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Schizophr Bull. 2019 Mar 7;45(2):436-449. doi: 10.1093/schbul/sby045.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population.

Liu S1,2,3,4, Wang H2, Song M1, Lv L5,6, Cui Y1, Liu Y1, Fan L1, Zuo N1, Xu K1, Du Y7,8, Yu Q7, Luo N1,3, Qi S1,3, Yang J9, Xie S1, Li J1, Chen J10, Chen Y11, Wang H11, Guo H12, Wan P12, Yang Y5,6,13, Li P14,15, Lu L14,15,16, Yan H14,15, Yan J14,15, Wang H17, Zhang H5,6,18, Zhang D14,15,19, Calhoun VD7,20, Jiang T1,3,16,21, Sui J1,3,7,21.

Author information

Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Automation, Harbin University of Science and Technology, Harbin, China.
University of Chinese Academy of Sciences, Beijing, China.
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.
The Mind Research Network, Albuquerque, NM.
School of Computer and Information Technology, Shanxi University, Taiyuan, China.
Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Zhumadian Psychiatric Hospital, Zhumadian, China.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.
Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China.
Queensland Brain Institute, University of Queensland, Brisbane, Australia.
Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Psychology, Xinxiang Medical University, Xinxiang, China.
Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM.
CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.


Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.


MCCA + jICA; auditory hallucination; diffusion MRI; functional network connectivity (FNC); multimodal fusion; resting-state fMRI; schizophrenia; structural MRI

[Available on 2020-03-01]

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