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2022 Fiscal Year Final Research Report

Multi-layer and multi-area data integration mothods for elucidating brain information dynamics

Planned Research

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Project AreaBrain information dynamics underlying multi-area interconnectivity and parallel processing
Project/Area Number 17H06310
Research Category

Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

Allocation TypeSingle-year Grants
Review Section Complex systems
Research InstitutionKyoto University

Principal Investigator

Ishii Shin  京都大学, 情報学研究科, 教授 (90294280)

Co-Investigator(Kenkyū-buntansha) 山崎 匡  電気通信大学, 大学院情報理工学研究科, 准教授 (40392162)
Project Period (FY) 2017-06-30 – 2022-03-31
Keywords脳情報動態 / カルシウムイメージング / 機能モジュール / エンコーダ / デコーダ / シミュレーション / 画像処理
Outline of Final Research Achievements

We developed a brain decoding method based on fMRI activities during human subjects were performing a decision-making task in an uncertain environment, and examined how the meta recognition is involve in the decision-making process (Katayama, et al., 2022). We examined how hierarchical inference and meta recognition crosstalked in a hierarchical decision-making task. Moreover, we elucidated brain information dynamics that parallel processes observation-based information and knowledge-based information when humans were performing a visual discrimination task.
We developed a decoding method to extract dynamically functional circuity from functional imaging data, and applied it to mice and marmosets Ca2+ imaging from their cortices. We completed functional identification of parietal visual association area of one marmoset.

Free Research Field

計算神経科学

Academic Significance and Societal Importance of the Research Achievements

不確実環境における意思決定ではメタ認知の関与が想定されるものの、いかに関わるのかはこれまでに明確ではなかった。本研究では、メタ認知が高まることで脳情報動態(動的な脳内表象)を明確にするということを明らかにした点で学術的意義がある。成果が評価され、2022年度のテレコム学際研究賞(奨励賞)を受賞した。
他のサブテーマはいずれも研究は完了しており、今後ハイインパクトの論文としての発表を通じて、学術的意義を謳う予定である。

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Published: 2024-01-30  

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