title: Can depression be diagnosed by response to mother's face? A personalized attachment-based paradigm for diagnostic fMRI.
abstract: OBJECTIVE
  Objective measurement of depression remains elusive. Depression has been associated with insecure attachment, and both have been associated with changes in brain reactivity in response to viewing standard emotional and neutral faces. In this study, we developed a method to calculate predicted scores for the Beck Depression Inventory II (BDI-II) using personalized stimuli: fMRI imaging of subjects viewing pictures of their own mothers.
  
  
  METHODS
  28 female subjects ages 18-30 (14 healthy controls and 14 unipolar depressed diagnosed by MINI psychiatric interview) were scored on the Beck Depression Inventory II (BDI-II) and the Adult Attachment Interview (AAI) coherence of mind scale of global attachment security. Subjects viewed pictures of Mother (M), Friend (F) and Stranger (S), during functional magnetic resonance imaging (fMRI). Using a principal component regression method (PCR), a predicted Beck Depression Inventory II (BDI-II) score was obtained from activity patterns in the paracingulate gyrus (Brodmann area 32) and compared to clinical diagnosis and the measured BDI-II score. The same procedure was performed for AAI coherence of mind scores.
  
  
  RESULTS
  Activity patterns in BA-32 identified depressed subjects. The categorical agreement between the derived BDI-II score (using the standard clinical cut-score of 14 on the BDI-II) and depression diagnosis by MINI psychiatric interview was 89%, with sensitivity 85.7% and specificity 92.8%. Predicted and measured BDI-II scores had a correlation of 0.55. Prediction of attachment security was not statistically significant.
  
  
  CONCLUSIONS
  Brain activity in response to viewing one's mother may be diagnostic of depression. Functional magnetic resonance imaging using personalized paradigms has the potential to provide objective assessments, even when behavioral measures are not informative. Further, fMRI based diagnostic algorithms may enhance our understanding of the neural mechanisms of depression by identifying distinctive neural features of the illness.
authors.affiliation: Department of Radiology, Department of Psychology, Hatch Research Center, Columbia University, Neurological Institute, New York, New York, United States of America.
authors.forename: Xian
authors.initials: X
authors.lastname: Zhang
authors.forename: Zimri S
authors.initials: ZS
authors.lastname: Yaseen
authors.forename: Igor I
authors.initials: II
authors.lastname: Galynker
authors.forename: Joy
authors.initials: J
authors.lastname: Hirsch
authors.forename: Arnold
authors.initials: A
authors.lastname: Winston
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D003362:Cost-Benefit Analysis; D003863:Depression / Q000175:diagnosis* / Q000523:psychology*; D004644:Emotions; D005145:Face; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D009035:Mothers; D057285:Precision Medicine / Q000191:economics; D025341:Principal Component Analysis; D055815:Young Adult