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Abstract #0227

Multimodal imaging classification of ADHD: brain functional connectivity and cortical thickness

Po-Hsiang Chan 1 , Yu-Sheng Tseng 1 , Chun-jung Chen 1 , Teng-Yi Huang 1 , and Tzu-Chao Chuang 2

1 Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipe, Taipei, Taiwan, 2 Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C, Taipei, Taiwan

This study aims to develop a classification method based on support vector machine (SVM) for attention deficit hyperactivity disorder (ADHD) patients. We proposed to use SVM to classify subject groups based on the brain functional connectivity obtained from resting-state fMRI datasets and brain cortical thickness obtained from 3D T1 MPRAGE datasets. We identified that SVM classifier did not perform well (accuracy of ~ 57%) if all the available features were selected into SVM training. Using the proposed feature selection approach, the maximum accuracies increased to 68 V 99 %. The results support that feature selection according to absolute weightings of a pre-trained SVM hyperplane is an efficient method to increase the classification accuracies.

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