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Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion

Author

Listed:
  • Jing Sui

    (Chinese Academy of Sciences
    The Mind Research Network
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Shile Qi

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Theo G. M. van Erp

    (University of California, Irvine)

  • Juan Bustillo

    (University of New Mexico)

  • Rongtao Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Dongdong Lin

    (The Mind Research Network)

  • Jessica A. Turner

    (The Mind Research Network
    Georgia State University)

  • Eswar Damaraju

    (The Mind Research Network)

  • Andrew R. Mayer

    (The Mind Research Network
    University of New Mexico)

  • Yue Cui

    (Chinese Academy of Sciences)

  • Zening Fu

    (The Mind Research Network)

  • Yuhui Du

    (The Mind Research Network)

  • Jiayu Chen

    (The Mind Research Network)

  • Steven G. Potkin

    (University of California, Irvine)

  • Adrian Preda

    (University of California, Irvine)

  • Daniel H. Mathalon

    (University of California
    San Francisco VA Medical Center)

  • Judith M. Ford

    (University of California
    San Francisco VA Medical Center)

  • James Voyvodic

    (Duke University)

  • Bryon A. Mueller

    (University of Minnesota)

  • Aysenil Belger

    (University of North Carolina School of Medicine)

  • Sarah C. McEwen

    (University of California)

  • Daniel S. O’Leary

    (University of Iowa Carver College of Medicine)

  • Agnes McMahon

    (University of Southern California)

  • Tianzi Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Vince D. Calhoun

    (The Mind Research Network
    University of New Mexico
    University of New Mexico)

Abstract

Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define—in three separate cohorts—promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.

Suggested Citation

  • Jing Sui & Shile Qi & Theo G. M. van Erp & Juan Bustillo & Rongtao Jiang & Dongdong Lin & Jessica A. Turner & Eswar Damaraju & Andrew R. Mayer & Yue Cui & Zening Fu & Yuhui Du & Jiayu Chen & Steven G., 2018. "Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05432-w
    DOI: 10.1038/s41467-018-05432-w
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    Cited by:

    1. 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.

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