Ganesh B Chand1, Pankhuri Singhal1, Dominic B Dwyer1, Junhao Wen1, Guray Erus1, Jimit Doshi1, Dhivya Srinivasan1, Elizabeth Mamourian1, Erdem Varol1, Aristeidis Sotiras1, Gyujoon Hwang1, Paola Dazzan1, Rene S Kahn1, Hugo G Schnack1, Marcus V Zanetti1, Eva Meisenzahl1, Geraldo F Busatto1, Benedicto Crespo-Facorro1, Christos Pantelis1, Stephen J Wood1, Chuanjun Zhuo1, Russell T Shinohara1, Haochang Shou1, Yong Fan1, Nikolaos Koutsouleris1, Antonia N Kaczkurkin1, Tyler M Moore1, Anurag Verma1, Monica E Calkins1, Raquel E Gur1, Ruben C Gur1, Marylyn D Ritchie1, Theodore D Satterthwaite1, Daniel H Wolf1, Christos Davatzikos1. 1. Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite).
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.
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.
Entities:
Keywords:
Genetics/Genomics; Machine Learning; Neuroanatomy; Neuroimaging; Polygenic Risk Scores; Schizophrenia Spectrum and Other Psychotic Disorders
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