3) regression of reactivity of current source density estimates versus originality scores (separately at each age group). Group differences were tested with null hypothesis that current source density estimates would be the same whatever the age group (for direct contrasts) or that there was no correlation between variables (for regression statistics). Current source density data were analyzed voxel-wise using statistical nonparametrical mapping implemented in the sLORETA package (SnPM; Pascual-Marqui, 2002). This methodology is based on estimating, via permutation, the empirical probability distribution for the max-statistic (e.g., the maximum of a t or an F statistic), under the null hypothesis. Five thousand randomizations (permutations) were used. Holmes’ nonparametric correction for multiple comparisons was applied (i.e., for the collection of tests performed for all voxels and for all discrete frequencies; Holmes, Blair, Watson, & Ford, 1996; Nichols & Holmes, 2002). Due to the nonparametric nature of the method, its validity need not rely on any assumption of Gaussianity. Complete overview of the methodology and details about the properties (e.g., pertaining to its nonparametric nature, and pertaining to how it properly corrects for multiple testing) can be found in (Nichols & Holmes, 2002). Journal of Psychophysiology (2020), 34(1), 19–34

Compared with YA, OA performed divergent task faster. There was also moderate evidence for no effect of age on originality scores. Analysis of variance revealed no significant age difference in originality scores. Observed effect size was small for that indicator (d = .246) according to Cohen’s guidelines that categorized effect sizes as “small, d = .2”, “medium, d = .5”, and “large, d = .8” (Cohen, 1988). For this Cohen’s d, the probability of superiority (PS) is 57% (Fritz, Morris, & Richler, 2012). That is, if we compared randomly chosen younger and older adults, the younger would have more originality scores than the older counterparts for 57% of the comparisons. Younger adults had lower level of education in comparison with OA due to the fact that most of younger participants did not yet complete basic higher education, whereas most of the older adults completed basic higher education and some of them also had PhD degree. OA and YA did not differ in executive control, scored in ms (see Table 1).

sLORETA Pairwise comparisons of current source density estimates in prestimulus interval in younger and older adults have revealed significant difference in all three rhythms under interest. Lower theta density estimates were shown in OA than YA with maximum values in anterior cingulate and medial frontal gyrus bilaterally (t < 7; p < .0003). In beta 1 and beta 2 bands, higher current source density estimates were revealed in OA as compared to YA with the strongest Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Figure 2. Age difference in current source density estimates in prestimulus time interval. In the theta rhythm, blue tones show negative t-values (indicators are lower in older than in younger adults). In beta 1 rhythm, yellow tones show positive t-values (indicators are higher in older than in younger adults). Pattern of activation in beta 2 rhythm replicates that one in beta 1. Statistically significant differences according to sLORETA solutions are colored (p < .05).

difference in the left prefrontal cortex (BA 10, superior and middle frontal gyrus; see Figure 2). Pairwise comparisons of reactivity of current source density estimates in younger and older adults have revealed significant effects in the theta, beta 1, beta 2 rhythms (see Figure 3). Divergent problem-solving was accompanied by higher theta ERS in limbic lobe (116 voxels) and frontal lobe (87 voxels) in OA than YA. Contrast showed the strongest difference (t > 4.73; p < .001) in anterior area, including cingulate gyrus bilaterally (BA 32, 24) and right medial frontal gyrus (BA 9). Contrast also yielded a significant effect in the beta 1 frequency band with older showing bigger ERD than younger ones predominantly in posterior brain region: parietal lobule (1,144 voxels), occipital lobe (761 voxels), insula (143 voxels), limbic lobe (614 voxels). Maximal difference (t < 7; p < .0003) was localized in posterior part of cingulate gyrus (BA 31, 23, 24), precuneus (BA 7, 31, 19), parietal lobÓ 2018 Hogrefe Publishing

ule (superior parietal lobule – BA 7, and left inferior parietal lobule – BA 40), also in postcentral gyrus (BA 40, 3, 7), paracentral lobule (BA 5, 31), sub-gyral (BA 40, 7, 31). Difference with lower level of significance (t < 4; p < .008) was observed in temporal (906 voxels), frontal lobe (676 voxels in precentral and postcentral gyrus, paracentral lobule, 881 voxels in superior, middle, inferior, and medial frontal gyrus). As with age-related difference in the beta 1 frequency band, beta 2 ERD also appeared to be higher in OA in comparison with YA in the following regions: parietal lobule (1,105 voxels), occipital lobe (761 voxels), insula (139 voxels), limbic lobe (614 voxels). The strongest difference (t < 7; p < .0003) was revealed in precuneus (BA 7, 31), superior parietal lobule (BA 7, 5) and posterior part of cingulate gyrus (BA 31), postcentral gyrus (BA 40, 3, 7). Difference with the weakest level of significance (t < 4.01; p < .008) was located in temporal (658 voxels) and frontal lobe (369 voxels in precentral gyrus and paracentral lobule, Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Age difference in task-related changes of current source density estimates

Figure 3. Age difference in reactivity of current source density estimates. In the theta rhythm, yellow tones show positive t-values (ERS is higher in older than in younger adults). In beta 1 and beta 2 rhythms, blue tones show negative t-values (ERD is higher in older than in younger adults). Statistically significant differences according to sLORETA solutions are colored (p < .05).

733 voxels in superior, middle, inferior, and medial frontal gyrus). Correlation statistics of reactivity of current source density estimates versus originality scores in YA and OA sepaJournal of Psychophysiology (2020), 34(1), 19–34

rately yielded a significant effect in beta 2 frequency band only. Higher originality scores were accompanied by higher beta 2 ERS of current source density estimates in precuneus in YA (r > .38; p < .05; BA 7) (see Figure 4). Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Figure 4. Correlation statistics of reactivity of current source density estimates versus originality scores in young adults. Statistically significant correlations according to sLORETA solutions are colored (p < .05).

Discussion Behavioral Data One can assume that both groups of older and younger adults showed comparable performance at the Alternate Uses Task, such as expressed by originality. Therefore, difference in terms of oscillatory brain activity might report not a difference in originality, but rather a manifestation of particular strategy implemented by different age groups. Years of completed education is one of the indicators of cognitive reserve (Scarmeas et al., 2004) – the ability to enhance performance via recruitment of different brain networks which may reflect the use of alternate cognitive strategies to cope with the cognitive decline (Stern, 2002). As we have stressed in introduction, the current study paid attention to successful aging and all older subjects were full-time employees, so the seniors of the current study may have a larger cognitive reserve. Consistent with this presupposition, older and younger participants did not differ in executive control of attention as measured by modified Flanker test (ANT). Parameters of verbal creativity are known to be positively correlated with indices of cognitive reserve (Palmiero, Giacomo, & Passafiume, 2016). Perhaps, high level of education and preserved executive functioning of older subjects contribute to the efficiency of divergent problem-solving.

Current Source Density Estimates in Prestimulus Time Interval Electrophysiological evidence suggests that baseline oscillatory brain activity changes in old age. Decrease in cortical slow wave activity along with increase in the power of fast rhythms was detected in healthy aging (Babiloni et al., Ó 2018 Hogrefe Publishing

2006; Cummins & Finnigan, 2007; Gaál, Boha, Stam, & Molnár, 2010; Leirer et al., 2011; Vlahou, Thurm, Kolassa, & Schlee, 2014; Volf & Gluhih, 2011). Our data of lower current source density estimates in theta but higher in beta 1, beta 2 frequency bands have confirmed the main pattern, which is typical for older adults.

Reactivity of Current Source Density Estimates in the Theta Rhythm sLORETA application made it possible to clarify that the difference previously identified in the ratio of theta power reactivity between the frontal and parietal regions in younger and older subjects is mainly due to large values of source density estimates’ reactivity in prefrontal structures, including the anterior cingulate gyrus (BA 32, 24) and right medial frontal gyrus (BA 9), in OA as compared with YA along with absence of significant difference in parietal activity. Electrophysiological evidence suggests that the midfrontal theta oscillations play an important role in cognitive control mechanisms (Cavanagh & Frank, 2014), for example, in error detection (Luu et al., 2004) and response conflict processing, including response competition and response inhibition (Cohen & Donner, 2013; Van Driel et al., 2015). Direct participation of the anterior cingulate cortex in generation of theta activity has been found via intracranial recordings from microelectrodes located in this region, while subjects were engaged in tasks requiring topdown regulation (Wang et al., 2005). Thus, age difference that was observed in the present study in the anterior cingulate and prefrontal cortex in the theta frequency range may be associated with cognitive control processes as we supposed. The results lent some support to initial assumption that controlling the decision-making processes is more important in aging. The hypothesis is in line with pattern Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

that the elderly tend to reinforce cognitive control at the end of the task (Jimura & Braver, 2010; Velanova, Lustig, Jacoby, & Buckner, 2007). It is known that amount of absolute power during a resting state can be the predictor for changes in band power (Basar, 1998, 1999; Klimesch, 1999). Considering the changes in theta frequency band, Klimesch (1999) notes that tonic decrease in power is associated with large phasic increase (synchronization) during task performance. The features of age difference in theta frequency band observed in the current study indicate the same pattern. Lower theta EEG indicators as measured by current source density estimates during prestimulus period (tonic decrease) are accompanied with higher theta EEG indicators as measured by induced changes in current source density estimates (larger synchronization) in older, than in younger subjects. Brain localization of the effects was mostly the same (anterior cingulate gyrus). Thus, young and aged brain could be prepared or predisposed to specific pattern of responses via maintenance of lower or higher initial power of theta oscillations.

Reactivity of Current Source Density Estimates in the Beta Rhythm The current research has confirmed the importance of beta rhythms to identify age-related difference in the brain activity at the final stage of divergent problem-solving, which was detected in the analysis of the EEG power (Privodnova et al., 2017) and shed light on important areas of the brain. The beta ERD was higher in OA than in YA, reaching the maximal difference in such structures as the posterior part of cingulate gyrus, precuneus, superior and inferior parietal lobule in the beta 1 rhythm, and the precuneus, superior parietal lobule, posterior part of cingulate gyrus in the beta 2 rhythm. The same direction of age difference and the similar localization of the effect allow to consider oscillatory activity in the wide beta frequency range without differentiation on the beta 1 and the beta 2 subbands. Age-Related Difference in Posterior Nodes of the DMN Cortical areas of interest, namely posterior cingulate gyrus and precuneus, are considered to be the core regions associated with the brain’s default network (DMN). It appears to be activated in the absence of an external task and to maintain internally focused and self-generated thought, including autobiographical memory retrieval and envisioning the future (Andrews-Hanna, Smallwood, & Spreng, 2014; Buckner et al., 2008). It is hypothesized that cognitive processes during the last stage of divergent problem-solving and idea assessment per Journal of Psychophysiology (2020), 34(1), 19–34

se rely on some regions of the executive and default network (Beaty, Benedek, Kaufman, & Silvia, 2015; Ellamil et al., 2012). Moreover, in simultaneous performance of functional magnetic resonance imaging (fMRI) and EEG on young participants, activation of DMN during wakeful rest (as assessed by resting-state functional connectivity) was linked to higher beta power (Hlinka, Alexakis, Diukova, Liddle, & Auer, 2010; Laufs et al., 2003; Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007). Thus, it is possible that OA involved such parts of default network as posterior cingulate gyrus and precuneus during idea evaluation to a lesser degree than YA. Recent functional evidence has also revealed age-related disruption in DMN functioning, especially in parieto-occipital zone, during performance of some goal-directed tasks (Lustig et al., 2003; Miller et al., 2008). Reduced functional connectivity of default network in the elderly (Damoiseaux et al., 2008; Sala-Llonch, Bartrés-Faz, & Junqué, 2015), particularly in the posterior nodes of the DMN (Jones et al., 2011), is one of the most consistent age-associated findings from investigation of brain networks. Besides brain imaging studies, EEG research of connectivity between brain regions via graph-theoretical analysis has also revealed reduction of number of nodes identified as hubs in the posterior default network, including the posterior cingulate, precuneus, and the occipital network in older adults. This effect was shown in beta frequency band highlighting special significance of this rhythm in terms of agerelated difference in DMN (Knyazev, Volf, & Belousova, 2015). Thus, the age difference in the beta range shown in this article may be related to the functional changes in posterior default network nodes, which are severely influenced by aging and which are functioning on EEG beta frequency. With respect to divergent problem-solving, aforementioned age-associated decline may be the basis for a difference in strategies implemented by younger and older people. Taking into account hypothesized preferential role of the posterior cingulate gyrus and precuneus in integrative functions (Buckner et al., 2008) and internally directed attention (Chen et al., 2015; Leech & Sharp, 2014; Mason et al., 2007), our results are in line with the assumption that the elderly tend to use patterns of recognition or heuristics (Seligman, Forgeard, & Kaufman, 2016), while their younger counterparts rely more on free recombination of elements from memory during internal attention (Benedek et al., 2012). Processes dealing with integration of information at the last stage of divergent problem-solving are important for achieving high efficiency in younger adults only. It was confirmed by positive correlation between originality scores and reactivity of current source density estimates in beta 2 band in precuneus in YA, while correlation was absent in OA. Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Age-Related Difference in Other Parietal and Occipital Areas Except for the posterior nodes of the DMN, strong higher beta ERD in OA in comparison with YA was also localized in other parietal and occipital areas related to sensory processing, including lingual gyrus, cuneus, fusiform gyrus (BA 17, 18, 19). It has been revealed that integrative structures of parietal lobe – such as posterior cingulate cortex, angular gyrus, precuneus, temporoparietal junction, parietal lobule, and sensory reactivation regions – were activated in episodic retrieval (Binder et al., 2009; Hall et al., 2014) and during such a complex and important for creativity process as imagination (Abraham & Bubic, 2015; Fink et al., 2014). From the standpoint of EEG research, experimental findings have brought evidence that beta oscillations with predominant parieto-occipital scalp distribution play essential role in providing visual attention (Gola et al., 2013; Güntekin et al., 2013; Kamiński et al., 2012; Kukleta et al., 2009) and internal focus of attention during meditation (Travis & Shear, 2010). Moreover, a large number of studies have reported that integrative perceptual processes, such as processing of tactile, visual, visual-motor information, crossmodal integration, and perceptual decision-making, were associated with the beta ERS, most prominent at centroparietal sites (Aissani et al., 2014; Donner & Siegel, 2011; Donner et al., 2007; Göschl et al., 2015). Overall, both functional significance of brain structures and the frequency components of EEG suggest that age difference in the wide beta range established in the current research may correspond to different modes of generated idea presentation during its evaluation. Probably, processes of retention and analysis of complex multimodal images of generated idea (unusual use of the common object), including perceptual, semantic, emotional representations, were pronounced in YA to a greater extent than in OA. Psychological studies of cognitive processes in the course of aging have revealed that, compared with younger subjects, older ones tend to use fewer details from episodic memory concerning “who, what, where, when.” It was found in such tasks as retrieval of past events from one’s life and episodic simulation of future events (Addis, Musicaro, Pan, & Schacter, 2010; Addis, Wong, & Schacter, 2008; Gaesser, Sacchetti, Addis, & Schacter, 2011; Levine, Svoboda, Hay, Winocur, & Moscovitch, 2002; Schacter, Gaesser, & Addis, 2013). Presumably, this effect is also present during the evaluation of creative ideas. It is not surprising, taking into account, that creative imagination and envisioning the future are thought to draw on common neural resources (Benedek et al., 2014). The revealed findings may be considered an indirect confirmation at the level of brain processes that older adults, as compared with younger ones, are less able to rely on the recombination of elements from episodic memory during verbal divergent thinking, Ó 2018 Hogrefe Publishing

which has been suggested on the basis of behavioral data (Madore et al., 2016).

Limitations of the Study Our study has some limitations. With sliding time window with width of 440 ms, method could cover one cycle of 2.3 Hz. In younger subjects, lower limit of individually adjusted theta frequency range varied from 3 to 5.7 Hz (M = 4.16), in older ones – from 2.7 to 5.67 Hz (M = 3.78). So the method could cover only one cycle of the lowest theta activity. This is the weak point of the method. Nevertheless, submitting longer time epochs allows for precise identification of a given frequency at the cost of temporal resolution (Keil, 2013). Time interval under analysis is short, but it was strategically preselected after our preliminary study (Privodnova et al., 2017), and we preferred to have good temporal resolution. The results obtained in the current study are in line with that one in our previous article, where Morlet wavelet analysis was used (Privodnova et al., 2017). Thus, we hope that in spite of the lower reliability of frequency assessment, we are dealing with local changes in the theta rhythm.

Conclusion For the first time, the spatial localization of age-related difference in reactivity of spectral EEG source density estimates at the evaluative stage of divergent problemsolving has been shown. In the current study, higher theta ERS of source density estimates was observed in older adults as compared with younger ones in prefrontal areas, such as anterior cingulate gyrus and right medial frontal gyrus. Comparable level of originality shown in the current study allows us to consider that identified age-related difference in induced EEG changes is not driven by difference in originality, but rather by a manifestation of particular strategies implemented by different age groups. Estimates of prestimulus theta current source density showed that differences in reactivity in older and younger adults may be due to age difference in prestimulus theta density. Based on localization of age-related difference, enhancement of executive control functions in older adults was supposed. ERD in the wide beta frequency band has been shown to be higher in older in comparison with younger adults. The difference was the strongest in the posterior cingulate gyrus, precuneus, and parietal lobule. This may correspond to less involvement of the integration processes and retention of mental multimodal images in older as compared with younger subjects. The importance of integration of information (it may be maintenance of the internal image of elements’ recombination) at the last stage of divergent Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

problem-solving in younger adults (but not in their older counterparts) was confirmed by positive correlation between originality scores and EEG reactivity in beta 2 band in this group only.

Ethics and Disclosure Statements All participants of the study provided written informed consent and the study was approved by the Ethics Committee of Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine.” 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.

Acknowledgments Funding: The reported study was funded by RFBR and Government of the Novosibirsk region according to the research project No. 17-46-540705.

References Abraham, A., & Bubic, A. (2015). Semantic memory as the root of imagination. Frontiers in Psychology, 6, 325. https://doi.org/ 10.3389/fpsyg.2015.00325 Addis, D. R., Musicaro, R., Pan, L., & Schacter, D. L. (2010). Episodic simulation of past and future events in older adults: Evidence from an experimental recombination task. Psychological Aging, 25, 369–376. https://doi.org/10.1037/a0017280 Addis, D. R., Pan, L., Musicaro, R., & Schacter, D. L. (2016). Divergent thinking and constructing episodic simulations. Memory, 24, 89–97. https://doi.org/10.1080/09658211.2014. 985591 Addis, D. R., Wong, A. T., & Schacter, D. L. (2008). Age-related changes in the episodic simulation of future events. Psychological Science, 19, 33–41. https://doi.org/10.1111/j.14679280.2008.02043.x Aissani, C., Martinerie, J., Yahia-Cherif, L., Paradis, A. L., & Lorenceau, J. (2014). Beta, but not gamma, band oscillations index visual form-motion integration. Public Library of Science, 9, e95541. https://doi.org/10.1371/journal.pone.0095541 Alpaugh, P. K., & Birren, J. E. (1977). Variables affecting creative contributions across the adult life span. Human Development, 20, 240–248. https://doi.org/10.1159/000271559 Alpaugh, P. K., Parham, I. A., Cole, K. D., & Birren, J. E. (1982). Creativity in adulthood and old age: An exploratory study. Educational Gerontology, 8, 101–116. https://doi.org/10.1080/ 0380127820080202 American Electroencephalographic Society. (1991). Guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology, 8, 200–202.

Journal of Psychophysiology (2020), 34(1), 19–34

Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316, 29–52. https://doi.org/ 10.1111/nyas.12360 Annett, M. (1970). A classification of hand preference by association analysis. The British Journal of Psychology, 61, 303–321. https://doi.org/10.1111/j.2044-8295.1970.tb01248.x Aron, A. R. (2007). The neural basis of inhibition in cognitive control. Neuroscientist, 13, 214–228. https://doi.org/10.1177/ 1073858407299288 Babiloni, C., Binetti, G., Cassarino, A., Dal Forno, G., Del Percio, C., Ferreri, F., . . . Rossini, P. M. (2006). Sources of cortical rhythms in adults during physiological aging: A multicentric EEG study. Human Brain Mapping, 27, 162–172. https://doi.org/10.1002/ hbm.20175 Babiloni, F., Cincotti, F., Carducci, F., Rossini, P. M., & Babiloni, C. (2001). Spatial enhancement of EEG data by surface Laplacian estimation: The use of magnetic resonance imaging-based head models. Clinical Neurophysiology, 112, 724–727. https:// doi.org/10.1016/S1388-2457(01)00494-1 Basadur, M. S. (1995). Optimal ideation-evaluation ratios. Creativity Research Journal, 8, 63–75. https://doi.org/10.1207/ s15326934crj0801_5 Basar, E. (1998). Brain function and oscillations: I. Brain oscillations. Principles and approaches. Berlin, Heidelberg, Germany: Springer. Basar, E. (1999). Brain function and oscillations: II. Integrative brain function. Neurophysiology and cognitive processes. Berlin, Heidelberg, Germany: Springer. Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5, 10964. https://doi.org/ 10.1038/srep10964 Benedek, M., Jauk, E., Fink, A., Koschutnig, K., Reishofer, G., Ebner, F., & Neubauer, A. C. (2014). To create or to recall? Neural mechanisms underlying the generation of creative new ideas. NeuroImage, 88, 125–133. https://doi.org/10.1016/j. neuroimage.2013.11.021 Benedek, M., Könen, T., & Neubauer, A. C. (2012). Associative abilities underlying creativity. Psychology of Aesthetics, Creativity, and the Arts, 6, 273–281. https://doi.org/10.1037/a0027059 Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and metaanalysis of 120 functional neuroimaging studies. Cerebral Cortex, 19, 2767–2796. https://doi.org/10.1093/cercor/bhp055 Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38. https://doi.org/10.1196/annals.1440.011 Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18, 414–421. https://doi.org/10.1016/j.tics.2014.04.012 Chen, Q. L., Xu, T., Yang, W. J., Li, Y. D., Sun, J. Z., Wang, K. C., . . . Qiu, J. (2015). Individual differences in verbal creative thinking are reflected in the precuneus. Neuropsychologia, 75, 441–449. https://doi.org/10.1016/j.neuropsychologia.2015.07.001 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York, NY: Psychology Press. Cohen, M. X., & Donner, T. H. (2013). Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology, 110, 2752–2763. https://doi.org/10.1152/jn.00479.2013 Congedo, M. (2006). Subspace projection filters for real-time brain electromagnetic imaging. IEEE Transactions on Biomedical

Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Engineering, 53, 1624–1634. https://doi.org/10.1109/ TBME.2006.878055 Cummins, T. D., & Finnigan, S. (2007). Theta power is reduced in healthy cognitive aging. International Journal of Psychophysiology, 66, 10–17. https://doi.org/10.1016/j.ijpsycho.2007.05. 008 Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J., Barkhof, F., Scheltens, P., Stam, C. J., . . . Rombouts, S. A. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18, 1856–1864. https://doi.org/10.1093/ cercor/bhm207 Dechent, P., Merboldt, K. D., & Frahm, J. (2004). Is the human primary motor cortex involved in motor imagery? Brain Research. Cognitive Brain Research, 19, 138–144. https://doi. org/10.1016/j.cogbrainres.2003.11.012 Der, G., & Deary, I. J. (2006). Age and sex differences in reaction time in adulthood: results from the United Kingdom Health and Lifestyle Survey. Psychology and aging, 21, 62–73. https://doi. org/10.1037/0882-7974.21.1.62 Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136, 822–848. https://doi.org/10.1037/a0019749 Donner, T. H., & Siegel, M. (2011). A framework for local cortical oscillation patterns. Trends in Cognitive Sciences, 15, 191–199. https://doi.org/10.1016/j.tics.2011.03.007 Donner, T. H., Siegel, M., Oostenveld, R., Fries, P., Bauer, M., & Engel, A. K. (2007). Population activity in the human dorsal pathway predicts the accuracy of visual motion detection. Journal of Neurophysiology, 98, 345–359. https://doi.org/ 10.1152/jn.01141.2006 Doppelmayr, M., Klimesch, W., Pachinger, T., & Ripper, B. (1998). Individual differences in brain dynamics: Important implications for the calculation of event-related band power. Biological Cybernetics, 79, 49–57. https://doi.org/10.1007/ s004220050457 Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., . . . Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104, 11073–11078. https://doi.org/10.1073/ pnas.0704320104 Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes of thought during the creative process. NeuroImage, 59, 1783–1794. https://doi.org/10.1016/ j.neuroimage.2011.08.008 Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon identification of a target letter in a non-search task. Perception and Psychophysics, 16, 143–149. https://doi.org/ 10.3758/bf03203267 Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14, 340–347. https://doi.org/10.1162/089892902317361886 Fink, A., Koschutnig, K., Hutterer, L., Steiner, E., Benedek, M., Weber, B., . . . Weiss, E. M. (2014). Gray matter density in relation to different facets of verbal creativity. Brain Structure and Function, 219, 1263–1269. https://doi.org/10.1007/ s00429-013-0564-0 Finke, R. A., Ward, T. M., & Smith, S. M. (1992). Creative cognition: Theory, research and applications. Cambridge, MA: MIT. Fozard, J. L., Vercryssen, M., Reynolds, S. L., Hancock, P. A., & Quilter, R. E. (1994). Age differences and changes in reaction time: The Baltimore longitudinal study of aging. Journals of Gerontology, 49, 179–189. Freunberger, R., Werkle-Bergner, M., Griesmayr, B., Lindenberger, U., & Klimesch, W. (2011). Brain oscillatory correlates of working

Ó 2018 Hogrefe Publishing

memory constraints. Brain Research, 1375, 93–102. https:// doi.org/10.1016/j.brainres.2010.12.048 Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141, 2–18. https://doi. org/10.1037/a0024338 Järveläinen, J., Schürmann, M., & Hari, R. (2004). Activation of the human primary motor cortex during observation of tool use. NeuroImage, 23, 187–192. https://doi.org/10.1016/j. neuroimage.2004.06.010 Gaál, Z. A., Boha, R., Stam, C. J., & Molnár, M. (2010). Agedependent features of EEG-reactivity-spectral, complexity, and network characteristics. Neuroscience Letters, 479, 79–84. https://doi.org/10.1016/j.neulet.2010.05.037 Gabora, L. (2005). Creative thought as a non-Darwinian evolutionary process. Journal of Creative Behavior, 39, 262–283. https:// doi.org/10.1002/j.2162-6057.2005.tb01261.x Gaesser, B., Sacchetti, D. C., Addis, D. R., & Schacter, D. L. (2011). Characterizing age-related changes in remembering the past and imagining the future. Psychological Aging, 26, 80–84. https://doi.org/10.1037/a0021054 Gola, M., Magnuski, M., Szumska, I., & Wróbel, A. (2013). EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. International Journal of Psychophysiology, 89, 334–341. https://doi.org/10.1016/ j.ijpsycho.2013.05.007 Gómez-Herrero, G. (2007). Automatic Artifact Removal (AAR) Toolbox v1.3 (Release 09.12. 2007) for MATLAB. Tampere, Finland: Tampere University of Technology. Göschl, F., Friese, U., Daume, J., König, P., & Engel, A. K. (2015). Oscillatory signatures of crossmodal congruence effects: An EEG investigation employing a visuotactile pattern matching paradigm. NeuroImage, 116, 177–186. https://doi.org/10.1016/ j.neuroimage.2015.03.067 Guilford, J. P., Christensen, P. R., Merrifield, P. R., & Wilson, R. C. (1978). Alternate uses: Manual of instructions and interpretations. Orange, CA: Sheridan Psychological Services. Güntekin, B., Emek-Savasß, D. D., Kurt, P., Yener, G. G., & Basßar, E. (2013). Beta oscillatory responses in healthy subjects and subjects with mild cognitive impairment. NeuroImage. Clinical, 3, 39–46. https://doi.org/10.1016/j.nicl.2013.07.003 Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., . . . Vanrumste, B. (2008). Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation, 5, 25. https://doi.org/10.1186/ 1743-0003-5-25 Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15, 536–548. https://doi. org/10.1038/nrn3747 Hall, S. A., Rubin, D. C., Miles, A., Davis, S. W., Wing, E. A., Cabeza, R., & Berntsen, D. (2014). The neural basis of involuntary episodic memories. Journal of Cognitive Neuroscience, 26, 2385–2399. https://doi.org/10.1162/jocn_a_00633 Hanakawa, T., Immisch, I., Toma, K., Dimyan, M. A., Van Gelderen, P., & Hallett, M. (2003). Functional properties of brain areas associated with motor execution and imagery. Journal of Neurophysiology, 89, 989–1002. https://doi.org/10.1152/ jn.00132.2002 Hlinka, J., Alexakis, C., Diukova, A., Liddle, P. F., & Auer, D. P. (2010). Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: An inter-subject analysis. NeuroImage, 53, 239–246. https://doi.org/10.1016/j. neuroimage.2010.06.002 Holmes, A. P., Blair, R. C., Watson, J. D., & Ford, I. (1996). Nonparametric analysis of statistic images from functional

Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

mapping experiments. Journal of Cerebral Blood Flow and Metabolism, 16, 7–22. https://doi.org/10.1097/00004647199601000-00002 Howard-Jones, P. A. (2002). A dual-state model of creative cognition for supporting strategies that foster creativity in the classroom. International Journal of Technology and Design Education, 12, 215–226. https://doi.org/10.1023/ A:1020243429353 Jimura, K., & Braver, T. S. (2010). Age-related shifts in brain activity dynamics during task switching. Cerebral Cortex, 20, 1420–1431. https://doi.org/10.1093/cercor/bhp206 Jones, D. T., Machulda, M. M., Vemuri, P., McDade, E. M., Zeng, G., Senjem, M. L., . . . Jack, C. R. Jr. (2011). Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology, 77, 1524–1531. https://doi.org/ 10.1212/WNL.0b013e318233b33d Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37, 163–178. Kamiński, J., Brzezicka, A., Gola, M., & Wróbel, A. (2012). β band oscillations engagement in human alertness process. International Journal of Psychophysiology, 85, 125–128. https://doi. org/10.1016/j.ijpsycho.2011.11.006 Keil, A. (2013). Electro- and magneto-encephalography in the study of emotion. In J. Armony & P. Vuilleumier (Eds.), The Cambridge handbook of human affective neuroscience (pp. 107–132). New York, NY: Cambridge University Press. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29, 169–195. https://doi.org/10.1016/ S0165-0173(98)00056-3 Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16, 606–617. https://doi.org/10.1016/j.tics.2012.10.007 Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews, 31, 377–395. https://doi.org/10.1016/j. neubiorev.2006.10.004 Knyazev, G. G., Volf, N. V., & Belousova, L. V. (2015). Age-related differences in electroencephalogram connectivity and network topology. Neurobiology of Aging, 36, 1849–1859. https://doi. org/10.1016/j.neurobiolaging.2015.02.007 Kröger, S., Rutter, B., Hill, H., Windmann, S., Hermann, C., & Abraham, A. (2013). An ERP study of passive creative conceptual expansion using a modified alternate uses task. Brain Research, 1527, 189–198. https://doi.org/10.1016/j.brainres. 2013.07.007 Kröger, S., Rutter, B., Stark, R., Windmann, S., Hermann, C., & Abraham, A. (2012). Using a shoe as a plant pot: Neural correlates of passive conceptual expansion. Brain Research, 1430, 52–61. https://doi.org/10.1016/j.brainres.2011.10.031 Kopp, B., Rist, F., & Mattler, U. (1996). N200 in the flanker task as a neurobehavioral tool for investigating executive control. Psychophysiology, 33, 282–294. Kukleta, M., Bob, P., Brázdil, M., Roman, R., & Rektor, I. (2009). Beta 2-band synchronization during a visual oddball task. Physiological Research, 58, 725–732. https://doi.org/10.1016/j. nicl.2013.07.003 Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., SalekHaddadi, A., & Kleinschmidt, A. (2003). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proceedings of the National Academy of Sciences of the United States of America, 100, 11053–11058. https://doi.org/10.1073/ pnas.1831638100

Journal of Psychophysiology (2020), 34(1), 19–34

Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137, 12–32. https://doi. org/10.1093/brain/awt162 Leirer, V. M., Wienbruch, C., Kolassa, S., Schlee, W., Elbert, T., & Kolassa, I. T. (2011). Changes in cortical slow wave activity in healthy aging. Brain Imaging and Behavior, 5, 222–228. https:// doi.org/10.1007/s11682-011-9126-3 Leon, S. A., Altmann, L. J. P., Abrams, L., Gonzalez, R. L. J., & Heilman, K. M. (2014). Divergent task performance in older adults: Declarative memory or creative potential? Creativity Research Journal, 26, 21–29. https://doi.org/10.1080/ 10400419.2014.873657 Levine, B., Svoboda, E., Hay, J. F., Winocur, G., & Moscovitch, M. (2002). Aging and autobiographical memory: Dissociating episodic from semantic retrieval. Psychological Aging, 17, 677–689. https://doi.org/10.1037/0882-7974.17.4.677 Lewis, A. G., & Bastiaansen, M. (2015). A predictive coding framework for rapid neural dynamics during sentence-level language comprehension. Cortex, 68, 155–168. https://doi.org/ 10.1016/j.cortex.2015.02.014 Lewis, A. G., Wang, L., & Bastiaansen, M. (2015). Fast oscillatory dynamics during language comprehension: Unification versus maintenance and prediction? Brain & Language, 148, 51–63. https://doi.org/10.1016/j.bandl.2015.01.003 Lustig, C., Snyder, A. Z., Bhakta, M., O’Brien, K. C., McAvoy, M., Raichle, M. E., . . . Buckner, R. L. (2003). Functional deactivations: Change with age and dementia of the Alzheimer type. Proceedings of the National Academy of Sciences of the United States of America, 100, 14504–14509. https://doi.org/10.1073/ pnas.2235925100 Luu, P., Tucker, D. M., & Makeig, S. (2004). Frontal midline theta and the error-related negativity: Neurophysiological mechanisms of action regulation. Clinical Neurophysiology, 115, 1821– 1835. https://doi.org/10.1016/S1388-2457(04)00129-4 Machielsen, W. C., Rombouts, S. A., Barkhof, F., Scheltens, P., & Witter, M. P. (2000). FMRI of visual encoding: Reproducibility of activation. Human Brain Mapping, 9, 156–164. https://doi.org/ 10.1002/(SICI)1097-0193(200003)9:3<156::AID-HBM4>3.0. CO;2-Q Madore, K. P., Jing, H. G., & Schacter, D. L. (2016). Divergent creative thinking in young and older adults: Extending the effects of an episodic specificity induction. Memory & Cognition, 44, 974–988. https://doi.org/10.3758/s13421-0160605-z Magyari, L., Bastiaansen, M. C., de Ruiter, J. P., & Levinson, S. C. (2014). Early anticipation lies behind the speed of response in conversation. Journal of Cognitive Neuroscience, 26, 2530– 2539. https://doi.org/10.1162/jocn_a_00673 Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007). Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 104, 13170–13175. https://doi.org/10.1073/pnas.0700668104 Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315, 393–395. https://doi.org/10.1126/science.1131295 Mayseless, N., Aharon-Peretz, J., & Shamay-Tsoory, S. (2014). Unleashing creativity: The role of left temporoparietal regions in evaluating and inhibiting the generation of creative ideas. Neuropsychologia, 64, 157–168. https://doi.org/10.1016/j. neuropsychologia.2014.09.022 McCrae, R. R., Arenberg, D., & Costa, P. T. Jr. (1987). Declines in divergent thinking with age: Cross-sectional, longitudinal, and cross-sequential analyses. Psychology and Aging, 2, 130–137. https://doi.org/10.1037/0882-7974.2.2.130

Ó 2018 Hogrefe Publishing

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Meijer, W. A., de Groot, R. M., van Gerven, P. M., van Boxtel, M. J., & Jolles, J. (2009). Level of processing and reaction time in young and middle-aged adults and the effect of education. European Journal of Cognitive Psychology, 21, 216–234. https:// doi.org/10.1080/09541440802091780 Miller, S. L., Celone, K., DePeau, K., Diamond, E., Dickerson, B. C., Rentz, D., . . . Sperling, R. A. (2008). Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proceedings of the National Academy of Sciences of the United States of America, 105, 2181–2186. https://doi.org/10.1073/pnas.0706818105 Neuper, C., Wörtz, M., & Pfurtscheller, G. (2006). ERD/ERS patterns reflecting sensorimotor activation and deactivation. Progress in Brain Research, 159, 211–222. https://doi.org/ 10.1016/S0079-6123(06)59014-4 Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 1, 1–25. Nunez, P. L., & Silberstein, R. B. (2000). On the relationship of synaptic activity to macroscopic measurements: Does coregistration of EEG with fMRI make sense? Brain Topography, 13, 79–96. https://doi.org/10.1023/A:1026683200895 Nunez, P. L., Silberstein, R. B., Cadusch, P. J., Wijesinghe, R. S., Westdorp, A. F., & Srinivasan, R. (1994). A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. Electroencephalography and Clinical Neurophysiology, 90, 40–57. https://doi.org/10.1016/ 0013-4694(94)90112-0 Palmiero, M., Giacomo, D. D., & Passafiume, D. (2014). Divergent thinking and age-related changes. Creativity Research Journal, 26, 456–460. https://doi.org/10.1080/10400419.2014.961786 Palmiero, M., Giacomo, D. D., & Passafiume, D. (2016). Can creativity predict cognitive reserve? Journal of Creative Behavior, 50, 7–23. https://doi.org/10.1002/jocb.62 Park, C. H., Chang, W. H., Lee, M., Kwon, G. H., Kim, L., Kim, S. T., & Kim, Y. H. (2015). Which motor cortical region best predicts imagined movement? NeuroImage, 113, 101–110. https://doi. org/10.1016/j.neuroimage.2015.03.033 Pascual-Marqui, R. D. (2002). Standardized low resolution brain electromagnetic tomography (sLORETA): Technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(Suppl D), 5–12. Pineda, J. A., & Hecht, E. (2009). Mirroring and mu rhythm involvement in social cognition: Are there dissociable subcomponents of theory of mind? Biological Psychology, 80, 306–314. https://doi.org/10.1016/j.biopsycho.2008.11.003 Posner, M. I., Rothbart, M. K., Sheese, B. E., & Tang, Y. (2007). The anterior cingulate gyrus and the mechanism of self-regulation. Cognitive, Affective, & Behavioral Neuroscience, 7, 391–395. https://doi.org/10.3758/CABN.7.4.391 Privodnova, E. Yu., & Volf, N. V. (2017). Impact of intelligence and executive control in verbal creativity in physiological aging. Siberian Scientific Medical Journal, 37, 55–61. Privodnova, E. Yu., Volf, N. V., & Knyazev, G. G. (2017). Specific features of the oscillatory brain activity during the final stage of creative problem solving in young and elderly subjects. Human Physiology, 43, 241–247. https://doi.org/10.1134/ S0362119717030161 Radel, R., Davranche, K., Fournier, M., & Dietrich, A. (2015). The role of (dis)inhibition in creativity: Decreased inhibition improves idea generation. Cognition, 134, 110–120. https:// doi.org/10.1016/j.cognition.2014.09.001 Razumnikova, O. M. (2016). Psychophysiology. Novosibirsk: NSTU.

Ó 2018 Hogrefe Publishing

Ruth, J. E., & Birren, J. E. (1985). Creativity in adulthood and old age: Relations to intelligence, sex, and mode of testing. International Journal of Behavioral Development, 8, 99–109. https://doi.org/10.1177/016502548500800107 Rutter, B., Kröger, S., Stark, R., Schweckendiek, J., Windmann, S., Hermann, C., & Abraham, A. (2012). Can clouds dance? Neural correlates of passive conceptual expansion using a metaphor processing task: Implications for creative cognition. Brain & Cognition, 78, 114–122. https://doi.org/10.1016/j.bandc.2011. 11.002 Sala-Llonch, R., Bartrés-Faz, D., & Junqué, C. (2015). Reorganization of brain networks in aging: A review of functional connectivity studies. Frontiers in Psychology, 6, 663. https://doi. org/10.3389/fpsyg.2015.00663 Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428. https://doi.org/10.1037/0033-295X.103.3.403 Scarmeas, N., Zarahn, E., Anderson, K. E., Honig, L. S., Park, A., Hilton, J., . . . Stern, Y. (2004). Cognitive reserve-mediated modulation of positron emission tomographic activations during memory tasks in Alzheimer disease. Archives of Neurology, 61, 73–78. https://doi.org/10.1001/archneur.61.1.73 Schacter, D. L., Gaesser, B., & Addis, D. R. (2013). Remembering the past and imagining the future in the elderly. Gerontology, 59, 143–151. https://doi.org/10.1159/000342198 Seligman, M. E. P., Forgeard, M., & Kaufman, S. B. (2016). Creativity and aging: What we can make with what we have left. In M. E. P. Seligman, P. Railton, & R. F. Baumeister (Eds.), Prospection and Life’s Enduring Questions (pp. 305–350) Homo Prospectus. New York, NY: Oxford University Press. Soper, D.S. (2017). Post hoc statistical power calculator for a student t-test [Computer software]. Retrieved from http:// www.danielsoper.com/statcalc Sowden, P. T., Pringle, A., & Gabora, L. (2015). The shifting sands of creative thinking: Connections to dual-process theory. Thinking & Reasoning, 21, 40–60. https://doi.org/10.1080/ 13546783.2014.885464 Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8, 448–460. https://doi.org/ 10.1017/S1355617702813248 Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. Stuttgart, Germany: Thieme. Travis, F., & Shear, J. (2010). Focused attention, open monitoring and automatic self-transcending: Categories to organize meditations from Vedic, Buddhist and Chinese traditions. Consciousness and Cognition, 19, 1110–1118. https://doi.org/ 10.1016/j.concog.2010.01.007 Van Driel, J., Sligte, I. G., Linders, J., Elport, D., & Cohen, M. X. (2015). Frequency band-specific electrical brain stimulation modulates cognitive control processes. Public Library of Science, 10, e0138984. https://doi.org/10.1371/journal. pone.0138984 Velanova, K., Lustig, C., Jacoby, L. L., & Buckner, R. L. (2007). Evidence for frontally mediated controlled processing differences in older adults. Cerebral Cortex, 17, 1033–1046. https:// doi.org/10.1093/cercor/bhl013 Vlahou, E. L., Thurm, F., Kolassa, I.-T., & Schlee, W. (2014). Resting-state slow wave power, healthy aging and cognitive performance. Scientific Reports, 4, 5101. https://doi.org/ 10.1038/srep05101 Volf, N. V., & Gluhih, A. A. (2011). Background cerebral electrical activity in healthy mental aging. Human Physiology, 37, 559– 567. https://doi.org/10.1134/S0362119711040207

Journal of Psychophysiology (2020), 34(1), 19–34

E. Yu. Privodnova et al., The Evaluation of Creative Ideas in Older and Younger Adults

Wang, C., Ulbert, I., Schomer, D. L., Marinkovic, K., & Halgren, E. (2005). Responses of human anterior cingulate cortex microdomains to error detection, conflict monitoring, stimulus-response mapping, familiarity, and orienting. Journal of Neuroscience, 25, 604–613. https://doi.org/10.1523/JNEUROSCI.4151-04.2005

Journal of Psychophysiology (2020), 34(1), 19–34

Received March 5, 2018 Revision received July 27, 2018 Accepted August 15, 2018 Published online December 14, 2018 Evgeniya Yu. Privodnova Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine” 630117, Novosibirsk Timakova str., 4 Russian Federation privodnovaeu@physiol.ru

Ó 2018 Hogrefe Publishing

Article

Changes in the Electroencephalographic Activity in Response to Odors Produced by Organic Compounds Minju Kim1, Jieun Song1, Kosuke Nishi2, Kandhasamy Sowndhararajan1,4, and Songmun Kim1,3 1

School of Natural Resources and Environmental Science, Kangwon National University, Gangwon-do, Republic of Korea

Department of Bioscience, Ehime University, Japan

Gangwon Perfume Alchemy Ltd. Co., Gangwon-do, Republic of Korea

Department of Botany, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India

Abstract: Volatile organic compounds are widely used to manufacture various products in addition to research purposes. They play an important role in the air quality of outdoor and indoor with a pleasant or unpleasant odor. It is well known that the odor of chemicals with different structures can affect brain functions differently. In general, organic compounds are mainly characterized by their functional groups. Acetic acid, acetaldehyde, acetone, and acetonitrile are widely used laboratory chemicals with the same methyl group, but different functional groups. Hence, the present study was aimed to investigate whether the exposure of these four chemicals (10%) exhibits the same electroencephalographic (EEG) activity or different. For this purpose, the EEG was recorded in 20 male healthy volunteers. The EEG was recorded from 32 electrodes located on the scalp, based on the International 10–20 system with modified combinatorial nomenclature. The results indicated that tested subjects are less sensitive to acetic acid odor than other three chemicals. The absolute theta activity significantly increased at Cp5 and F8 regions, and the relative mid-beta (RMB) significantly decreased at Fc1 region during the exposure of acetic acid. On the other hand, acetaldehyde, acetone, and acetonitrile produced EEG changes in many indices such as relative theta, relative gamma, relative high beta, relative beta, relative slow beta, the ratio of alpha to high beta, and spectral edge frequencies. However, there was no significant change in the absolute wave activity. Although acetaldehyde, acetone, and acetonitrile odors affected almost similar EEG indices, they exhibited changes in different brain regions. The variations in the EEG activity of these chemicals may be due to the activation of different olfactory receptors, odor characteristics, and structural arrangements. Keywords: electroencephalography, functional group, acetonitrile, acetaldehyde, acetone, acetic acid

In general, chemical compounds differ in their ability to produce odors and an odor might be due to an individual chemical or a mixture of chemicals. Strong odors of chemicals may cause irritation to different organs including the eye, nose, throat, or lung. These odors may also cause the human being to feel a burning sensation, resulting in coughing, wheezing, or other respiratory problems (Dorman et al., 2008). Further, an odor can affect psychophysiological conditions of the human being such as mood, anxiety, and stress. Volatility, hydrophobicity, and molecular weight < 300 Da are the general requirements for an odorant. Among the various chemicals, volatile organic compounds play a major role in the air quality of outdoor as well as indoor due to their adverse impacts on the health of human beings. The odor detection threshold is used to determine the air quality in relation to human being comfort. Odor detection thresholds may be extremely different even for two different compounds with an identical molecular structure. A number of authors have studied relationships Ó 2019 Hogrefe Publishing

between the molecular structure and qualities of odorants (Laing, Legha, Jinks, & Hutchinson, 2003; Zarzo, 2012). The major classes of volatile organic compounds are mainly characterized by their functional groups. The functional groups are specific groups of atoms within molecules that possess very distinctive properties irrespective to other atoms present in a molecular structure. Acetic acid, acetaldehyde, acetone, and acetonitrile are among the chemicals widely used in several laboratories for various research purposes. They are also extensively used to manufacture numerous products such as paints, pharmaceuticals, adhesives, inks, pesticides, perfumery products, and cleaners (Dick, 2006; IARC, 1985; Nagasawa et al., 2013). In the structural point of view, these chemicals possess the same type of methyl group with different functional groups (Figure 1). Acetic acid (CH3COOH) is a carboxylic acid, colorless liquid with a strong vinegar-like odor. It is the chief characterizing component of vinegar. Acetic acid can be obtained by oxidation of acetaldehyde Journal of Psychophysiology (2020), 34(1), 35–49 https://doi.org/10.1027/0269-8803/a000234

M. Kim et al., EEG Activity of Organic Compounds

H H

O C

Acetaldehyde

H H

Acetone

O C

H Acetic acid

H O

H Acetonitrile

Figure 1. Chemical structure of volatile organic compounds used in this study.

or by the hydrolysis of acetonitrile. Acetaldehyde (C2H4O) is a colorless liquid with a pungent, fruity odor, but it has an irritating odor at the concentration of 50 ppm (Ruth, 1986). Acetaldehyde can be obtained by the oxidation of ethanol and by the reduction of acetic acid. Acetaldehyde is mainly used as a chemical intermediate in the production of various products (IARC, 1985). Small quantities of this chemical are also utilized as a food additive. It is an intermediate in the metabolism of higher plants; thus, it can be detected in numerous fruits and vegetables (EPA, 2007; NRC, 2009). Acetone (C3H6O) is the simplest form of a ketone, colorless, and flammable liquid with a fruity odor. Since acetone is miscible well with water, it is used as an intermediate in the production of various chemicals, such as methylacrylates, bisphenol A, chloroform, and other ketones (Buron, Hacquemand, Pourié, & Brand, 2009). In the case of acetonitrile (CH3CN), it is the simplest nitrile and possesses an ether-like odor. In particular, workrelated exposures occur by the inhalation of solvent vapor. Heavy solvent exposure is linked to various harmful effects including mild cognitive impairment (Dick, 2006). For example, the short-term exposure to acetaldehyde causes irritation of the eyes, skin, and respiratory tract, whereas symptoms of long-term intoxication of acetaldehyde resemble those of alcoholism (Hoffmann & Hecht, 1990; IARC, 1985). Generally, numerous chemicals have an irritant odor at higher concentrations, but it is perceived as a pleasant odor at lower concentrations. It is well known that human beings have about 300 active olfactory receptor genes. Previous studies reported Journal of Psychophysiology (2020), 34(1), 35–49

that olfactory receptors are acted in a combinatorial fashion for detecting structurally different odorants, such that each olfactory receptor can bind different odor molecules, and each odor molecule is capable of binding to various olfactory receptors (Cometto-Muñiz & Abraham, 2010; Malnic, Hirono, Sato, & Buck, 1999). In this context, numerous studies are being performed to investigate the effect of olfactory stimulation on human beings in order to understand whether odorants can produce functional changes in the brain (Sowndhararajan & Kim, 2016). Functional changes in the brain can be determined by using electroencephalography (EEG) via brain wave activities (delta, theta, alpha, beta, and gamma). The odorants significantly affect brain functions through the olfactory system based on the type of odor (pleasant or unpleasant; Angelucci et al., 2014; Sowndhararajan & Kim, 2016). Previously, some studies reported the effect of different chemicals on the brain function using EEG. The frontal theta power band is increased in the chronic solvent encephalopathy patient group when compared with the laboratory control group (Keski-Säntti, Kovala, Holm, Hyvärinen, & Sainio, 2008). Significant increases of dominant alpha frequency and alpha percentage were observed during the early phase exposure of m-xylene (Seppalainen et al., 1991). Another study indicated that smelling an unpleasant odor (valeric acid) resulted in a cortical deactivation by increasing alpha 2 power (Brauchli, Ruegg, Etzweiler, & Zeier, 1995). During the performance of a mental arithmetic task, low alcohol dosages significantly increased theta power activity especially in the frontal area (Boha et al., 2009). Based on the above, the present study aimed to determine the effect of exposure of chemicals with the same methyl group, but different functional groups (acetic acid, acetaldehyde, acetone, and acetonitrile) on the human EEG activity to examine whether their action on the brain wave activity is the same or different.

Materials and Methods Materials Acetic acid (CAS No. 64-19-7), acetaldehyde (CAS No. 7507-0), acetone (CAS No. 67-64-1), and acetonitrile (CAS No. 75-05-8) were purchased from Sigma (St. Louis, MO, USA). The purchased chemicals were stored at room temperature until used for the EEG study.

Subjects The current study followed the Declaration of Helsinki on Biomedical Research Involving Human Subjects. This study Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

Acetic acid

4.5

Acetaldehyde

Acetone

Figure 2. The pleasantness of odor of acetic acid, acetaldehyde, acetone, and acetonitrile according to 5-point rating scale (0 = extremely pleasant; 5 = extremely unpleasant). Error bars depict the values expressed as mean (n = 5) ± standard deviation.

Acetonitrile

Pleasantness (5 point rating scale)

4 3.5 3 2.5 2 1.5 1 0.5 0 75%

50%

25%

10%

Concentration (%)

was approved by the Institution Review Board, Kangwon National University, Chuncheon, Republic of Korea (IRB No. KWNUIRB-2017-12-004-001). A total of 20 male healthy volunteers (aged 20–25 years) participated in this study. The EEG study details were advertised in our department notice board to recruit the volunteer subjects. The inclusion criteria for the subjects were non-smokers and right-handed without any abnormalities in terms of olfaction. The purpose of this study was clearly informed to subjects. Alcohol consumption and medications were prohibited from 2 days before the experiment. In addition, the subjects were instructed not to consume any caffeinated drinks during the day of the EEG experiment. There were no statistically significant differences between the groups. The subjects were asked to provide informed consent before participation.

Odor Evaluation The odor of these four chemicals was evaluated by the volunteer subjects. Different concentrations (75, 50, 25, and 10%) of these chemicals were prepared by diluting with dipropylene glycol. For this purpose, one drop of each chemical was separately added on a commercial odor-strip and placed at 5 cm from the nose of the subject. Then the characteristics of the odor were determined according to the strength of smell described by the observers using 5-point scale (0 = extremely pleasant to 5 = extremely unpleasant). Based on the pleasantness, 10% concentration of these chemicals was used for the EEG study (Figure 2). Ó 2019 Hogrefe Publishing

Experimental Design In the present study, a single group pretest and posttest experimental design was used (20 male subjects). The subjects were informed that the aim of this study was to investigate the effect of exposure of odors on the EEG activity. The subjects were instructed to sit quietly, close their eyes, and breathe normally but to remain awake during the EEG measurement. After the EEG recordings, the subjects were asked to give their preference and impression of odor molecules.

EEG Recordings A QEEG-64FX system was used for recording the EEG readings (LAXTHA Inc., Daejeon, Republic of Korea). The EEG recordings were performed using an electrode cap from 32 channels located on the scalp at Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Afz, Cz, Fz, Pz, Fpz, Oz, Af3, Af4, Fc1, Fc2, Fc5, Fc6, Cp1, Cp2, Cp5, and Cp6 sites according to the International 10–20 system with modified combinatorial nomenclature (Figure 3). These 32 electrodes were referenced to the ipsilateral earlobe electrodes. The EEG sampling rate of the measured subjects was 250 Hz, filtered in the range of 2.5–50 Hz, and readings were stored in a computer by the 24-bit analog-to-digital conversion. The electrodes (silver/ silver chloride) were applied over an elastic cap with plastic electrode holders. The ECI electrode gel (Electro-gelTM, Electro-Cap International Inc., Eaton, OH, USA) was applied into each electrode to connect with the surface of Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

such as 15 s air with no odor presentation and 15 s air with odor presentation (Figure 4). EEG data batch processing and the brain 3D-mapping of EEG power spectra were performed using Telescan software (LXSMD61, LAXTHA Inc., Daejeon, Republic of Korea). Fast Fourier transform was used to measure the mean power values (microvolt square, μV2) of each segment. The mean power spectrum values were calculated for 25 EEG indices including absolute and relative power spectra of theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) waves (Table 1) (Seo, Sowndhararajan, & Kim, 2016). Data were analyzed using SPSS statistical package 18 (SPSS, Inc., Chicago, IL, USA). The EEG power spectrum values air with no odor and air with acetic acid, acetaldehyde, acetone, and acetonitrile odors were analyzed by a paired Student’s t-test and the p value < .05 was considered significant.

Figure 3. The electrode placement locations according to the International 10–20 system with modified combinatorial nomenclature.

Results Acetic Acid

the scalp in order to drop the electric resistance of the scalp below 5 kΩ (Kim et al., 2017).

Odor Administration Acetic acid, acetaldehyde, acetone, and acetonitrile (10% v/v) were dissolved in dipropylene glycol and used as fragrance stimuli. The EEG recording room was maintained at 24 °C with the relative humidity of 50%. The EEG was recorded 15 s air with no odor and 15 s air with acetic acid or acetaldehyde or acetone or acetonitrile exposure with 30 s interval time. The total EEG recording time was 300 s for each subject. Figure 4 depicts the experimental procedure and recording sequences of the EEG measurement. Acetic acid or acetaldehyde or acetone or acetonitrile (10 ml in a 15 ml glass vial) was placed inside a sample chamber (250 ml), and the odorless fresh air was pumped into the chamber at an air flow of 3 L/min. The air outflow chamber was placed 5 cm in front of the subject’s nose. According to the subjects’ perspective, the odor intensity of these chemicals was not too strong.

Data Analysis A total of eight segments were selected for four odors from 300 s of EEG recordings (Figure 5). The first 45 s and the last 45 s with interval time 30 s of EEG recordings were not included in EEG data analysis. Out of 300 s, 120 s of EEG recordings were included in EEG data analysis. In these, each odor condition had two EEG segments (30 s) Journal of Psychophysiology (2020), 34(1), 35–49

Out of 25 EEG power spectra analyzed, significant changes were observed only in two indices during the exposure of acetic acid odor. Absolute theta (AT) wave activity significantly increased at Cp5 (788.7–3,2691.7 μV2) and F8 (1,337.9–39,830.3 μV2) regions, and relative mid-beta (RMB) activity significantly decreased at Fc1 (0.1647– 0.1107 μV2) region during the exposure of air with acetic acid odor when compared to air with no odor (Figure 6).

Acetaldehyde Figure 7 shows the t-mapping of EEG power spectra changes during the exposure of air with no odor and air with acetaldehyde odor conditions. Significant changes were observed in nine indices due to the exposure of acetaldehyde odor. However, there was no significant change in absolute wave activity. Significant decreases of relative theta (RT) in all electrode sites and the ratio of alpha to high beta (RAHB) at Af4 and Fc2 sites were observed during the exposure of acetaldehyde when compared to air with no odor exposure. On the other hand, relative low beta (RLB), relative high beta (RHB), relative beta (RB), relative gamma (RG), the ratio of sensorimotor rhythm (SMR) to theta (RST), spectral edge frequency 50% (SEF50), and spectral edge frequency 90% (SEF90) significantly increased due to the exposure of acetaldehyde odor. Among them, RLB and RHB activities significantly increased in all 32 electrode sites. In addition, RG, RST, and SEF50 significantly increased in 31 out of 32 electrode sites with the exception of P3, Fc6, and Fc1 sites, respectively. Out of 32 electrode sites, RB (except at Fpz and Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

ARDUINO microprocessor

EEG

Relay module

Solenoid valve (SV)

SV Fresh air

Odor air

Nose mask Gas flow meter No odor container

Odor container

Gas regulator

Gas cylinder

Figure 4. Experimental procedure of the EEG measurement. AA = acetic acid; AD = acetaldehyde; AC = acetone; AN = acetonitrile. The EEG was recorded 15 s air with no odor and 15 s air with acetic acid or acetaldehyde or acetone or acetonitrile exposure with 30 s interval time for each odor condition.

T8 sites) and SEF90 (except at T8 and Fc1 sites) significantly increased at 30 electrode sites.

Acetone The odor of acetone produced significant changes in 13 indices. The t-mapping of the brain clearly expressed the alteration of EEG power spectra due to the exposure of acetone odor (Figure 8). RT (except at C3 and T8 sites) and RAHB (at Fp1, Fpz, F3, F4, Fz, Fc5, C4, T7, Cz, Cp1, Cp5, P3, P7, and P8 sites) significantly decreased during Ó 2019 Hogrefe Publishing

the exposure of acetone odor than no odor exposure (Figure 8). Significant increases of relative alpha (RA), relative fast alpha (RFA), RLB, RHB, RB, RG, RST, Ratio of SMR mid-beta to theta (RSMT), SEF50, SEF90, and spectral edge frequency 50% of alpha (ASEF) were observed during the exposure of acetone odor. Among these 11 indices, RLB significantly increased in all 32 electrode sites and ASEF only at Fc5, F2, and Oz sites. Further, out of 32 electrode sites, RHB (except at C3 site), RST (except at C3 site), SEF50 (except at C3 and T8 sites), RB (except at Pz, T3, T8, and Fc6 sites), and RFA (except at Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

Table 1. EEG power spectrum indices used in this study S. No.

EEG indices

The full name of the EEG power spectrum indices

Wavelength range (Hz)

Absolute theta

4–8

Absolute alpha

8–13

Absolute beta

13–30

Absolute gamma

30–50

ASA

Absolute slow alpha

8–11

AFA

Absolute fast alpha

11–13

ALB

Absolute low beta

12–15

AMB

Absolute mid-beta

15–20

AHB

Absolute high beta

Relative theta

Relative alpha

(8–13)/(4–50)

Relative beta

(13–30)/(4–50) (30–50)/(4–50)

20–30 (4–8)/(4–50)

Relative gamma

RSA

Relative slow alpha

(8–11)/(4–50)

RFA

Relative fast alpha

(11–13)/(4–50)

RLB

Relative low beta

(12–15)/(4–50)

RMB

Relative mid-beta

(15–20)/(4–50) (20–30)/(4–50)

RHB

Relative high beta

RST

Ratio of SMR to theta

(12–15)/(4–8)

RMT

Ratio of mid-beta to theta

(15–20)/(4–8)

RSMT

Ratio of SMR mid-beta to theta

(12–20)/(4–8)

RAHB

Ratio of alpha to high beta

(8–13)/(20–30)

SEF50

Spectral edge frequency 50%

SEF90

Spectral edge frequency 90%

4–50

ASEF

Spectral edge frequency 50% of alpha

8–13

T8, Fc5, Fc1, and Fc6 sites) significantly increased in 31, 31, 30, 28, and 28 electrode sites, respectively.

Acetonitrile In the case of acetonitrile, significant changes were observed in 10 out of 25 indices during the odor exposure compared to no odor exposure. RT (except at Af3 site) and RAHB (at Cz, F8, Fz, Fc2, and Oz sites) values significantly decreased due to the exposure of acetonitrile odor (Figure 9). Significant increases of RFA, RLB, RHB, RB, RG, RST, SEF50, and SEF90 were observed during the exposure of acetonitrile odor when compared to that of no odor exposure. In these, RHB significantly increased in all 32 electrode sites. In the case of RFA, a significant increase was observed in 20 electrode sites. Interestingly, out of 32 electrode sites, significant changes of RG, RT, and RST were not observed only at Af3 site. In addition, SEF50 (except at Af3, Af4, and Fz sites) and SEF90 (except at Af3, Fpz, and O2 sites) significantly increased in 29 electrode sites. Furthermore, a significant increase of relative beta was observed at Cz, Fc2, Cp5, Oz, Fc5, F8, F2, O2, T3, Fc1, and Fc6 sites due to the exposure of acetonitrile odor. Journal of Psychophysiology (2020), 34(1), 35–49

4–50

Discussion In the present study, acetic acid, acetaldehyde, acetone, and acetonitrile were used to stimulate the olfactory system via inhalation in order to understand their effect on brain wave activity. The changes of 25 EEG power spectra between no odor and odor exposure conditions were analyzed from 32 electrode sites. In the results, we presented the t-mapping of EEG power spectra changes under no odor and odor conditions for four different odors. The findings of the present study clearly revealed that all four chemicals showed significantly (p < .05) different EEG activity as well as produced changes in different brain regions. A significant increase of absolute theta wave activity was observed at Cp5 and F8 sites during the exposure of acetic acid odor. However, there was no significant change in absolute wave activity during the exposure of acetaldehyde, acetone, and acetonitrile (Figure 10). In general, the increase in theta wave activity is highly linked with the reduction of awakening state of the brain. On the other hand, reduction in theta wave activity is correlated with the formation of memory. Further, theta wave has been considered to maintain attention during the performance Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

Figure 5. A typical example of EEG recordings from 32 channels.

Figure 6. The t-mapping of significant changes of EEG power spectra under no odor and acetic acid odor conditions.

Ó 2019 Hogrefe Publishing

of a difficult task (Greenberg, Burke, Haque, Kahana, & Zaghloul, 2015; Razumnikova, 2007; Sowndhararajan, Cho, Yu, Song, & Kim, 2016). Matsubara et al. (2011) studied the effect of inhalation of essential oil from the leaves of Abies sibirica on subjects during and after the performance of a visual display terminal work. The authors found that the essential oil reduced the arousal levels after the visual display terminal work by increasing theta wave activity. When compared with no odor condition, RLB, RHB, RB, and RG power bands significantly increased during the exposure of acetaldehyde, acetone, and acetonitrile with the exception of acetic acid. The relative power is a last resort kind of measure to be used when there is no equilibration of the absolute power band. In addition, the relative power mainly depends on absolute power in order to interpret relative power. Among them, the beta power bands play a major role in the enhancement of various cognitive performances in human beings. In general, a higher beta power band activity is connected with alertness and concentration states of the brain (Lee, Lee, & Chung, 2014). Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

Figure 7. The t-mapping of significant changes of EEG power spectra under no odor and acetaldehyde odor conditions.

An increase in the beta power activity in posterior regions is highly associated with the reading speed enhancement and reorganization of language learning task (Edagawa & Kawasaki, 2017; Weiss & Mueller, 2012). Sayowan, Siripornpanich, Hongratanaworakit, Kotchabhakdi, and Ruangrungsi (2013) also found that the inhalation of jasmine oil significantly increased the beta wave activity in the anterior center and left posterior regions, and these changes may be associated with the enhancement of active and fresh Journal of Psychophysiology (2020), 34(1), 35–49

feelings in human being. Further, Howells, Stein, and Russell (2010) reported that increased relative beta power in the left parietal region may reflect increased arousal states required to maintain attention during attentional tasks. In detoxified alcohol-dependent patients, significant increases of absolute and relative beta powers and significant decreases of alpha and delta/theta powers were observed as compared with normal controls (Saletu, Anderer, Saletu-Zyhlarz, Arnold, & Pascual-Marqui, 2002). Based Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

Figure 8. The t-mapping of significant changes of EEG power spectra under no odor and acetone odor conditions.

on the previous reports, it can be stated that the beta wave activity occurs during the alertness state of the brain. Out of 32 electrode sites, RG significantly increased in 31 sites during the exposure of acetaldehyde (except at P3 site) and acetonitrile (except at Af3 site) odors. RG also Ó 2019 Hogrefe Publishing

significantly increased in 21 electrode sites due to the exposure of acetone odor. Minguillon, Lopez-Gordo, and Pelayo (2016) proposed the prefrontal RG as a marker for stress assessment, and it was more discriminative between stress levels when compared to alpha asymmetry, theta, alpha, Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

Figure 9. The t-mapping of significant changes of EEG power spectra under no odor and acetonitrile odor conditions.

beta, and gamma power bands in the prefrontal cortex. In addition, relative fast alpha increased during the exposure of acetone and acetonitrile odors. In particular, increase in the alpha power band is related to relaxation state of the brain, and a decrease in the alpha power band is related to stress state of the brain (Basar, 2012; Iijima, Osawa, Nishitani, & Iwata, 2009; Sayorwan et al., 2012). During the heart coherent meditation, the relative power increase in the alpha power band and absolute power band decrease in high beta power band could reflect relaxation state of the brain (Kim, Rhee, & Kang, 2014). Hence, the increased Journal of Psychophysiology (2020), 34(1), 35–49

alpha wave activity may be linked with the process of relaxation. The RST also significantly increased due to the exposure of acetaldehyde, acetone, and acetonitrile odors. The SMR (12–15 Hz) and theta activity (4–7 Hz) are important EEG frequency bands used by neurofeedback researchers and clinicians (de Zambotti, Bianchin, Magazzini, Gnesato, & Angrilli, 2012). Kober, Witte, Ninaus, Neuper, and Wood (2013) stated that the SMR activity in the EEG is highly associated with the physically relaxed and mentally focused states of the brain. In another study, Ros et al. (2009) found Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

Figure 10. A schematic representation of inhalation of acetaldehyde, acetic acid, acetone, and acetonitrile odors on human EEG activity. AT = absolute theta; RMB = relative mid-beta; RT = relative theta; RAHB = ratio of alpha to high beta; RLB = relative low beta; RHB = relative high beta; RB = relative beta; RG = relative gamma; RST = ratio of SMR to theta; RSMT = Ratio of SMR mid-beta to theta; SEF50 = spectral edge frequency 50%; SEF90 = spectral edge frequency 90%; RA = relative alpha; RFA = relative fast alpha; ASEF = spectral edge frequency 50% of alpha. Arrows show the increases and decreases of EEG power spectra during the exposure of odors.

that increased SMR/theta activity was positively correlated with improvement in surgical performance. Further, SMR activity is associated with decreased anxiety and impulsivity (Gruzelier, Egner, & Vernon, 2006). The spectral edge frequencies such as SEF50 and SEF90 also significantly increased during the exposure of acetaldehyde, acetone, and acetonitrile odors when compared with no odor condition. The SEF reflects the frequency below which a defined power of total power spectrum is located. The SEF may increase in the course of light anesthesia and then decrease during deeper levels (Tonner & Bein, 2006). Schwender et al. (1996) also found that the SEF90 is decreased with increasing the concentration of anesthetics. Among the four chemicals, acetaldehyde has a strong, fruity odor and it can make breathing difficult at high concentrations. Acetaldehyde has been found in ripe fruits, essential oils, roasted coffee, heated milk, and tobacco smoke (Hoffmann & Hecht, 1990; IARC, 1985). In a rat study, there were no effects on the olfactory epithelium or other parts of the respiratory tract after repeated Ó 2019 Hogrefe Publishing

subchronic exposure to acetaldehyde at 50 ppm concentration. On the other hand, repeated subchronic exposure of rats to acetaldehyde at higher concentrations (> 500 ppm) has resulted in inflammation, injury to the respiratory epithelium, and loss of neurons (Dorman et al., 2008). However, the present study revealed that the exposure of acetaldehyde (at 10% concentration) produced some positive psychological changes in human being by increasing relative beta power values. Acetic acid is a key aroma compound in different types of vinegar (Callejón, Morales, Troncoso, & Silva Ferreira, 2008). Cometto-Muñiz and Abraham (2010) studied the structure-activity relationships on the odor threshold level of homologous carboxylic acids (formic, acetic, butyric, hexanoic, and octanoic acids) by human beings. In their study, threshold levels decreased for formic, acetic, and butyric acid. Further, there were no significant effects of gender on odor detectability. Olfactory detection threshold levels were positively correlated with carbon chain length of carboxylic acids and their branching next to the functional carboxyl group (Güven & Journal of Psychophysiology (2020), 34(1), 35–49

Laska, 2012). Several studies have also shown that carboxylic acids are strong ligands for a number of olfactory receptors (Grosmaitre et al., 2009; Repicky & Luetje, 2009). In another study, Muttray et al. (2005) investigated the acute effects of a solvent mixture (acetone and toluene) on the human beings’ central nervous system. For this purpose, 12 healthy men were exposed to a mixture containing acetone (25 ppm) and toluene (250 ppm) or to air (control) for 4.5 hr. The results revealed that the mixture of toluene and acetone did not produce any adverse effect. In regard to EEG findings, possible subclinical effects on the central nervous system cannot be avoided. In chronic experiments on rats, inhalation of vapors of an organic solvent, No. 646 (a mixture of 50% toluene and acetone) exhibited behavioral alterations, modulation of electrical activity in the neocortex, hippocampus, and medial olfactory region by increasing the powers of some spectral components (Levicheva & Berchenko, 2014). Wysocki, Dalton, Brody, and Lawley (1997) investigated the sensitivity of olfaction and irritation for 2-propanone (acetone) and 1-butanol in acetone-exposed workers and suggested that exposures to acetone stimulate changes in acetone sensitivity that are specific to acetone. According to the World Health Organization (1998), acetone is not considered to be genotoxic or mutagenic. However, previous studies reported that acetone exhibited neuropsychological effects in workers in relation to concentration and duration of exposure (Buron et al., 2009). In the present study, the results revealed that the acetic acid odor exhibited changes only in two EEG indices (AT and RMB). On the other hand, other three chemicals produced changes in numerous EEG indices with similar patterns of activity. The variation in the activity of acetic acid (strong vinegar-like odor) and other three chemicals might be due to the fragrance type. Acetaldehyde and acetone have a similar type of odor characteristics (fruity odor), whereas acetonitrile has an ether-like odor. In addition, our previous studies clearly suggested that the mixture of chemicals, especially essential oils (Magnolia kobus, Mentha arvensis, Angelica gigas, and Zizyphus jujuba) produced changes only in a few number of EEG indices (Cho, Sowndhararajan, Jung, Jhoo, & Kim, 2013, 2015; Cho, Yu, et al., 2013; Sowndhararajan, Seo, Kim, Kim, & Kim, 2017). However, the odor of individual chemicals such as geosmin, 2-methylisoborneol, (+)-α-pinene, and (+)-βpinene significantly produced changes in numerous EEG power spectra (Kim, Sowndhararajan, Park, & Kim, 2018; Kim et al., 2017). In a similar line, acetaldehyde, acetone, and acetonitrile odors also exhibited changes in a greater number of EEG indices in different brain regions. Odor molecules play an important role in the human brain function including emotions, thoughts, and memory Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

(Touhara & Vosshall, 2009). It is clearly known that odor molecules that enter the nose are detected by millions of olfactory sensory neurons. The detection of odor molecules is facilitated by 1,000 different G-protein-coupled receptors, and the olfactory receptor cells are particularly tuned to distinct odor characteristics. Numerous findings indicate that each olfactory neuron expresses only one olfactory receptor gene (Malnic et al., 1999; Simoes de Souza & Antunes, 2007). Olfactory receptors in the mammalian olfactory system show a combinatorial response to odor molecules. For example, a single odor produces the response from several receptors, and a single olfactory receptor also responds to multiple odor molecules. Hence, every odor molecule has been believed to have a distinctive combination of responses from various olfactory receptors (Floriano, Vaidehi, Goddard, Singer, & Shepherd, 2000). In general, odor molecules can differ in a variety of parameters, such as size, shape, functional groups, and charge. The functional group of chemicals would seem to play a major role in the choice of a specific receptor site. Zarzo (2012) reported that the influence of functional group is noticeable in odor character and odor detectability. In addition, there are a number of receptors that do not discriminate between some chemical functional groups, including aldehydes, alcohols, and acids (Araneda, Peterlin, Zhang, Chesler, & Firestein, 2004). Olfactory thresholds may also be extremely different even for two volatile organic chemicals with a similar molecular structure (Zarzo, 2012). The oxygen-containing moiety in the stimulating odor molecules included aldehyde, ketone, ester, or acid functional groups, with alcohols rarely triggering mitral cells of the olfactory bulb (Laing et al., 2003). In an animal study, odor molecules with multiple oxygen-containing functional groups as well as other odor molecules with high water solubility specifically activate posterior olfactory bulb glomeruli (Johnson, Arguello, & Leon, 2007). It was also reported that even a minor alteration in the structure of an odor molecule can produce a remarkable shift in its perceived odor. For example, octanol has an orange with rose-like odor, whereas octanoic acid has a rancid with sweet odor. However, octanoic acid can be obtained from octanol by replacing its hydroxyl group with the carboxyl group. According to the concentration of odorants, indole has a putrid odor at higher concentrations, but it is perceived as floral odor at lower concentrations (Malnic et al., 1999). In the present study, odorants were exposed to only men at the concentration of 10%. Furthermore, EEG was recorded for a short duration (15 s during no odor condition and 15 s during odor condition). Moreover, the odors were sequentially administered to each subject. The lack of randomization is also an important limitation of this study. Therefore, it is still unknown whether these chemicals will Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

show the same EEG activity over a slightly longer duration using both genders. In light of these limitations, further studies are warranted in relation to EEG recordings for a longer duration, randomization of odor presentation, different concentrations of chemicals, and odor exposure to both men and women in order to understand their exact action on brain wave activity.

Conclusions This study revealed that acetaldehyde, acetic acid, acetone, and acetonitrile odors exhibited markedly different EEG activity by affecting different brain regions. Among these, acetaldehyde, acetone, and acetonitrile odors showed almost similar EEG activity by altering different relative and ratio values. However, a significant change of absolute wave (AT) was observed only for acetic acid odor. At the concentration of 10%, men are highly responded well to all the odors with the exception of acetic acid odor. The present study clearly indicates that chemicals with different functional groups produced significantly different EEG activity, and these variations might be due to the structural arrangement of chemicals, odor types, and the sensitivity of olfactory receptors.

References Angelucci, F. L., Silva, V. V., Dal Pizzol, C., Spir, L. G., Praes, C. E., & Maibach, H. (2014). Physiological effect of olfactory stimuli inhalation in humans: An overview. International Journal of Cosmetic Science, 36, 117–123. https://doi.org/10.1111/ics.12096 Araneda, R. C., Peterlin, Z., Zhang, X., Chesler, A., & Firestein, S. (2004). A pharmacological profile of the aldehyde receptor repertoire in rat olfactory epithelium. The Journal of Physiology, 555, 743–756. https://doi.org/10.1113/jphysiol.2003.058040 Basar, E. (2012). A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology. International Journal of Psychophysiology, 86, 1–24. https://doi.org/10.1016/j.ijpsycho.2012.07.002 Boha, R., Molnar, M., Gaal, Z. A., Czigler, B., Rona, K., Kass, K., & Klausz, G. (2009). The acute effect of low–dose alcohol on working memory during mental arithmetic: I. Behavioral measures and EEG theta band spectral characteristics. International Journal of Psychophysiology, 73, 133–137. https://doi. org/10.1016/j.ijpsycho.2009.02.006 Brauchli, P., Ruegg, P. B., Etzweiler, F., & Zeier, H. (1995). Electrocortical and autonomic alteration by administration of a pleasant and an unpleasant odor. Chemical Senses, 20, 505–515. https://doi.org/10.1093/chemse/20.5.505 Buron, G., Hacquemand, R., Pourié, G., & Brand, G. (2009). Inhalation exposure to acetone induces selective damage on olfactory neuroepithelium in mice. Neurotoxicology, 30, 114–120. https://doi.org/10.1016/j.neuro.2008.11.005 Callejón, R. M., Morales, M. L., Troncoso, A. M., & Silva Ferreira, A. C. (2008). Targeting key aromatic substances on the typical aroma of sherry vinegar. Journal of Agricultural and Food Chemistry, 56, 6631–6639. https://doi.org/10.1021/jf703636e

Ó 2019 Hogrefe Publishing

Cho, H., Sowndhararajan, K., Jung, J. W., Jhoo, J. W., & Kim, S. (2013). Fragrance chemicals in the essential oil of Mentha arvensis reduce the level of mental stress. Journal of Life Science, 23, 933–940. https://doi.org/10.5352/JLS.2013.23.7.9 Cho, H., Sowndhararajan, K., Jung, J. W., Jhoo, J. W., & Kim, S. (2015). Fragrant chemicals in the supercritical carbon dioxide extract of Magnolia kobus DC. Flower buds increase the concentration state of brain function. Journal of Essential Oil Bearing Plants, 18, 1059–1069. https://doi.org/10.1080/ 0972060X.2014.901619 Cho, H., Yu, B., Sowndhararajan, K., Jung, J. W., Jhoo, J. W., & Kim, S. (2013). Effect of essential oil from San-Jo-In (Zizyphus jujuba Mill. seeds) on human electroencephalographic activity. Journal of Life Science, 23, 1170–1176. https://doi.org/10.5352/ JLS.2013.23.9.1170 Cometto-Muñiz, J. E., & Abraham, M. H. (2010). Structure-activity relationships on the odor detectability of homologous carboxylic acids by humans. Experimental Brain Research, 207, 75–84. https://doi.org/10.1007/s00221-010-2430-0 de Zambotti, M., Bianchin, M., Magazzini, L., Gnesato, G., & Angrilli, A. (2012). The efficacy of EEG neurofeedback aimed at enhancing sensory-motor rhythm theta ratio in healthy subjects. Experimental Brain Research, 221, 69–74. https://doi. org/10.1007/s00221-012-3148-y Dick, F. D. (2006). Solvent neurotoxicity. Occupational and Environmental Medicine, 63, 221–226. https://doi.org/10.1136/ oem.2005.022400 Dorman, D. C., Struve, M. F., Wong, B. A., Gross, E. A., Parkinson, C., Willson, G. A., . . . Andersen, M. E. (2008). Derivation of an inhalation reference concentration based upon olfactory neuronal loss in male rats following subchronic acetaldehyde inhalation. Inhalation Toxicology, 20, 245–256. https://doi.org/ 10.1080/08958370701864250 Edagawa, K., & Kawasaki, M. (2017). Beta phase synchronization in the frontal-temporalcerebellar network during auditory-tomotor rhythm learning. Scientific Reports, 7, 42721. https://doi. org/10.1038/srep42721 EPA (US Environmental Protection Agency). (2007). Acetaldehyde. (Hazard Summary-Created in April 1992; Revised in January 2000). Retrieved from Technology Transfer Network, Air Toxics website, US Environmental Protection Agency. https://www. epa.gov/haps/health-effects-notebook-hazardous-airpollutants Floriano, W. B., Vaidehi, N., Goddard, W. A., Singer, M.S., & Shepherd, G. M. (2000). Molecular mechanisms underlying differential odor responses of a mouse olfactory receptor. Proceedings of the National Academy of Sciences of the United States of America, 97, 10712–10716. Greenberg, J. A., Burke, J. F., Haque, R., Kahana, M. J., & Zaghloul, K. A. (2015). Decreases in theta and increases in high frequency activity underlie associative memory encoding. NeuroImage, 114, 257–263. https://doi.org/10.1016/j.neuroimage. 2015.03.077 Grosmaitre, X., Fuss, S. H., Lee, A. C., Adipietro, K. A., Matsunami, H., Mombaerts, P., & Ma, M. (2009). SR1, a mouse odorant receptor with an unusually broad response profile. Journal of Neuroscience, 29, 14545–14552. https://doi.org/10.1523/ JNEUROSCI.2752-09.2009 Gruzelier, J., Egner, T., & Vernon, D. (2006). Validating the efficacy of neurofeedback for optimising performance. Progress in Brain Research, 159, 421–431. https://doi.org/10.1016/S0079-6123 (06)59027-2 Güven, S. C., & Laska, M. (2012). Olfactory sensitivity and odor structure-activity relationships for aliphatic carboxylic acids in CD-1 mice. PLoS One, 7, e34301. https://doi.org/10.1371/ journal.pone.0034301

Journal of Psychophysiology (2020), 34(1), 35–49

Hoffmann, D., & Hecht, S. S. (1990). Advances in tobacco carcinogenesis. In C. S. Cooper & P. L. Grover (Eds.), Handbook of experimental pharmacology, chemical carcinogenesis and mutagenesis 1 (Vol. 94, pp. 63–102). Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-642-74775-5_3 Howells, F. M., Stein, D. J., & Russell, V. A. (2010). Perceived mental effort correlates with changes in tonic arousal during attentional tasks. Behavioral and Brain Functions, 6, 39. https://doi.org/10.1186/1744-9081-6-39 IARC (International Agency for Research on Cancer). (1985). Allyl compounds, aldehydes, epoxides and peroxides [IARC Monographs on the Evaluation of the Carcinogenic Risk of Chemicals to Humans, Vol. 36]. Lyon, France: IARC. Iijima, M., Osawa, M., Nishitani, N., & Iwata, M. (2009). Effects of incense on brain function: Evaluation using electroencephalograms and event–related potentials. Neuropsychobiology, 59, 80–86. https://doi.org/10.1159/000209859 Johnson, B. A., Arguello, S., & Leon, M. (2007). Odorants with multiple oxygen-containing functional groups and other odorants with high water solubility preferentially activate posterior olfactory bulb glomeruli. Journal of Comparative Neurology, 502, 468–482. https://doi.org/10.1002/cne.21322 Keski-Säntti, P., Kovala, T., Holm, A., Hyvärinen, H. K., & Sainio, M. (2008). Quantitative EEG in occupational chronic solvent encephalopathy. Human & Experimental Toxicology, 27, 315–320. https://doi.org/10.1177/0960327107082231 Kim, D. K., Rhee, J. H., & Kang, S. W. (2014). Reorganization of the brain and heart rhythm during autogenic meditation. Frontiers in Integrative Neuroscience, 7, 109. https://doi.org/10.3389/ fnint.2013.00109 Kim, M., Sowndhararajan, K., Kim, T., Kim, J. E., Yang, J. E., & Kim, S. (2017). Gender differences in electroencephalographic activity in response to the earthy odorants geosmin and 2-methylisoborneol. Applied Sciences, 7, 876. https://doi.org/ 10.3390/app7090876 Kim, M., Sowndhararajan, K., Park, S. J., & Kim, S. (2018). Effect of inhalation of isomers, (+)-α-pinene and (+)-β-pinene on human electroencephalographic activity according to gender difference. European Journal of Integrative Medicine, 17, 33–39. https://doi.org/10.1016/j.eujim.2017.11.005 Kober, S. E., Witte, M., Ninaus, M., Neuper, C., & Wood, G. (2013). Learning to modulate one’s own brain activity: The effect of spontaneous mental strategies. Frontiers in Human Neuroscience, 7, 695. https://doi.org/10.3389/fnhum.2013.00695 Laing, D. G., Legha, P. K., Jinks, A. L., & Hutchinson, I. (2003). Relationship between molecular structure, concentration and odor qualities of oxygenated aliphatic molecules. Chemical Senses, 28, 57–69. https://doi.org/10.1093/chemse/ 28.1.57 Lee, B. G., Lee, B. L., & Chung, W. Y. (2014). Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals. Sensors, 14, 17915–17936. https://doi.org/10.3390/s141017915 Levicheva, N. A., & Berchenko, O. G. (2014). Brain electrical activity and peculiarities of the self-stimulation reaction in pubertate rats with addiction to inhalation of organic solvent vapors. Neurophysiology, 46, 521–523. https://doi.org/10.1007/ s11062-015-9483-4 Malnic, B., Hirono, J., Sato, T., & Buck, L. B. (1999). Combinatorial receptor codes for odors. Cell, 96, 713–723. https://doi.org/ 10.1016/S0092-8674(00)80581-4 Matsubara, E., Fukagawa, M., Okamoto, T., Ohnuki, K., Shimizu, K., & Kondo, R. (2011). The essential oil of Abies sibirica (Pinaceae) reduces arousal levels after visual display terminal work. Flavour and Fragrance Journal, 26, 204–210. https://doi.org/ 10.1002/ffj.2056

Journal of Psychophysiology (2020), 34(1), 35–49

M. Kim et al., EEG Activity of Organic Compounds

Minguillon, J., Lopez-Gordo, M. A., & Pelayo, F. (2016). Stress assessment by prefrontal relative gamma. Frontiers in Computational Neuroscience, 10, 101. https://doi.org/10.3389/ fncom.2016.00101 Muttray, A., Martus, P., Schachtrup, S., Müller, E., Mayer-Popken, O., & Konietzko, J. (2005). Acute effects of an organic solvent mixture on the human central nervous system. European Journal of Medical Research, 10, 381–388. Nagasawa, Y., Samoto, H., Ukai, H., Okamoto, S., Itoh, K., Hanada, T., . . . Ikeda, M. (2013). Use of organic solvents in large research institutions in Japan. Environmental Health and Preventive Medicine, 18, 341–348. https://doi.org/10.1007/ s12199-012-0327-1 NRC (National Research Council). (2009). Acetaldehyde in emergency and continuous exposure guidance levels for selected submarine contaminants (Vol. 3), Washington, DC: National Academy Press. Razumnikova, O. M. (2007). Creativity related cortex activity in the remote associates task. Brain Research Bulletin, 73, 96–102. https://doi.org/10.1016/j.brainresbull.2007.02.008 Repicky, S. E., & Luetje, C. W. (2009). Molecular receptive range variation among mouse odorant receptors for aliphatic carboxylic acids. Journal of Neurochemistry, 109, 193–202. https://doi.org/10.1111/j.1471-4159.2009.05925.x Ros, T., Moseley, M. J., Bloom, P. A., Benjamin, L., Parkinson, L. A., & Gruzelier, J. H. (2009). Optimizing microsurgical skills with EEG neurofeedback. BMC Neuroscience, 10, 87. https://doi.org/ 10.1186/1471-2202-10-87 Ruth, J. H. (1986). Odor thresholds and irritation levels of several chemical substances: A review. American Industrial Hygiene Association Journal, 47, A142–A151. https://doi.org/10.1080/ 15298668691389595 Saletu, B., Anderer, P., Saletu-Zyhlarz, G. M., Arnold, O., & Pascual-Marqui, R. D. (2002). Classification and evaluation of the pharmacodynamics of psychotropic drugs by single-lead pharmaco-EEG, EEG mapping and tomography (LORETA). Methods and Findings in Experimental and Clinical Pharmacology, 24, 97–120. Sayorwan, W., Siripornpanich, V., Piriyapunyaporn, T., Hongratanaworakit, T., Kotchabhakdi, N., & Ruangrungsi, N. (2012). The effects of lavender oil inhalation on emotional states, autonomic nervous system, and brain electrical activity. Journal of the Medical Association of Thailand, 95, 598–606. Sayowan, W., Siripornpanich, V., Hongratanaworakit, T., Kotchabhakdi, N., & Ruangrungsi, N. (2013). The effects of jasmine oil inhalation on brain wave activities and emotions. Journal of Health Research, 27, 73–77. Schwender, D., Daunderer, M., Mulzer, S., Klasing, S., Finsterer, U., & Peter, K. (1996). Spectral edge frequency of the electroencephalogram to monitor “depth” of anaesthesia with isoflurane and propofol. British Journal of Anaesthesia, 77, 179–184. Seo, M., Sowndhararajan, K., & Kim, S. (2016). Influence of binasal and uninasal inhalations of essential oil of Abies koreana twigs on electroencephalographic activity of human. Behavioural Neurology, 2016, 9250935. https://doi.org/10.1155/2016/ 9250935 Seppalainen, A. M., Laine, A., Salmi, T., Verkkala, E., Riihimäki, V., & Luukkonen, R. (1991). Electroencephalographic findings during experimental human exposure to m-xylene. Archives of Environmental Health, 46, 16–24. https://doi.org/10.1080/ 00039896.1991.9937424 Simoes de Souza, F. M., & Antunes, G. (2007). Biophysics of olfaction. Reports on Progress in Physics, 70, 451–491. https:// doi.org/10.1088/0034-4885/70/3/R04 Sowndhararajan, K., Cho, H., Yu, B., Song, J., & Kim, S. (2016). Effect of inhalation of essential oil from Inula helenium L. root

Ó 2019 Hogrefe Publishing

M. Kim et al., EEG Activity of Organic Compounds

on electroencephalographic (EEG) activity of the human brain. European Journal of Integrative Medicine, 8, 453–457. https:// doi.org/10.1016/j.eujim.2016.01.005 Sowndhararajan, K., & Kim, S. (2016). Influence of fragrances on human psychophysiological activity: With special reference to human electroencephalographic response. Scientia Pharmaceutica, 84, 724–751. https://doi.org/10.3390/ scipharm84040724 Sowndhararajan, K., Seo, M., Kim, M., Kim, H., & Kim, S. (2017). Effect of inhalation of essential oil and supercritical carbon dioxide extract from the root of Angelica gigas Nakai on human EEG activity. Complementary Therapies in Clinical Practice, 28, 161–168. https://doi.org/10.1016/j.ctcp.2017.05.010 Tonner, P. H., & Bein, B. (2006). Classic electroencephalographic parameters: Median frequency, spectral edge frequency etc. Best Practice & Research: Clinical Anaesthesiology, 20, 147–159. https://doi.org/10.1016/j.bpa.2005.08.008 Touhara, K., & Vosshall, L. B. (2009). Sensing odorants and pheromones with chemosensory receptors. Annual Review of Physiology, 71, 307–332. https://doi.org/10.1146/annurev. physiol.010908.163209 Weiss, S., & Mueller, H. M. (2012). “Too many betas do not spoil the broth”: The role of beta brain oscillations in language processing. Frontiers in Psychology, 3, 201. https://doi.org/ 10.3389/fpsyg.2012.00201 WHO (World Health Organization). (1998). Environmental health criteria 207, acetone. Geneva, Switzerland: International Programme on Chemical Safety. Retrieved from http://www. inchem.org/documents/ehc/ehc/ehc207.htm Wysocki, C. J., Dalton, P., Brody, M. J., & Lawley, H. J. (1997). Acetone odor and irritation thresholds obtained from acetoneexposed factory workers and from control (occupationally unexposed) subjects. American Industrial Hygiene Association Journal, 58, 704–712. https://doi.org/10.1080/15428119791012342 Zarzo, M. (2012). Effect of functional group and carbon chain length on the odor detection threshold of aliphatic compounds. Sensors, 12, 4105–4112. https://doi.org/10.3390/s120404105

Ó 2019 Hogrefe Publishing

History Received April 16, 2018 Revision received September 20, 2018 Accepted September 24, 2018 Published online February 28, 2019 Conflict of Interest 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. Funding This study was supported by the research grant from Kangwon National University, Chuncheon and the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) through the Encouragement Program for The Industries of Economic Cooperation Region (Project No. R0004940). Songmun Kim School of Natural Resources and Environmental Science Kangwon National University Chuncheon 24341 Republic of Korea perfume@kangwon.ac.kr Kandhasamy Sowndhararajan Department of Botany Kongunadu Arts and Science College Coimbatore 641029 Tamil Nadu India sowndhar1982@gmail.com

Journal of Psychophysiology (2020), 34(1), 35–49

Article

Some Compliments (and Insults) Are More Heartfelt High Cardiac Awareness Increases P2 Amplitudes to Emotional Verbal Stimuli That Involve the Body Erik M. Benau and Ruth Ann Atchley Department of Psychology, University of Kansas, Lawrence, KS, USA

Abstract: Previous research suggests that individuals with increased awareness of internal bodily states (i.e., high interoceptive awareness) are more sensitive to emotional stimuli, particularly stimuli that are negative or threatening. Concurrently, there is increasing evidence that words that are more body-referent (e.g., bonehead) are processed faster, perceived more accurately, and generate larger neuroelectrical signals than those that are less body-referent (e.g., idiot). The present study examined individual differences in interoceptive awareness (IA) to these more embodied words. While electroencephalogram (EEG) was recorded, participants passively viewed insults, compliments, and neutral stimuli, half of which were more embodied (e.g., bonehead, beautiful) and half of which were less embodied (e.g., idiot, friendly). Results showed that the high perceivers generated a larger P2 to embodied compliments than less embodied compliments while average perceivers generated a larger P2 to embodied insults than to less embodied insults. The results provide preliminary evidence that good cardiac awareness is not only associated with increased sensitivity to negative stimuli, but to stimuli pertaining to the body itself. Keywords: interoception, embodiment, cardiac perception, attentional bias, event-related potentials

Interoception is the awareness of the body’s internal states (e.g., satiety, fatigue, cardiac activity; Craig, 2010). Interoceptive awareness (IA) is positively associated with frequency and intensity of self-reported emotional experience (Garfinkel & Critchley, 2013; Schandry, 1981). One proposed mechanism for this relation is that heightened IA facilitates the subjective association of external stimuli to their aversive or pleasant properties with greater intensity than those with average IA (Garfinkel & Critchley, 2013). Increasing neurobehavioral data support this postulation. For example, IA has been found to be positively associated with psychophysiological indices of attention and arousal to emotional pictures and words than to neutral stimuli (Garfinkel & Critchley, 2013; Herbert, Pollatos, Flor, Enck, & Schandry, 2010; Herbert, Pollatos, & Schandry, 2007). Individuals with increased IA have also demonstrated greater sensitivity to idiographic and self-relevant stimuli (Ainley, Maister, Brokfeld, Farmer, & Tsakiris, 2013; Ainley, Tajadura-Jimenez, Fotopoulou, & Tsakiris, 2012; Babo-Rebelo, Wolpert, Adam, Hasboun, & Tallon-Baudry, 2016). Thus, heightened sensitivity to internal sensations has been found to translate to awareness of external stimuli pertinent to the individual. In the present study, we used Event-Related Potentials (ERPs) to explore whether, and how, stimuli containing information pertaining to the body are more salient for high Journal of Psychophysiology (2020), 34(1), 50–59 https://doi.org/10.1027/0269-8803/a000235

IA individuals. The study is exploratory in nature and focused on the P200 (P2) component, which has been of increasing utility in elucidating the time course and topography of emotional processing (Carretie, Albert, LopezMartin, & Tapia, 2009; Lei et al., 2017; M.-K. Kim, Kim, Oh, & Kim, 2013). The P2 is typically the second positive deflection that is maximal around 200 ms post-stimulus onset, though its exact function is not yet established (Citron, 2012; M.-K. Kim et al., 2013). Studies that examined the P2 have shown that it may reflect similar evaluative processes as other early attention-related components associated with threat detection (Dennis & Chen, 2007; M.-K. Kim et al., 2013; Thomas, Johnstone, & Gonsalvez, 2007) and sensory integration (Klasen, Kreifelts, Chen, Seubert, & Mathiak, 2014; Zinchenko, Kanske, Obermeier, Schroger, & Kotz, 2015). Personal relevance of stimuli has been found to increase P2 amplitudes (Carretie et al., 2008; Fields & Kuperberg, 2012; Speed, Levinson, Gross, Kiosses, & Hajcak, 2017; Tacikowski, Cygan, & Nowicka, 2014). The P2 has been found to be elevated to emotional stimuli, compared to neutral stimuli, even under rapid, backward-masked paradigms (Lei et al., 2017). The P2 is not often analyzed in relation to individual differences in emotion processing, and the choice to focus on this component was based on examination of the data after study completion (discussed further below). Ó 2019 Hogrefe Publishing

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Insults and compliments have increased personal relevance as they are directly intended to generate emotional response by the recipient or perceiver (Archer, 2015; Carretie et al., 2008). When elements of the body are more salient in an insult or compliment (e.g., numbskull, beautiful), those stimuli are perceived faster and more accurately than those with less embodied concepts (e.g., idiot, friendly) (Wellsby, Siakaluk, Pexman, & Owen, 2010). More embodied stimuli may be less ambiguous, reducing the time and resources needed for processing, as indexed by greater amplitudes of emotion-related ERP components and faster, more accurate lexical judgments (Benau et al., 2018; Wellsby et al., 2010). To our knowledge, individual differences in the perception of embodiment and emotionality have not yet been explored; the study of the influence of a variable such as IA on embodiment processing is an intuitive start to this work. The goal of the present study was to investigate how IA interacts with emotional and/or embodied components in lexical stimuli. To achieve this goal, healthy undergraduate participants viewed compliments, insults, and neutral stimuli (half of which were more embodied than the other half) while electroencephalograms (EEGs) were recorded. We hypothesized that individuals with increased IA would be more sensitive to emotional stimuli with greater reference to the body due to their personal relevance. Our predictions regarding valence were less firm. If individuals with high IA are more sensitive to threatening stimuli, then they may perceive insults (especially when more embodied) as more salient than positive and neutral stimuli, thus elevating the components of interest. However, it is equally possible that individuals with increased IA will see compliments (particularly more embodied compliments) as more salient and/or self-referent, resulting in similar elevations.

Method Participants Thirty-seven self-selected participants completed the study in exchange for course credit. By self-report, participants were right-handed native speakers of English, free of previous traumatic brain injury and learning disability. One participant’s data were lost to recording failure, and three others were removed from analyses due to excess artifact (described further below). The final sample consisted of 34 undergraduates (19 women).

Stimuli We used the same stimuli as Benau et al. (2018). A list of 240 words contained 80 insults, 80 compliments, and 80 Ó 2019 Hogrefe Publishing

neutral stimuli. Half of each category had “more embodied,” features related to some element of the body (e.g., numbskull, beautiful, tooth), and the other half was “less embodied” (e.g., stupid, friendly, crate). Detailed description of the stimulus generation and rating procedures can be found in Benau et al. (2018). Briefly, insult and neutral stimuli were taken from Siakaluk, Pexman, Dalrymple, Stearns, and Owen (2011, Appendix A). For compliments, we followed similar stimulus generation techniques as Siakaluk et al. (2011); 36 undergraduates and members of our laboratory, respectively, generated a total of 170 compliments that were then rated by a separate group of undergraduate participants (not otherwise involved in any other part of the study). They rated the stimuli based on how much the compliment contained an element of the body and how much the word was a compliment. Three separate groups rated the words on a scale of 1–9 on pleasantness (extremely unpleasant to extremely pleasant), intensity (extremely intense to entirely neutral), and frequency of hearing or seeing the word (nearly never to multiple times per day), respectively. Stimuli in each category were rated as comparable on intensity, valence, length, and frequency.

Procedure The University of Kansas Institutional Review Board approved all procedures of this experiment, and participants completed informed consent prior to participation. Following consent and questionnaires, participants applied the heart rate monitor and completed the cardiac awareness task. They then removed the heart rate monitor, and we applied the EEG cap. We then described the passiveviewing and rating tasks and addressed any questions participants had prior to the start of the task. Depression, Anxiety, and Stress Scale 42-Item (DASS-42) The DASS-42 (Brown, Chorpita, Korotitsch, & Barlow, 1997) measures depression, anxiety, and general distress within three eponymous subscales. The instrument has been found to be valid and reliable within both clinical and general samples of adults (e.g., Crawford, Cayley, Lovibond, Wilson, & Hartley, 2011). Body Consciousness Questionnaire (BCQ) The BCQ (Miller, Murphy, & Buss, 1981) is a valid and reliable instrument that measures subjective cognizance of internal bodily states and physical appearance (Miller et al., 1981). The BCQ has three subscales: Private Body Consciousness (i.e., awareness of internal bodily sensations), Public Body Consciousness (i.e., perception of outward appearance), and Body Competence (i.e., confidence in physical abilities). The BCQ was included to compare self-reported and objective IA. Journal of Psychophysiology (2020), 34(1), 50–59

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Cardiac Awareness Task We used a heartbeat perception task based on those used amply elsewhere (e.g., Herbert et al., 2010; Schandry, 1981). After applying the heart rate monitor and without taking a pulse, participants were asked to count the number of heartbeats they felt within three trials of 25 s, 35 s, and 45 s. Their estimated count of heartbeats was recorded and compared to the actual count of heartbeats. Passive-Viewing and Rating Tasks We informed participants that they were going to complete a stimuli rating task and would preview each word first. During the passive-viewing task, a fixation cross appeared on screen for 1 s followed by a word presented for 1.25 s. A 1.5 s blank screen served as an inter-trial interval. Participants were given three untimed breaks and were asked to refrain from blinking and other muscle movements until this time. After the passive-viewing task, participants completed the rating task. Each word was presented for 1 s, after which a 9-point valence Self-Assessment Manikin (Suk, 2006) appeared beneath the word. Participants first rated the word from 1 (= insult) to 9 (= compliment) with 5 marked as “neutral.” Immediately after response, a similar 9-point intensity scale appeared using the arousal Manikin with 1 = not intense and 9 = very intense. The next word appeared after both responses. Ratings were collected via a USB keyboard. Presentation order was randomized across tasks and participants. Participants completed a practice block of 10 words (not used in the actual task) prior to recording.

Apparatus Heart Rate RR peaks were recorded using a Polar V800 heart rate monitor with the H7 heart rate sensor using the Polar Flow application (Polar Electro Oy, Kempele, Finland). The Polar V800 has been previously found to be a valid and reliable method for attaining counts of RR peaks (Giles, Draper, & Neil, 2015). Electroencephalogram (EEG) All equipment and software used for EEG acquisition and analysis were manufactured by Compumedics (Charlotte, NC). EEG was recorded with 32 Ag-AgCl electrodes mounted in an elastic cap (Quik-cap) according to the International 10–20 system1 via NuAmps-40 amplifier. Ocular

artifacts were monitored via bipolar leads placed above and below the left eye and at the outer canthi of both eyes. Impedances were kept below 5 kΩ. EEG was sampled at 1,000 Hz and filtered online with a low-pass filter of 100 Hz (40 dB attenuation). Participants rested their chin on a stand 51 cm from the screen. Stimulus Presentation Stimuli were presented using E-Prime software version 2.2 (Psychology Software Tools, Inc., Pittsburgh, PA). Words were presented in lower case Courier New font, size 18, as white text on black background on an LCD monitor connected to a PC.

Data Reduction Cardiac Data Cardiac awareness was calculated as: [(1 (|beats guessed – beats recorded|)/beats recorded)]. “Good” perceivers were defined as those with an average perception score .85 (n = 15), while “average” perceivers (n = 19) were the remainder of the participants. The use of 85% accuracy to define “high” and “average” groups has been used amply in previous electrophysiological studies of emotional processing (e.g., Herbert et al., 2010; Montoya, Schandry, & Müller, 1993; Pollatos, Kirsch, & Schandry, 2005a; Pollatos & Schandry, 2008; Schandry, 1981). These initial studies using taxonomic analyses indicate that this cut score substantially distinguishes individuals with high and low IA who have different emotional and cognitive processes. Further description of the groups is provided below. EEG Data EEG was re-referenced offline to averaged mastoids and filtered with a 0.01–30.0 Hz band-pass. Ocular artifacts were corrected using the proprietary, semi-automated covariance algorithm within Curry software based on Semlitsch, Anderer, Schuster, and Presslich (1986). Trials were removed if artifact remained after correction that contained deflections from baseline ±70 μv in any channel. Stimulus-locked EEG was segmented to 1,000 ms after stimulus onset with a 200 ms baseline. The P2 was scored between 170 ms and 300 ms at electrode FCz where it was observed to be most prominent. We also examined the Late Positive Potential (LPP; 470–600 ms and 600–800 ms) and Early Posterior Negativity (EPN; 200–280 ms) in posterior electrodes2 using analysis of

Electrodes in the array were: FP1, FP2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T7, T8, C3, Cz, C4, TP7, TP8, CP3, CPz, CP4, P7, P3, Pz, P4, P8, O1, Oz, O2, A1, A2. The LPP was examined in CPz, Pz, and Oz both individually and as a variable in the ANOVA. Similarly, the EPN was examined in O1, Oz, and O2. No interpretable significant effects or interactions emerged in these ANOVA beyond main effects of electrode (Pz showed largest LPP amplitudes while O2 showed most negative EPN amplitudes).

Journal of Psychophysiology (2020), 34(1), 50–59

Ó 2019 Hogrefe Publishing

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Average Perceivers More Embodied

High Perceivers

Less Embodied

More Embodied

Less Embodied

Compliments

Insults

Neutral

Figure 1. Topographical maps showing the averaged activity from 200–280 ms, the typical window for an EPN. Note that red indicates positive polarity, and blue indicates negative polarity.

variance (ANOVA) parallel to the ones described below. There were no main effects or interactions that approached significance for these components. For brevity, we have omitted those analyses, though data are available upon request. Topographical maps for each condition in both groups for the EPN and LPP are shown in Figures 1 and 2, respectively.

Data Analysis Behavioral ratings of intensity and valence, as well as P2 amplitudes, were assessed using 2 (Group: average vs. good perceivers) 2 (Embodiment: embodied vs. non-embodied stimuli) 3 (Stimulus Type: insults, compliments, and neutral stimuli) mixed-model repeated-measures ANOVA. Follow-up pairwise comparisons were completed with Bonferroni correction.

Results Group Characteristics Descriptive statistics of the two groups can be seen in Table 1. As expected, performance on cardiac awareness task significantly differed between the two groups with a Ó 2019 Hogrefe Publishing

large effect size (p < .001, d = 2.32). High and average perceivers were comparable in terms of gender and age (ps > .05). Cardiac awareness scores did not significantly differ between men and women (p > .5). Average and high perceivers did not significantly differ on the total score or any subscale score of the DASS or BCQ (ps > .38).

Stimuli Ratings Descriptive statistics for behavioral ratings and P2 amplitudes can be viewed in Table 2. There was no significant main effect of group, nor did group interact with assessment of stimulus type and embodiment for valence (compliment vs. insult) or arousal (ps > .21). For valence, there was a significant main effect of stimulus type, F(2, 62) = 510.71, p < .001, ηp2 = .94, such that insults were rated as less pleasant (M = 2.36, SE = 0.14) than neutral stimuli (M = 5.03, SE = 0.03), which were rated as less pleasant than compliments (M = 7.43, SE = 0.10; all ps < .001). There was also a significant main effect of embodiment, F(1, 31) = 13.31, p = .001, ηp2 = .30, such that more embodied stimuli were rated more unpleasant (M = 5.02, SE = 0.05) than less embodied stimuli (M = 4.85, SE = 0.04). Given the small actual difference in the ratings of embodiment (0.17 on a 9-point scale), it is unclear if the difference is practically meaningful. There were no other Journal of Psychophysiology (2020), 34(1), 50–59

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

(A)

High Perceivers

Average Perceivers More Embodied

Less Embodied

More Embodied

Less Embodied

Compliments

Insults

Neutral

(B) +3µV

High Perceivers

More Embodied Less Embodied

-3µV

+3µV

Average Perceivers

-3µV -200ms

200ms

400ms

600 ms

800ms

Figure 2. (A) Topographical maps showing the averaged activity from 470–600 ms, the typical window for an LPP. Note the increased positivity (red coloration) for high perceivers to embodied stimuli compared to less embodied stimuli. (B) Waveforms depicting the same time window collapsed across more and less embodied stimuli from electrode Pz, where amplitudes were maximal. Vertical lines indicate 470–600 ms, which we had defined as the LPP.

significant main effects or interactions in terms of valence. In terms of intensity, there was a significant main effect of stimulus type, F(2, 62) = 161.12, p < .001, ηp2 = .71, such that insults (M = 6.01, SE = 0.18) and compliments (M = 5.46, SE = 0.26) were rated as significantly more intense than neutral stimuli (M = 3.04, SE = 0.27; ps < .001). Overall, insults were rated as somewhat more intense than compliments, though at a non-significant trend level (p = .052). The main effect of stimulus type may be interpreted in light of a significant interaction with embodiment, F(2, 62) = 13.17, p < .001, ηp2 = .30, such that more Journal of Psychophysiology (2020), 34(1), 50–59

embodied insults and compliments were rated as more intense than their less embodied counterparts (ps < .03). It was even the case that more embodied neutral stimuli were rated as more intense than the less embodied neutral words (p = .010).

P2 There was a significant interaction of embodiment, stimulus type, and group, F(2, 64) = 3.98, p = .024, ηp2 = .11, such that average perceivers elicited a significantly larger P2 to more embodied insults than to less embodied insults Ó 2019 Hogrefe Publishing

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Table 1. Descriptive statistics of the present sample Average

High

Age

18.26 (0.65)

21.00 (5.01)

19.47 (3.57)

BCQ – Private Body

11.63 (2.95)

11.00 (2.57)

11.36 (2.77)

BCQ – Public Body

14.37 (3.61)

13.71 (3.43)

14.09 (3.49)

BCQ – Body Competence

10.00 (2.40)

BCQ – Total

36.00 (6.55)

9.93 (2.59) 34.64 (6.69)

Whole sample

9.97 (2.44)

4.90 (4.05)

5.86 (4.57)

5.30 (4.24)

DASS – Anxiety

4.89 (5.29)

5.29 (6.39)

5.06 (5.69)

DASS – Stress

9.53 (4.29)

10.14 (5.97)

9.79 (4.99)

Cardiac awareness score

0.66 (0.16)

0.93 (0.04)

0.77 (0.18)

N Women (N Men)

11 (8)

8 (7)

Table 2. Descriptive statistics for response data (on a scale of 1–9) for pleasantness, intensity, and P2 amplitudes in μV Stimulus type

Embodiment level

Insults

19 (15)

Average perceiver

High perceiver

Total

Less

2.43 (0.97)

2.40 (0.74)

2.42 (0.87)

2.38 (0.85)

2.22 (0.66)

Compliments

2.31 (0.77)

7.68 (0.61)

7.43 (0.86)

7.57 (0.73)

Less

7.26 (0.47)

7.34 (0.69)

7.29 (0.56)

Neutral

5.10 (0.22)

5.09 (0.13)

5.10 (0.19)

Less

4.95 (0.16)

4.98 (0.10)

4.96 (0.13)

4.97 (0.05)

4.91 (0.06)

Pleasantness

35.42 (6.53)

DASS – Depression

Total

Intensity Note. With the exception of the distribution of men and women, all values are [M (SD)]. BCQ = Body Consciousness Questionnaire; DASS = Depression Anxiety Stress Scales.

(p = .024). In contrast, high perceivers elicited a larger P2 to more embodied compliments than to less embodied compliments (p = .038). Figures 3A and 3B present the waveforms and head maps related to this interaction. No other pairwise comparison approached significance, nor were there any additional significant main effects or interactions.

Discussion The goal of the present study was to examine how individual differences in interoceptive awareness may be associated with the perception and interpretation of more and less embodied emotional stimuli, respectively. We hypothesized that high perceivers would show increased P2 amplitudes to more embodied insults and compliments. This hypothesis had qualified support. There were no group differences in behavioral ratings; however, we did not hypothesize group differences because the rating task was untimed and was a second viewing of each stimulus. Interesting group differences emerged in the P2. High perceivers elicited larger P2 amplitudes for embodied compliments than less embodied compliments while average perceivers did not. The inverse was true for insults: Average perceivers elicited a larger P2 to more embodied insults than less embodied insults while high perceivers did not. Emerging evidence suggests the P2 indexes neural networks sensitive to cross-modal sensory processing, perhaps reflecting early assessment of stimuli for their valence, prosody, salience, and self-reference in order to allocate attention and activate avoid or approach mechanisms (e.g., Humphreys & Sui, 2016; Klasen et al., 2014; Zinchenko et al., 2015). These processes may explain how and why the P2 increases when stimuli are personally relevant (e.g., Fields & Kuperberg, 2012), including the recognition of familiar names compared to unknown Ó 2019 Hogrefe Publishing

Insults

6.16 (0.98)

5.58 (1.34)

Less

6.36 (0.90)

5.96 (1.23)

6.19 (1.05)

Compliments

5.94 (1.52)

5.52 (1.55)

5.77 (1.52)

Less

5.17 (1.54)

5.17 (1.59)

5.17 (1.54)

Neutral

3.11 (1.38)

3.07 (1.79)

3.09 (1.54)

Less

3.03 (1.41)

2.94 (1.68)

2.99 (1.51)

4.96 (0.25)

4.71 (0.29)

Total

5.91 (1.17)

P2 Insults

1.8 (1.49)

1.39 (1.24)

Less

1.35 (1.54)

1.55 (1.33)

1.44 (1.44)

Compliments

1.62 (1.33)

2.69 (3.53)

2.07 (2.52)

Less

1.76 (1.25)

1.01 (1.93)

1.44 (1.59)

Neutral

1.95 (1.46)

1.65 (1.87)

1.82 (1.63)

Less

1.83 (1.28)

1.51 (2.03)

1.7 (1.62)

1.72 (0.29)

1.63 (0.34)

Total

1.63 (1.38)

Note. Total rows and columns are estimated marginal means [M (SE)] while all other values are [M (SD)]; all values are rounded.

names (e.g., Tacikowski et al., 2014). Similar to the present study, individuals with somatization tendencies showed increased P2 amplitudes to body-related stimuli (J.-Y. Kim, Oh, & Bae, 2014). The embodied compliments tended to relate to physical appearance, and IA is positively associated with body satisfaction (Ainley & Tsakiris, 2013). In contrast, the embodied insults tended to be more varied and ambiguous in concept. In general samples, the P2 has been shown to be an index of threat evaluation (Dennis & Chen, 2007; Klasen et al., 2014; Thomas et al., 2007). Therefore, positive embodied concepts may be more immediately salient and relevant for individuals with high IA. However, we did not observe the high IA participants showing this same cognitive processing boost for the threat related insults, reflecting a discriminability between these highly emotional and arousing stimulus types. Together, these findings support the postulation that individuals with high IA perceive positive embodied concepts as more personally relevant (Ainley et al., 2012), even if the body-related elements of stimuli are somewhat implicit. These findings are also consistent Journal of Psychophysiology (2020), 34(1), 50–59

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

(A)

(B) Compliments Less Embodied

More Embodied

Insults Less Embodied

More Embodied

High Perceivers

Average Perceivers

Figure 3. (A) Waveforms depicting the Group Embodiment Stimulus type interaction for the P2 component at electrode FCz. High perceivers are the top row while average perceivers are the lower row; compliments are the left two figures while insults are the right two figures. More embodied stimuli are the solid line while less embodied stimuli are the dashed line. Dashed vertical line represents stimulus onset. *p < .05. (B) Topographical maps depicting the P2 component averaged across the 170–250 ms time window. Note that red indicates positive polarity, and blue indicates negative polarity.

with findings that IA facilitates awareness and recognition of self-relevant stimuli and concepts (Ainley et al., 2012, 2013; Babo-Rebelo et al., 2016). In contrast, the average perceivers showed a very different pattern for the P2 results. For the more typical participants, the P2 seems to have responded as a threat detector. These results are consistent with the findings of Lei et al. (2017), who also examined the differences in P2 amplitude comparing neutral and negative stimuli and found the component to be associated with automatic semantic processing of threatening stimuli. It is notable that other components of interest regarding emotional processing (i.e., LPP and EPN) were not Journal of Psychophysiology (2020), 34(1), 50–59

significantly associated with valence or group (data not shown), while the associations with emotional stimuli were associated with modulations in P2 amplitude. Emerging evidence suggests that task and stimuli repetition can suppress the LPP and EPN (Ferrari, Codispoti, & Bradley, 2017), while this effect is less seen for P2 amplitudes (e.g., Hsu, Hamalainen, & Waszak, 2014; Laszlo, Stites, & Federmeier, 2012). LPP and EPN attenuation occurs rapidly in cognitively simple tasks – such as a passive-viewing task used in the present study – compared to tasks requiring assessment or appraisal (Olofsson, Nordin, Sequeira, & Polich, 2008; Speed et al., 2017; Zinchenko et al., 2015). Compared to our 240 stimuli, for example, Herbert et al. (2007) used Ó 2019 Hogrefe Publishing

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

just 120, and Pollatos, Kirsch, and Schandry (2005b) used 60. Thus, our comparatively lengthier, less challenging task may have reduced the LPP and EPN. Future studies would do well to examine to what degree task demands and/or number of trials and/or can impact the expression of these components. Alternatively, it is possible that the effects observed here were not driven by the P2, per se, but by a longer-lasting process that overlaps with it (see Figure 2 and the later time points of Figure 3A). The lack of significant interactions or main effects observed in the later time windows in posterior electrodes may be explained by the multiple comparison approach we took with the data. The goal of the present exploratory study, and our chosen analytical approach, was to explore whether IA interacts with embodiment and valence in the perception of positive and negative stimuli. Future research should consider utilizing other techniques, such as spatiotemporal principal components analysis (Dien, 2012) or a cluster corrected mass univariate technique (Pernet, Chauveau, Gaspar, & Rousselet, 2011) to confirm and expand upon the present findings. It is certainly possible that there were subtle modulations of the LPP or some other later component or process that could explain the present findings both within and between groups, and using these analyses may clarify whether, and how, additional processes contribute to what we have defined as the P2. Two additional findings of note in the present study have potentially important implications. First, while others found that those with high IA are sensitive to emotional stimuli regardless of valence (e.g., Herbert et al., 2007), we did not observe this finding in the present study. Instead, the high perceivers showed greater sensitivity to positive embodied stimuli, which we argue are more self-relevant for that group (Ainley et al., 2012, 2013) and would result in greater amplitudes (Babo-Rebelo et al., 2016; Carretie et al., 2008; Fields & Kuperberg, 2012; Speed et al., 2017). Second, similar with much previous research (Garfinkel & Critchley, 2013), the two groups did not significantly differ in self-reported body consciousness, suggesting the group differences that emerged occurred as a result of objective, and not subjective, interoceptive abilities.

Limitations and Future Directions This study has some limitations that should be mentioned. First, it is important to note the exploratory nature of these findings that the present results should be interpreted cautiously, and replication is needed to be more certain of our conclusions. For brevity, we did not show the null results of additional components for which we had a priori hypotheses based on the work of Herbert and colleagues (2007) Ó 2019 Hogrefe Publishing

and Herbert, Junghofer, and Kissler (2008), including the LPP, EPN, and P300. It is unclear why Herbert and colleagues successfully elicited the EPN while we did not, which, as discussed above, may be a result of repetition effects. Future studies should consider using alternative, briefer tasks to perhaps assess the online, speeded judgment of stimuli. The list of insults and compliments we utilized was, by design, relatively mild, and additional studies with more intense stimuli may clarify these individual differences. Additionally, future work should include a general negative or positive category, or more idiographic and individualized stimuli. We dichotomized the sample using 85% accuracy as a cut score on the cardiac awareness task. This threshold is well established based on taxonomic methods demonstrating two groups with different affective, cognitive, and physiological processing, particularly to emotional stimuli, despite few or no differences in any other demographic or self-report measure (e.g., Herbert et al., 2010; Montoya et al., 1993; Pollatos et al., 2005a; Pollatos & Schandry, 2008; Schandry, 1981). In addition to recruiting a larger, more diverse sample, future research would do well to use additional measures of IA to generalize and expand the present findings and/or maintain the cardiac data as a continuous variable.

Conclusion The findings of this exploratory study demonstrate that average perceivers showed increased sensitivity in the P2 only to negative embodied stimuli (i.e., insults), which might indicate that the P2 is additionally sensitive to threat, as are other later ERP components such as the LPP. In contrast, those with high IA are more sensitive to the embodiment of positive stimuli (i.e., compliments) in the P2 component. Therefore, individuals with increased IA may be sensitive not just to their own bodily sensations and explicitly self-relevant stimuli, but to stimuli referring to the body itself.

References Ainley, V., Maister, L., Brokfeld, J., Farmer, H., & Tsakiris, M. (2013). More of myself: Manipulating interoceptive awareness by heightened attention to bodily and narrative aspects of the self. Consciousness and Cognition, 22, 1231–1238. https://doi. org/10.1016/j.concog.2013.08.004 Ainley, V., Tajadura-Jimenez, A., Fotopoulou, A., & Tsakiris, M. (2012). Looking into myself: Changes in interoceptive sensitivity during mirror self-observation. Psychophysiology, 49, 1504–1508. https://doi.org/10.1111/j.1469-8986.2012.01468.x Ainley, V., & Tsakiris, M. (2013). Body conscious? Interoceptive awareness, measured by heartbeat perception, is negatively correlated with self-objectification. PLoS One, 8, e55568. https://doi.org/10.1371/journal.pone.0055568

Journal of Psychophysiology (2020), 34(1), 50–59

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Archer, D. (2015). Slurs, insults, (backhanded) compliments and other strategic facework moves. Language Sciences, 52, 82–97. https://doi.org/10.1016/j.langsci.2015.03.008 Babo-Rebelo, M., Wolpert, N., Adam, C., Hasboun, D., & TallonBaudry, C. (2016). Is the cardiac monitoring function related to the self in both the default network and right anterior insula? Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371. https://doi.org/10.1098/ rstb.2016.0004 Benau, E. M., Gregersen, S. C., Siakaluk, P. D., O’Hare, A., Johnson, E. K., & Atchley, R. A. (2018). Sweet-cheeks vs. peabrain: Embodiment, valence, and task all influence the emotional salience of language. Cognition & Emotion, 32, 691–708. https://doi.org/10.1080/02699931.2017.1342602 Brown, T. A., Chorpita, B. F., Korotitsch, W., & Barlow, D. H. (1997). Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behaviour Research and Therapy, 35, 79–89. https://doi.org/10.1016/S0005-7967(96) 00068-X Carretie, L., Albert, J., Lopez-Martin, S., & Tapia, M. (2009). Negative brain: An integrative review on the neural processes activated by unpleasant stimuli. International Journal of Psychophysiology, 71, 57–63. https://doi.org/10.1016/j.ijpsycho. 2008.07.006 Carretie, L., Hinojosa, J. A., Albert, J., Lopez-Martin, S., De La Gandara, B. S., Igoa, J. M., & Sotillo, M. (2008). Modulation of ongoing cognitive processes by emotionally intense words. Psychophysiology, 45, 188–196. https://doi.org/10.1111/ j.1469-8986.2007.00617.x Citron, F. M. (2012). Neural correlates of written emotion word processing: A review of recent electrophysiological and hemodynamic neuroimaging studies. Brain and Language, 122, 211–226. https://doi.org/10.1016/j.bandl.2011.12.007 Craig, A. D. (2010). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Neuroscience Reviews, 3, 655–665. https://doi.org/10.1038/nrn894 Crawford, J. R., Cayley, C., Lovibond, P. F., Wilson, P. H., & Hartley, C. (2011). Percentile norms and accompanying interval estimates from an Australian General Adult Population Sample for self-report mood scales (BAI, BDI, CRSD, CES-D, DASS, DASS21, STAI-X, STAI-Y, SRDS, and SRAS). Australian Psychologist, 46, 3–14. https://doi.org/10.1111/j.1742-9544.2010.00003.x Dennis, T. A., & Chen, C. C. (2007). Neurophysiological mechanisms in the emotional modulation of attention: The interplay between threat sensitivity and attentional control. Biological Psychology, 76, 1–10. https://doi.org/10.1016/j.biopsycho. 2007.05.001 Dien, J. (2012). Applying principal components analysis to eventrelated potentials: A tutorial. Developmental Neuropsychology, 37, 497–517. https://doi.org/10.1080/87565641.2012.697503 Ferrari, V., Codispoti, M., & Bradley, M. M. (2017). Repetition and ERPs during emotional scene processing: A selective review. International Journal of Psychophysiology, 111, 170–177. https://doi.org/10.1016/j.ijpsycho.2016.07.496 Fields, E. C., & Kuperberg, G. R. (2012). It’s all about you: An ERP study of emotion and self-relevance in discourse. NeuroImage, 62, 562–574. https://doi.org/10.1016/j.neuroimage.2012.05. 003 Garfinkel, S. N., & Critchley, H. D. (2013). Interoception, emotion and brain: New insights link internal physiology to social behaviour. Commentary on: “Anterior insular cortex mediates bodily sensibility and social anxiety” by Terasawa et al. (2012). Social, Cognitive, and Affective Neuroscience, 8, 231–234. https://doi.org/10.1093/scan/nss140 Giles, D., Draper, N., & Neil, W. (2015). Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. European

Journal of Psychophysiology (2020), 34(1), 50–59

Journal of Applied Physiology, 116, 563–571. https://doi.org/ 10.1007/s00421-015-3303-9 Herbert, B. M., Pollatos, O., Flor, H., Enck, P., & Schandry, R. (2010). Cardiac awareness and autonomic cardiac reactivity during emotional picture viewing and mental stress. Psychophysiology, 47, 342–354. https://doi.org/10.1111/j.1469-8986. 2009.00931.x Herbert, B. M., Pollatos, O., & Schandry, R. (2007). Interoceptive sensitivity and emotion processing: An EEG study. International Journal of Psychophysiology, 65, 214–227. https://doi.org/ 10.1016/j.ijpsycho.2007.04.007 Herbert, C., Junghofer, M., & Kissler, J. (2008). Event related potentials to emotional adjectives during reading. Psychophysiology, 45, 487–498. https://doi.org/10.1111/j.1469-8986.2007. 00638.x Hsu, Y. F., Hamalainen, J. A., & Waszak, F. (2014). Repetition suppression comprises both attention-independent and attention-dependent processes. NeuroImage, 98, 168–175. https:// doi.org/10.1016/j.neuroimage.2014.04.084 Humphreys, G. W., & Sui, J. (2016). Attentional control and the self: The Self-Attention Network (SAN). Cognitive Neuroscience, 7, 5–17. https://doi.org/10.1080/17588928.2015. 1044427 Kim, J.-Y., Oh, S.-S., & Bae, K.-Y. (2014). Automatic attentional bias in individuals with somatization tendencies: An event-related potential study. Journal of Korean Neuropsychiatric Association, 53, 206. https://doi.org/10.4306/jknpa.2014.53.4.206 Kim, M.-K., Kim, M., Oh, E., & Kim, S.-P. (2013). A review on the computational methods for emotional state estimation from the human EEG. Computational and Mathematical Methods in Medicine, 2013, 573734. https://doi.org/10.1155/2013/573734 Klasen, M., Kreifelts, B., Chen, Y. H., Seubert, J., & Mathiak, K. (2014). Neural processing of emotion in multimodal settings. Frontiers in Human Neuroscience, 8, 822. https://doi.org/ 10.3389/fnhum.2014.00822 Laszlo, S., Stites, M., & Federmeier, K. D. (2012). Won’t get fooled again: An event-related potential study of task and repetition effects on the semantic processing of items without semantics. Language and Cognitive Processes, 27, 257–274. https://doi. org/10.1080/01690965.2011.606667 Lei, Y., Dou, H., Liu, Q., Zhang, W., Zhang, Z., & Li, H. (2017). Automatic processing of emotional words in the absence of awareness: The critical role of P2. Frontiers in Psychology, 8, 592. https://doi.org/10.3389/fpsyg.2017.00592 Miller, L. C., Murphy, R., & Buss, A. H. (1981). Consciousness of body: Private and public. Journal of Personality and Social Psychology, 41, 397–406. https://doi.org/10.1037/0022-3514. 41.2.397 Montoya, P., Schandry, R., & Müller, A. (1993). Heartbeat evoked potentials (HEP): Topography and influence of cardiac awareness and focus of attention. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 88, 163–172. https://doi.org/10.1016/0168-5597(93)90001-6 Olofsson, J. K., Nordin, S., Sequeira, H., & Polich, J. (2008). Affective picture processing: An integrative review of ERP findings. Biological Psychology, 77, 247–265. https://doi.org/ 10.1016/j.biopsycho.2007.11.006 Pernet, C. R., Chauveau, N., Gaspar, C., & Rousselet, G. A. (2011). LIMO EEG: A toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic Data. Computational Intelligence and Neuroscience, 2011, 1–11. https://doi.org/10.1155/2011/ 831409 Pollatos, O., Kirsch, W., & Schandry, R. (2005). Brain structures involved in interoceptive awareness and cardioafferent signal processing: A dipole source localization study. Human Brain Mapping, 26, 54–64. https://doi.org/10.1002/hbm.20121

Ó 2019 Hogrefe Publishing

E. M. Benau & R. A. Atchley, Cardiac Perception and Embodied Insults

Pollatos, O., Kirsch, W., & Schandry, R. (2005). On the relationship between interoceptive awareness, emotional experience, and brain processes. Cognitive Brain Research, 25, 948–962. https://doi.org/10.1016/j.cogbrainres.2005.09.019 Pollatos, O., & Schandry, R. (2008). Emotional processing and emotional memory are modulated by interoceptive awareness. Cognition & Emotion, 22, 272–287. https://doi.org/10.1080/ 02699930701357535 Schandry, R. (1981). Heart beat perception and emotional experience. Psychophysiology, 18, 483–488. https://doi.org/ 10.1111/j.1469-8986.1981.tb02486.x Semlitsch, H. V., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23, 695–703. Siakaluk, P. D., Pexman, P. M., Dalrymple, H. A. R., Stearns, J., & Owen, W. J. (2011). Some insults are more difficult to ignore: The embodied insult Stroop effect. Language and Cognitive Processes, 26, 1266–1294. https://doi.org/10.1080/01690965.2010. 521021 Speed, B. C., Levinson, A. R., Gross, J. J., Kiosses, D. N., & Hajcak, G. (2017). Emotion regulation to idiographic stimuli: Testing the autobiographical emotion regulation task. Neuropsychologia. Advance online publication. https://doi.org/ 10.1016/j.neuropsychologia.2017.04.032 Suk, H.-J. (2006). Color and Emotion-a study on the affective judgment across media and in relation to visual stimuli (Unpublished doctoral dissertation). Universität Mannheim, Mannheim, Germany Tacikowski, P., Cygan, H. B., & Nowicka, A. (2014). Neural correlates of own and close-other’s name recognition: ERP evidence. Frontiers in Human Neuroscience, 8, 194. https://doi. org/10.3389/fnhum.2014.00194 Thomas, S. J., Johnstone, S. J., & Gonsalvez, C. J. (2007). Eventrelated potentials during an emotional Stroop task. International Journal of Psychophysiology, 63, 221–231. https://doi. org/10.1016/j.ijpsycho.2006.10.002 Wellsby, M., Siakaluk, P. D., Pexman, P. M., & Owen, W. J. (2010). Some insults are easier to detect: The embodied insult detection effect. Frontiers in Psychology, 1, 198. https://doi. org/10.3389/fpsyg.2010.00198

Ó 2019 Hogrefe Publishing

Zinchenko, A., Kanske, P., Obermeier, C., Schroger, E., & Kotz, S. A. (2015). Emotion and goal-directed behavior: ERP evidence on cognitive and emotional conflict. Social, Cognitive, and Affective Neuroscience, 10, 1577–1587. https://doi.org/10.1093/ scan/nsv050 History Received January 10, 2018 Revision received October 27, 2018 Accepted November 8, 2018 Published online February 28, 2019 Acknowledgments The authors would like to thank Joshua Jenkins, Jin Han, Brittany Dozier, and Elizabeth Konecny for their assistance in collecting and processing data. Funding No funding was obtained for this study. Conflict of Interest The authors have no potential conflicts of interest to declare. Authorship Ruth Ann Atchley is now at the Department of Psychology, University of South Florida, Tampa, FL, USA. Erik M. Benau is now at the Department of Psychiatry, Columbia University, New York, NY, USA.

Erik M. Benau New York State Psychiatric Institute and Columbia University 1051 Riverside Dr. Pardes 3511 New York, NY 10032 USA Erik.benau@nyspi.columbia.edu

Journal of Psychophysiology (2020), 34(1), 50–59

Article

The Effect of the Menstrual Cycle on Daily Measures of Heart Rate Variability in Athletic Women Renée L. Kokts-Porietis1, Nathaniel R. Minichiello1,2, and Patricia K. Doyle-Baker1,3 1

Faculty of Kinesiology, University of Calgary, AB, Canada

TCR Sport Lab, Calgary, AB, Canada

Faculty of Environmental Design, University of Calgary, AB, Canada

Abstract: Heart rate variability (HRV) is a biomarker used to reflect both healthy and pathological state(s). The effect of the menstrual cycle and menstrual cycle phases (follicular, luteal) on HRV remains unclear. Active eumenorrheic women free from exogenous hormones completed five consecutive weeks of daily, oral basal body temperature (BBT) and HRV measurements upon waking. Descriptive statistics were used to characterize shifts in the HRV measures: standard deviation of NN intervals (SDNN), root mean square of successive difference (rMSSD), high (HF) and low frequency (LF) across the menstrual cycle and between phases. All HRV measures were assessed by medians (Mdn), median difference of consecutive days (MdnΔ) and variance. Seven participants (M ± SD; age: 28.60 ± 8.40 year) completed the study with regular menstrual cycles (28.40 ± 2.30 days; ovulation day 14.57 ± 0.98 day). Median rMSSD displayed a nonlinear decrease across the menstrual cycle and plateau around the day of ovulation. A negative shift before ovulation in MdnΔ, rMSSD, SDNN, and LF as well as peak on luteal phase Day 4 in rMSSD and SDNN was observed. Median variance increased in rMSSD (150.06 ms2) SDNN (271.12 ms2), and LF variance (0.001 sec2/Hz) from follicular to luteal phase. Daily HRV associated with the parasympathetic nervous system was observed to decrease nonlinearly across the menstrual cycle. Keywords: menstrual cycle, heart rate variability, luteal phase, follicular phase, athletic women

Heart rate variability (HRV) is used to assess the interplay and contribution of the sympathetic and parasympathetic branches of the autonomic nervous system and subsequently provide insight into homeostasis (Ernst, 2017). It is used in multiple clinical and athletic performance settings (Buchheit, 2014) and is an accepted biomarker reflecting both a healthy and pathological state (Ernst, 2017; Young & Benton, 2018). Baseline measures of HRV, however are impacted by individual characteristics such as age, ethnicity (Hill et al., 2015), physical activity, fitness level (Grant, Viljoen, Van Rensburg, & Wood, 2012), and body mass index (Vallejo, Márquez, Borja-Aburto, Cárdenas, & Hermosillo, 2005). Koenig and Thayer’s (2016) metaanalysis examined sex differences in HRV and reported that autonomic control in women was characterized by a relative dominance of parasympathetic activity, despite greater average heart rates. The menstrual cycle and associated hormones have been suggested to contribute to the discrepancy in HRV seen between males and females (Koenig & Thayer, 2016; Salerni et al., 2015). Characterized by predictable fluctuations of hormones, chiefly estrogen and progesterone, the menstrual cycle Journal of Psychophysiology (2020), 34(1), 60–68 https://doi.org/10.1027/0269-8803/a000237

can be separated into the follicular and luteal phases. The menstrual cycle is approximately 28 days long during normal menstruation (eumenorrhea) though variations in the length may lead to cycles naturally range from 26 to 36 days (Mihm, Gangooly, & Muttukrishna, 2011). Based on a typical 28-day menstrual cycle, low levels of estrogen and progesterone are observed at the beginning of the follicular phase (start of menses, Day 1) with estrogen reaching peak concentrations 24–36 hr before ovulation ( Day 14; Mihm et al., 2011). Notably during the follicular phase estrogen levels may fluctuate in response to either minor waves of follicular growth or the development of a dominant follicle that later regresses, present in almost a quarter of menstrual cycles (Baerwald, Adams, & Pierson, 2012). Following ovulation in the luteal phase (Day 15–28) both estrogen and progesterone concentrations are elevated (Mihm et al., 2011). Previous literature has suggested that a decrease in parasympathetic HRV occurs across the menstrual cycle from the follicular to luteal phase (Bai, Li, Zhou, & Li, 2009; McKinley et al., 2009; Tenan, Brothers, Tweedell, Hackney, & Griffin, 2014). The occurrence of Ó 2019 Hogrefe Publishing

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

parasympathetic withdrawal has largely been determined by studies that utilize less than five different time points which, may lead to an oversimplification of the results (Tenan et al., 2014). The current study employed daily measures to determine how HRV shifted over the menstrual cycle and whether the phases (follicular, luteal) affected the variance in HRV measurements. Through improved characterization of the expected variation across the menstrual cycle and the variance between phases, we hope, as do others that HRV will be better able to distinguish healthy versus pathological states in the female population (Voss, Schroeder, Heitmann, Peters, & Perz, 2015). This is particularly important as HRV measurements become increasingly popular in athletic performance settings (Buchheit, 2014) since, better understanding of naturally occurring variation due to the menstrual cycle in female athletes is needed to aid in the identification of abnormal variation produced by overtraining. The objectives of this research were to determine: (1) how daily measures of time (SDNN, rMSSD) and frequency (HF, LF) HRV fluctuated over the course of the menstrual cycle and (2) whether the menstrual cycle phase (follicular, luteal) affected the variance in HRV time and frequency measures in a population of healthy athletic eumenorrheic females.

Method Participants The current study was a part of a larger project titled: Hormonal Effects in Riders Study (HERS) which employed the rolling recruitment of athletic eumenorrheic healthy women (N = 10) living in Calgary, Alberta, Canada. Recruitment occurred through posters at the University of Calgary and word of mouth among female cyclists between May and September of 2017. Inclusion criteria were based on self-reported training (cyclists or triathletes) or cross training (rowers or runners) on the bike for greater than three hours a week to ensure a minimum physical activity level and familiarity for the HERS cycle ergometer protocol. Participants were free from any exogenous hormones, non-smokers, between 18 and 45 years of age at the time of participation and, had access to a Smartphone. Women with irregular menstrual cycles (less than 26 or greater than 35 days; Mihm et al., 2011), amenorrhea (not menstruating), or postmenopausal (self-reported) were excluded from the study results (n = 3). The research was approved by the Conjoint Health Research Ethics Board (EID 15-2777) at the University of Calgary and prior to involvement each participant completed an informed consent.

Ó 2019 Hogrefe Publishing

Protocols Participants completed at least five consecutive weeks of daily HRV recordings and a single complete menstrual cycle was identified with a commercially available Smartphone application known as “HRV4training” (Altini, 2013). The HRV recording protocol instructed participants to record 1-minute measures each morning before getting out of bed in the supine position to align with measurement recommendations for athletic populations (Buchheit, 2014). Participants were informed to breathe normally, keep their eyes closed and, stay relaxed to ensure similar conditions for daily HRV recording. If the recording quality was not reported as “good” or “optimal” by the HRV4Training application, participants were prompted to repeat it. Basal body temperature (BBT) was taken each morning by a Geratherm digital thermometer to two decimal points (Medical digital thermometer GerathermÒ basal digital GT3230, Geratherm Medical AG, Geschwenda, Germany). The Sensiplan method of defining a BBT rise to signal ovulation has occurred was used in the current study as were the exceptions described below (Freundl, FrankHerrmann, Brown, & Blackwell, 2014). This method defines a temperature rise as three consecutive temperature readings, all higher than the preceding six, with the last of the three being at least 0.2 °C greater than any of the six preceding days. The first of the three days with elevated temperatures is referred to as the “BBT shift day” and the day prior was recorded as ovulation. Two exceptions to this definition of BBT rise are used in practice: (1) if the third temperature reading is not greater than 0.2 °C above the preceding six, a fourth (the next day) will be used and (2) within the three higher readings there may be a value that drops down to or below the level of the preceding six, this lower value can be ignored. However, the two exceptions may not be used together (Freundl et al., 2014). If ovulation could not be identified by this method, the midpoint of the participant’s menstrual cycle was considered the day of ovulation. Prior to the five weeks of HRV and BBT measurements, participant characteristics were determined through body composition scan completed with a Dual-energy X-ray absorptiometry (DXA) machine (Hologic QDR 4500, Hologic, Inc., Bedford, MA) located at the Human Performance Lab, University of Calgary, Alberta.

HRV Indexes and Measurement Tools The frequency domain uses the signal energy of power within a frequency band and can be expressed as absolute (milliseconds; ms2) or relative power (Shaffer & Ginsberg, 2017). In practice, pre-specified frequency bands such as

Journal of Psychophysiology (2020), 34(1), 60–68

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

the high- (HF: 0.15–0.40 Hz), low- (LF: 0.01–0.15 Hz), and very-low (VLF: < 0.04 Hz) frequency components are used (Koenig & Thayer, 2016). The amount of power from the HF and LF bands may vary in relation to changes in the autonomic nervous system. Some researchers refer to parasympathetic activity as cardiac vagal tone since the vagus nerve is the main nerve of the parasympathetic nervous system (Laborde, Mosley, & Thayer, 2017). As the parasympathetic nervous system reacts faster to internal and external changes (< 1 sec) than the sympathetic nervous system, the HF band in addition to time domain indexes of the root mean square of successive differences (rMSSD) are generally considered to represent cardiac vagal tone (Laborde, Mosley and Thayer, 2017). This chiefly dictates variations in beat-to-beat intervals in shortterm measurements of HRV at rest (Ernst, 2017), where as SDNN is generally seen as a mixed measure of both sympathetic and parasympathetic influence. The interpretation of the LF band has caused debate in the literature and it has been argued that in resting conditions it reflects not cardiac sympathetic innervation as previously thought but baroreflex activity (Shaffer & Ginsberg, 2017). Traditionally, HRV has been measured with electrocardiogram (ECG) though in more recent years other methods including photoplethysmography (PPG) have developed (Shaffer & Ginsberg, 2017). While, the agreement between ear PPG and ECG has been reported as insufficient for clinical purposes in rMSSD, HF, and LF HRV measurements while good in SDNN using Bland Altman ratios (Weinschenk, Beise, & Lorenz, 2016). The emergence of Smartphones with finger-tip PPG capability offers an alternative. This alternative has been validated and confirmed to provide reliable HRV when participants are in the supine position (Altini & Amft, 2016; Charlot, Cornolo, Brugniaux, Richalet, & Pichon, 2009). Through Bland-Altman analysis the agreement between ECG and finger-tip PPG using the iPhone 6 while seated has been reported as moderate for rMSSD (BA ratio: 0.106), good agreement for SDNN (BA ratio: 0.035), and LF (BA ratio: 0.089), and poor agreement for HF (BA ratio 0.249; Banhalmi et al., 2018). The HRV4Training Smartphone application measures HRV with finger-tip PPG and an acceptable agreement between the PPG HRV4Training smartphone application and ECG has been reported in short-term recordings (Plews et al., 2017). A mean bias of 1.4 ms (90% CI [0.2, 2.6]) in rMSSD between HRV4Training and ECG under normal respiratory conditions has been reported by Plews and colleagues (2017). RMSSD values typically range from 50–250 ms, and 1.4 ms is therefore considered a very small bias by the authors (Plews et al., 2017). Variations from ECG results were not available for other HRV indexes. The Geratherm basal digital thermometer has a resolution of 0.01 °C and an accuracy of ± 0.10 °C between Journal of Psychophysiology (2020), 34(1), 60–68

35.50 °C and 42.00 °C. The thermometer has a guarantee of quality by the European Commission Directive 93/42/ EWG of the Council for medical devices.

Data Analysis All data were analyzed using SPSS V24 software. Participant characteristics including age (years), height (cm), weight (kg), percent body fat (%), menstrual cycle length (day) and, day of ovulation (day) are reported as means and standard deviation [SD, (±)]. HRV was aligned by the BBT method of determining ovulation, as mentioned above. As the inter-individual variation in menstrual cycle length typically occurs in the follicular phase, participants’ menstrual cycles were aligned at ovulation to better display the two phases (Fehring, Schneider, & Raviele, 2006). Ovulation was defined as “Day 0” and days prior to ovulation were classified as the follicular phase while, days following were considered the luteal phase. The four HRV outcome variables (SDNN, rMSSD, HF and LF bands) were analyzed independently to identify trends in each index. Descriptive statistics and graphical representation (box plots) of HRV indexes arranged by days from ovulation were employed to visually compare the variation in each HRV measurement over the course of the menstrual cycle. Nonparametric methods rather than data transformation were utilized to preserve the clinical relevance of the HRV values. Reference lines through daily medians were used to assess the trend of HRV indexes and were compared to the median reference line of the entire menstrual cycle. Since HRV measures can have large inter-individual variation (Ernst, 2017) HRV indexes were displayed both as daily measures of HRV (ms, sec/Hz) and the median difference between consecutive days (MdnΔ) to standardize individual baseline levels. The variance of menstrual cycle phase was displayed graphically (box plots) as was the difference between the follicular and luteal phases. Outlying data points were marked by participant number and missing HRV data were computed as the average of the day preceding and following the missed day in order to compute the MdnΔ.

Results Three participants were excluded from the results as their menstrual cycles were outside the 26 to 36-day range considered eumenorrheic (Mihm et al., 2011). Participant characteristics (n = 7) are presented in Table 1. Two participants missed two data collection days in the luteal phase and one participant missed three data collections (1 follicular Ó 2019 Hogrefe Publishing

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

Table 1. Participant characteristics (n = 7) Variable

Mean, SD (±)

Age (years)

28.6 ± 8.4

Height (cm)

167.00 ± 6.30

Weight (kg)

66.41 ± 7.89

Percent body fat (%)

21.10 ± 4.90

Menstrual cycle length (day)

28.40 ± 2.30

Day of ovulation (day)

14.57 ± 0.98

phase, 2 luteal phase). Additionally, one subject did not record data for the first 5 days of their menstrual cycle. Daily rMSSD medians displayed a negative trend across the menstrual cycle as illustrated in Figure 1A. A plateau of rMSSD medians began 5 days before ovulation (Day 0) and was not interrupted until luteal phase Day 4. SDNN showed larger oscillatory patterns in daily medians which began three days before ovulation and peaked on ovulation, luteal phase Day 4, and luteal phase Day 11 (Figure 1B). Both rMSSD and SDNN exhibited greater interquartile ranges and outlying data points following ovulation. The MdnΔ rMSSD decreased three days prior to ovulation and

displayed a sudden increase between Days 3 and 4 of the luteal phase (Figure 1C), whereas MdnΔ SDNN illustrated large fluctuations at the beginning of the menstrual cycle and decreased the day before ovulation. Oscillatory peaks of MdnΔ SDNN occurred between luteal phase Days 3 and 4, and 8 and 9 (Figure 1D). HF daily medians were stable through the menstrual cycle with a slight decrease around ovulation and largest interquartile range on luteal phase Day 5 (Figure 2A). The MdnΔ HF displayed larger variation at the beginning and end of the menstrual cycle compared to the middle (Figure 2B). The beginning of the menstrual cycle also showed a large fluctuation in LF medians followed by a stable period before ovulation. Daily LF medians were slightly lower around ovulation and had oscillatory peaks on Day 4 and Day 10 of the luteal phase (Figure 2C). All frequency domain measures displayed between 8 and 18 outlying data points. The variance between the follicular phase and luteal phase had a median increase in rMSSD (150.06 ms2), SDNN (271.12 ms2), and LF variance (sec2/Hz) as seen in Figures 3A–3C. One participant did not follow the trend of increased variance in both median SDNN and rMSSD

Figure 1. Daily time domain HRV (a) rMSSD (ms) (b) SDNN (ms) (c) MdnΔ rMSSD (ms) and (d) MdnΔ SDNN (ms) represented as days from ovulation (Day 0) in athletic women.

Ó 2019 Hogrefe Publishing

Journal of Psychophysiology (2020), 34(1), 60–68

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

Figure 2. Daily frequency domain HRV indexes (a) HF (sec/Hz) (b) MdnΔ HF (sec2) (c) LF (sec2) and (d) MdnΔ LF(sec2) represent as days from ovulation (Day 0) in athletic women.

and an additional participant did not display the increased variance in rMSSD. The variance in HF between the follicular phase and luteal phase did not demonstrate a trend (Figure 3D). At the subject level difference in HF variance presented three participants who increased, three who experienced no change and, one who decreased from follicular phase to luteal phase.

Discussion Daily HRV Across the Menstrual Cycle In short-term resting recordings, the primary source of the variation according to Shaffer and Ginsberg is parasympathetically mediated respiratory sinus arrhythmia, especially with slow, paced breathing (PB) protocols (2017). In this current study parasympathetic activity was reflected by the short-term recordings of rMSSD, SDNN, and HF HRV. The decrease in rMSSD HRV across the menstrual cycle revealed a plateau around ovulation followed by a Journal of Psychophysiology (2020), 34(1), 60–68

peak on luteal phase Day 4 representing a non-linear reduction in parasympathetic activity. The parasympathetic activity reflected by SDNN HRV also showed a subtle decrease in the luteal phase compared to the follicular phase and increased oscillatory amplitude during the luteal phase. While the results of this study are in agreement with the decreases of rMSSD and SDNN from the follicular phase to the luteal phase reported in the literature (Brar, Singh, & Kumar, 2015; McKinley et al., 2009; Tenan et al., 2014), the present study suggests this decrease does not occur in a linear fashion. This discrepancy may be attributable to the number of time points measured in each of these studies: Brar et al. (2015) utilized three-time points (Days 2, 10, and 21), Tenan et al. (2014) collected HRV from five different time points across the menstrual cycle however, the linear modeling used prevents a comparison of the raw data patterns, and McKinley et al. (2009) measured HRV during the mid-follicular phase (between Days 4 and 10) and mid-luteal phase (3 days post luteinizing hormone surge to 5 days before the next predicted menstrual cycle). However, the interpretation of HRV indexes particularly SDNN may differ based on short versus Ó 2019 Hogrefe Publishing

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

LF fluctuated during menses at the beginning of the follicular phase, remained stable until ovulation, then increased in the luteal phase. Previously Brar et al. (2015) reported LF power to decrease from Day 2 to 10 of the follicular phase (22.36 ± 58.30 ms2, 8.06 ± 18.03 ms2) as well as an increase in standard deviation from Day 10 to 21 (31.3 ± 71.70 ms2). The current study is in agreement with the reduction of LF power in the mid-follicular phase and larger variations seen at the beginning of the follicular phase reported by Brar et al. (2015).

Potential Mechanisms of Non-Linear Decrease of Parasympathetic Activity

Figure 3. Variance of (a) rMSSD (ms2) (b) SDNN (ms2) (c) LF (sec2/Hz) (d) HF (sec2/Hz) in the FP, LP and, difference between the menstrual cycle phases in athletic women.

24-hour HRV recording and the comparison to McKinley et al. (2009) results should be done with caution. Previously HF HRV has been reported to decrease from follicular phase to luteal phase (McKinley et al., 2009; Tenan et al., 2014). In the current study HF did not produce an obvious trend as only a slight decrease around ovulation was observed. The presence of multiple outliers may suggest that this sample size was not sufficient to produce a trend. Alternatively, the expression of HF as sec/Hz from the HRV4Training application rather than the conventional ms2 or neutral units may not have had the necessary precision to display a trend (Malik et al., 1996). Ó 2019 Hogrefe Publishing

The interaction between estrogen and the parasympathetic system may partially explain the appearance of a non-linear decrease in HRV, especially around ovulation. Greater vagus nerve parasympathetic activity has been observed in women compared to men (Salerni et al., 2015). This has been partially attributed to the action of estrogen to enhance choline uptake and acetylcholine synthesis, the primary neurotransmitter of the vagus nerve (Salerni et al., 2015). Sex hormones have also been shown to contribute to the modification of the electro-physiological properties of the heart by regulating the amount and activity of potassium ion channels. Specifically, the down-regulation of rapidly activating delayed rectifier current (IKr) channels has been observed in the presence of physiological concentrations of estrogen (Salerni et al., 2015). The reduction of IKr channels results in lengthened time intervals between contraction and repolarization of the heart and lowers heart rate (Salerni et al., 2015). In the current study, there was an oscillatory decrease in rMSSD until 5 days prior to ovulation which corresponds to a period of non-uniform follicle growth and variable low levels of estrogen (Baerwald et al., 2012; Farage, Neill, & MacLean, 2009). An exponential rise in estrogen associated with primary follicle growth after follicular phase Days 6–9 (Baerwald et al., 2012) may relate to the following plateau in rMSSD. Furthermore, the decrease in MdnΔ rMSSD corresponded to the fall of estrogen 24–36 hr prior to ovulation (Farage et al., 2009; Mihm et al., 2011) and the second increase in estrogen aligns with the peak in time domain HRV (luteal phase Day 4). This observation may suggest that in the early follicular phase or at ovulation concentrations of estrogen were not adequate to mitigate the decrease in parasympathetic activity, but with increased concentrations at the end of the follicular phase or luteal phase Day 4 estrogen had reached a critical level. As estrogen levels were not measured in the current study this mechanism is purely speculative. Journal of Psychophysiology (2020), 34(1), 60–68

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

The period of reduced SDNN (luteal phase Day 4–11) and LF (luteal phase Day 5–10) corresponds to the elevated estrogen and progesterone concentrations during the luteal phase (Mihm et al., 2011). While less is understood about the effects of progesterone, it has been linked to increase in heart rate, body temperature and, breathing rate seen in the luteal phase (Lebrun & Joyce, 2013). At higher physiological concentrations progesterone is associated with increased epinephrine and norepinephrine, as well as shorter cardiac repolarization (Salerni et al., 2015). The decrease in the SDNN in the mid-luteal phase may therefore relate to the increase in progesterone. Additionally, progesterone has been demonstrated to increase plasma volume with and without estrogen presence through greater protein retention and overall expansion of extracellular fluid volume (Stachenfeld, 2005). The increase in plasma volume may be particularly relevant for the LF HRV association with the baroreflex (Shaffer & Ginsberg, 2017) though the impact of sex hormones on baroreflex control of heart rate in the literature remains mixed (Brunt et al., 2013).

Variance Between Menstrual Cycle Phases The variance between the follicular phase and luteal phase had a median increase in rMSSD (150.06 ms2) and SDNN (271.12 ms2), although two participants did not follow the rMSSD trend and one of these participants did not show an increase in SDNN variance as well. Lifestyle and emotional stress such as worrying has been shown to create prolonged decreased rMSSD HRV values (Verkuil, Brosschot, Tollenaar, Lane, & Thayer, 2016). This may partially explain why a participant experienced decreased time domain HRV in the luteal phase compared to the follicular phase. Three participants had an increase in HF variance while three showed no change and, one decreased from follicular phase to luteal phase. In a systematic review of normal short-term measurements of HRV, Nunan, Sandercock, and Brodie, (2010) reported that HF (ms2) had the largest variation [coefficient of variation (CV) = 118%] of all HRV indexes with a range of 3,548 ms2 across 36 studies reporting HF. The large CV in HF and units used by the HRV4Training application (mentioned above) in addition to this small sample size may, explain the HF results. The LF variance displayed an 0.001 sec2/Hz median increase between the menstrual cycle phases which parallels the greater standard deviation seen after ovulation from Brar et al. (2015) (Day 2 ± 58.30 ms2, Day 10 ± 18.03 ms2, Day 21 ± 71.70 ms2). Clinical relevance cannot be easily assessed due to the discrepancy of units in the current study compared to the literature. Journal of Psychophysiology (2020), 34(1), 60–68

Greater Variance in the Luteal Phase Greater variance in the luteal phase may relate to different hormonal regulation between phases or the presence of progesterone not experienced in the follicular phase. Gonadotropin-releasing hormone (GnRH) indirectly promotes the production of both estrogen and progesterone and in turn receives feedback action from these hormones (Clarke, 2015). The release of GnRH occurs in a pulsatile manner which becomes more infrequent and produces higher amplitude pulses during the luteal phase compared to the follicular phase (Clarke, 2015). The infrequent large pulses of GnRH may contribute to or result from the varying concentrations of estrogen and progesterone in the luteal phase. These hormones have been shown to have antagonistic effects in the cardiovascular system (Lebrun & Joyce, 2013) such as cardiac repolarization (mentioned above), vascular tone and, catecholamine production and removal (Salerni et al., 2015). The impact estrogen and progesterone have on cardiac stability is visible through the greater prevalence of arrhythmias observed in the luteal phase (von Holzen, Capaldo, Wilhelm, & Stute, 2016). The decrease in parasympathetic tone at the end of the luteal phase may also contribute to the larger variance observed. Some authors have suggested this relates to physical, psychological, or behavioural premenstrual syndrome experienced by 90% of eumenorrheic women (Matsumoto et al., 2006).

Limitations The use of self-reported inclusion criteria in the current study reduced the ability to confirm menstrual cycle status before enrollment. Accordingly, to ensure the intended population (eumenorrheic females) was represented, it was necessary to remove three participants from the results. To mitigate participant burden and equipment requirements the smartphone HRV4Training application was used for short-term recordings after waking each morning. Participants’ wake time was not controlled nor was the specific smartphone type recorded by the study team. While recording HRV immediately after waking attempts to standardize the influence of circadian rhythm within the study participants, it limits direct comparison to pervious literature that measures HRV during a different time of day. The HRV agreement between finger-tip PPG recorded with a smartphone and ECG has been previously reported between moderate and good for rMSSD, SDNN, and LF though HF showed poor agreement (Banhalmi et al., 2018). Missing data were computed from the day prior to and after the missed day, this may reduce the variability observed and skew the results toward the null. However, as the variance was larger in the luteal phase even with a greater Ó 2019 Hogrefe Publishing

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

prevalence of missed days the results were not likely critically impacted. Additionally, the HRV4Training application uses different units for the frequency domain (sec/Hz) commonly seen in the literature, which limited the current study’s ability to compare these measurements. Since it has been recommended that athletes be monitored with rMSSD rather than other parasympathetic HRV indexes, the frequency domain may not be as relevant for this population (Buchheit, 2014). Sex hormone measurements and participant’s premenstrual symptomatology were not available in the current study to determine inter-individual differences in hormone sensitivity which may be relevant for future research. Overall the small sample size and athletic nature of the participants reduces the generalizability of the observed trends and it is recommended that the findings be confirmed by larger studies in the future.

Conclusion This is the first study to employ daily monitoring of HRV in athletic eumenorrheic females to evaluate novel trends in autonomic activity across the menstrual cycle. Through daily measurements, a nonlinear decrease in parasympathetic tone with a plateau around ovulation was observed which suggests the previous HRV trends identified in the literature may not represent all time points of the menstrual cycle. The evaluation of expected variance observed in each menstrual cycle phase is of particular importance in the female population for the clinical use of HRV to distinguish between natural and pathological trends. Future research should aim to confirm these results in athletic women and additionally women of other physical states in order to identify clinically relevant female-specific reference ranges that account for the influence of the menstrual cycle.

References Altini, M. (2013). HRV4Training. Retrieved from http://www. hrv4training.com/ Altini, M., & Amft, O. (2016). HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, 2610–2613. https://doi.org/10.1109/ EMBC.2016.7591265 Baerwald, A. R., Adams, G. P., & Pierson, R. A. (2012). Ovarian antral folliculogenesis during the human menstrual cycle: A review. Human Reproduction Update, 18, 73–91. https://doi. org/10.1093/humupd/dmr039

Ó 2019 Hogrefe Publishing

Banhalmi, A., Borbas, J., Fidrich, M., Bilicki, V., Gingl, Z., & Rudas, L. (2018). Analysis of a pulse rate variability measurement using a smartphone camera. Journal of Healthcare Engineering, 2018, 4038034. https://doi.org/10.1155/2018/4038034 Bai, X., Li, J., Zhou, L., & Li, X. (2009). Influence of the menstrual cycle on nonlinear properties of heart rate variability in young women. American Journal of Physiology: Heart and Circulatory Physiology, 297, H765–H774. https://doi.org/10.1152/ ajpheart.01283.2008 Brar, T. K., Singh, K. D., & Kumar, A. (2015). Effect of different phases of menstrual cycle on heart rate variability (HRV). Journal of Clinical and Diagnostic Research, 9, CC01–CC04. https://doi.org/10.7860/JCDR/2015/13795.6592 Brunt, V. E., Miner, J. A., Kaplan, P. F., Halliwill, J. R., Strycker, L. A., & Minson, C. T. (2013). Short-term administration of progesterone and estradiol independently alter carotid-vasomotor, but not carotid-cardiac, baroreflex function in young women. American Journal of Physiology: Heart and Circulatory Physiology, 305, H1041–H1049. https://doi.org/10.1152/ ajpheart.00194.2013 Buchheit, M. (2014). Monitoring training status with HR measures: Do all roads lead to Rome? Frontiers in Physiology, 5, 1–19. https://doi.org/10.3389/fphys.2014.00073 Charlot, K., Cornolo, J., Brugniaux, J. V., Richalet, J. P., & Pichon, A. (2009). Interchangeability between heart rate and photoplethysmography variabilities during sympathetic stimulations. Physiological Measurement, 30, 1357–1369. https://doi.org/ 10.1088/0967-3334/30/12/005 Clarke, I. J. (2015). Hypothalamus as an endocrine organ. Comprehensive Physiology, 5, 217–253. https://doi.org/10.1002/ cphy.c140019 Ernst, G. (2017). Heart-rate variability – more than heart beats? Frontiers in Public Health, 5, 1–12. https://doi.org/10.3389/ fpubh.2017.00240 Farage, M. A., Neill, S., & MacLean, A. B. (2009). Physiological changes associated with the menstrual cycle. Obstetrical & Gynecological Survey, 64, 58–72. https://doi.org/10.1097/ OGX.0b013e3181932a37 Fehring, R., Schneider, M., & Raviele, K. (2006). Variability in the phases of the menstrual cycle. Journal of Obstetric, Gynecologic and Neonatal Nursing, 35, 276–384. https://doi.org/10.1111/ j.1552-6909.2006.00051.x Freundl, G., Frank-Herrmann, P., Brown, S., & Blackwell, L. (2014). A new method to detect significant basal body temperature changes during a woman’s menstrual cycle. The European Journal of Contraception & Reproductive Health Care, 19, 392– 400. https://doi.org/10.3109/13625187.2014.948612 Grant, C. C., Viljoen, M., Van Rensburg, D. C. J., & Wood, P. S. (2012). Heart rate variability assessment of the effect of physical training on autonomic cardiac control. Annals of Noninvasive Electrocardiology, 17, 219–229. https://doi.org/ 10.1111/j.1542-474X.2012.00511.x Hill, L. K., Hu, D., Koenig, J., Sollers, J. III, Kapuku, G., Wang, X., . . . Thayer, J. (2015). Ethnic differences in resting heart rate variability: A systematic review and meta-analysis. Psychosomatic Medicine, 77, 16–25. https://doi.org/10.1097/ PSY.0000000000000133 Koenig, J., & Thayer, J. F. (2016). Sex differences in healthy human heart rate variability: A meta-analysis. Neuroscience and Biobehavioral Reviews, 64, 288–310. https://doi.org/10.1016/j. neubiorev.2016.03.007 Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research – Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, 1–18. https:// doi.org/10.3389/fpsyg.2017.00213

Journal of Psychophysiology (2020), 34(1), 60–68

R. L. Kokts-Porietis et al., Menstrual Cycle And Daily Heart Rate Variability

Lebrun, C. M., & Joyce, S. M. (2013). Effects of female reproductive hormones on sports performance. In N. Constantini & A. C. Hackney (Eds), Endocrinology of physical activity and sport (pp. 281–322). Totowa, NJ: Humana Press. https://doi.org/10.1007/ 978-1-62703-314-5 Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., . . . Schwartz, J. (1996). Heart rate variability. Standards of measurement, physiological interpretation, and clinical use (Eds.), European Heart Journal, 17, 354–381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868 Matsumoto, T., Ushiroyama, T., Morimura, M., Moritani, T., Hayashi, T., Suzuki, T., & Tatsumi, N. (2006). Autonomic nervous system activity in the late luteal phase of eumenorrheic women with premenstrual symptomatology. Journal of Psychosomatic Obstetrics and Gynecology, 27, 131–139. https://doi.org/ 10.1080/01674820500490218 McKinley, P. S., King, A. R., Shapiro, P. A., Slavov, I., Fang, Y., Chen, I. S., . . . Sloan, R. P. (2009). The impact of menstrual cycle phase on cardiac autonomic regulation. Psychophysiology, 46, 904–911. https://doi.org/10.1111/j.1469-8986.2009. 00811.x Mihm, M., Gangooly, S., & Muttukrishna, S. (2011). The normal menstrual cycle in women. Animal Reproduction Science, 124, 229–236. https://doi.org/10.1016/j.anireprosci.2010.08.030 Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33, 1407–1417. https://doi.org/10.1111/j.15408159.2010.02841.x Plews, D. J., Scott, B., Altini, M., Wood, M., Kilding, A. E., & Laursen, P. B. (2017). Comparison of heart rate variability recording with smart phone photoplethysmographic, Polar H7 chest strap and electrocardiogram methods. International Journal of Sports Physiology and Performance, 12, 1324– 1328. https://doi.org/10.1123/ijspp.2016-0668 Salerni, S., Di Francescomarino, S., Cadeddu, C., Acquistapace, F., Maffei, S., & Gallina, S. (2015). The different role of sex hormones on female cardiovascular physiology and function: Not only oestrogens. European Journal of Clinical Investigation, 45, 634–645. https://doi.org/10.1111/eci.12447 Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 1– 17. https://doi.org/10.3389/fpubh.2017.00258 Stachenfeld, N. S. (2005). Progesterone increases plasma volume independent of estradiol. Journal of Applied Physiology, 98, 1991–1997. https://doi.org/10.1152/japplphysiol.00031.2005 Tenan, M. S., Brothers, R. M., Tweedell, A. J., Hackney, A. C., & Griffin, L. (2014). Changes in resting heart rate variability across the menstrual cycle. Psychophysiology, 51, 996–1004. https:// doi.org/10.1111/psyp.12250 Vallejo, M., Márquez, M. F., Borja-Aburto, V. H., Cárdenas, M., & Hermosillo, A. G. (2005). Age, body mass index, and menstrual

Journal of Psychophysiology (2020), 34(1), 60–68

cycle influence young women’s heart rate variability: A multivariable analysis. Clinical Autonomic Research, 15, 292–298. https://doi.org/10.1007/s10286-005-0272-9 Verkuil, B., Brosschot, J. F., Tollenaar, M. S., Lane, R. D., & Thayer, J. F. (2016). Prolonged Non-metabolic heart rate variability reduction as a physiological marker of psychological stress in daily life. Annals of Behavioral Medicine, 50, 704–714. https:// doi.org/10.1007/s12160-016-9795-7 von Holzen, J. J., Capaldo, G., Wilhelm, M., & Stute, P. (2016). Impact of endo- and exogenous estrogens on heart rate variability in women: A review. Climacteric, 19, 222–228. https://doi.org/10.3109/13697137.2016.1145206 Voss, A., Schroeder, R., Heitmann, A., Peters, A., & Perz, S. (2015). Short-term heart rate variability – Influence of gender and age in healthy subjects. PLoS ONE, 10, 1–33. https://doi.org/ 10.1371/journal.pone.0118308 Weinschenk, S. W., Beise, R. D., & Lorenz, J. (2016). Heart rate variability (HRV) in deep breathing tests and 5-min short-term recordings: Agreement of ear photoplethysmography with ECG measurements, in 343 subjects. European Journal of Applied Physiology, 116, 1527–1535. https://doi.org/10.1007/s00421016-3401-3 Young, H. A., & Benton, D. (2018). Heart-rate variability. Behavioural Pharmacology, 29, 140–151. https://doi.org/10.1097/ FBP.0000000000000383 History Received August 25, 2018 Revision received January 10, 2019 Accepted January 11, 2019 Published online May 7, 2019 Acknowledgments The author would like to acknowledge the participants for their involvement. Conflict of Interest The authors report no conflict of interest. Funding This work was supported by the Sport Science Association of Alberta (SSAA) through Alberta Sport Connection. Patricia K. Doyle-Baker Faculty of Kinesiology University of Calgary 2500 University Drive NW Calgary, AB T2N 1N4 Canada pdoyleba@ucalgary.ca

Ó 2019 Hogrefe Publishing