title: Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.
abstract: OBJECTIVE
  Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).
  
  
  METHODS
  Twenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM) analysis tested for traditional univariate group level differences in GM.
  
  
  RESULTS
  The pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.
  
  
  CONCLUSIONS
  The study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.
authors.affiliation: Department of Child and Adolescent Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom.
authors.forename: Lena
authors.initials: L
authors.lastname: Lim
authors.forename: Andre
authors.initials: A
authors.lastname: Marquand
authors.forename: Ana A
authors.initials: AA
authors.lastname: Cubillo
authors.forename: Anna B
authors.initials: AB
authors.lastname: Smith
authors.forename: Kaylita
authors.initials: K
authors.lastname: Chantiluke
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
authors.forename: Mitul
authors.initials: M
authors.lastname: Mehta
authors.forename: Katya
authors.initials: K
authors.lastname: Rubia
mesh_terms: D000293:Adolescent; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D001321:Autistic Disorder / Q000175:diagnosis*; D001931:Brain Mapping; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D046709:Pattern Recognition, Physiological / Q000502:physiology