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Full-text held externally
- PMID: 26187550
- UKPMCID: 26187550
- DOI: 10.1016/j.pscychresns.2015.07.001
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Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.
Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland
Psychiatry research. 2015;.
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Full-text held externally
- PMID: 26187550
- UKPMCID: 26187550
- DOI: 10.1016/j.pscychresns.2015.07.001
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.
Keyword(s)
Anterior temporal lobe; Major depressive disorder; Self-blame