Improved ASD classification using dynamic functional connectivity and multi-task feature selection

Jin Liu, Yu Sheng, Wei Lan, Rui Guo, Yufei Wang, Jianxin Wang

Abstract: Highlights•We propose an efficient method for diagnosing ASD.•Dynamic functional connectivity features have complementary information.•We proposed an improved multi-task feature selection method.•A multi-kernel SVM method is applied.•Our method achieves classification accuracy of 76.8%.