Abstract #3091

Predicting treatment outcome of schizophrenia based on white matter tract integrity using a support vector classifier

Wen-Bin Luo¹, Jing-Ying Huang^2,3, Yung-Chin Hsu², and Wen-Yih Isaac Tseng^2,4,5,6

¹School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan, ²Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan, ³Department of Radiology, Wei Gong Memorial Hospital, Miaoli, Taiwan, ⁴Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan, ⁵Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan, ⁶Molecular Imaging Center, National Taiwan University, Taipei, Taiwan

Although white matter tract microstructure has been implicated in treatment outcome of schizophrenia, its predictive capability on first-episode patients remains unknown. In the study, diffusion spectrum imaging (DSI) data were acquired from both chronic and first-episode patients, reconstructed by mean apparent propagator (MAP) MRI and analyzed with tract-based automatic analysis (TBAA). Stepwise statistical analysis was then performed to identify specific segments of white matter tracts that were significantly different between remitted and non-remitted chronic patients. We built a support vector classifier on the preprocessed data matrix. The resulting model yielded fair validation and test accuracy on chronic and first-episode patients, respectively.

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