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.

The differential diagnosis of psychiatric disorders is relatively challenging for several reasons. In this context, we believe that task-based magnetic resonance imaging (MRI) can serve as a tool for differential diagnosis. The aim of this study was to explore the commonalities in brain activities among individuals with psychiatric disorders and to identify the key brain regions that can distinguish between these disorders.

The PubMed, MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and Google Scholar databases were searched for whole-brain functional MRI studies that compared psychiatric patients and normal controls. The psychiatric disorders included schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), obsessive–compulsive disorder, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Studies using go–nogo paradigms were selected, we then conducted activation likelihood estimation (ALE) meta-analysis, factor analysis, and regression analysis on these studies subsequently.

A total of 152 studies (108 with patients) were selected and a consistent pattern was found, that is, decreased activities in the same brain regions across six disorders. Factor analysis clustered six disorders into three pairs: SCZ and ASD, MDD and BD, and ADHD and BD. Furthermore, the heterogeneity of SCZ and ASD was located in the left and right thalamus; and the heterogeneity of MDD and BD was located in the thalamus, insula, and superior frontal gyrus.

The results can lead to a new classification method for psychiatric disorders, benefit the differential diagnosis at an early stage, and help to understand the biobasis of psychiatric disorders.