Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion

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. Potkin, Adrian Preda, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah C. McEwen, Daniel S. O’Leary, Agnes McMahon, Tianzi Jiang and Vince D. Calhoun ()
Additional contact information
Jing Sui: Chinese Academy of Sciences
Shile Qi: 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
Dongdong Lin: The Mind Research Network
Jessica A. Turner: The Mind Research Network
Eswar Damaraju: The Mind Research Network
Andrew R. Mayer: The Mind Research Network
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
Judith M. Ford: University of California
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
Vince D. Calhoun: The Mind Research Network

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-05432-w

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