| dc.contributor.author | Sato J.R. | |
| dc.contributor.author | Moll J. | |
| dc.contributor.author | Green S. | |
| dc.contributor.author | Deakin J.F.W. | |
| dc.contributor.author | Thomaz C.E. | |
| dc.contributor.author | Zahn R. | |
| dc.date.accessioned | 2019-08-19T23:45:25Z | |
| dc.date.accessioned | 2023-05-03T20:39:03Z | |
| dc.date.available | 2019-08-19T23:45:25Z | |
| dc.date.available | 2023-05-03T20:39:03Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | 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. | |
| dc.identifier.issn | 1872-7506 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/89698 | |
| dc.description.abstract | © 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. | |
| dc.relation.ispartof | Psychiatry Research - Neuroimaging | |
| dc.rights | Acesso Aberto | |
| dc.title | Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression | |
| dc.type | Artigo | |
