title: Connectivity Analysis and Feature Classification in Attention Deficit Hyperactivity Disorder Sub-Types: A Task Functional Magnetic Resonance Imaging Study.
abstract: Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychiatric disorder. Patients with different ADHD subtypes show different behaviors under different stimuli and thus might require differential approaches to treatment. This study explores connectivity differences between ADHD subtypes and attempts to classify these subtypes based on neuroimaging features. A total of 34 patients (13 ADHD-IA and 21 ADHD-C subtypes) underwent functional magnetic resonance imaging (fMRI) with six task paradigms. Connectivity differences between ADHD subtypes were assessed for the whole brain in each task paradigm. Connectivity measures of the identified regions were used as features for the support vector machine classifier to distinguish between ADHD subtypes. The effectiveness of connectivity measures of the regions were tested by predicting ADHD-related Diagnostic and Statistical Manual of Mental Disorders (DSM) scores. Significant connectivity differences between ADHD subtypes were identified mainly in the frontal, cingulate, and parietal cortices and partially in the temporal, occipital cortices and cerebellum. Classifier accuracy for distinguishing between ADHD subtypes was 91.18 % for both gambling punishment and emotion task paradigms. Linear prediction under the two task paradigms showed significant correlation with DSM hyperactive/impulsive score. Our study identified important brain regions from connectivity analysis based on an fMRI paradigm using gambling punishment and emotion task paradigms. The regions and associated connectivity measures could serve as features to distinguish between ADHD subtypes.
authors.affiliation: Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
authors.forename: Bo-Yong
authors.initials: BY
authors.lastname: Park
authors.affiliation: Graduate School of Human ICT Convergence, Sungkyunkwan University, Suwon, Korea.
authors.forename: Mansu
authors.initials: M
authors.lastname: Kim
authors.affiliation: Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
authors.forename: Jongbum
authors.initials: J
authors.lastname: Seo
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
authors.forename: Jong-Min
authors.initials: JM
authors.lastname: Lee
authors.affiliation: School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea. hyunjinp@skku.edu.|Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea. hyunjinp@skku.edu.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Park
mesh_terms: D000328:Adult; D001289:Attention Deficit Disorder with Hyperactivity / Q000145:classification* / Q000000981:diagnostic imaging* / Q000473:pathology; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D063132:Connectome; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000473:pathology; D009434:Neural Pathways / Q000473:pathology; D010296:Parietal Lobe / Q000473:pathology; D060388:Support Vector Machine; D013702:Temporal Lobe / Q000473:pathology
keywords: ADHD; ADHD subtypes; Connectivity; SVM classifier; Task fMRI