Brain structure network analysis based on local similarity for neurodegenerative disorders

Research Project

Project/Area Number	18K12025
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 90110:Biomedical engineering-related
Research Institution	The University of Tokyo (2020-2021) National Center of Neurology and Psychiatry (2018-2019)
Principal Investigator	Maikusa Norihide 東京大学, 大学院総合文化研究科, 特任助教 (80631069)
Project Period (FY)	2018-04-01 – 2022-03-31
Project Status	Completed (Fiscal Year 2021)
Budget Amount *help	¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000) Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000) Fiscal Year 2020: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000) Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000) Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywords	MRI / 脳変性疾患 / 認知症 / 脳構造ネットワーク解析 / T1強調像 / 個人内脳構造ネットワーク / 構造ネットワーク / Similarity Matrix / Graph analysis / Brain / Graph解析
Outline of Final Research Achievements	Although the small-world configuration was maintained in the patient group, a global reduction in clustering coefficients was observed in the left and right TLE. At the local level, patients with left-sided TLE showed a widespread reduction in clustering coefficients beyond the ipsilateral temporal lobe and a decrease in characteristic path length at the ipsilateral temporal pole. In contrast, right-sided TLE patients showed a decrease in clustering coefficients confined to the ipsilateral temporal lobe. Compared to controls, the TLE patients showed a disruption of global and local network properties, suggesting a shift toward a randomized network. This network change was more extensive in left-sided TLE patients than in right-sided TLE patients.
Academic Significance and Societal Importance of the Research Achievements	本研究の成果により、局所的類似度に基づく脳構造ネットワーク解析が脳変性疾患における疾患の識別に有効であることが示された。これは従来の解剖学的関心領域内の脳体積測定や、Voxel Based Morphometryに代わる新たな解析手法になりうることを示唆している。