Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not ade...

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Bibliographic Details
Main Authors: Jacqueline K. Harris, Stefanie Hassel, Andrew D. Davis, Mojdeh Zamyadi, Stephen R. Arnott, Roumen Milev, Raymond W. Lam, Benicio N. Frey, Geoffrey B. Hall, Daniel J. Müller, Susan Rotzinger, Sidney H. Kennedy, Stephen C. Strother, Glenda M. MacQueen, Russell Greiner
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:NeuroImage: Clinical
Subjects:
Resting State
fMRI
Machine learning
Treatment response
Depression
Functional connectivity
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158222001851

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http://www.sciencedirect.com/science/article/pii/S2213158222001851

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