The spontaneous brain activity of disgust: Perspective from resting state fMRI and resting state EEG

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals

STUDY OBJECTIVES

Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants.

METHODS

Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed.

RESULTS

Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033).

CONCLUSIONS

Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

Electroencephalography Microstate Class D is a Brain Marker of Subjective Sleep Quality for College Students with High Habitual Sleep Efficiency

Subjective sleep quality is an individual's subjective sleep feeling, and its effective evaluation is the premise of improving sleep quality. However, people with autism or mental disorders often experience difficulties in verbally expressing their subjective sleep quality. To solve the above problem, this study provides a non-verbal and convenient brain feature to assess subjective sleep quality. Reportedly, microstates are often used to characterize the patterns of functional brain activity in humans. The occurrence frequency of microstate class D is an important feature in the insomnia population. We therefore hypothesize that the occurrence frequency of microstate class D is a physiological indicator of subjective sleep quality. To test this hypothesis, we recruited college students from China as participants [N = 61, mean age = 20.84 years]. The Chinese version of the Pittsburgh Sleep Quality Index scale was used to measure subjective sleep quality and habitual sleep efficiency, and the state characteristics of the brain at this time were assessed using closed eyes resting-state brain microstate class D. The occurrence frequency of EEG microstate class D was positively associated with subjective sleep quality (r = 0.32, p < 0.05). Further analysis of the moderating effect showed that the occurrence frequency of microstate class D was significantly and positively correlated with subjective sleep quality in the high habitual sleep efficiency group. However, the relationship was not significant in the low sleep efficiency group (βsimple = 0.63, p < 0.001). This study shows that the occurrence frequency of microstate class D is a physiological indicator of assessing subjective sleep quality levels in the high sleep efficiency group. This study provides brain features for assessing subjective sleep quality of people with autism and mental disorders who cannot effectively describe their subjective feelings.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

EEG Microstates in Social and Affective Neuroscience

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.

A resting state EEG study on depressed persons with suicidal ideation

Background

Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life-threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain region. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD.

Methods

Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with and MDD persons with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between-group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) between PSD data and the scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS).

Results

When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered.

Conclusions

The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self-referencing. Future studies should confirm these findings in a larger population sample.

Ewww-Investigating the neural basis of disgust in response to naturalistic and pictorial nauseating stimuli

Emotion induction in psychological and neuroscientific research has been mostly done by presenting participants with picture or film material. However, it is debatable whether this passive approach to emotion induction results in an affective state comparable to real-life emotions, and if the neural correlates of emotion processing are ecologically valid. To investigate the appropriateness of pictures for the induction of emotions, we presented 56 participants in a within-subjects design with naturalistic disgusting and neutral stimuli as well as with pictures of said stimulus material while recording continuous EEG data. We calculated asymmetry indices (AIs) for alpha power as an index of emotion processing and emotion regulation at the F3/4, F5/6, F7/8, and O1/2 electrode pairs. Participants reported higher disgust ratings for disgusting naturalistic compared to disgusting pictorial stimuli. Investigating changes in the EEG signal in participants with a pronounced disgust response (n = 38), we found smaller AIs for naturalistic stimuli compared to pictures. Moreover, in this disgusted sub-sample, there were smaller AIs in response to naturalistic disgusting stimuli compared to pictorial disgusting and neutral stimuli at the O1/2 electrode pair indicating stronger activation of the right relative to the left hemisphere by naturalistic stimuli. As the right hemisphere has been shown to display dominance in processing negative and withdrawal-associated emotions, this might indicate that naturalistic stimuli are more appropriate for the induction of emotions than picture stimuli. To improve the validity of results from emotion induction, future research should incorporate stimulus material that is as naturalistic as possible.

Effect of trait anxiety on cognitive flexibility: Evidence from event-related potentials and resting-state EEG

Individuals with anxiety often exhibit cognitive flexibility impairment; however, the neural underpinnings of this cognitive impairment remain unclear. In this study, 45 participants were instructed to complete a task-switching assessment of shifting function by EEG technology, and 200 participants were included in microstate analysis to study why cognitive flexibility is impaired and the neuromechanism. Behaviorally, a positive correlation between trait anxiety scores and set shifting cost was found. At the EEG level, there was a positive correlation between trait anxiety scores and frontal P2 peaks under the shifting condition, which was related to the activation of the stimulus-response associations by attention. Furthermore, microstate analysis was used to analyze EEG functional networks, and TA scores had significant positive correlations with the Occurrence of class D and the Contribution of class D, which was related to the dorsal attention network. These results provided direct neuroelectrophysiological evidence that trait anxiety impairs cognitive flexibility when shifting is required.