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

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

    Research output: Contribution to journalArticlepeer-review

    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 languageEnglish
    JournalPsychiatry Research
    DOIs
    Publication statusPublished - 5 Jul 2015

    Keywords

    • Anterior temporal lobe
    • Major depressive disorder
    • Self-blame

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