Large-scale brain functional network abnormalities in social anxiety disorder

Abstract

Background Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. Methods Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. Results SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. Conclusions SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple‐network model

“…Specifically, we found that the DMN was divided into anterior and posterior parts while the ECN was bilaterally divided. Our results were consistent with previous studies on RSNs (Li et al, 2018 , 2020 ; Zhang et al, 2022 ). In both ASD and TDC groups, the mean EC strength showed similar negative patterns, indicating that inhibition was the predominant effect between RSNs in the triple‐network.…”

“…Classical resting state networks (RSN), defined through independent component analysis (ICA) of large samples of functional brains, exhibit great population stability. This approach has been successfully employed in a wide variety of neurological disorders, including traumatic brain injury (Li et al, 2020 ), substance addiction (Li et al, 2018 ), social anxiety disorder (Zhang et al, 2022 ), Alzheimer's disease (Schumacher et al, 2021 ), and ASD (Bi et al, 2018 ). Functionalities of several RSNs have been linked to specific cognitive processes such as the regulation of attention to internal versus external stimuli (Green et al, 2016 ), working memory, fluid intelligence (Zielinski et al, 2022 ), and social cognition (Esménio et al, 2019 ).…”

Dynamic effective connectivity among large‐scale brain networks mediates risk of anxiety

“…Dissociation of resting‐state functional connectivity in the executive control network is reported to contribute to the development of anxiety (Geiger et al, 2016 ). While previous studies using the conventional functional connectivity approach have elucidated the relationship between anxiety and brain functional networks (Liao, Chen, et al, 2010 ; Modi et al, 2015 ; Zhang, Yang, et al, 2022 ), this current work provides deeper insights into directed information flow changes between networks. Effective connectivity from the low‐level visual network to the higher‐order attention network was positively related to anxiety, which may underlie attentional biases formed by high perceptual load conditions in high anxious subjects.…”

Reduced resting-state functional connectivity between insula and inferior frontal gyrus and superior temporal gyrus in hoarding disorder

“…In other psychiatric disorders, mega-analyses using a large amount of rsfMRI data have been conducted (20,21). Many studies have focused on resting-state functional connectivity and reported abnormalities that may lead to the discovery of biomarkers related to psychiatric disorders (21)(22)(23)(24)(25). Up to date, there is only one study focusing rsfMRI in HD, and that study using independent component analysis (ICA) (26).…”