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.
journal_name
Psychiatry Resjournal_title
Psychiatry researchauthors
Sato JR,Moll J,Green S,Deakin JF,Thomaz CE,Zahn Rdoi
10.1016/j.pscychresns.2015.07.001subject
Has Abstractpub_date
2015-08-30 00:00:00pages
289-91issue
2eissn
0165-1781issn
1872-7123pii
S0925-4927(15)30025-1journal_volume
233pub_type
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