Abnormal dynamics of functional connectivity in first‐episode and treatment‐naive patients with obsessive–compulsive disorder

Abstract

The aim of this study was to assess the whole-brain dynamic functional connectivity of first-episode and treatment-naive patients with obsessive-compulsive disorder (OCD) and to investigate the clinical correlations of abnormal changes in dynamic functional connectivity. Methods: Twenty-nine patients in our hospital diagnosed with first-episode OCD and 29 healthy controls matched for age, sex, and education were included in our study. Resting-state functional magnetic resonance imaging scans were performed on a 3.0-Tesla magnetic resonance scanner in our hospital. Three temporal metrics of connectivity state expression were calculated: (i) fraction of time; (ii) mean dwell time; and (iii) number of transitions. The Yale-Brown Obsessive-Compulsive Scale was used to assess the severity of OCD symptoms. Results: In the comparison of dynamic functional connectivity indicators, we found that there were significant differences in the number of transitions among the four functional connectivity states but no significant differences in the fraction of time or the mean dwell time. The total Yale-Brown Obsessive-Compulsive Scale score was positively correlated with the number of transitions. In the validation analysis, when the size of the sliding window changed, there was still a significant difference in the number of transitions between OCD patients and healthy controls. Conclusion: The functional networks of OCD patients have lost the correct dynamic rhythm, which may be considered as a potential marker for OCD and for new directions for its intervention.

Altered brain functional network dynamics in obsessive–compulsive disorder

“…In contrast to findings in State 1, we did not find significant between‐group differences in the occurrence of States 2 and 3, or number of transitions among those states. These findings differ somewhat from findings of Liu et al (2020)), who found an altered number of transitions. The patients in their study were all first‐episode and treatment‐naive, while half of patients in our study were previously medicated.…”

“…This study focused on the peak FC impairment of specific networks (FPN, DMN, and SAN) rather than the whole brain, and other key indices of dFC were not obtained. Liu et al used spatial group independent component analysis (GICA) and the sliding‐window approach to assess whole‐brain dFC, and found an altered number of transitions among different dynamic states in OCD patients that correlated with obsessive–compulsive symptom severity (Liu et al, 2020). This study used a low model order GICA and did not explore alterations of dynamic topological properties.…”

Abnormal brain functional network dynamics in obsessive–compulsive disorder patients and their unaffected first‐degree relatives

“…One previous study examined temporal properties of dFNC in OCD (Liu et al, 2020 ). It is hard to directly compare the studies because the data‐driven algorithm extracted four different states in the Liu study between which the OCD group showed more frequent switching than controls, whereas our study the algorithm indicated two states with no significant difference in number of transitions.…”

“…They focused on a subset of networks (default mode network, frontoparietal network, and salience network) and did not examine whole brain connectivity patterns. Liu and colleagues examined first‐episode and treatment‐naive patients with obsessive–compulsive disorder (OCD) (Liu et al, 2020 ) and did not examine patients undergoing treatment. Neither study examined nonaffected relatives in order to examine whether dFNC patterns might serve to identify endophenotypes for OCD.…”

Temporal variability of regional intrinsic neural activity in drug‐naïve patients with obsessive–compulsive disorder

“…More recently, the temporal dynamic properties of functional network connectivity in the resting state have gained increased attention because a more thorough understanding of the brain's function can be obtained through its analysis (Liao et al, 2019 ). The aberrant dynamic functional network connectivity patterns emerging from this analysis have been identified as important features of many psychiatric disorders, such as schizophrenia (Wang, Zhang, Sun, Hu, & Chen, 2016 ), major depressive disorder (MDD) (Qiu et al, 2018 ) and OCD (Gürsel et al, 2020 ; Liu et al, 2020 ).…”