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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.
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1 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; The Mind Research Network, Albuquerque, NM, USA; University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
3 Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
4 Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
5 The Mind Research Network, Albuquerque, NM, USA
6 The Mind Research Network, Albuquerque, NM, USA; Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, USA
7 The Mind Research Network, Albuquerque, NM, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
8 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
9 Department of Psychiatry, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA
10 Department of Radiology, Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
11 Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
12 Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, USA
13 Department of Psychiatry, University of California, San Diego, CA, USA
14 Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa, IA, USA
15 USC Stevens Neuroimaging and Informatics Institute, University of Southern California, San Diego, CA, USA
16 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
17 The Mind Research Network, Albuquerque, NM, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA