2017 Fiscal Year Final Research Report
Estimation of neural activity propagation pathway in the brain by applying deep learning
| Project/Area Number |
15KT0111
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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 | 特設分野 |
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| Research Field |
Mathematical Sciences in Search of New Cooperation
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| Research Institution | Keio University |
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Principal Investigator |
TAKATA Norio 慶應義塾大学, 医学部(信濃町), 特任講師 (50415212)
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| Co-Investigator(Kenkyū-buntansha) |
中江 健 京都大学, 情報学研究科, 特定研究員 (70617472)
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| Co-Investigator(Renkei-kenkyūsha) |
TANAKA Kenji 慶應義塾大学, 医学部, 准教授 (30329700)
OKANO Hideyuki 慶應義塾大学, 医学部, 教授 (60160694)
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| Research Collaborator |
YOSHIDA Keitaro
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| Project Period (FY) |
2015-07-10 – 2018-03-31
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| Keywords | 深層学習 / マウス / fMRI / 光遺伝学 / 経路推定 / 脳活動伝搬 |
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| Outline of Final Research Achievements |
In this study, we tried to estimate the activity propagation path in the brain and elucidate the existence of rules and features in the propagation path. Specifically, when the start site and the termination site of brain activity are clear, we aimed to clarify the nature of the propagation pathway such as whether the neural activity propagates in parallel or in series in the brain circuit. In the beginning, path estimation was performed on a small number of ROIs using fMRI measurement data (data obtained by measuring responses of the brain upon optogenetic activation of the hippocampus of an anesthetized mouse; Takata et al. 2015 PLoS One). As a result, we could estimate three paths as potential candidates.
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| Free Research Field |
in vivo脳生理学
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