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

Tao Chen; Jia Huang; Ji-Fang Cui; Zhi Li; Muireann Irish; Ya Wang; Raymond C K Chan

doi:10.1016/j.ajp.2022.103430

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

Asian J Psychiatr. 2023 Mar:81:103430. doi: 10.1016/j.ajp.2022.103430. Epub 2022 Dec 28.

Authors

Tao Chen¹, Jia Huang², Ji-Fang Cui³, Zhi Li², Muireann Irish⁴, Ya Wang⁵, Raymond C K Chan⁶

Affiliations

¹ Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; The University of Sydney, Brain and Mind Centre, Sydney, Australia; The University of Sydney, School of Psychology, Sydney, Australia.
² Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
³ Institute of Educational Information and Statistics, National Institute of Education Sciences, Beijing, China.
⁴ The University of Sydney, Brain and Mind Centre, Sydney, Australia; The University of Sydney, School of Psychology, Sydney, Australia.
⁵ Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China. Electronic address: [email protected].
⁶ Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China. Electronic address: [email protected].

PMID: 36608611
DOI: 10.1016/j.ajp.2022.103430

Abstract

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 connectivity 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.

Keywords: Connectome-based predictive modelling (CPM); Machine learning; Resting-state functional connectivity; Schizotypal trait.

Publication types

Letter

MeSH terms

Brain
Connectome* / methods
Humans
Magnetic Resonance Imaging / methods
Phenotype
Schizophrenia*