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

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.

Highlights

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  We use machine learning to test a new fMRI biomarker of depression vulnerability.

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  This is based on guilt-selective anterior temporal functional connectivity changes.

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  The classification algorithm uses Maximum Entropy Linear Discriminant Analysis.

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  The remitted depression group is distinguished from controls with 78.3% accuracy.

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  This shows a high biomarker potential for detecting vulnerability to depression.