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 | Schizophrenia patients | Healthy controls | t value | P value |
Left bankssts area | 1016.20 ± 170.13 | 1133.20 ± 186.00 | -2.488 | 0.015 |
Left inferior temporal area | 3492.30 ± 557.45 | 3856.30 ± 423.39 | -2.544 | 0.013 |
Left lateral occipital area | 4925.30 ± 719.19 | 5489.80 ± 480.09 | -3.111 | 0.003 |
Left lingual area | 2812.00 ± 444.30 | 3236.70 ± 479.05 | -3.471 | 0.001 |
Left lingual thickness | 2.08 ± 0.17 | 1.99 ± 0.10 | 2.121 | 0.037 |
Left superior frontal volume | 23123.00 ± 2824.16 | 24744.00 ± 2448.14 | -2.187 | 0.032 |
Right cuneus thickness | 2.01 ± 0.14 | 1.89 ± 0.14 | 2.938 | 0.004 |
Right lateral occipital thickness | 2.18 ± 0.15 | 2.07 ± 0.15 | 2.738 | 0.008 |
- 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