title: Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents.
abstract: BACKGROUND
  There are no known biological measures that accurately predict future development of psychiatric disorders in individual at-risk adolescents. We investigated whether machine learning and fMRI could help to: 1. differentiate healthy adolescents genetically at-risk for bipolar disorder and other Axis I psychiatric disorders from healthy adolescents at low risk of developing these disorders; 2. identify those healthy genetically at-risk adolescents who were most likely to develop future Axis I disorders.
  
  
  METHODS
  16 healthy offspring genetically at risk for bipolar disorder and other Axis I disorders by virtue of having a parent with bipolar disorder and 16 healthy, age- and gender-matched low-risk offspring of healthy parents with no history of psychiatric disorders (12-17 year-olds) performed two emotional face gender-labeling tasks (happy/neutral; fearful/neutral) during fMRI. We used Gaussian Process Classifiers (GPC), a machine learning approach that assigns a predictive probability of group membership to an individual person, to differentiate groups and to identify those at-risk adolescents most likely to develop future Axis I disorders.
  
  
  RESULTS
  Using GPC, activity to neutral faces presented during the happy experiment accurately and significantly differentiated groups, achieving 75% accuracy (sensitivity = 75%, specificity = 75%). Furthermore, predictive probabilities were significantly higher for those at-risk adolescents who subsequently developed an Axis I disorder than for those at-risk adolescents remaining healthy at follow-up.
  
  
  CONCLUSIONS
  We show that a combination of two promising techniques, machine learning and neuroimaging, not only discriminates healthy low-risk from healthy adolescents genetically at-risk for Axis I disorders, but may ultimately help to predict which at-risk adolescents subsequently develop these disorders.
authors.affiliation: Department of Computer Science, University College London, United Kingdom. j.mourao-miranda@cs.ucl.ac.uk
authors.forename: Janaina
authors.initials: J
authors.lastname: Mourão-Miranda
authors.forename: Leticia
authors.initials: L
authors.lastname: Oliveira
authors.forename: Cecile D
authors.initials: CD
authors.lastname: Ladouceur
authors.forename: Andre
authors.initials: A
authors.lastname: Marquand
authors.forename: Michael
authors.initials: M
authors.lastname: Brammer
authors.forename: Boris
authors.initials: B
authors.lastname: Birmaher
authors.forename: David
authors.initials: D
authors.lastname: Axelson
authors.forename: Mary L
authors.initials: ML
authors.lastname: Phillips
mesh_terms: D000293:Adolescent; D001185:Artificial Intelligence; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000209:etiology / Q000523:psychology; D016022:Case-Control Studies; D002648:Child; D016241:Child of Impaired Parents / Q000523:psychology*; D005260:Female; D005500:Follow-Up Studies; D059907:Functional Neuroimaging; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis* / Q000209:etiology / Q000523:psychology; D019964:Mood Disorders / Q000175:diagnosis* / Q000209:etiology / Q000523:psychology; D046709:Pattern Recognition, Physiological; D011379:Prognosis; D012372:ROC Curve; D012307:Risk Factors