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

Abstract

Electroencephalography (EEG) measures the brain’s electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects ( n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts’ brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

Microstates-based resting frontal alpha asymmetry approach for understanding affect and approach/withdrawal behavior

“…The aim is to obtain a low value of CV. The active microstate prototypes obtained in this study are consistent with the normative EEG microstate classes identified by many studies 40,41,87,[109][110][111] . Following the selection of an active number of microstate prototypes, the EEG of each volunteer is re-expressed as a sequence of microstate classes by back-fitting these active microstate prototypes on each volunteer's EEG Figure 9.…”

“…However, the microstate analysis employed in the current study uses an average referencing scheme for frontal alpha asymmetry estimation. The present study follows average referencing for microstate analysis as various studies 40,87 adequately understand the cognitive phenomena through average-referenced microstate estimations. Further, the effect of different EEG referencing schemes on microstate estimations is still not clearly understood.…”

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…To date, measures of the duration, rate of occurrence, and the sequence of specific microstate configurations have been associated with individual differences in age and gender (Koenig et al, 2002;Tomescu et al, 2018), as well as clinical and neurological diagnoses and dysfunction (Rieger et al, 2016;Zoubi et al, 2019). Information regarding the association of microstate parameters with personality attributes or with cognitive performance measures is even more limited.…”

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

“…Microstates appear to be useful to identify novel diagnostic and treatment options for schizophrenia patients, 28 for the diagnosis of psychotic disorders 164 and bipolar disorder, 187 to reveal depressive symptomatology during depressive episodes 165 and to identify aberrant neural dynamics in patients affected by mood and anxiety disorders. 166 Resting state measures such as frontal alpha asymmetry, prefrontal theta cordance or EEG-vigilance regulation seem to have the potential to serve as predictive biomarkers to inform the choice of treatment, for example, in affective disorders. 38,39,188 In this view, the COVID-19 impact on psychiatric electrophysiological research was tremendously negative.…”

“…158,159 The results of these microstates and functional connectivity analyses may have an impact on clinical applications such as schizophrenia, 28,162,163 psychosis, 164 depression, 165 and mood and anxiety. 166 A more recent application of functional connectivity concerns the study of how information is exchanged between individuals during the performance of a common task (hyperbrain studies). The results from hyperbrain scanning have helped elucidate neural mechanisms of social interaction and have identified neural networks and electrophysiological biomarkers associated with cooperative and competitive behaviors (for reviews, see Czeszumski et al 167 and Balconi and Vanuelli 168 ).…”