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

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).


Instruments for clinical symptoms assessments.
1.1 The PHQ-9 (Kroenke et al., 2001) is a 9-question instrument with a 2-week time frame (based on diagnostic criteria of depression from DSM-IV ), given to patients in a primary care setting to screen, diagnose and measure the severity of depression in last 2 weeks. Each item on the measure is rated on a 4-point scale ("0" =Not at all) to "3" =nearly every day)). The total score can range from 0 to 27, in which a higher score indicates a greater severity of depression.
1.2 The Rumination Response Scale (RRS) (Treynor et al., 2003) is a 22-question assesses depressive thoughts and responses and focuses on the self, symptoms, and possible causes/consequence of associated mood. Each questions consists of scale ranging from 1 (almost never) to 4 (almost always). The RRS has been shown to be a reliable and valid measure with internal consistency of (α = 0.93).
1.3 The State-Trait Anxiety Inventory (STAI) has 20 items for assessing state anxiety and 20 items for trait anxiety (Spielberger, 1983). The State Anxiety Scale (S-Anxiety) screens and measures the current state of anxiety, asking how respondents feel "right now," using questions that measure subjective feelings of apprehension, nervousness, tension, worry, and activation/arousal of the autonomic nervous system. The Trait Anxiety Scale (T-Anxiety) evaluates relatively stable aspects of "anxiety proneness," consisting of general states of calmness, confidence, and security. The higher score indicates greater anxiety.
1.5 The PROMIS_Depression scale consists of 4 items, and asked participants how often in the last 7 days they had experienced depression, including feeling hopeless, worthless, helpless, or depressed (Cella et al., 2010). These items were scored the same way as PROMIS Anxiety on a 5-point Likert scale ranging from 1 to 5.

Model Description
Transition probability ~ Group * Symptom + Age + Gender We ran GLM for each connection (Transition probability or y in the model) and symptom independently using "lme4" package from R (Bates et al., 2014). We reported the estimated coefficient and p-values.
Results are presented in supplementary Table S4. Table S4: The GLM analysis for the interaction between group and symptoms. Table S1. Demographic information of the study Note: PHQ-9 = Patient Health Questionnaire-9; STAI = State-Trait Anxiety Inventory; PROMIS = Patient-Reported Outcomes Measurement Information System. Values outside parentheses are means and values in parentheses are standard deviations. * Levels of Studying are assigned as follows:

Association between EEG-ms transition probabilities and clinical instruments
Note