| Project/Area Number |
15K19829
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Radiation science
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
Kasahara Kazumi 沖縄科学技術大学院大学, 神経計算ユニット, 特別研究員 (30706164)
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| Research Collaborator |
Doya Kenji
Hanakawa Takashi
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | MRI / バイオマーカー / 個人差 / ブレイン・マシン・インターフェイス / リハビリテーション / 可塑性 / Diffusion Tensor Imaging / 拡散スペクトラムイメージング / 白質微細構造 / 軸索 / 髄鞘 / 訓練 / 安静時脳機能連絡 / Functional Connectivity / rsfMRI / Diffusion tensor imaging |
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| Outline of Final Research Achievements |
The outcome of conventional physical therapy after brain injury varies greatly among patients. We would like to predict this variability and develop personalized therapy which is customized to each patient’s condition. The purpose of the present study is to develop non-invasive biomarkers, using MRI, which have the potential to predict the variability of therapy-effect and elucidate its mechanism. So far the study found that cortical volumes of motor-related areas measured by MRI correlated with the ability to control Brain-Machine Interfaces. In addition, by using resting-state functional MRI we found that motor skill learning enhanced the functional connectivity between Substantia Nigra and Thalamus. These results suggest that MRI based bio-markers might be of great value in the development of personalized therapy options for patients who suffered from brain injury.
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| Academic Significance and Societal Importance of the Research Achievements |
ブレイン・マシン・インターフェイスは、病気や怪我によって失われた脳機能を代替する最先端の医工学技術である。さらにこの技術は、リハビリテーション治療に応用するとその治療効果を高めるという報告もある。本研究成果であるMRIによるブレイン・マシン・インターフェイスの操作能力の予測、そして長期学習が誘導する可塑的変化の解明は、リハビリテーションの治療効果の個体差が何に起因するのか理解し、将来的な個人の病状に最適化したテーラーメイドな治療法の開発の一助となると考える。
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