Obsessive-compulsive personality disorder (OCPD) is one of the most prevalent personality disorders in general population. However, neural mechanisms underlying OCPD remain elusive. The aim of this study is to use functional magnetic resonance imaging (fMRI) to examine whether OCPD patients will exhibit altered spontaneous brain activity as compared to healthy controls (HC). Resting-state fMRI data were acquired in 37 OCPD patients and 37 matched HC. Amplitudes of low-frequency fluctuation (ALFF) were calculated and compared between the two groups. Correlation analysis was performed between regional ALFF values and OCPD severity scores. Significant group differences in regional ALFF were found in multiple brain regions. Compared to HCs, OCPD subjects had increased ALFF in bilateral caudate, left precuneus, left insula, and left medial superior frontal gyrus, and decreased ALFF in the right fusiform gyrus and left lingual gyrus. The ALFF values in the left precuneus correlated with OCPD severity scores. We excluded patients with comorbidity and did not conduct cognitive function assessments. Our findings are also limited to cross-sectional analysis. OCPD patients exhibit altered spontaneous neural activity as compared to healthy controls in multiple brain regions, which may reflect different characteristic symptoms of OCPD pathophysiology, including cognitive inflexibility, excessive self-control, lower empathy, and visual attention bias.

Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.