Aberrant Dynamics of the Default Mode Network Activity in Patients with Obsessive-Compulsive Disorder
Cognitive Neurodynamics（2025）
Abstract
Obsessive compulsive disorder (OCD) is a serious mental disease highly associated with the dysfunctional dynamics of brain networks. The default mode network (DMN) is an essential brain network responding to various cognitive functions, which has been reported the abnormality in OCD patients. However, the underlying evolutions of temporal-spatial neurodynamics in fast-changing DMN activity of OCD remains largely unclear. In the present work, we tried to address this issue by tracking the neurodynamic structure of electrophysiological DMN activity in OCD patients during the resting state. To achieve this purpose, we reconstructed the DMN source activity from the scalp EEG signal with the exact low-resolution brain electromagnetic tomography (LORETA) algorithm, and applied coactive micropattern (CAMP) analysis developed by our previous studies to characterize DMN dynamics. We observed three CAMPs with distinct spatial structures in DMN dynamics for the normal subjects, while there were four different CAMPs in OCD patients. More interestingly, the temporal features of CAMPs which displayed similar spatial structures in two groups were also significantly altered in OCD patients compared with those in normal subjects, implying an abnormal DMN dynamic structure for OCD. Moreover, stronger DMN source connectivity in different CAMP networks emerged in OCD patients, where the posterior cingulate cortex and frontal cortex might be two hub regions with more increased connectivity. Overall, our results showed pathological alterations in the dynamic configuration of the DMN in OCD patients and revealed the neural brain network mechanism for OCD. All these findings could deepen our understanding of the neural pathology of OCD and might provide potential electrophysiological markers for diagnosing OCD patients clinically.
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Key words
Obsessive-compulsive disorder,Default mode network,Coactive micropatterns,Brain dynamics
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