Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability
Article
Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Gururajan, Raj, Zhou, Xujuan, Li, Yuefeng and Acharya, U. Rajendra. 2023. "Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability." Computer Methods and Programs in Biomedicine. 242. https://doi.org/10.1016/j.cmpb.2023.107771
| Article Title | Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability |
|---|---|
| ERA Journal ID | 5039 |
| Article Category | Article |
| Authors | Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Gururajan, Raj, Zhou, Xujuan, Li, Yuefeng and Acharya, U. Rajendra |
| Journal Title | Computer Methods and Programs in Biomedicine |
| Journal Citation | 242 |
| Article Number | 107771 |
| Number of Pages | 10 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Ireland |
| ISSN | 0169-2607 |
| 1872-7565 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2023.107771 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260723004376 |
| Abstract | Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries. |
| Keywords | Deep learning; Repetitive transcranial magnetic stimulation; Explainable AIDepression; Explainable AI |
| Related Output | |
| Is part of | Cutting through the hype: artificial intelligence for clinical decision support in psychiatry |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| Belmont Private Hospital, Australia | |
| School of Business | |
| Queensland University of Technology |
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