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 BelgerSarah C. McEwen, Daniel S. O’Leary, Agnes McMahon, Tianzi Jiang, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

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.

Original languageEnglish (US)
Article number3028
JournalNature communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

Bibliographical note

Funding Information:
This work was supported by the Chinese National Natural Science Foundation Nos. 81471367, 61773380, and 61703253, the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02060005), the National High-Tech Development Plan (863 program, No. 2015AA020513), “100 Talents Plan” of Chinese Academy of Sciences, and the NIH grants R01EB005846, 1R01MH094524, and P20GM103472 as well as NSF grant 1539067.

Publisher Copyright:
© 2018, The Author(s).

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