| Project/Area Number |
20K11493
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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 59030:Physical education, and physical and health education-related
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| Research Institution | Kochi University of Technology |
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Principal Investigator |
Kadota Hiroshi 高知工科大学, 情報学群, 准教授 (00415366)
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| Co-Investigator(Kenkyū-buntansha) |
関口 浩文 上武大学, ビジネス情報学部, 教授 (20392201)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 運動学習 / fMRI / MRI |
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| Outline of Research at the Start |
我々の日常は運動することで営まれており，ヒトの持つ運動学習能力にかかわる神経基盤の解明は神経科学における重要課題であると考えられる．本研究では，経頭蓋直流電気刺激（tDCS）を用いて脳活動の興奮性を変えることで，運動学習能力と脳活動との関係性について明らかにしていく．また機能的磁気共鳴画像法（fMRI）を用いて運動学習中の脳活動を計測することで，全脳を対象に運動学習能力に関わる脳機能の解明を行っていく．
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| Outline of Final Research Achievements |
Humans can acquire various motor skills through motor learning, and able to perform appropriate movements in our daily lives. The purpose of this study was to clarify the relationship between the brain state before motor learning and motor learning ability. We recorded the resting-state brain activity (functional brain images) and structural brain images, and then the participants were asked to train juggling. We conducted correlation analysis and machine learning on the brain images and the degree of motor learning. Correlation analysis revealed that there was a correlation between resting-state brain activity and the degree of motor learning in the left sensorimotor area. The results of machine learning suggested that motor learning ability could be predicted from brain images.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究により，運動学習前の脳画像を解析することで運動学習能力を推定できる可能性が示唆された．個人の運動学習能力が予測できるようになれば，リハビリテーションやスポーツトレーニングなどにおいて個人に合わせた効率的な運動学習につながる可能性が考えられる．
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