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

Research output: Contribution to journalArticle

João R Sato, Jorge Moll, Sophie Green, John F W Deakin, Carlos E Thomaz, Roland Zahn

Original languageEnglish
JournalPsychiatry Research
Publication statusE-pub ahead of print - 5 Jul 2015


King's Authors


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.

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