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

Phillips, ML (2012) Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS ONE, 7 (2).

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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. © 2012 Mourão-Miranda et al.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Phillips, ML
Date: 15 February 2012
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 7
Number: 2
DOI or Unique Handle: 10.1371/journal.pone.0029482
Schools and Programs: School of Medicine > Psychiatry
Refereed: Yes
MeSH Headings: 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
Other ID: NLM PMC3280237
PubMed Central ID: PMC3280237
PubMed ID: 22355302
Date Deposited: 13 Sep 2012 15:46
Last Modified: 12 Jan 2019 10:55
URI: http://d-scholarship.pitt.edu/id/eprint/14138

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