Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population

Ganesh B. Chand, Pankhuri Singhal, Dominic B. Dwyer, Junhao Wen, Guray Erus, Jimit Doshi, Dhivya Srinivasan, Elizabeth Mamourian, Erdem Varol, Aristeidis Sotiras, Gyujoon Hwang, Paola Dazzan, Rene S. Kahn, Hugo G. Schnack, Marcus V. Zanetti, Eva Meisenzahl, Geraldo F. Busatto, Benedicto Crespo-Facorro, Christos Pantelis, Stephen J. WoodChuanjun Zhuo, Russell T. Shinohara, Haochang Shou, Yong Fan, Nikolaos Koutsouleris, Antonia N. Kaczkurkin, Tyler M. Moore, Anurag Verma, Monica E. Calkins, Raquel E. Gur, Ruben C. Gur, Marylyn D. Ritchie, Theodore D. Satterthwaite, Daniel H. Wolf, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

OBJECTIVE: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS: This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS: Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS: The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Original languageEnglish
Pages (from-to)650-660
Number of pages11
JournalAmerican Journal of Psychiatry
Volume179
Issue number9
DOIs
StatePublished - 1 Sep 2022
Externally publishedYes

Keywords

  • Genetics/Genomics
  • Machine Learning
  • Neuroanatomy
  • Neuroimaging
  • Polygenic Risk Scores
  • Schizophrenia Spectrum and Other Psychotic Disorders

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