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Network analysis of schizotypal personality traits and their association with other subclinical psychiatric features
Rui-Ting Zhang1, Han-Yu Zhou1, Yong-Ming Wang2
1Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China.
Abstract:
Schizotypal personality (SP) traits have been found to be correlated with autistic traits, obsessive-compulsive traits, depressive symptoms and anxiety symptoms. However, the overall pattern of the relationship remains unclear. The purpose of this study was to investigate the network structure between SP traits and other subclinical features (symptoms or traits) and test the replicability of these relationships. A total of 2204 college students completed measurements for SP traits, autistic traits, obsessive-compulsive traits, depressive symptoms and anxiety symptoms, and a validated subsample of 814 completed the same questionnaires again three months later. Using network analysis, we constructed the network structure of subclinical features and then tested its replicability. We found that interpersonal features were the bridge node connecting SP traits and autistic traits (social skill: r = 0.50; attention switching: r = 0.14; communication: r = 0.12), while cognitive-perceptual (obsessing: r = 0.05; neutralizing: r = 0.06) and disorganization (obsessing: r = 0.11) features were the SP traits associated with obsessive-compulsive traits. In addition to interpersonal features (r = 0.10), disorganization (r = 0.12) and cognitive-perceptual (r = 0.05) features were also the overlap between depressive symptoms and SP traits. Anxiety symptoms only connected with interpersonal (r = 0.05) but not cognitive-perceptual features of SP traits. The network showed high predictability (43%) and interpersonal features of SP traits had the highest expected influence (1.67) among all nodes, which may be a potential target for intervention. High similarities were found on network structure (r = 0.86) and expected influence (r = 0.96), and no significant difference on global connectivity was found between these two networks (difference value = 0.45, p = 0.135), suggesting the replicability of the network structure.
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