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

Mourão-Miranda, Janaina, Oliveira, Leticia, Ladouceur, Cecile D., Marquand, Andre, Brammer, Michael, Birmaher, Boris, Axelson, David and Phillips, Mary L. 2012. Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS ONE 7 (2) , e29482. 10.1371/journal.pone.0029482

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Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Medicine
Subjects: R Medicine > R Medicine (General)
Additional Information: © 2012 Mourão-Miranda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher: Public Library of Science
ISSN: 1932-6203
Date of First Compliant Deposit: 30 March 2016
Last Modified: 06 Nov 2015 11:42
URI: http://orca.cf.ac.uk/id/eprint/79890

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