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

Divergent spatiotemporal signatures characterize impaired facial emotional recognition in major depressive disorder: An event-related microstate study

BACKGROUND

Major depressive disorder (MDD) is a chronic affective mental disorder with intricate neuropathological characteristics. Microstate analysis has proved its ability to reveal the relatively stable features in a specific brain process. However, the relationship between event-related microstate networks and affective dysfunctions in patients with MDD is not well known.

METHODS

The 128-channel electroencephalogram (EEG) data from 24 MDD patients and 29 healthy controls (HCs) with facial emotion recognition (FER) tasks were used in this study. The analysis encompassed both event-related microstate parameters and specific microstate network metrics. The microstate parameters included Mean Global Field Power (mGFP), Mean Duration (mDur), Time Coverage (TC), and Segment Count Density (SegD). The network metrics evaluated were the clustering coefficient (CC), path length (Lp), global efficiency (Eg), and local efficiency (Eloc).

RESULTS

Three event-related microstates (MS-P1, MS-N170, and MS-P2) were estimated. Compared with HCs, the MDD patients showed significantly increased mGFP in MS-P1 with the sad emotion and decreased microstate parameters in MS-P2 with happy (mDur and TC) and sad (SegD and TC) emotions. Correlation results showed that MS-P1 with the sad emotion was positively related to clinical outcomes. MS-P2 with happy and sad emotions negatively correlated with clinical scores. Additionally, the microstate networks confirmed that MDD patients had decreased network efficiency of the happy emotion in MS-P1 while increased efficiency in dealing with the negative emotion in MS-P2.

CONCLUSIONS

By analyzing event-related microstates and brain networks, we provided a novel approach to demonstrate the divergent patterns for FER processing and the atypical dynamic coordination and integration of affective mechanisms underlying emotional deficits in MDD.

Correlations between depressive symptoms, verbal working memory, and physical activity in university students: evidence based on resting EEG

BACKGROUND

Depressive symptoms are prevalent among university students and are closely correlated with verbal working memory. An increasing amount of evidence suggests that physical activity can mitigate depressive symptoms through multiple mechanisms. The objective of this study was to explore the correlations among depressive symptoms, verbal working memory, and physical activity in university students and to explore the potential correlations between these factors and EEG indicators.

METHODS

A case‒control study design was employed to enroll 136 university students, and convenience samples were used to collect 5-minute resting EEG data with their eyes closed. Physical activity was evaluated using the Physical Activity Scale-3, depressive symptoms were assessed with the Beck Depression Inventory-II, and the N-back task was used to measure the accuracy rate and reaction time of verbal working memory. Pearson's correlation coefficient was calculated to assess the correlation between the variables, the PROCESS macro in SPSS (Model 4) was applied to analyze the mediating role of verbal working memory in the correlation between physical activity and depressive symptoms, and the bootstrap method was used to calculate the mediating effect.

RESULTS

The results showed that the verbal working memory reaction time and physical activity scores of university students with depressive symptoms were significantly different from those of university students without depressive symptoms (P < 0.05), but no difference was found in the accuracy rate of verbal working memory (t = 0.580, P > 0.05). Physical activity was negatively correlated with depressive symptom scores (r=-369, P < 0.05) and with the reaction time of verbal working memory (r=-0.334, P < 0.05). Mediation analyses indicated that the verbal working memory reaction time partially mediated (β = -0.039, 95% CI = -0.096 to -0.001) the correlation between physical activity and depressive symptoms in university students. EEG indicators in the frontal regions of the brain, including beta2 power values for (FP1, FP2) and delta power values for (F3, F4) and (F7, F8), were negatively correlated with depressive symptom scores and verbal working memory reaction time and positively correlated with the level of physical activity (P < 0.05).

CONCLUSIONS

Differences in physical activity and VWM reaction time between university students with depressive symptoms and those without depressive symptoms. Three variables, depressive symptom scores, VWM, and physical activity, had mutually related EEG indicators, which may provide a useful addition to the clinical identification and targeting of interventions in the population with depressive symptoms. Verbal working memory partially mediated the relationship between physical activity and depressive symptoms, but the mediating path coefficient accounted for a low percentage.

CLINICAL TRIAL NUMBER

Not applicable.

Facilitating cognitive neuroscience research with 80-sensor optically pumped magnetometer magnetoencephalography (OPM-MEG)

Recent advancements in optically pumped magnetometer magnetoencephalography (OPM-MEG) make it a promising alternative to conventional SQUID-MEG systems. Nonetheless, as reported in the literature, current OPM-MEG systems are often constrained by a limited number of sampling points, which restricts their capability to match the full-head coverage offered by SQUID-MEG systems. Additionally, whether OPM-MEG can deliver results comparable to SQUID-MEG in practical cognitive neuroscience applications remains largely unexplored. In this study, we introduce a high-density, full-head coverage OPM-MEG system with 80 sensors and systematically compare the performance of OPM-MEG and SQUID-MEG, from sensor- to source-level analysis, across various classic cognitive tasks. Our results demonstrate that visual and auditory evoked fields captured using OPM-MEG align closely with those obtained from SQUID-MEG. Furthermore, steady-state visual evoked field and finger-tapping-induced beta power change recorded with OPM-MEG are accurately localized to corresponding brain regions, with activation centers highly congruent to those observed with SQUID-MEG. For resting-state recordings, the two modalities exhibit similar power distributions, functional connectomes, and microstate clusters. These findings indicate that the 80-sensor OPM-MEG system provides spatial and temporal characteristics comparable to those of traditional SQUID-MEG. Thus, our study offers empirical evidence supporting the efficacy of high-density OPM-MEG and suggests that OPM-MEG, with dense sampling capability, represents a compelling alternative to conventional SQUID-MEG, facilitating further exploration of human cognition.

A Single Session of tDCS Stimulation Can Modulate an EEG Microstate Associated With Anxiety in Patients With Depression

PURPOSE

Microstate analysis involves examining the temporal dynamics of electroencephalogram (EEG) signals and serves as a crucial method for exploring the neural basis of psychiatric disorders. This study investigates the effects of transcranial direct current stimulation (tDCS) on specific microstate parameter maps-D and C in patients with depression, specifically targeting the dorsomedial prefrontal cortex (DMPFC) and left dorsolateral prefrontal cortex (DLPFC).

METHODS

We conducted an open-label, between-subject, crossover trial involving 19 patients clinically diagnosed with depression. A 1 mA electrical current was administered, with anodal stimulation specifically targeting the DMPFC or the left DLPFC. Microstate maps were derived from resting-state EEG recordings obtained prior to and following the application of tDCS. The EEG data were categorized into five distinct microstate classes for subsequent analysis.

FINDINGS

The findings revealed a significant increase in the duration of microstate class D following stimulation in both groups, while microstate class C exhibited no notable changes. Additionally, a significant association was identified between the transition from microstate D to C and alterations in the State-Trait Anxiety Inventory-State (STAI-S) scores after left DLPFC stimulation.

CONCLUSION

Microstate map D appears to be associated with psychiatric disorders and executive functions, whereas map C may relate to the salience network and mind-wandering. Our findings suggest that microstate maps D and C are responsive to tDCS stimuli, indicating their potential as objective tools for anxiety assessment. Employing transition-focused parameters in EEG microstate analysis may enhance the tracking of rapidly fluctuating emotional states, rather than relying solely on duration metrics. Furthermore, the integration of non-invasive brain stimulation techniques, such as tDCS, with EEG microstate analysis holds significant promise for elucidating the neural mechanisms involved in depression.

TRIAL REGISTRATION

UMIN-CTR Clinical Trial: UMIN000015046.

Differentiation with electroencephalography microstate in temporal lobe epilepsy with and without cognitive decline

OBJECTIVE

Electroencephalography (EEG) microstate analysis is widely used in the study of various neurological and psychiatric disorders. We aim to assess whether EEG microstates are altered in TLE patients with and without cognitive decline.

METHODS

This study included a total of 47 temporal lobe epilepsy (TLE) patients with or without cognitive decline and 14 healthy controls (HCs). All participants underwent 64-channel EEG monitoring. Two-minute epochs of preprocessed eye closed awake EEG data were extracted for microstate analysis. Participants were divided into groups based on intelligence quotient (IQ) scores assessed by the Wechsler Intelligence Scale: normal cognition group (n = 23, IQ ≥ 90) and abnormal cognition group (n = 24, IQ < 90). We conducted frequency spectral analysis on each frequency sub-bands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz) of the three groups. Then the following microstate parameters were extracted for analysis over the full frequency band (1-30 Hz) and frequency sub-bands: duration, coverage, occurrence, and transition probability. Statistical analysis using multivariate analysis of variance (MANOVA) with Bonferroni correction (α = 0.0025).

RESULT

Microstate analysis in the beta sub-band revealed significant differences among groups. TLE patients with abnormal cognition showed increased occurrence of Map-D; significantly higher transition probabilities from Map-A, Map-B, and Map-C to Map-D; and distinct microstate characteristics compared to patients with normal cognition and HCs. In frequency spectral analysis, the power across all frequency sub-bands showed no significant differences among TLE patients with normal cognition, TLE patients with abnormal cognition, and HCs.

CONCLUSION

Beta sub-band EEG microstates, particularly Map-D characteristics, may serve as potential neurophysiological markers for cognitive decline in TLE patients.

Association between Electroencephalographic microstates abnormalities and cognitive dysfunction in drug-naive MDD

OBJECTIVES

This study aims to investigate the link between Electroencephalography (EEG) microstate anomalies and cognitive impairments in individuals with drug-naive Major depressive disorder (MDD).

METHODS

We recruited 29 patients with drug-naive MDD and 30 healthy controls. The Hamilton Depression Rating Scale (HDRS-17) measured symptom severity, the MATRICS Consensus Cognitive Battery (MCCB) assessed neurocognitive function, and resting-state EEG data were collected using 64 scalp electrodes. Analysis of EEG microstates was conducted via the Microstate Analysis plugin for EEGLAB.

RESULTS

MDD group had lower scores in six neurocognitive MCCB domains. For EEG microstates, four similar ones (A - D) were found in both groups. Notably, microstate C duration was lower in MDD group (t = 4.549, P < 0.001), microstate D occurrence (t = 2.258, P = 0.028) and proportion (t = 3.733, P < 0.001) were lower in MDD group. There were significant differences in all 4 microstate transition probabilities between groups. For example, A - B, B - A etc. transitions were higher in MDD, while A - C, A - D etc. were lower.The proportion of microstate D was found positively correlated with Speed of processing (SOP) score (r = 0.499, df = 26, P = 0.007) and Working memory (WM) score (r = 0.451, df = 26, P = 0.016). The Occurrence of microstate D was found positively correlated with SOP score (r = 0.383, df = 26, P = 0.044) and WM score (r = 0.389, df = 26, P = 0.041).

CONCLUSIONS

MDD patients show alterations in sub-second brain dynamics, characterized by a decreased proportion and occurrence of microstate D and shorter duration of microstate C, and significant shifts in microstate transition probabilities. These changes correlate with cognitive deficits across several domains, including processing speed and working memory.

EEG microstate as a biomarker of personalized transcranial magnetic stimulation treatment on anhedonia in depression

Anhedonia, a core feature of major depressive disorder (MDD), presents significant treatment challenges with conventional methods. Circuit-targeted, personalized repetitive transcranial magnetic stimulation (rTMS) has shown potentiation by focusing on disruptions in specific networks related to anhedonia. However, how rTMS modulates brain network dynamics in anhedonia is not yet fully understood. This research sought to explore these effects using EEG microstate analysis. In this double-blind, randomized, sham-controlled study, resting-state functional MRI was employed to pinpoint the left dorsolateral prefrontal cortex (DLPFC) region that exhibited the strongest functional connectivity to the nucleus accumbens (NAcc), used as the target for rTMS stimulation. Rest-state EEG data from 49 depressive patients with anhedonia(active=26, sham=23) were analyzed both at baseline and after treatment. In addition, a group of 15 healthy participants was included to serve as baseline controls. Resting-state EEG data were collected at baseline and post-treatment. Using polarity-insensitive k-means clustering, EEG microstates were segmented into five categories (A-E). Circuit-targeted rTMS significantly alleviated symptoms of anhedonia and depression. Compared to healthy controls, patients with anhedonia showed reduced microstate B and C occurrence, along with increased microstate D duration. After rTMS targeting the DLPFC-NAcc pathway, the active treatment group exhibited normalization of microstate C occurrence and a reduction in microstate E duration. Notably, the increase in microstate C was significantly correlated with improvements in anticipatory anhedonia, and these changes were observed specifically in treatment responders. The findings suggest that microstate C is linked to anhedonia and could serve as a reliable biomarker for personalized rTMS treatment. These results provide insights into the neural mechanisms underlying rTMS for anhedonia and highlight the potential of EEG microstate analysis in guiding personalized treatment strategies for depression.

Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis

Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.

EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is associated with abnormal neural activities and brain connectivity. Electroencephalography (EEG) microstate is a voltage topographical representation of temporary brain network activations. Most research on EEG microstates in SCZ has focused on differences between patients and healthy controls (HC). However, changes in EEG microstates among SCZ patients across various stages of physiological and cognitive development have not been thoroughly assessed. Consequently, we stratified patients with SCZ into four age-specific cohorts (20-29 years (brain maturation), 30-39 years (stabilization), 40-49 years (early aging), and 50-59 years (advanced aging)) to evaluate EEG microstate alterations. Additionally, we assessed changes in EEG microstates in first-episode psychosis (FEP) before and after an 8-week treatment period.

METHODS

We acquired 19-channel resting-state EEG from 140 chronic SCZ patients, aged 20 to 59 years, as well as from 19 FEP and 20 healthy controls. FEP patients underwent an 8-week inpatient follow-up. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the K-Means clustering algorithm was applied to classify the data into 4 microstates. Subsequently, templates of these microstates were used to fit EEG signals from each patient, and the collected microstate parameters were analyzed.

RESULTS

Patients with SCZ aged 20 to 29 years demonstrated an increased time coverage of microstate class D compared to other age cohorts. In individuals aged 30-39 years, the parameters of microstate class B-specifically time coverage and occurrence-exhibited significant reductions relative to those in the 40-49 and 50-59 years age groups. Compared to healthy controls, microstates class A parameters were significantly reduced in SCZ patients, while microstates class C parameters were prolonged; after 8 weeks of treatment, microstates class A parameters increased and microstates class C parameters decreased.

CONCLUSIONS

Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.

Experimentally induced central sensitization is accompanied by alterations in electroencephalographical microstate parameters

ABSTRACT

Pain perception is a dynamic and time-varying phenomenon. The high temporal resolution of electroencephalography (EEG) can be leveraged to gain insight into its cortical dynamics. Electroencephalography microstate analysis is a novel technique that parses multichannel EEG signals into a limited number of quasi-stable topographies (microstates) that have a meaningful temporal structure and have been linked to the activity of resting state networks. In recent years, several studies have investigated alterations in EEG microstate parameters associated with acute and chronic pain states, with mixed results. In the present study, we used high-frequency stimulation (HFS), in healthy human volunteers, to induce mechanical hypersensitivity (a perceptual correlate of central sensitization) and investigated (1) changes in microstate parameters before vs after the induction of mechanical hypersensitivity and (2) whether microstate parameters before HFS were linked to the development of mechanical hypersensitivity. Results showed that the duration of microstate E, typically related to the activity of the salience/default mode network, was consistently decreased post-HFS. The global explained variance of microstates A (Auditory network) and E and coverage of microstate A were positively associated with mechanical hypersensitivity. Conversely, the transition probabilities from microstates B (Visual network) to A and the bidirectional transition probabilities between B and C (saliency and default mode networks) were negatively associated with mechanical hypersensitivity. We discuss these findings in the context of the functional significance of EEG microstates. Our results highlight the potential utility of microstate analysis in understanding pain processing and its potential link to changes in the nociceptive system.

Impaired cognitive flexibility in major depressive disorder: Evidences from spatial-temporal ERPs analysis

BACKGROUND

Major Depressive Disorder (MDD) may exhibit impairments in cognitive flexibility. This study investigated whether the cognitive flexibility deficits in MDD are evident across general stimuli or specific to emotional stimuli, while exploring the underlying neuropsychological mechanism.

METHODS

A total of 41 MDD patients and 42 healthy controls (HCs) were recruited. Event-related potentials (ERPs) were recorded when participants performed a non-emotional and an emotional task switching paradigm (N-ETSP and ETSP), both of which assessed cognitive flexibility. Microstate and source localization analysis were applied to reflect brain activity among different brain areas during task switching.

RESULTS

In the N-ETSP, MDD group showed larger P3 difference wave (Pd3) amplitudes and longer P2 difference wave (Pd2) latencies. In the ETSP, MDD group displayed smaller N2 difference wave (Nd2) amplitudes and larger Pd3 amplitudes. The comparison of sLORETA images of emotional switch task and emotional repeat task showed that MDD group had increased activation in the precentral gyrus in microstate2 of the P2 time window and had reduced activation in the middle occipital gyrus in microstate3 of the N2 time window.

LIMITATIONS

The cross-sectional design failed to capture dynamic changes in cognitive flexibility in MDD.

CONCLUSIONS

MDD demonstrated impaired cognitive flexibility respond to both non-emotional and emotional stimuli, with greater impairment for negative emotional stimuli. These deficits are evident in abnormal ERPs component during the early attention stage and the later task preparation stage. Furthermore, abnormal emotional switching cost in MDD appears to be related to early abnormal perceptual control in the parietal-occipital cortex.

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.

Resting-state EEG microstates predict mentalizing ability as assessed by the Reading the Mind in the Eyes test

Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Risk psychosocial factors associated with postpartum depression trajectories from birth to six months

PURPOSE

The purpose of this study was to assess the trajectory of women's depressive symptoms during the first six months postpartum, identify risk factors (sociodemographic, obstetric and personality) associated with classes, and examine associations between classes and postpartum PTSD at two months and bonding at six months.

METHODS

The final sample included 212 women who gave birth in the maternity wards of a large tertiary health center that were approached at 1-3 days, two months, and six months postpartum and completed a demographic questionnaire and measures of neuroticism (BFI) and postpartum depression (EPDS), postpartum PTSD (City Birth Trauma Scale) and bonding (PBQ). Obstetric data were taken from the medical files.

RESULTS

Cluster analysis revealed three distinctive clusters: "stable-low" (64.2%), "transient-decreasing" (25.9%), and "stable-high" (9.9%). Neuroticism, general-related PTSD symptoms, and bonding were associated with differences between all trajectories. Birth-related PTSD symptoms were associated with differences between both stable-high and transient-decreasing trajectories and the stable-low trajectory. No obstetric or demographic variables were associated with differences between trajectories.

CONCLUSION

We suggest that screening women for vulnerabilities such as high levels of neuroticism and offering treatment can alleviate the possible deleterious effects of high-symptom depression trajectories that may be associated with their vulnerability.

Human brain imaging with high-density electroencephalography: Techniques and applications

Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression

BACKGROUND

Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.

METHODS

This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.

RESULTS

Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.

CONCLUSIONS

Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts

Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression

Background

Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear.

Methods

Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed.

Results

Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items.

Conclusion

These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Valence-specific EEG microstate modulations during self-generated affective states

Introduction

This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders.

Methods

Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear).

Results

The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states.

Discussion

This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.

Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia

Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.

Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals

Autism spectrum disorder (ASD) is not a discrete disorder and that symptoms of ASD (i.e., so-called "autistic traits") are found to varying degrees in the general population. Typically developing individuals with sub-clinical yet high-level autistic traits have similar abnormities both in behavioral performances and cortical activation patterns to individuals diagnosed with ASD. Thus it's crucial to develop objective and efficient tools that could be used in the assessment of autistic traits. Here, we proposed a novel machine learning-based assessment of the autistic traits using EEG microstate features derived from a brief resting-state EEG recording. The results showed that: (1) through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and correlation analysis, the mean duration of microstate class D, the occurrence rate of microstate class A, the time coverage of microstate class D and the transition rate from microstate class B to D were selected to be crucial microstate features which could be used in autistic traits prediction; (2) in the support vector regression (SVR) model, which was constructed to predict the participants' autistic trait scores using these four microstate features, the out-of-sample predicted autistic trait scores showed a significant and good match with the self-reported scores. These results suggest that the resting-state EEG microstate analysis technique can be used to predict autistic trait to some extent.

EEG Microstates in Mood and Anxiety Disorders: A Meta-analysis

To reduce the psycho-social burden increasing attention has focused on brain abnormalities in the most prevalent and highly co-occurring neuropsychiatric disorders, such as mood and anxiety. However, high inter-study variability in these patients results in inconsistent and contradictory alterations in the fast temporal dynamics of large-scale networks as measured by EEG microstates. Thus, in this meta-analysis, we aim to investigate the consistency of these changes to better understand possible common neuro-dynamical mechanisms of these disorders.In the systematic search, twelve studies investigating EEG microstate changes in participants with mood and anxiety disorders and individuals with subclinical depression were included in this meta-analysis, adding up to 787 participants.The results suggest that EEG microstates consistently discriminate mood and anxiety impairments from the general population in patients and subclinical states. Specifically, we found a small significant effect size for B microstates in patients compared to healthy controls, with larger effect sizes for increased B presence in unmedicated patients with comorbidity. In a subgroup meta-analysis of ten mood disorder studies, microstate D showed a significant effect size for decreased presence. When investigating only the two anxiety disorder studies, we found a significantly small effect size for the increased microstate A and a medium effect size for decreased microstate E (one study). However, more studies are needed to elucidate whether these findings are diagnostic-specific markers.Results are discussed in relation to the functional meaning of microstates and possible contribution to an explanatory mechanism of overlapping symptomatology of mood and anxiety disorders.

EEG Microstate Analysis in Patients with Disorders of Consciousness and Its Clinical Significance

Disorders of Consciousness are divided into two major categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of patients with vegetative and minimally conscious state are needed. EEG microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with disorders of consciousness. Our prospective observational study included 28 individuals with a disorder of consciousness. Control group included 18 healthy subjects with proper EEG data. We made clinical evaluations using patient behavior scales. We also analyzed the EEGs using microstate analysis. In our study, microstate D coverage differed substantially between vegetative and minimally conscious state patients. Also, there was a strong connection between microstate D characteristics and clinical scale scores. Consequently, we have demonstrated that the most accurate parameter for representing consciousness level is microstate D. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness.

Current State of EEG/ERP Microstate Research

The analysis of EEG microstates for investigating rapid whole-brain network dynamics during rest and tasks has become a standard practice in the EEG research community, leading to a substantial increase in publications across various affective, cognitive, social and clinical neuroscience domains. Recognizing the growing significance of this analytical method, the authors aim to provide the microstate research community with a comprehensive discussion on methodological standards, unresolved questions, and the functional relevance of EEG microstates. In August 2022, a conference was hosted in Bern, Switzerland, which brought together many researchers from 19 countries. During the conference, researchers gave scientific presentations and engaged in roundtable discussions aiming at establishing steps toward standardizing EEG microstate analysis methods. Encouraged by the conference's success, a special issue was launched in Brain Topography to compile the current state-of-the-art in EEG microstate research, encompassing methodological advancements, experimental findings, and clinical applications. The call for submissions for the special issue garnered 48 contributions from researchers worldwide, spanning reviews, meta-analyses, tutorials, and experimental studies. Following a rigorous peer-review process, 33 papers were accepted whose findings we will comprehensively discuss in this Editorial.

Altered resting-state brain oscillation and the associated cognitive impairments in late-life depression with different depressive severity: An EEG power spectrum and functional connectivity study

OBJECTIVE

Cognitive impairments are prevalent in late-life depression (LLD). However, it remains unclear whether there are concurrent brain oscillation alterations in resting condition across varying level of depression severity. This cross-sectional study aims to investigate the characteristics of altered resting-state oscillations, including power spectrum and functional connectivity, and their association with the cognitive impairments in LLD with different depression severity.

METHODS

A total of 65 patients with LLD and 40 elder participants without depression were recruited. Global cognition and subtle cognitive domains were evaluated. A five-minute resting-state electroencephalography (EEG) was conducted under eyes-closed conditions. Measurements included the ln-transformed absolute power for power spectrum analysis and the weighted phase lag index (wPLI) for functional connectivity analysis.

RESULTS

Attentional and executive dysfunction were exhibited in Moderate-Severe LLD group. Enhanced posterior upper gamma power was observed in both LLD groups. Additionally, enhanced parietal and fronto-parietal/occipital theta connectivity were observed in Moderate-Severe LLD group, which were associated with the attentional impairment.

LIMITATIONS

Limitations include a small sample size, concomitant medication use, and a relatively higher proportion of females.

CONCLUSIONS

Current study observed aberrant brain activity patterns in LLD across different levels of depression severity, which were linked to cognitive impairments. The altered posterior brain oscillations may be trait marker of LLD. Moreover, cognitive impairments and associated connectivity alterations were exhibited in moderate-severe group, which may be a state-like marker of moderate-to severe LLD. The study deepens understanding of cognitive impairments with the associated oscillation changes, carrying implications for neuromodulation targets in LLD.

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.

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.

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.

Changes in electroencephalography microstates are associated with reduced levels of vigilance after sleep deprivation

Total sleep deprivation (TSD) negatively affects cognitive functions, especially vigilance attention, but studies on vigilance changes in terms of electroencephalography (EEG) microstates after TSD are limited. This study investigates the impact of TSD on vigilance attention, EEG microstates and its relationship. Thirty healthy adult males completed a psychomotor vigilance task (PVT) before, 24 h after, and 36 h after TSD while their EEG was recorded during rest. Microstate analysis revealed significant changes in the occurrence and contribution of microstate class B after TSD. Moreover, changes in the probability of transitioning between microstate classes A and D were observed, correlating with decreased vigilance. Specifically, a positive correlation was found between transitioning from class B to class C and vigilance, while a trend of negative correlation was observed between transitioning between classes A and D and vigilance. These findings indicate abnormal activity in the salience network and dorsal attention network following sleep deprivation. TSD impairs vigilance attention, as demonstrated by the effects on EEG microstate class B and the transitions between classes A and D. The study suggests its potential as an early warning indicator for predicting vigilance attention after sleep deprivation.

EEG microstate co-specificity in schizophrenia and obsessive-compulsive disorder

The past 20 years of research on EEG microstates has yielded the hypothesis that the imbalance pattern in the temporal dynamics of microstates C (increased) and D (decreased) is specific to schizophrenia. A similar microstate imbalance has been recently found in obsessive-compulsive disorder (OCD). The aim of the present high-density EEG study was to examine whether this pathological microstate pattern is co-specific to schizophrenia and OCD. We compared microstate temporal dynamics using Bayesian analyses, transition probabilities analyses and the Topographic Electrophysiological State Source-Imaging method for source reconstruction in 24 OCD patients and 28 schizophrenia patients, respectively, free of comorbid psychotic and OCD symptoms, and 27 healthy controls. OCD and schizophrenia patients exhibited the same increased contribution of microstate C, decreased duration and contribution of microstate D and greater D → C transition probabilities, compared with controls. A Bayes factor of 4.424 for the contribution of microstate C, 4.600 and 3.824, respectively, for the duration and contribution of microstate D demonstrated that there was no difference in microstate patterns between the two disorders. Source reconstruction further showed undistinguishable dysregulations between the Salience Network (SN), associated with microstate C, and the Executive Control Network (ECN), associated with microstate D, and between the ECN and cognitive cortico-striato-thalamo-cortical (CSTC) loop in the two disorders. The ECN/CSTC loop dysconnectivity was slightly worsened in schizophrenia. Our findings provide substantial evidence for a common aetiological pathway in schizophrenia and OCD, i.e. microstate co-specificity, and same anomalies in salience and external attention processing, leading to co-expression of symptoms.

Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network

Mild cognitive impairment (MCI) is the initial phase of Alzheimer's disease (AD). The cognitive decline is linked to abnormal connectivity between different regions of the brain. Most brain network studies fail to consider the changes in brain patterns and do not reflect the dynamic pathological characteristics of patients. Therefore, this paper proposes a method for constructing brain networks based on microstate sequences. It also analyzes the microstate temporal parameters and introduces a new feature, the brain homeostasis coefficient (Bhc), to quantify the stability of patient brain connections. The results showed that microstate class B parameters were higher in the MCI than in the HC group. Additionally, the Bhc values in most channels of the MCI and AD groups were lower than those of the HC group, with the most significant differences observed in the right frontal lobe. These differences were statistically significant (P < 0.05). The findings indicate that connectivity in the right frontal lobe may be most severely disrupted in patients with cognitive impairment. Furthermore, the Montreal Cognitive Assessment score showed a strong positive correlation with Bhc. This suggests that Bhc could be a novel biomarker for evaluating cognitive function in patients with cognitive impairment.

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.

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.

Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion

Introduction

At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear.

Methods

In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied.

Results

Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls.

Discussion

The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

Associations between abnormal electroencephalogram microstates and childhood emotional abuse in adolescent depression

OBJECTIVES

Childhood traumatic experiences greatly influenced the brain network activities of patients with depression, and there is an urgent need to explore the temporal dynamics for these changes. This study aims to investigate the abnormalities of resting-state electroencephalogram (EEG) microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience.

METHODS

Using resting-state EEG microstate analysis, we explored the temporal dynamics of brain activity in patients with adolescent depression. This study selected 66 adolescents with depression as a patient group, and 27 healthy adolescents as a healthy control group. A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates. Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation.

RESULTS

In this study, significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group. Notably, there was a statistically significant difference (P<0.05) in the occurrence of microstate A across 2 groups, exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores (Spearman's rho=-0.31, P=0.013). Furthermore, patient-specific, non-random transitions from microstate B to A (Spearman's rho=-0.30, P=0.015) and C to A (Spearman's rho=-0.31, P=0.013) were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group, showing statistically significant differences when comparing to the healthy controls (P<0.05). Upon stratification into quartiles based on the emotional abuse factor scores, the occurrence of microstate A, as well as the transition rates from microstates B to A and C to A, retained statistical significance following adjustment for multiple comparisons (all P<0.05).

CONCLUSIONS

The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse. Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks. EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.

Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers

Background

Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents.

Materials and methods

A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software.

Results

In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future.

Conclusion

This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.

Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model

Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.

A resting-state electroencephalographic microstates study in depressed adolescents with non-suicidal self-injury

Neuroimaging studies have revealed abnormal brain activities in depressed teenagers who engage in non-suicidal self-injury (NSSI). We used resting-state electroencephalography (EEG) microstate analysis, which indicates the brief overlap of brain network activation for exploring the characteristics of large-scale cortical activities in depressed adolescents engaged with NSSI to clarify the underlying temporal mechanism. A modified k-means cluster algorithm was used to segment 64-channel resting-state EEG data into microstates. Data from 27 healthy adolescents, 37 adolescents with major depressive disorder (MDD), and 53 adolescents with both MDD and NSSI were examined in this study. The resting-state microstate parameters were compared among groups using the one-way ANOVA and Spearman correlation. Then the associations between significantly different microstate parameters and the depressive severity and self-harming data in the patient groups were further analyzed. The MDD group had higher contribution (p < 0.01), occurrence (p < 0.01) of microstate A, and higher microstate E→A transition (p < 0.05) than the HC and the NSSI group. The MDD group showed a distinctly longer duration (p < 0.05) of microstate A and microstate A→C transition than the HC. The transition probability from B to C was increased in the NSSI group compared to the HC. In the MDD group, the HAMD correlated with the duration of microstate A (Spearman's rho = 0.34, p = 0.044), as the PHQ-9 correlated with its occurrence (Spearman's rho = 0.37, p = 0.028). This research revealed that whereas depressive adolescents with NSSI and MDD displayed similar patterns with healthy controls in EEG microstate, the MDD group did not. Additionally, the non-random transition from microstate E→A may protect against recent self-harm in adolescents with MDD.

Electroencephalography microstates imbalance across the spectrum of early psychosis, autism, and mood disorders

BACKGROUND

Electroencephalography (EEG) microstates translate resting-state temporal dynamics of neuronal networks throughout the brain and could constitute possible markers of psychiatric disorders. We tested the hypothesis of an increased imbalance between a predominant self-referential mode (microstate C) and a decreased attentional mode (microstate D) in psychosis, mood, and autism spectrum disorders.

METHODS

We retrospectively included 135 subjects from an early psychosis outpatient unit, with available eyes-closed resting-state 19 electrodes EEG. Individual-level then group-level modified K-means clustering in controls provided four microstate maps that were then backfitted to all groups. Differences between microstate parameters (occurrence, coverage, and mean duration) were computed between controls and each group, and between disease groups.

RESULTS

Microstate class D parameters were systematically decreased in disease groups compared with controls, with an effect size increasing along the psychosis spectrum, but also in autism. There was no difference in class C. C/D ratios of mean duration were increased only in SCZ compared with controls.

CONCLUSIONS

The decrease in microstate class D may be a marker of stage of psychosis, but it is not specific to it and may rather reflect a shared dimension along the schizophrenia-autism spectrum. C/D microstate imbalance may be more specific to schizophrenia.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Spatiotemporal Precision of Neuroimaging in Psychiatry

Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.

Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method

The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.

Microstate analysis in infancy

BACKGROUND

Microstate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the "atom of thought," meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes.

METHODS

The microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament.

RESULTS

Four microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R.

CONCLUSION

Three microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.

Do we really measure what we think we are measuring?

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our argument with the example of resting-state electroencephalogram (EEG). We used multiple analysis methods, contrary to typical EEG studies where one analysis method is used. We found, first, that many EEG features correlated significantly with cognitive tasks. However, these EEG features correlated weakly with each other. Similarly, in a second analysis, we found that many EEG features were significantly different in older compared to younger participants. When we compared these EEG features pairwise, we did not find strong correlations. In addition, EEG features predicted cognitive tasks poorly as shown by cross-validated regression analysis. We discuss several explanations of these results.

Depression and Cognitive Impairment: Current Understanding of Its Neurobiology and Diagnosis

BACKGROUND

Eye movement is critical for obtaining precise visual information and providing sensorimotor processes and advanced cognitive functions to the brain behavioral indicator.

METHODS

In this article, we present a narrative review of the eye-movement paradigms (such as fixation, smooth pursuit eye movements, and memory-guided saccade tasks) in major depression.

RESULTS

Characteristics of eye movement are considered to reflect several aspects of cognitive deficits regarded as an aid to diagnosis. Findings regarding depressive disorders showed differences from the healthy population in paradigms, the characteristics of eye movement may reflect cognitive deficits in depression. Neuroimaging studies have demonstrated the effectiveness of different eye movement paradigms for MDD screening.

CONCLUSION

Depression can be distinguished from other mental illnesses based on eye movements. Eye movement reflects cognitive deficits that can help diagnose depression, and it can make the entire diagnostic process more accurate.

Event-related microstate dynamics represents working memory performance

In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.