A Systematic Characterization of Structural Brain Changes in Schizophrenia.
10.1007/s12264-020-00520-8
- Author:
Wasana EDIRI ARACHCHI
1
;
Yanmin PENG
1
;
Xi ZHANG
1
;
Wen QIN
2
;
Chuanjun ZHUO
2
;
Chunshui YU
1
;
Meng LIANG
3
Author Information
1. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, China.
2. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
3. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, China. liangmeng@tmu.edu.cn.
- Publication Type:Journal Article
- Keywords:
Cortical thickness;
Deformation-based morphometry;
Multivariate pattern analysis;
Schizophrenia;
Structural MRI;
Tensor-based morphometry;
Voxel-based morphometry
- From:
Neuroscience Bulletin
2020;36(10):1107-1122
- CountryChina
- Language:English
-
Abstract:
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
