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Prediction of Treatment Response in Patients with Major Depressive Disorder: A Meta-Analysis of Functional Magnetic Resonance Imaging Studies

Published online by Cambridge University Press:  01 September 2022

M. Torres ,
P. Manghera  and
C. Miller
M. Torres*
Affiliation:
California State University, Fresno, Psychology, Clovis, United States of America
P. Manghera
Affiliation:
California State University, Fresno, Psychology, Fresno, United States of America
C. Miller
Affiliation:
California State University, Fresno, Psychology, Clovis, United States of America
*
*Corresponding author.

Abstract

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Introduction

Identifying the optimal treatment for individuals with major depressive disorder (MDD) is often a long and complicated process. Functional magnetic resonance imaging (fMRI) studies have been used to help predict and explain differences in treatment response among individuals with MDD.

Objectives

We conducted a comprehensive meta-analysis of treatment prediction studies utilizing fMRI in patients with MDD to provide evidence that neural activity can be used to predict response to antidepressant treatment.

Methods

A multi-level kernel density analysis was applied to these primary fMRI studies, in which we analyzed brain activation patterns of depressed patients (N= 364) before receiving antidepressant treatment.

Results

The results of this analysis demonstrated that hyperactivity in six brain regions significantly predicted treatment response in patients with MDD: the right anterior cingulate, right cuneus, left fusiform gyrus, left middle frontal gyrus, right cingulate gyrus, and left superior frontal gyrus.

Conclusions

This study provides evidence that neural activity, as measured by standard fMRI paradigms, can be used to successfully predict response to antidepressant treatment. This may be used in the future clinically to improve decision-making processes and treatment outcomes for patients.

Disclosure

No significant relationships.

Keywords

treatment responsemeta-analysisFunctional Magnetic Resonance Imagingmajor depressive disorder
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S297
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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