Phenotypic Integrated Framework for Classification of ADHD Using fMRI

Attention Deficit Hyperactive Disorder (ADHD) is one of the most common disorders affecting young children, and its underlying mechanism is not completely understood. This paper proposes a phenotypic integrated machine learning framework to investigate functional connectivity alterations between ADHD and control subjects not diagnosed with ADHD, employing fMRI data. Our aim is to apply computational techniques to (1) automatically classify a person’s fMRI signal as ADHD or control, (2) identify differences in functional connectivity of these two groups and (3) evaluate the importance of phenotypic information for classification. In the first stage of our framework, we determine the functional connectivity of brain regions by grouping brain activity using clustering algorithms. Next, we employ Elastic Net regression to select the most discriminant features from the dense functional brain network and integrate phenotypic information. Finally, a support vector machine classifier is trained to classify ADHD subjects vs. control. The proposed framework was evaluated on a public dataset ADHD-200, and our classification results outperform the state-of-the-art on some subsets of the data.