Deep Convolutional Neural Network for ADHD Classification Using Resting-State fMRI

Abstract

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common diagnosed mental disorder among children and may persist into adulthood. ADHD is known for its symptoms of inattention, hyperactivity, and impulsiveness. ADHD is a neurodevelopmental disease and widely affects brain functions; thus, investigating brain functional connectivity is more effective in childhood. The exact mechanism of how ADHD affects brain neural connections has not been discovered, and discriminating children with ADHD from the control group is a challenging issue. Deep learning (DL) methods yielded promising results in disease diagnosis. DL and neuroimaging tools such as functional resonance imaging (fMRI) were combined in order to differentiate between the neural activities of ADHD and typically developing children (TDC) patients. This study suggests a DL-based procedure that is used for classifying these TDC and ADHD groups. At the first step, resting-state fMRI (rsfMRI) data from the NYU imaging site from the ADHD-200 global competition public dataset were preprocessed in order to remove artifacts. Next, our algorithm uses functional parcellation to divide brain regions into 412 parcels. Our algorithm extracts features and classes ADHD and TDC patients at the same time, while some other methods extract features and classify subjects with different algorithms. A 5-fold cross-validation is applied to investigate classification results. Our results show that the proposed procedure in this study outperforms other methods in the state-of-the-art by an accuracy of 76.096%.