Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function

Shalaila S. Haas, Gaelle E. Doucet, Mathilde Antoniades, Amirhossein Modabbernia, Cheryl M. Corcoran, René S. Kahn, Joseph Kambeitz, Lana Kambeitz-Ilankovic, Stefan Borgwardt, Paolo Brambilla, Rachel Upthegrove, Stephen J. Wood, Raimo K.R. Salokangas, Jarmo Hietala, Eva Meisenzahl, Nikolaos Koutsouleris, Sophia Frangou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 languageEnglish
Article number100252
Number of pages8
JournalSchizophrenia Research: Cognition
Volume29
Early online date2 Apr 2022
DOIs
Publication statusPublished - 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

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