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

Psychiatry Res. 2015 Jul 5;

Authors: Sato JR, Moll J, Green S, Deakin JF, Thomaz CE, Zahn R

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.

PMID: 26187550 [PubMed - as supplied by publisher]