title: Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.
abstract: BACKGROUND
  There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographic, clinical questionnaire, environmental, neuropsychological, neuroimaging, and genetic information can predict therapeutic response following methylphenidate administration.
  
  
  METHODS
  The present study included 83 attention deficit hyperactivity disorder youth. At baseline, parents completed the ADHD Rating Scale-IV and Disruptive Behavior Disorder rating scale, and participants undertook the continuous performance test, Stroop color word test, and resting-state functional MRI scans. The dopamine transporter gene, dopamine D4 receptor gene, alpha-2A adrenergic receptor gene (ADRA2A) and norepinephrine transporter gene polymorphisms, and blood lead and urine cotinine levels were also measured. The participants were enrolled in an 8-week, open-label trial of methylphenidate. Four different machine learning algorithms were used for data analysis.
  
  
  RESULTS
  Support vector machine classification accuracy was 84.6% (area under receiver operating characteristic curve 0.84) for predicting methylphenidate response. The age, weight, ADRA2A MspI and DraI polymorphisms, lead level, Stroop color word test performance, and oppositional symptoms of Disruptive Behavior Disorder rating scale were identified as the most differentiating subset of features.
  
  
  CONCLUSIONS
  Our results provide preliminary support to the translational development of support vector machine as an informative method that can assist in predicting treatment response in attention deficit hyperactivity disorder, though further work is required to provide enhanced levels of classification performance.
authors.affiliation: Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan).
authors.forename: Jae-Won
authors.initials: JW
authors.lastname: Kim
authors.affiliation: Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan).
authors.forename: Vinod
authors.initials: V
authors.lastname: Sharma
authors.affiliation: Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan). ryannd@upmc.edu.
authors.forename: Neal D
authors.initials: ND
authors.lastname: Ryan
mesh_terms: D000367:Age Factors; D001289:Attention Deficit Disorder with Hyperactivity / Q000145:classification* / Q000188:drug therapy* / Q000235:genetics / Q000503:physiopathology; D001835:Body Weight; D001921:Brain / Q000503:physiopathology; D000697:Central Nervous System Stimulants / Q000627:therapeutic use*; D002648:Child; D003367:Cotinine / Q000652:urine; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007854:Lead / Q000097:blood; D008279:Magnetic Resonance Imaging; D008297:Male; D008774:Methylphenidate / Q000627:therapeutic use*; D050484:Norepinephrine Plasma Membrane Transport Proteins / Q000235:genetics; D011110:Polymorphism, Genetic; D011379:Prognosis; D018341:Receptors, Adrenergic, alpha-2 / Q000235:genetics; D012146:Rest; D057190:Stroop Test; D060388:Support Vector Machine; D016896:Treatment Outcome
chemical_list: C547768:ADRA2A protein, human; D000697:Central Nervous System Stimulants; D050484:Norepinephrine Plasma Membrane Transport Proteins; D018341:Receptors, Adrenergic, alpha-2; C493227:SLC6A2 protein, human; D008774:Methylphenidate; D007854:Lead; D003367:Cotinine
keywords: ADHD; machine learning; methylphenidate; prediction; treatment response