Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

Abstract

Abstract Introduction The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. Methods In this paper, we developed a novel pipeline based on convolutional LSTM‐based deep learning (DL) to classify 25 female and 25 male patients 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 versus pre‐rTMS female nonresponders (model 3), pre‐rTMS male responder vs. pre‐rTMS male nonresponder (model 4), and pre‐rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. Conclusion 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.

Keywords

• EEG
• Deep Learning
• rTMS
• depression
• Sex Differences
• Iran