Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression

Psychiatry Res. 2015 Aug 30;233(2):289-91. doi: 10.1016/j.pscychresns.2015.07.001. Epub 2015 Jul 5.

Abstract

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability.

Keywords: Anterior temporal lobe; Major depressive disorder; Self-blame.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Biomarkers
  • Case-Control Studies
  • Depressive Disorder, Major / diagnosis*
  • Depressive Disorder, Major / physiopathology*
  • Diagnosis, Computer-Assisted*
  • Echo-Planar Imaging / methods*
  • Female
  • Guilt
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Machine Learning*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Nerve Net / physiopathology
  • Reference Values
  • Sensitivity and Specificity
  • Temporal Lobe / physiopathology

Substances

  • Biomarkers