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Abstract #0229

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

Chang-Le Chen1,2, Yung-Chin Hsu1, Susan Shur-Fen Gau2,3, and Wen-Yih Isaac Tseng1,2,4

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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