Abstract
Objective: Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.
Methods: We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.
Results: Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (PFDR = 0.004) and crystallized intelligence (PFDR = 0.01), cognitive flexibility (PFDR = 0.02), inhibitory control (PFDR = 0.01), working memory (PFDR = 0.0005), and processing speed (PFDR = 0.04).
Conclusions: We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.
Original language | English |
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Article number | 100252 |
Number of pages | 8 |
Journal | Schizophrenia Research: Cognition |
Volume | 29 |
Early online date | 2 Apr 2022 |
DOIs | |
Publication status | Published - Sept 2022 |
Bibliographical note
Funding Information:PRONIA is a Collaboration Project funded by the European Union under the EU 7th Framework Programme under grant agreement no. 602152 . This work was supported by the National Institute of Mental Health under grant R01MH113619 , R01MH116147 and R01MH107558 , and the National Institute of General Medical Sciences under grant P20GM144641 .
Publisher Copyright:
© 2022 The Authors
Keywords
- Clinical high-risk for psychosis
- General population
- Neuroimaging
- Social function
- Support vector machine
ASJC Scopus subject areas
- Cognitive Neuroscience
- Psychiatry and Mental health