Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Zhang, Xun, Lai, Han, Li, Qingyuan, Yang, Xun, Pan, Nanfang, He, Min, Kemp, Graham J ORCID: 0000-0002-8324-9666, Wang, Song and Gong, Qiyong (2023) Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis. CEREBRAL CORTEX, 33 (16). pp. 9627-9638.

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Abstract

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Item Type:	Article
Uncontrolled Keywords:	graph theory, psychoradiology, social anxiety disorder, structural covariance network, support vector machine
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Sep 2023 09:34
Last Modified:	25 Sep 2023 09:34
DOI:	10.1093/cercor/bhad231
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172989