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

Research output: Contribution to journalArticlepeer-review


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 (US)
Article number100252
JournalSchizophrenia Research: Cognition
StatePublished - Sep 2022


  • 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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