Integrated deep learning model for personalized transcranial magnetic stimulation
| Project/Area Number |
22K12765
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 90110:Biomedical engineering-related
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| Research Institution | University of Hyogo |
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Principal Investigator |
Rashed Essam 兵庫県立大学, 情報科学研究科, 教授 (60837590)
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| Co-Investigator(Kenkyū-buntansha) |
平田 晃正 名古屋工業大学, 工学(系)研究科(研究院), 教授 (00335374)
ゴメスタメス ホセデビツト 千葉大学, フロンティア医工学センター, 准教授 (60772902)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | Deep learning / brain stimulation / TMS / Segmentation / Brain stimulation |
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| Outline of Research at the Start |
Transcranial magnetic stimulation (TMS) is commonly used in several clinical procedures. Due to large variability of efficacy, planning of personalized stimulation is highly desired. However, personalized TMS requires a complicated data processing pipeline for individual head model generation to provide target-specific stimulation. This project aims at the development of accurate and reliable deep learning model to handle data processing, subject variabilities and other physical effects with integrated deep learning framework.
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| Outline of Annual Research Achievements |
In this year, we have have conducted a comprehensive training process for the developed deep learning models. The training consider optimizing the process of TMS focalization of specific brain region (motor cortex) and how to estimate stimulation parameters in different scenarios. The SHARM dataset (https://arxiv.org/abs/2309.06677) developed last year is used in the training process with TMS data obtain from our research collaborators.
After the initial training, the model parameters are optimized using several validation studies to achieve superior network performance. We have evaluated different versions of the network architecture to validate potential variations such as adding attention layers and include BN/dropout layers. Now, we are in the phase of preparing publications.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The research is progress smoothly than planned. We initially plan to complete three work packages in the first year (development of the deep learning model, data collection, and TMS simulation). Which are all successfully completed. Furthermore, we have reported results through several presentations and invited talks. Work package 4 that include network training (scheduled for FY2023) was partially completed in FY2022. Moreover, we have completed Work Package 5 (validation and optimization) which was planned to be completed within the third year.
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| Strategy for Future Research Activity |
The research plan for FY2024 include two work packages (Result reporting) and (deployment of open-source software). We have made some conference publications and already submitted one journal paper to international journal and currently under review. Further publications is expected based on results we already have in hands.
Additional experiment will be conducted for extension of the achieved results in terms of TMS focal point optimization. Also, we will prepare documentation for the open-source software to ease sharing with research community.
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Report
(2 results)
Research Products
(8 results)
