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Deep learning approach to evaluate sex differences in response to neuromodulation in Major Depressive Disorder

Published online by Cambridge University Press:  01 September 2022

S. Seenivasan ,
M. Adamson  and
A. Phillips
S. Seenivasan*
Affiliation:
Palo Alto Veterans Affairs, Psychiatry, Palo Alto, United States of America
M. Adamson
Affiliation:
Palo Alto Veterans Affairs, Polytrauma, Palo Alto, United States of America
A. Phillips
Affiliation:
Palo Alto Veterans Affairs, Psychiatry, Palo Alto, United States of America
*
*Corresponding author.

Abstract

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Introduction

Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions.

Objectives

The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients.

Methods

In this paper, we developed a novel pipeline based on convolutional LSTM-based deep learning (DL) to classify 25 female and 25 male subjects based on their rTMS treatment response.

Results

Five different classification models were generated, namely pre/post-rTMS female (model 1), pre/post-rTMS male (model 2), pre-rTMS female responder vs. pre-rTMS female non-responders (model 3), pre-rTMS male responder vs. pre-rTMS male non-responder (model 4), and pre-rTMS responder vs. non-responder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively.

Conclusions

These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients.

Disclosure

No significant relationships.

Keywords

EEGrTMSdeep learningsex differences
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S182
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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