Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression
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47
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Artigo
Data
2015
Autores
Sato J.R.
Moll J.
Green S.
Deakin J.F.W.
Thomaz C.E.
Zahn R.
Moll J.
Green S.
Deakin J.F.W.
Thomaz C.E.
Zahn R.
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Psychiatry Research - Neuroimaging
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Sato, João R.; MOLL, JORGE; GREEN, SOPHIE; DEAKIN, JOHN F.W.; THOMAZ, CARLOS E.; ZAHN, ROLAND. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Research. Neuroimaging (Print), v. 233, n. 2, p. 289-291, 2015.
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© 2015 Published by Elsevier Ireland Ltd.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.