Mourão-Miranda, J and Oliveira, L and Ladouceur, CD and Marquand, A and Brammer, M and Birmaher, B and Axelson, D and 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).
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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| Item Type: |
Article
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| Status: |
Published |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Mourão-Miranda, J | | | | | Oliveira, L | | | | | Ladouceur, CD | cdl9@pitt.edu | CDL9 | | | Marquand, A | | | | | Brammer, M | | | | | Birmaher, B | birmaher@pitt.edu | BIRMAHER | | | Axelson, D | | | | | Phillips, ML | | | |
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| 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: |
26 Jan 2019 10:55 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/14138 |
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