| Project/Area Number |
16K00312
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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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| Research Field |
Intelligent informatics
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| Research Institution | Doshisha University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
渡邉 真也 室蘭工業大学, 大学院工学研究科, 准教授 (30388136)
杉本 昌弘 慶應義塾大学, 政策・メディア研究科(藤沢), 特任教授 (30458963)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 脳機能イメージング / fMRI / fNIRS / ヒトの状態推定 / 進化計算 / メタボローム / 脳機能 / スパースモデリング / コミュニケーション / マインドフルネス / 脳機能ネットワーク解析 / 協調作業 / 進化的計算 |
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| Outline of Final Research Achievements |
There are two primary goals of this study, and they are to be accomplished in the following phases. The goal of the first phase is to identify the region of brain functions which determines the cluster of the subject's characteristics, and this goal is to be achieved using the brain functional data obtained by non-invasive human brain functional imaging systems, such as fMRI and fNIRS. The next goal is to identify metabolites highly relevant to cluster identification by measuring metabolic changes through analyses of Salivary metabolism. As we carried out the first phase, we made a dynamic network analysis to estimate individual state and developed a multi-objective genetic algorithm-based evolutionary method to identify functional brain regions to be focused on. In the second phase, we performed metabolome analysis and examination of saliva, but could not reach a point having enough evidence to propose a method of identifying the target substance.
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| Academic Significance and Societal Importance of the Research Achievements |
深い知識を有しない場合や既存の研究例が存在しない場合、非侵襲な脳機能イメージング装置で脳機能データが取得できたとしても、注目すべき脳部位はどこであるかを決定することは難しい。今後、ますます種々のタスクに対して、様々な環境で脳機能情報が取得されることが予想されるが、データドリブンな方式で注目すべき脳部位が決定できることは大いに意義がある。また、fMRIやfNIRSなどの装置は大規模であるため実際の利用においては汎用性が低い。唾液などのメタボローム情報から脳機能状態が決定できれば、汎用性が高まり活用の範囲が拡がる。
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