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

Standard functional magnetic resonance imaging (IMRI) 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 IMRI signature as a biomarker of MD vulnerability. Crown Copyright (C) 2015 Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).