Copyright
©The Author(s) 2024.
World J Psychiatry. Jun 19, 2024; 14(6): 804-811
Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804
Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804
Variables | Coefficients | |
Surface area | Bankssts | -0.00005308106 |
Inferior temporal | -0.00004908730 | |
Lateral occipital | -0.00003610912 | |
Lingual | -0.00022333510 | |
Insula | 0.000007207081 | |
Isthmus cingulate | 0.000156094500 | |
Paracentral | 0.000248385300 | |
Gray matter volume | Superior frontal | -0.00001763374 |
Temporal pole | 0.000149290400 | |
Cortical thickness | Lingual | 0.062200590000 |
Cuneus | 0.037246120000 | |
Lateral occipital | 0.239341600000 | |
Par sopercularis | 0.000000005200 |
- Citation: Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14(6): 804-811
- URL: https://www.wjgnet.com/2220-3206/full/v14/i6/804.htm
- DOI: https://dx.doi.org/10.5498/wjp.v14.i6.804