Sex differences in rTMS treatment response: A deep learning‐based EEG investigation. Issue 8 (25th July 2022)

• Record Type: Journal Article
• Authors: Adamson, M.; Hadipour, A. L.; Uyulan, C.; Erguzel, T.; Cerezci, O.; Kazemi, R.; Phillips, A.; Seenivasan, S.; Shah, S.; Tarhan, N.
• Is Part Of: Brain and behavior. Volume 12:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-25
• Subjects: EEG -- Deep Learning -- rTMS -- depression -- Sex Differences -- Iran; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.2696
• Languages: English
• ISSNs: 2162-3279

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. Abstract : Maheen Adamson, Srija Seenivasan, Caglar Uyulan, Turker Erguzel, Onder Cerezci, Reza Kazemi, Siddhi Shah, Angela Phillips, Nevzat Tarhan. Sex differences in rTMS treatment response: A deep learning‐based EEG investigation. We developed a novel pipeline based on convolutional LSTM‐based deep learning to classify 25 female and 25 male patients based on their rTMS treatment response. Five different classification models were generated: pre‐/post‐rTMS female (model 1), pre‐/post‐rTMS male (model 2), pre‐rTMS female responder /nonresponder (model 3), pre‐rTMS male responder/nonresponder (model 4), and pre‐rTMS responder/nonresponder (model 5), achieving respectively 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, and indicating the potential of our approach to be used as a response predictor especially regarding sex‐specific antidepressant effects of rTMS in MDD patients. These results indicate the potential of our approach to be used as a response predictor of sex‐specific antidepressant effects of rTMS in MDD patients and to help guide clinicians to administer more personalized interventions.