Journal of attention disorders | 2019
Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing.
Abstract
OBJECTIVE\nThis study focused on the ADHD classification through functional connectivity (FC) analysis.\n\n\nMETHOD\nAn ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data.\n\n\nRESULTS\nOur method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method.\n\n\nCONCLUSION\nResults demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.