Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Issue 10 (13th May 2014)

• Record Type: Journal Article
• Authors: Johnston, Blair A.; Mwangi, Benson; Matthews, Keith; Coghill, David; Konrad, Kerstin; Steele, J. Douglas
• Is Part Of: Human brain mapping. Volume 35:Issue 10(2014:Oct.)
• Page Start: 5179
• Page End: 5189
• Publication Date: 2014-05-13
• Subjects: ADHD -- brainstem -- machine learning -- DARTEL; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.22542
• Languages: English
• ISSNs: 1065-9471

Abstract

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 T 1 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 35:5179–5189, 2014 . © 2014 Wiley Periodicals, Inc.