title: Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala.
abstract: OBJECTIVE
  Pediatric bipolar disorder is currently diagnosed based on signs and symptoms, and without objective diagnostic biomarkers. In the present study, we investigated the utility of structural neuroanatomical signatures of the amygdala to objectively differentiate individual subjects with pediatric bipolar disorder from matched healthy controls.
  
  
  METHODS
  Structural T1 -weighted neuroimaging scans were obtained from 16 children and adolescents with unmedicated DSM-IV bipolar disorder (11 males, five females) and 16 matched healthy controls (11 males, five females). Voxel-based gray matter morphometric features extracted from a bilateral region-of-interest within the amygdala were used to develop a multivariate pattern analysis model which was utilized in predicting novel or 'unseen' individual subjects as either bipolar disorder or healthy controls.
  
  
  RESULTS
  The model assigned 25 out of 32 subjects the correct label (bipolar disorder/healthy) translating to a 78.12% diagnostic accuracy, 81.25% sensitivity, 75.00% specificity, 76.47% positive predictive value, and 80.00% negative predictive value and an area under the receiver operating characteristic curve (ROC) of 0.81. The predictions were significant at p = 0.0014 (χ(2) test p-value).
  
  
  CONCLUSIONS
  These results reaffirm previous reports on the existence of neuroanatomical abnormalities in the amygdala of pediatric patients with bipolar disorder. Remarkably, the present study also demonstrates that neuroanatomical signatures of the amygdala can predict individual subjects with bipolar disorder with a relatively high specificity and sensitivity. To the best of our knowledge, this is the first study to present a proof-of-concept diagnostic marker of pediatric bipolar disorder based on structural neuroimaging scans of largely medication-naïve patients.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Texas Center of Excellence on Mood Disorders, UT Houston Medical School, Houston, TX, USA.
authors.forename: Benson
authors.initials: B
authors.lastname: Mwangi
authors.forename: Danielle
authors.initials: D
authors.lastname: Spiker
authors.forename: Giovana B
authors.initials: GB
authors.lastname: Zunta-Soares
authors.forename: Jair C
authors.initials: JC
authors.lastname: Soares
mesh_terms: D000293:Adolescent; D000679:Amygdala / Q000473:pathology*; D001185:Artificial Intelligence; D001714:Bipolar Disorder / Q000175:diagnosis*; D002648:Child; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009445:Neuroanatomy / Q000379:methods*; D012372:ROC Curve
keywords: amygdala; machine learning; multivariate pattern analysis; neuroimaging; pediatric bipolar disorder