Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification

Blair A. Johnston, Benson Mwangi, Keith Matthews, David Coghill, Kerstin Konrad, J. Douglas Steele (Lead / Corresponding author)

    Research output: Contribution to journalArticle

    47 Citations (Scopus)

    Abstract

    Despite extensive research, psychiatry remains an essentially clinical and, therefore, subjective clinical discipline, with no objective biomarkers to guide clinical practice and research. Development of psychiatric biomarkers is consequently important. A promising approach involves the use of machine learning with neuroimaging, to make predictions of diagnosis and treatment response for individual patients. Herein, we describe predictions of attention deficit hyperactivity disorder (ADHD) diagnosis using structural T1 weighted brain scans obtained from 34 young males with ADHD and 34 controls and a support vector machine. We report 93% accuracy of individual subject diagnostic prediction. Importantly, automated selection of brain regions supporting prediction was used. High accuracy prediction was supported by a region of reduced white matter in the brainstem, associated with a pons volumetric reduction in ADHD, adjacent to the noradrenergic locus coeruleus and dopaminergic ventral tegmental area nuclei. Medications used to treat ADHD modify dopaminergic and noradrenergic function. The white matter brainstem finding raises the possibility of "catecholamine disconnection or dysregulation" contributing to the ADHD syndrome, ameliorated by medication. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.

    Original languageEnglish
    Pages (from-to)5179-5189
    Number of pages11
    JournalHuman Brain Mapping
    Volume35
    Issue number10
    Early online date13 May 2014
    DOIs
    Publication statusPublished - Oct 2014

    Keywords

    • ADHD
    • brainstem
    • Machine learning
    • DARTEL

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