EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects

The neurophysiological mechanisms of emotional conflict are influenced by social associations information of varying valence

Previous research has indicated that emotional valence can influence the resolution of emotional conflicts, with this effect benefiting from the prioritized processing of negative emotions. In this study, a social learning paradigm was utilized to train participants to associate different neutral faces with distinct social meanings (e.g., stingy, generous). These learned neutral faces were then combined with emotion words of varying valence to create a novel face-word Stroop task. This task was employed to investigate whether social affective associations of different valences continue to impact emotional conflict processing. Concurrently, electroencephalogram data was recorded while participants completed the task. Behavioral results revealed that when participants were presented with neutral faces associated with negative social associations, emotional conflict resolution is facilitated, whereas when faced with neutral faces linked to positive social associations, the emotional conflict effect was significantly present. Consistency between event-related potentials and microstate results indicated that negative social associations facilitated conflict resolution, while positive social associations required participants to recruit more cognitive resources to inhibit irrelevant emotional interference. These findings further expand the factors influencing emotional conflict and relevant neurophysiological explanations.

Electroencephalographic Resting-State Microstates are Unstable in Visual Snow Syndrome

OBJECTIVE

To study large-scale network processing in visual snow syndrome (VSS) with and without migraine, we applied resting-state electroencephalographic microstate analysis.

BACKGROUND

VSS is characterized by a spectrum of visual symptoms, with the main symptom being perceived as flickering dots throughout the visual field. The syndrome is associated with migraine and tinnitus and is considered a network disorder, but the cause and pathophysiology are still largely unknown.

METHODS

In this case-control study, resting-state electroencephalography (EEG) recordings were selected from a cohort of 21 subjects with VSS (8 females, 33 ± 9.56 years, 14 migraines) and 21 matched controls (8 females, 33 ± 11.1 years, 14 migraines). An analysis of parameters between the four canonical microstate Classes A-D was performed.

RESULTS

VSS patients showed an overall shorter duration (p = 0.001, Cohen's d = -0.48) and lower mean amplitude (p < 0.001, Cohen's d = -0.76) of microstates compared to the controls. In addition, we found an aberrant syntax of microstate Class A (auditive and visual processing) with more (p = 0.001, Cohen's d = 0.57) transitions to Class B (visual) and less (p = 0.011, d = -0.71) to Class C (interoceptive, salience) compared to controls.

CONCLUSION

Visual snow syndrome (VSS) is a complex disorder, affecting widespread neural network activity. In VSS, electroencephalography (EEG) microstate analysis revealed unstable microstates as well as aberrant transition probabilities, indicating disturbed large-scale network activity. The analysis of microstate dynamics in VSS complements the results of imaging with high temporal resolution and could contribute to the development of future treatment approaches.

A comparative study of EEG microstate dynamics during happy and sad music videos

EEG microstates offer a unique window into the dynamics of emotional experiences. This study delved into the emotional responses of happiness and sadness triggered by music videos, employing microstate analysis and eLoreta source-level investigation in the alpha band. The results of the microstate analysis showed that regardless of gender, participants during happy music video significantly upregulated class D microstate and downregulated class C microstate, leading to a significantly enhanced global explained variance (GEV), coverage, occurrence, duration, and global field power (GFP) for class D. Conversely, sad music video had the opposite effect. The eLoreta study revealed that during the happy state, there was enhanced CSD in the central parietal regions across both genders and diminished functional connectivity in the precuneus for female participants compared to the sad state. Class D and class C microstates are linked to attention and mind-wandering, respectively. The findings suggest that (1) increased class D and CSD activity could explain heightened attentiveness observed during happy music, and (2) increased class C activity and functional connectivity could explain enhanced mind wandering observed during sad music. Additionally, female participants exhibited significantly higher mean occurrence than males, and the sad state showed significantly higher mean occurrence than the happy state.

Similarities and differences between chronic primary pain and depression in brain activities: Evidence from resting-state microstates and auditory Oddball task

BACKGROUND

In 2019, the International Association for the Study of Pain introduced the concept of 'chronic primary pain (CPP)', characterized by persistent non-organic pain with emotional and functional abnormalities. Underdiagnosed and linked to depression, CPP has poorly understood neural characteristics. Electroencephalogram (EEG) microstates enable detailed examination of brain network dynamics at the millisecond level. Incorporating task-related EEG features offers a comprehensive neurophysiological signature of brain dysfunction, facilitating exploration of potential neural mechanisms.

METHODS

This study employed resting-state and task-related auditory Oddball EEG paradigm to evaluate 20 healthy controls, 20 patients with depression, and 20 patients with CPP. An 8-minute recording of resting-state EEG was conducted to identify four typical microstates (A-D). Additionally, power spectral density (PSD) features were examined during an auditory Oddball paradigm.

RESULTS

Both CPP and Major Depressive Disorder (MDD) patients exhibited reduced occurrence rate and transition probabilities of other microstates to microstate C during resting-state EEG. Furthermore, more pronounced increase in Gamma PSD was observed in the occipital region of CPP during the Oddball task. In CPP, both resting-state microstate C and task-related Gamma PSD correlated with pain and emotional indicators. Notably, microstate C occurrence positively correlated with occipital Gamma PSD in MDD.

CONCLUSION

Conclusively, both CPP and MDD display dynamic abnormalities within the salient network, closely associated with pain and depressive symptoms in CPP. Unlike MDD, CPPs' dynamic network changes appear unrelated to perceptual integration function, indicating differing microstate functional impacts. Combining resting-state microstates and Oddball tasks may offer a promising avenue for identifying potential biomarkers in objectively assessing chronic primary pain.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Altered cortical synaptic lipid signaling leads to intermediate phenotypes of mental disorders

Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1R345T/WT). Prg-1R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.

Resting state EEG microstate profiling and a machine-learning based classifier model in epilepsy

Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate statistics. We found significant differences in all parameters related to Microstate D (fronto-parietal network) in TLE patients and Microstate B (visual processing) in IGE patients compared to HCs. Occurrence, duration and time coverage of Microstate B was highest in IGE when compared to the other groups. We also found significant deviations in transition probabilities for both epilepsy groups, particularly into Microstate C (salience network) in IGE. Classification accuracy into clinical groups was found to exceed 70% using microstate parameters which improved on incorporating neuropsychological test differences. To the best of our knowledge, the current study is the first to compare and validate the use of microstate features to discriminate between two disparate epilepsy syndromes (TLE, IGE) and HCs using machine learning suggesting that resting state EEG microstates can be used for endophenotyping and to study resting state dysfunction in epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10095-z.

Neuroimaging evidence of disturbed self-appraisal in posttraumatic stress disorder: A systematic review

BACKGROUND

The experience of self-hood in posttraumatic stress disorder (PTSD) is altered cognitively and somatically. Dysfunctional negative cognitions about the self are a central mechanism of PTSD symptomatology and treatment. However, while higher-order brain models of disturbances in self-appraisal (i.e., cognitive processes relating to evaluating the self) have been examined in other psychiatric disorders, it is unclear how normative brain function during self-appraisal is impaired in PTSD.

METHODS

This paper presents a PRISMA systematic review of functional neuroimaging studies (n = 5), to establish a neurobiological account of how self-appraisal processes are disturbed in PTSD. The review was prospectively registered with PROSPERO (CRD42023450509).

RESULTS

Self-appraisal in PTSD is linked to disrupted activity in core self-processing regions of the Default Mode Network (DMN); and regions involved in cognitive control and emotion regulation, salience and valuation.

LIMITATIONS

Because self-appraisal in PTSD is relatively under-studied, only a small number of studies could be included for review. Cross-study heterogeneity in analytic approaches and trauma-exposure history prohibited a quantitative meta-analysis.

CONCLUSIONS

This paper proposes a mechanistic account of how neural dysfunctions may manifest clinically in PTSD and inform targeted selection of appropriate treatment options. We present a research agenda for future work to advance the field.

Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double-blind, cross-over randomized clinical trial

Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.

Resting-State EEG Microstates and Power Spectrum in Borderline Personality Disorder: A High-Density EEG Study

Borderline personality disorder (BPD) is a debilitating psychiatric condition characterized by emotional dysregulation, unstable sense of self, and impulsive, potentially self-harming behavior. In order to provide new neurophysiological insights on BPD, we complemented resting-state EEG frequency spectrum analysis with EEG microstates (MS) analysis to capture the spatiotemporal dynamics of large-scale neural networks. High-density EEG was recorded at rest in 16 BPD patients and 16 age-matched neurotypical controls. The relative power spectrum and broadband MS spatiotemporal parameters were compared between groups and their inter-correlations were examined. Compared to controls, BPD patients showed similar global spectral power, but exploratory univariate analyses on single channels indicated reduced relative alpha power and enhanced relative delta power at parietal electrodes. In terms of EEG MS, BPD patients displayed similar MS topographies as controls, indicating comparable neural generators. However, the MS temporal dynamics were significantly altered in BPD patients, who demonstrated opposite prevalence of MS C (lower than controls) and MS E (higher than controls). Interestingly, MS C prevalence correlated positively with global alpha power and negatively with global delta power, while MS E did not correlate with any measures of spectral power. Taken together, these observations suggest that BPD patients exhibit a state of cortical hyperactivation, represented by decreased posterior alpha power, together with an elevated presence of MS E, consistent with symptoms of elevated arousal and/or vigilance. This is the first study to investigate resting-state MS patterns in BPD, with findings of elevated MS E and the suggestion of reduced posterior alpha power indicating a disorder-specific neurophysiological signature previously unreported in a psychiatric population.

EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome

Fragile X syndrome (FXS) is one of the most common inherited causes of intellectual disabilities. While there is currently no cure for FXS, EEG is considered an important method to investigate the pathophysiology and evaluate behavioral and cognitive treatments. We conducted EEG microstate analysis to investigate resting brain dynamics in FXS participants. Resting-state recordings from 70 FXS participants and 71 chronological age-matched typically developing control (TDC) participants were used to derive microstates via modified k-means clustering. The occurrence, mean global field power (GFP), and global explained variance (GEV) of microstate C were significantly higher in the FXS group compared to the TDC group. The mean GFP was significantly negatively correlated with non-verbal IQ (NVIQ) in the FXS group, where lower NVIQ scores were associated with greater GFP. In addition, the occurrence, mean duration, mean GFP, and GEV of microstate D were significantly greater in the FXS group than the TDC group. The mean GFP and occurrence of microstate D were also correlated with individual alpha frequencies in the FXS group, where lower IAF frequencies accompanied greater microstate GFP and occurrence. Alterations in microstates C and D may be related to the two well-established cognitive characteristics of FXS, intellectual disabilities and attention impairments, suggesting that microstate parameters could serve as markers to study cognitive impairments and evaluate treatment outcomes in this population. Slowing of the alpha peak frequency and its correlation to microstate D parameters may suggest changes in thalamocortical dynamics in FXS, which could be specifically related to attention control. (250 words).

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

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.

On the Reliability of the EEG Microstate Approach

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.

A Potential Source of Bias in Group-Level EEG Microstate Analysis

Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Between and within individuals, microstates can differ both in terms of characteristic topographies and temporal dynamics, which leads to analytic challenges as the measurement of microstate dynamics is dependent on assumptions about their topographies. Here we focus on the analysis of group differences, using simulations seeded on real data from healthy control subjects to compare approaches that derive separate sets of maps within subgroups versus a single set of maps applied uniformly to the entire dataset. In the absence of true group differences in either microstate maps or temporal metrics, we found that using separate subgroup maps resulted in substantially inflated type I error rates. On the other hand, when groups truly differed in their microstate maps, analyses based on a single set of maps confounded topographic effects with differences in other derived metrics. We propose an approach to alleviate both classes of bias, based on a paired analysis of all subgroup maps. We illustrate the qualitative and quantitative impact of these issues in real data by comparing waking versus non-rapid eye movement sleep microstates. Overall, our results suggest that even subtle chance differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.

Combined effects of neuroticism and negative emotional context on spontaneous EEG dynamics

Neuroticism is a personality trait with great clinical relevance, defined as a tendency to experience negative affect, sustained self-generated negative thoughts and impaired emotion regulation. Here, we investigated spontaneous brain dynamics in the aftermath of negative emotional events and their links with neuroticism in order to shed light on the prolonged activity of large-scale brain networks associated with the control of affect. We recorded electroencephalography (EEG) from 36 participants who were asked to rest after watching neutral or fearful video clips. Four topographic maps (i.e. microstates classes A, B, C and D) explained the majority of the variance in spontaneous EEG. Participants showed greater presence of microstate D and lesser presence of microstate C following exposure to fearful stimuli, pointing to changes in attention- and introspection-related networks previously associated with these microstates. These emotional effects were more pronounced for participants with low neuroticism. Moreover, neuroticism scores were positively correlated with microstate C and negatively correlated with microstate D, regardless of previous emotional stimulation. Our results reveal distinctive effects of emotional context on resting-state EEG, consistent with a prolonged impact of negative affect on the brain, and suggest a possible link with neuroticism.

All-night spectral and microstate EEG analysis in patients with recurrent isolated sleep paralysis

The pathophysiology of recurrent isolated sleep paralysis (RISP) has yet to be fully clarified. Very little research has been performed on electroencephalographic (EEG) signatures outside RISP episodes. This study aimed to investigate whether sleep is disturbed even without the occurrence of a RISP episode and in a stage different than conventional REM sleep. 17 RISP patients and 17 control subjects underwent two consecutive full-night video-polysomnography recordings. Spectral analysis was performed on all sleep stages in the delta, theta, and alpha band. EEG microstate (MS) analysis was performed on the NREM 3 phase due to the overall high correlation of subject template maps with canonical templates. Spectral analysis showed a significantly higher power of theta band activity in REM and NREM 2 sleep stages in RISP patients. The observed rise was also apparent in other sleep stages. Conversely, alpha power showed a downward trend in RISP patients' deep sleep. MS maps similar to canonical topographies were obtained indicating the preservation of prototypical EEG generators in RISP patients. RISP patients showed significant differences in the temporal dynamics of MS, expressed by different transitions between MS C and D and between MS A and B. Both spectral analysis and MS characteristics showed abnormalities in the sleep of non-episodic RISP subjects. Our findings suggest that in order to understand the neurobiological background of RISP, there is a need to extend the analyzes beyond REM-related processes and highlight the value of EEG microstate dynamics as promising functional biomarkers of RISP.

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function

BACKGROUND

A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography (EEG) in people with depression. However, the consistency of findings on EEG microstates in patients with depression is poor, and few studies have reported the relationship between EEG microstates, cognitive scales, and depression severity scales.

AIM

To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.

METHODS

A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We collected information relating to demographic and clinical characteristics, as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Chinese version) and EEG.

RESULTS

Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher [depression (DEP): Duration 84.58 ± 24.35, occurrence 3.72 ± 0.56, contribution 30.39 ± 8.59; CON: Duration 72.77 ± 10.23, occurrence 3.41 ± 0.36, contribution 24.46 ± 4.66; Duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; Contribution F = 10.82, P = 0.011] while the duration, occurrence, and contribution of microstate D were significantly lower (DEP: Duration 70.00 ± 15.92, occurrence 3.18 ± 0.71, contribution 22.48 ± 8.12; CON: Duration 85.46 ± 10.23, occurrence 3.54 ± 0.41, contribution 28.25 ± 5.85; Duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in patients with depression. A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B (r = 0.405, P = 0.049).

CONCLUSION

EEG microstate, especially C and D, is a possible biomarker in depression. Patients with depression had a more frequent transition from microstate C to B, which may relate to more negative rumination and visual processing.

Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis

We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.

Altered EEG Microstates Dynamics in Individuals with Subthreshold Depression When Generating Negative Future Events

Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.

Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome

Objective: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods: We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. Results: Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. Conclusions: The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

BACKGROUND

Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently.

OBJECTIVE

This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety.

METHODS

Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate.

RESULTS

Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods.

CONCLUSIONS

Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI.

TRIAL REGISTRATION

PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.

Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders

Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.

Elevated decision uncertainty and reduced avoidance drives in depression, anxiety and substance use disorders during approach-avoidance conflict: a replication study

BACKGROUND

Decision-making under approach-avoidance conflict (AAC; e.g., sacrificing quality of life to avoid feared outcomes) may be affected in multiple psychiatric disorders. Recently, we used a computational (active inference) model to characterize information processing differences during AAC in individuals with depression, anxiety and/or substance use disorders. Individuals with psychiatric disorders exhibited increased decision uncertainty (DU) and reduced sensitivity to unpleasant stimuli. This preregistered study aimed to determine the replicability of this processing dysfunction.

METHODS

A new sample of participants completed the AAC task. Individual-level computational parameter estimates, reflecting decision uncertainty and sensitivity to unpleasant stimuli ("emotion conflict"; EC), were obtained and compared between groups. Subsequent analyses combining the prior and current samples allowed assessment of narrower disorder categories.

RESULTS

The sample in the present study included 480 participants: 97 healthy controls, 175 individuals with substance use disorders and 208 individuals with depression and/or anxiety disorders. Individuals with substance use disorders showed higher DU and lower EC values than healthy controls. The EC values were lower in females, but not males, with depression and/or anxiety disorders than in healthy controls. However, the previously observed difference in DU between participants with depression and/or anxiety disorders and healthy controls did not replicate. Analyses of specific disorders in the combined samples indicated that effects were common across different substance use disorders and affective disorders.

LIMITATIONS

There were differences, although with small effect size, in age and baseline intellectual functioning between the previous and current sample, which may have affected replication of DU differences in participants with depression and/or anxiety disorders.

CONCLUSION

The now robust evidence base for these clinical group differences motivates specific questions that should be addressed in future research: can DU and EC become behavioural treatment targets, and can we identify neural substrates of DU and EC that could be used to measure severity of dysfunction or as neuromodulatory treatment targets?

The EEG microstate representation of discrete emotions

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.

Feature extraction based on microstate sequences for EEG-based emotion recognition

Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.

Microstate feature fusion for distinguishing AD from MCI

Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.

Electroencephalogram microstates and functional connectivity of cybersickness

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem-cybersickness-which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time-space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A-E), including four widely reported canonical microstates (A-D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.

EEG microstate in first-episode drug-naive adolescents with depression

BACKGROUND

A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far.

METHODS

Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression.

RESULTS

Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained.

CONCLUSION

Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.

EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy

BACKGROUND

Major depressive disorder (MDD) is associated with abnormal neural activities and brain connectivity. EEG microstate is a voltage topology map that reflects transient activations of the brain network. A limited number of studies on EEG microstate in MDD have focused on differences between patients and healthy controls. However, EEG microstate changes in MDD patients before and after drug treatment have not been evaluated. We assessed EEG microstate characteristics and evaluated changes in brain network dynamics in MDD patients before and after drug treatment. Moreover, we evaluated the neuro-electrophysiological mechanisms of antidepressant therapies.

METHODS

64-channel resting EEG was obtained from 101 patients with first-episode untreated depression (0 week) and 45 healthy controls (HC) from January to December 2020. MDD patients were treated with selective serotonin reuptake inhibitors (SSRI). EEG data for 51 MDD patients who had completed an 8-week follow-up was collected. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the atomize and agglomerate hierarchical clustering (AAHC) was into 4 microstates. Next, EEG signals from each patient were fitted using templates of 4 microstates. Finally, microstate indices were collected and analyzed.

RESULTS

Global clustering generated 4 microstates (A, B, C, D) in all subjects, which explained 65-84% of the global variance. Compared to HC, the duration of microstate D reduced while those of microstates A and B increased in MDD patients. After the 8-week treatment period, the duration and coverage of microstate D increased, the frequency of microstate A and transition probability of microstate D to A reduced, while transition probability of microstate B to D and D to B increased in MDD patients. There were no differences in microstate features between HC and MDD at 8 weeks. In patients with first-episode untreated depression, lower average durations of microstate D, relatively higher frequencies of microstate C and lower transition probabilities of microstate D to B correlated with better effects after 8 weeks. The higher occurrence and proportion of microstate C at 8 weeks was positively correlated with the HAMD score and reduction rate. The same observation was reached for the transition probability of microstate A to C. However, the transition probability of microstate D to B showed a negative correlation with the HAMD score at 8 weeks.

CONCLUSION

Microstate D is a potential electrophysiological trait of MDD and can predict treatment outcomes of SSRIs. Therefore, EEG microstate analysis may not only be an objective method for evaluating treatment outcomes of depression, but is also a potential new approach for exploring the neuro-electrophysiological mechanisms of antidepressant therapy. Public title: Multidimensional diagnosis, individualized treatment and management techniques based on clinic-pathological characteristics of depressive disorder; Registration number: ChiCTR1900026600; Date of registration: 2019-10-15; URL: http://www.chictr.org.cn/index.aspx.

Brain Responses to a Self-Compassion Induction in Trauma Survivors With and Without Post-traumatic Stress Disorder

Self-compassion (SC) is a mechanism of symptom improvement in post-traumatic stress disorder (PTSD), however, the underlying neurobiological processes are not well understood. High levels of self-compassion are associated with reduced activation of the threat response system. Physiological threat responses to trauma reminders and increased arousal are key symptoms which are maintained by negative appraisals of the self and self-blame. Moreover, PTSD has been consistently associated with functional changes implicated in the brain's saliency and the default mode networks. In this paper, we explore how trauma exposed individuals respond to a validated self-compassion exercise. We distinguish three groups using the PTSD checklist; those with full PTSD, those without PTSD, and those with subsyndromal PTSD. Subsyndromal PTSD is a clinically relevant subgroup in which individuals meet the criteria for reexperiencing along with one of either avoidance or hyperarousal. We use electroencephalography (EEG) alpha-asymmetry and EEG microstate analysis to characterize brain activity time series during the self-compassion exercise in the three groups. We contextualize our results with concurrently recorded autonomic measures of physiological arousal (heart rate and skin conductance), parasympathetic activation (heart rate variability) and self-reported changes in state mood and self-perception. We find that in all three groups directing self-compassion toward oneself activates the negative self and elicits a threat response during the SC exercise and that individuals with subsyndromal PTSD who have high levels of hyperarousal have the highest threat response. We find impaired activation of the EEG microstate associated with the saliency, attention and self-referential processing brain networks, distinguishes the three PTSD groups. Our findings provide evidence for potential neural biomarkers for quantitatively differentiating PTSD subgroups.

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.

Telling functional networks apart using ranked network features stability

Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.

Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression

Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.

Continuous Scoring of Depression from EEG Signals via a Hybrid of Convolutional Neural Networks

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.

EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with Autism Spectrum Development

EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD. This article is protected by copyright. All rights reserved.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

Decline in the integration of top-down and bottom-up attentional control in older adults with mild cognitive impairment

Individuals with mild cognitive impairment (MCI) have deficits in goal-directed top-down and stimulus-driven bottom-up attentional control. However, it remains unclear whether and how the interaction between the two processes is altered in individuals with MCI. We collected electroencephalography (EEG) data from 30 older adults with MCI and 30 demographically matched healthy controls (HCs) when they were performing a perceptual decision-making task, in which we manipulated the cognitive load involved in task-relevant top-down processing and the surprise level involved in task-irrelevant bottom-up processing. We found the significant group difference in the interaction between top-down and bottom-up processes. HCs showed enlarged P3 and strengthened event-related microstate C on high (vs. low) surprise level trials under high cognitive load, while there was no such surprise effect suggesting distraction under low cognitive load. In contrast, participants with MCI showed increased P2 and P3 amplitudes and strengthened microstates C and D on high (vs. low) surprise level trials under low cognitive load yet no surprise effect under high load. These results suggested that participants with MCI were distracted by task-irrelevant information under low cognitive load, while under high load, they might experience a passive inhibition on the task-irrelevant bottom-up processing because of the exhaustion of attentional resources; in addition, this altered interaction observed in the MCI group occurred at the stages of selective attention and uncertainty reduction.

Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

EEG microstate features according to performance on a mental arithmetic task

In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG

INTRODUCTION

The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic.

METHODS

Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities.

RESULTS

Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants.

CONCLUSIONS

The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences. A publicly available and widely used benchmarking EEG dataset called DEAP was used, in which 32 participants watched 40 one-minute music videos with their 32channel EEG recorded. Four quasi-stable prototypical microstates were obtained, and their temporal parameters were extracted as features. In random forest regression, the microstate features showed better performances for decoding valence (model fitting mean squared error (MSE) = 3.85±0.28 and 4.07 ± 0.30, respectively, p = 0.022) and comparable performances for decoding arousal (MSE = 3.30±0.30 and 3.41 ±0.31, respectively, p = 0.169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.

Reliability and Individual Specificity of EEG Microstate Characteristics

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.