Inference between neuron subsets and their resulting physiological phenotypes using the data obtained by neurogenic tagging

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K19281
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Review Section: Medium-sized Section 44:Biology at cellular to organismal levels, and related fields
• Research Institution: National Institute of Genetics
• Principal Investigator: Hirata Tatsumi 国立遺伝学研究所, 遺伝形質研究系, 教授 (80260587)
• Co-Investigator(Kenkyū-buntansha): 遠里 由佳子 立命館大学, 情報理工学部, 教授 (80346171)
• Project Period (FY): 2021-07-09 – 2024-03-31
• Keywords: 誕生日タグづけ / 大規模データ / 情報処理 / 因果推論 / 脳機能 / マウス

Outline of Final Research Achievements

The data acquired using the “neurogenic tagging method” were analyzed informatically. The NeuroGT database has been open to public to showcase microscopic images of neurons that are classified by the “neurogenic tagging method”. Using the datasets, we devised a method to construct a 3D brain. We also developed a segmentation system that identifies the eight major regions of the mouse brain through deep learning using the teaching data created by the. Furthermore, using the collected data sets of behavioral phenotypes that appear in individual mice when the neural activities of neurogenic tagged neurons are chemogenetically manipulated, we inferred relationships between neuronal subsets and their outputs in terms of physical functions. A relationship hypothesized informatically is currently being verified.

Free Research Field

神経科学

Academic Significance and Societal Importance of the Research Achievements

人工知能を用いた大規模データの情報解析は、世界の研究のあり方を大きく変えてしまった。本研究はまさしくその流れに沿ったものであり、多量の独自研究データを情報学的に解析することで、実験的に検証可能な仮説を得たところである。最終的にこの仮説を実験的に検証できれば、学術的のみならず医学的にも意義深い情報に昇華できるのはもちろん、この流れの研究の有効性を示すモデルケースを提示できると考えている。