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

Abstract

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

• EEG
• rTMS
• deep learning
• sex differences