Reverse Engineering of Connectome: Elucidating Brain Information Processing Architecture by Network Analysis

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K12060
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: The University of Tokyo
• Principal Investigator: OKAMOTO HIROSHI 東京大学, 大学院工学系研究科(工学部), 特任研究員 (00374067)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: コネクトーム / 複雑ネットワーク / 機能モジュール / コミュニティ / 視覚野 / 腹側経路 / 背側経路 / 階層構造

Outline of Final Research Achievements

We developed a method for extracting the hierarchical structure of soft-overlapping communities from complex networks, which is called modular decomposition of Markov chain (MDMC). We examined the block diagram of brain information processing from the connectome using MDMC, a process referred to as reverse engineering of the connectome. We identified ventral and dorsal pathways as communities in the high-resolution mouse visual cortical connectome. We also extracted the hierarchical structure of the communities and identified two additional modules that corresponded to a visual field gate and ventral-dorsal bridge. We showed that these community structures identified by MDMC are stable; therefore, the mouse visual cortex actually has functions corresponding to these communities.

Free Research Field

ネットワーク神経科学

Academic Significance and Societal Importance of the Research Achievements

霊長類の視覚野はオブジェクト認識に関わる腹側経路と空間認識に関わる背側経路に機能分化していることが、マクロレベルの実験・観測を通じて知られてきた。高解像コネクトームのネットワーク分析を行い、マウス視覚野も腹側経路と背側経路に分岐することを、ミクロレベルから初めて明らかにした。本研究が提案したコネクトームのリバースエンジニアリングの方法は、脳以外の、生物・工学・社会システムの機能分析にも展開可能である。