Obsessive-compulsive symptoms and resting-state functional characteristics in pre-adolescent children from the general population

普通人群中青春期前儿童的强迫症症状和静息态功能特征

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Abstract

While functional brain characteristics of obsessive-compulsive disorder have been extensively studied, literature on network topology and subnetwork connectivity related to obsessive-compulsive symptoms (OCS) is sparse. Here we investigated the functional brain characteristics of OCS in children from the general population using a multiscale approach. Since we previously observed OCS-related differences in thalamus morphology, we also focused on the network participation of thalamic subregions. The study included 1701 participants (9-12 years) from the population-based Generation R study. OCS were measured using the Short Obsessive-Compulsive Disorder Screener. We studied the brain network at multiple scales: global network topology, subnetwork connectivity and network participation of thalamic nodes (pre-registration: https://osf.io/azr9c ). Modularity, small-worldness and average participation coefficient were calculated on the global scale. We used a data-driven consensus community approach to extract a partition of five subnetworks involving thalamic subregions and calculate the within- and between-subnetwork functional connectivity and topology. Multiple linear regression models were fitted to model the relationship between OCS and functional brain measures. No significant associations were found when using our preregistered definition of probable OCS. However, post-hoc analyses showed that children endorsing at least one OCS (compared with controls) had higher modularity, lower connectivity between frontoparietal, limbic and visual networks as well as altered participation of the lateral prefrontal thalamus node. Our results suggest that network characteristics of OCS in children from the general population are partly symptom-specific and severity-dependent. Thorough assessment of symptom dimensions can deepen our understanding of OCS-related brain networks.

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