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

60 Scopus citations


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
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General Physics and Astronomy
  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology


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