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  • °ÔÀÓ¼³°è | Cases and Studies of Game Design in Lottery & Gambling | êý戏设计

    date : 2018-12-28 21:27|hit : 1447
    Article] Connectivity Analysis and Feature Classification in Attention Deficit Hyperactivity Disorder Sub-Types: A Task Functional Magnetic Resonance Imaging Study
    DocNo of ILP : 11787

    Document Type : Article

    Document Title : Connectivity Analysis and Feature Classification in Attention Deficit Hyperactivity Disorder Sub-Types: A Task Functional Magnetic Resonance Imaging Study

    Authors : Park, BY; Kim, M; Seo, J; Lee, JM; Park, H

    Author Full Name : Park, Bo-yong; Kim, Mansu; Seo, Jongbum; Lee, Jong-min; Park, Hyunjin

    Author Keywords : Connectivity; ADHD; ADHD subtypes; Task fMRI; SVM classifier

    Keywords Plus? : HUMAN CONNECTOME PROJECT; DEFICIT/HYPERACTIVITY-DISORDER; BRAIN NETWORKS; ADHD; FMRI; MRI; ADOLESCENTS; ACTIVATION; CHILDREN; CORTEX

    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.

    Web of Science Categories : Clinical Neurology; Neurosciences

    Year Published : 2016

    Publisher : SPRINGER

    Publisher City : DORDRECHT

    Language : English

    Cited Reference Count : 50

    reply : 0
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