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

Author

Listed:
  • 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])

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.

Suggested Citation

  • 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 & Jia, 2022. "Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32513-8
    DOI: 10.1038/s41467-022-32513-8
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    References listed on IDEAS

    as
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