Abstract

Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.

Du et al used fMRI data from patients with schizophrenia (SZ) and autism spectrum disorder (ASD) to analyze multi-scale brain functional connectivity and cerebral grey matter density and volume. They demonstrated the presence of convergent and divergent changes in SZ and ASD that correlated with clinical features.

Details

Title
Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder
Author
Du Yuhui 1   VIAFID ORCID Logo  ; Fu Zening 2 ; Xing Ying 3 ; Lin, Dongdong 2 ; Pearlson Godfrey 4 ; Kochunov, Peter 5 ; Elliot, Hong L 5 ; Shile, Qi 2 ; Salman Mustafa 2 ; Abrol Anees 2   VIAFID ORCID Logo  ; Calhoun, Vince D 2   VIAFID ORCID Logo 

 Shanxi University, School of Computer and Information Technology, Taiyuan, China (GRID:grid.163032.5) (ISNI:0000 0004 1760 2008); Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502) 
 Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502) 
 Shanxi University, School of Computer and Information Technology, Taiyuan, China (GRID:grid.163032.5) (ISNI:0000 0004 1760 2008) 
 Yale University, Department of Psychiatry, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 University of Maryland, School of Medicine, Maryland Psychiatric Research Center, Department of Psychiatry, Baltimore, USA (GRID:grid.411024.2) (ISNI:0000 0001 2175 4264) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2572355052
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.