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

Sato J.R.Moll J.Green S.Deakin J.F.W.Thomaz C.E.Zahn R.2019-08-192019-08-192015Sato, 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, 2015.1872-7506https://repositorio.fei.edu.br/handle/FEI/1273© 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.Acesso AbertoMachine learning algorithm accurately detects fMRI signature of vulnerability to major depressionArtigo10.1016/j.pscychresns.2015.07.001Anterior temporal lobeMajor depressive disorderSelf-blame