ADHD-200 Patient Characterization and Classification using Resting State Networks: A Dissertation

Attention Deficit/Hyperactivity Disorder (ADHD) is a common psychiatric disorder of childhood that is characterized by symptoms of inattention, impulsivity/hyperactivity, or a combination of both. Intrinsic brain dysfunction in ADHD can be examined through various methods including resting state functional Magnetic Resonance Imaging (rs-fMRI), which investigates patients’ functional brain connections in the absence of an explicit task. To date, studies of group differences in resting brain connectivity between patients with ADHD and typically developing controls (TDCs) have revealed reduced connectivity within the Default Mode Network (DMN), a resting state network implicated in introspection, mind-wandering, and day-dreaming. However, few studies have addressed the use of resting state connectivity measures as a diagnostic aide for ADHD on the individual patient level. In the current work, we attempted first to characterize the differences in resting state networks, including the DMN and three attention networks (the salience network, the left executive network, and the right executive network), between a group of youth with ADHD and a group of TDCs matched for age, IQ, gender, and handedness. Significant overand under-connections were found in the ADHD group in all of these networks compared with TDCs. We then attempted to use a support vector machine (SVM) based on the information extracted from resting state network connectivity to classify participants as “ADHD” or “TDC.” The IFGmiddle temporal network (66.8% accuracy), the parietal association network (86.6% specificity and 48.5% PPV), and a physiological noise component (sensitivity 39.7% and NPV 69.6%) performed the best classifications. Finally, we attempted to combine and