Abstract #0229

Neural network classification of ADHD based on white matter connectograms derived from diffusion spectrum imaging

Chang-Le Chen^1,2, Yung-Chin Hsu¹, Susan Shur-Fen Gau^2,3, and Wen-Yih Isaac Tseng^1,2,4

¹Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan, ²Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan, ³Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan, ⁴Molecular Imaging Center, National Taiwan University, Taipei, Taiwan

The diagnosis of ADHD relies on psychiatrists’ knowledge and subjective experience. Many studies aimed to develop an objective method to assist diagnosis, but the performance of classification between ADHD and controls was not acceptable for clinical use. Here, we proposed a neural network model based on white matter information to classify ADHD and typically developing controls. Diffusion spectrum imaging and tract-based automatic analysis were used to measure properties of white matter. The neural network classification model was developed with high accuracy in the training and test data. It might be helpful to provide an objective way for diagnosis of ADHD.

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