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.
Original language | English |
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Journal | Psychiatry Research |
DOIs | |
Publication status | Published - 5 Jul 2015 |
Keywords
- Anterior temporal lobe
- Major depressive disorder
- Self-blame