| Project/Area Number |
16K16649
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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 |
Basic / Social brain science
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
Satoshi Hirose 国立研究開発法人情報通信研究機構, 脳情報通信融合研究センター脳情報通信融合研究室, 研究員 (70590058)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | fMRI / 脳 / 個人差 / 学習法 / 脳機能解析 / 脳機能行動解析 / 新規学習法 |
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| Outline of Final Research Achievements |
This study aimed to develop a novel training method, where a person imitate brain activation pattern of experts during a particular task, such as mental calculation, in order to improve his/her task performance. For the purpose, brain activations during the task (mental calculation) were measured by means of functional magnetic resonance imaging (fMRI). Task performance is also measured outside the scanner. The relation between brain activation patterns and task performance is extracted by using machine learning techniques. However, we could not extract robust index of the task performance from the fMRI signal. To achieve the goal, it is essential to develop of noise reduction method for fMRI signal and robust and fine-tuned machine learning methods for analyzing fMRI data, and perform larger scale experiment.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は脳の使い方の個性を体系的、明示的に捉える方法を提案し、それを基にまったく新しい精神活動や運動のトレーニング法を提案するための初めての試みであった。本研究において、被験者数が少なかったなどの原因により、fMRI信号から学習に使用するのに十分な精度で課題の成績と関連する脳活動のパターンを同定することができなかったが、今後、解析法の開発や、大規模実験の実施により、上記目標が達成できる可能性を示唆した。
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