Improved ASD classification using dynamic functional connectivity and multi-task feature selection - Liu, Jin; Sheng, Yu; Lan, Wei; Guo, Rui; Wang, Yufei; Wang, Jianxin (Pattern recognition letters, 2020)
•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%. Accurate diagnosis of autism spectrum disorder (ASD), which is a neurodevelopmental disorder and often accompanied by abnormal social skills, communication skills, interests and behavior patterns, has always been a challenging task in clinical practice. Recent studies have shown great potential for using fMRI data to distinguish ASD from typical control (TC). However, it has always been a challenging problem to extract which features from fMRI data and how to combine these different types of features to achieve improved ASD/TC classification performance. To address this problem, in this study we propose an improved ASD/TC classification framework based on dynamic functional connectivity (DFC) and multi-task feature selection. Our proposed ASD/TC classification framework is evaluated on 871 subjects with fMRI data from the Autism Brain Imaging Data Exchange I (ABIDE I) via a 10-fold cross validation strategy. Experimental results show that our proposed method achieves an accuracy of 76.8% and an area under the receiver operating characteristic curve (AUC) of 0.81 for ASD/TC classification. In addition, compared with some existing state-of-the-art methods, our proposed method achieves better accuracy and AUC for ASD/TC classification. Overall, our proposed ASD/TC classification framework is effective and promising for automatic diagnosis of ASD in clinical practice.