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

Article English OPEN
Sato, João R.; Moll, Jorge; Green, Sophie; Deakin, John F.W.; Thomaz, Carlos E.; Zahn, Roland;
(2015)
  • Publisher: Elsevier/North-Holland Biomedical Press
  • Journal: Psychiatry Research, volume 233, issue 2, pages 289-291 (issn: 0165-1781, eissn: 1872-7123)
  • Publisher copyright policies & self-archiving
  • Identifiers: doi: 10.1016/j.pscychresns.2015.07.001, pmc: PMC4834459
  • Subject: Major depressive disorder | Anterior temporal lobe | Self-blame | Neuroscience (miscellaneous) | Short Communication | Radiology Nuclear Medicine and imaging | Psychiatry and Mental health
    mesheuropmc: behavioral disciplines and activities

<p>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 questi... View more