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

TY - JOUR

T1 - Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents

AU - Mourao-Miranda, Janaina

AU - Oliveira, Leticia

AU - Ladouceur, Cecile D.

AU - Marquand, Andre

AU - Brammer, Michael

AU - Birmaher, Boris

AU - Axelson, David

AU - Phillips, Mary L.

PY - 2012/2/15

Y1 - 2012/2/15

N2 - 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.

AB - 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.

KW - Magnetic Resonance Imaging

KW - Functional Neuroimaging

KW - ROC Curve

KW - Bipolar Disorder

KW - Humans

KW - Prognosis

KW - Child

KW - Longitudinal Studies

KW - Child of Impaired Parents

KW - Pattern Recognition, Physiological

KW - Mental Disorders

KW - Artificial Intelligence

KW - Risk Factors

KW - Mood Disorders

KW - Case-Control Studies

KW - Follow-Up Studies

KW - Adolescent

KW - Female

KW - Male

U2 - 10.1371/journal.pone.0029482

DO - 10.1371/journal.pone.0029482

M3 - Article

C2 - 22355302

SN - 1932-6203

VL - 7

JO - PL o S One

JF - PL o S One

IS - 2

M1 - e29482

ER -