Deep learning approach to evaluate sex differences in response to neuromodulation in Major Depressive Disorder. (1st June 2022)

• Record Type: Journal Article
• Authors: Seenivasan, S.; Adamson, M.; Phillips, A.
• Is Part Of: European psychiatry. Volume 65:Supplement 1(2022)
• Page Start: S182
• Page End: S182
• Publication Date: 2022-06-01
• Subjects: EEG -- rTMS -- deep learning -- sex differences; Psychiatry -- Periodicals; Mental illness -- Periodicals; Electronic journals; 616.89
• DOI: 10.1192/j.eurpsy.2022.480
• Languages: English
• ISSNs: 0924-9338

Abstract

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.