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

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 Res

journal_title

Psychiatry research

authors

Sato JR,Moll J,Green S,Deakin JF,Thomaz CE,Zahn R

doi

10.1016/j.pscychresns.2015.07.001

subject

Has Abstract

pub_date

2015-08-30 00:00:00

pages

289-91

issue

2

eissn

0165-1781

issn

1872-7123

pii

S0925-4927(15)30025-1

journal_volume

233

pub_type

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