Inhibitory control deficits represent a core cognitive feature in patients with obsessive-compulsive disorder (OCD), yet their neurophysiological mechanisms remain unclear. Electroencephalogram (EEG) microstates offer a novel framework for investigating large-scale brain network dynamics in psychiatric disorders. This cross-sectional study aimed to investigate EEG microstate dynamics in OCD patients and their relationships with cognitive and response inhibition.

A total of 91 OCD patients and 76 healthy controls were recruited. Participants completed an Emotional Stroop Task (EST) and a stop-signal task (SST). Resting-state EEG was recorded, and microstate parameters and transition probabilities were analyzed. Correlations between microstate parameters and inhibitory control performance were assessed.

OCD patients exhibited significant deficits in both response inhibition and cognitive inhibition, with both types of deficits positively correlated with obsessive symptom severity. Microstate analysis revealed increased mean duration and time coverage of microstate A, but decreased mean duration, occurrence, and coverage of microstate C in OCD patients. Additionally, transition probabilities from microstates B and D to C were significantly elevated. Notably, the mean duration of microstate C was positively correlated with stop-signal reaction time (SSRT), while the occurrence of microstates C and D and the time coverage of microstate D were negatively correlated with the Stroop effect.

These findings reveal dissociable microstate mechanisms associated with distinct inhibitory dysfunction domains in OCD patients. Resting-state microstate dynamics may represent candidate neurobiological markers for cognitive subtype classification in OCD and may help inform the development of targeted therapeutic interventions.

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.

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.

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.

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.

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.

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.

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.

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.