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Network structure of anticipatory pleasure and risk features: Evidence from a large college sample
Rui-Ting Zhang1,2, Yi Wang1, Zhuo-Ya Yang1,2
1Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Abstract:
Investigating the relationship between anticipatory pleasure deficits and risk features of mental disorders not only theoretically benefits the understanding of anhedonia, but could also facilitate early detection and intervention of mental disorders. Using network analysis, the present study examined the pattern of relationship between anticipatory pleasure and risk features of schizophrenia spectrum, depressive, anxiety, autism spectrum, and obsessive-compulsive disorders in a large sample of college students (n = 2152). It was found that interpersonal features of schizotypal personality traits and poor social skills of autistic traits showed strong correlation with low social anticipatory pleasure. Depressive symptoms severity was weakly associated with reduced abstract anticipatory pleasure, while obsessive-compulsive traits were weakly associated with high contextual anticipatory pleasure. No significant correlation was found between anxiety symptoms severity and anticipatory pleasure. Social anticipatory pleasure had the highest strength centrality among all anticipatory pleasure components, while interpersonal features of schizotypal personality traits had the highest strength centrality in the whole network. Our findings suggest that impaired anticipatory pleasure, especially social anticipatory pleasure, is a particular feature of schizotypal personality traits and autistic traits. Our findings may have implications for intervention in that the social component may be a target to improve anhedonia in individuals with schizotypal and autistic traits, while interpersonal features may be a key treatment target given that it was central to the relationship between anticipatory pleasure and risk features.
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