Whole brain network analysis
| Project/Area Number |
15K00418
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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 |
Life / Health / Medical informatics
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Okamoto Hiroshi 国立研究開発法人理化学研究所, 脳神経科学研究センター, 客員研究員 (00374067)
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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 |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 複雑ネットワーク / コネクトーム / コミュニティ / モジュール / ランダムウォーク / マルコフ連鎖 / 機械学習 / 階層構造 / 全脳アーキテクチャ / リバースエンジニアリング / 全脳ネットワーク / 階層クラスタリング / 二部グラフ / ノンパラメトリッククラスタリング / テンポラルネットワーク / テンポラルコミュニティ / セルアセンブリ / EMアルゴリズム |
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| Outline of Final Research Achievements |
We have established a method to approach the whole brain architecture, the mechanism of information processing in the whole brain. This method is based on community detection in networks, where communities refer to sets of nodes in the network that are mutually connected at higher probability and are associated with functional modules. Applying this method to whole brain networks will therefore reveal the functional configuration diagram of the whole brain architecture. We have examined real brain network data by using this method and acquired novel findings: The mouse visual cortical area is functionally differentiated into ventral and dorsal pathways as with those of human and primate; hierarchical organization of the whole brain networks is generally non-tree structured, which suggests efficiency and flexibility of brain information processing.
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| Academic Significance and Societal Importance of the Research Achievements |
脳は情報をネットワークとして処理する。本研究の目的は、脳情報処理の仕組みに脳ネットワークの分析を通じてせまる方法を構築することである。コミュニティ構造抽出方法を確立することによりこの目的をほぼ達成し、脳情報処理を全面的に解明するための橋頭保を築くことができた。さらに、本研究が構築した方法は、現実世界における脳以外の様々なネットワーク（例えば、文書引用関係、SNSにおける知人関係、購買履歴における商品と消費者との関係）にも適用可能であり、社会・産業における様々な価値にもつながることが期待される（例えば、購買履歴から特徴的な購買層・商品群を見つけてそれを販売・宣伝戦略立案に役立てる）。
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Report
(5 results)
Research Products
(22 results)
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[Journal Article] Relation prediction in knowledge graph by Multi-Label Deep Neural Network2019
Author(s)
Onuki, Y., Murata, T., Nukui, S., Inagi, S., Qiu, X.-L., Watanabe, M., Okamoto, H.
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Journal Title
Applied Network Science
Volume: 4
Issue: 1
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Predicting Relations Between RDF Entities by Deep Neural Network2017
Author(s)
Tsuyoshi, M., Onuki, Y., Nukui,S., Inagi, S., Qiu, Xu-le, Watanabe, M. & Okamoto, H.
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Journal Title
Lecture Notes in Computer Science
Volume: 10577
Pages: 343-354
Related Report
Peer Reviewed / Int'l Joint Research
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[Presentation] Connectome informatics: Reverse engineering of whole brain networks2018
Author(s)
Okamoto, H., Suzuki, Y., Hayami, T., Negishi, S., Tamaru, H., Mizutani, H., Yamakawa, H.
Organizer
Advances in Neuroinformatics 2018 (https://www.neuroinf.jp/aini2018/)
Related Report
Int'l Joint Research
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[Presentation] Predicting relations of embedded RDF entities by Deep Neural Network2017
Author(s)
Onuki, Y., Tsuyoshi, M., Nukui, S., Inagi, S., Qiu, Xu-le, Watanabe, M. & Okamoto, H.
Organizer
The 16th International Semantic Web Conference (ISWC 2017), Poster
Related Report
Int'l Joint Research
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