Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network

Shile Qi (), Jing Sui (), Godfrey Pearlson, Juan Bustillo, Nora I. Perrone-Bizzozero, Peter Kochunov, Jessica A. Turner, Zening Fu, Wei Shao, Rongtao Jiang, Xiao Yang, Jingyu Liu, Yuhui Du, Jiayu Chen (), Daoqiang Zhang () and Vince D. Calhoun
Additional contact information
Shile Qi: Nanjing University of Aeronautics and Astronautics
Jing Sui: Beijing Normal University
Godfrey Pearlson: Yale School of Medicine
Juan Bustillo: University of New Mexico
Nora I. Perrone-Bizzozero: University of New Mexico
Peter Kochunov: University of Maryland School of Medicine
Jessica A. Turner: Georgia State University
Zening Fu: [Georgia State University, Georgia Institute of Technology, Emory University]
Wei Shao: Nanjing University of Aeronautics and Astronautics
Rongtao Jiang: Yale University
Xiao Yang: West China Hospital of Sichuan University
Jingyu Liu: [Georgia State University, Georgia Institute of Technology, Emory University]
Yuhui Du: Shanxi University
Jiayu Chen: [Georgia State University, Georgia Institute of Technology, Emory University]
Daoqiang Zhang: Nanjing University of Aeronautics and Astronautics
Vince D. Calhoun: [Georgia State University, Georgia Institute of Technology, Emory University]

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.

Date: 2022
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DOI: 10.1038/s41467-022-32513-8

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