Neural correlates of schizotypal traits: Findings from connectome-based predictive modelling

被引：0
|
作者：
Chen, Tao [1 ,2 ,3 ,4 ]
Huang, Jia [1 ,2 ]
Cui, Ji-fang [5 ]
Li, Zhi [1 ,2 ]
Irish, Muireann [3 ,4 ]
Wang, Ya [1 ,2 ]
Chan, Raymond C. K. [1 ,2 ]
机构：
[1] Inst Psychol, CAS Key Lab Mental Hlth, Neuropsychol & Appl Cognit Neurosci Lab, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
[3] Univ Sydney, Brain & Mind Ctr, Sydney, Australia
[4] Univ Sydney, Sch Psychol, Sydney, Australia
[5] Natl Inst Educ Sci, Inst Educ Informat & Stat, Beijing, Peoples R China
来源：
ASIAN JOURNAL OF PSYCHIATRY | 2023年 / 81卷
基金：
澳大利亚研究理事会; 美国国家科学基金会;
关键词：
Schizotypal trait; Connectome-based predictive modelling (CPM); Machine learning; Resting-state functional connectivity;
D O I：
10.1016/j.ajp.2022.103430
中图分类号：
R749 [精神病学];
学科分类号：
100205 ;
摘要：
Schizotypal traits can be conceptualized as a phenotype for schizophrenia spectrum disorders. As such, a better understanding of schizotypal traits could potentially improve early identification and treatment of schizophrenia. We used connectome-based predictive modelling (CPM) based on whole-brain resting-state functional connec-tivity to predict schizotypal traits in 82 healthy participants. Results showed that only the negative network could reliably predict an individual's schizotypal traits (r = 0.29). The 10 nodes with the highest edges in the negative network were those known to play a key role in sensation and perception, cognitive control as well as motor control. Our findings suggest that CPM might be a promising approach to improve early identification and prevention of schizophrenia from a spectrum perspective.
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页数：3
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