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Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo
Ravan, Maryam Gediya, Harshil Basco, Kennette James Hasey, Gary
Ravan, Maryam
Gediya, Harshil
Basco, Kennette James
Hasey, Gary
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2024-09-26
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Abstract
Objective: Predicting an individual’s response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment. Methods: Pre-treatment EEG data were collected from study participants treated with either sertraline (N=105), placebo (N=119), or bupropion (N=35). After preprocessing, the robust exact low-resolution electromagnetic tomography (ReLORETA) brain source localization method was used to reconstruct the source signals in 54 brain regions. Connectivity between regions was determined using symbolic transfer entropy (STE). A convolutional neural network (CNN) classified participants as responders or non-responders to each treatment. Results: Classification accuracy was 91.0 %, 95.4%, and 86.8% for sertraline, placebo, and bupropion, respectively. The most highly predictive features were connectivity between i) the anterior cingulate cortex and superior parietal lobule (alpha frequency), ii) the anterior cingulate cortex and orbitofrontal area (beta frequency), and iii) the orbitofrontal area and anterior cingulate cortex (gamma frequency). Conclusion: CNN analysis of EEG connectivity may accurately predict response to sertraline, bupropion, and placebo. Significance: The suggested method may offer clinicians an accessible and cost-effective tool for speedy treatment and helps pharmaceutical firms to test new antidepressants efficiently.
Citation
Ravan, M., Noroozi, A., Gediya, H., Basco, K.J. and Hasey, G. (2024) Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo. Clinical Neurophysiology, 167, pp. 198-208. https://doi.org/10.1016/j.clinph.2024.09.002
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Journal article
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en
Description
This is an author's accepted manuscript of an article published by Elsevier in Clinical Neurophysiology on 26/09/2024, available online: https://www.sciencedirect.com/science/article/pii/S138824572400261X. The accepted manuscript may differ from the final published version.
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ISSN
1388-2457
EISSN
1872-8952