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

• Published in: PloS one 2012-02, Vol.7 (2), p.e29482-e29482
• Main Authors: Mourão-Miranda, Janaina, Oliveira, Leticia, Ladouceur, Cecile D, Marquand, Andre, Brammer, Michael, Birmaher, Boris, Axelson, David, Phillips, Mary L
• Format: Article
• Language: English
• Subjects: Adolescent; Adolescents; Adults; Artificial Intelligence; At risk youth; Behavior disorders; Biology; Bipolar disorder; Bipolar Disorder - diagnosis; Bipolar Disorder - etiology; Bipolar Disorder - psychology; Case-Control Studies; Child; Child development; Child of Impaired Parents - psychology; Childhood mental disorders; Computer Science; Development and progression; Diagnostic imaging; Disease susceptibility; Disorders; Families & family life; Female; Follow-Up Studies; Functional magnetic resonance imaging; Functional Neuroimaging; Gaussian process; Humans; Identification; Learning algorithms; Longitudinal Studies; Machine learning; Magnetic Resonance Imaging; Male; Medical imaging; Medical research; Medicine; Mental depression; Mental disorders; Mental Disorders - diagnosis; Mental Disorders - etiology; Mental Disorders - psychology; Mood; Mood Disorders - diagnosis; Mood Disorders - etiology; Mood Disorders - psychology; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Offspring; Parents; Parents & parenting; Pattern recognition; Pattern Recognition, Physiological; Phenomenology; Prognosis; Psychiatry; Risk; Risk Factors; ROC Curve; Studies; Teenagers; Trends
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0029482

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. 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. 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. 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.