Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents

PLoS One. 2012;7(2):e29482. doi: 10.1371/journal.pone.0029482. Epub 2012 Feb 15.


Introduction: 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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Artificial Intelligence
  • Bipolar Disorder / diagnosis*
  • Bipolar Disorder / etiology
  • Bipolar Disorder / psychology
  • Case-Control Studies
  • Child
  • Child of Impaired Parents / psychology*
  • Female
  • Follow-Up Studies
  • Functional Neuroimaging*
  • Humans
  • Longitudinal Studies
  • Magnetic Resonance Imaging*
  • Male
  • Mental Disorders / diagnosis*
  • Mental Disorders / etiology
  • Mental Disorders / psychology
  • Mood Disorders / diagnosis*
  • Mood Disorders / etiology
  • Mood Disorders / psychology
  • Pattern Recognition, Physiological*
  • Prognosis
  • ROC Curve
  • Risk Factors