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

Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland

Psychiatry research. 2015;.

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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.

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Digital Object Identifier:
10.1016/j.pscychresns.2015.07.001
Pubmed Identifier:
26187550
Pii Identifier:
S0925-4927(15)30025-1
Access state:
Active

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Record metadata

Manchester eScholar ID:
uk-ac-man-scw:269614
Created by:
Deakin, Bill
Created:
29th July, 2015, 11:41:26
Last modified by:
Deakin, Bill
Last modified:
29th July, 2015, 11:41:26

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