2021 Fiscal Year Final Research Report

Disturbed hippocampal intra-network in major depressive disorder

Research Project

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Project/Area Number	19K17214
Research Category	Grant-in-Aid for Early-Career Scientists
Allocation Type	Multi-year Fund
Review Section	Basic Section 52040:Radiological sciences-related
Research Institution	Kyoto University (2020-2021) University of Occupational and Environmental Health, Japan (2019)
Principal Investigator	Watanabe Keita 京都大学, オープンイノベーション機構, 特定准教授 (70565663)
Project Period (FY)	2019-04-01 – 2022-03-31
Keywords	大うつ病 / 海馬 / ネットワーク
Outline of Final Research Achievements	Evaluating intra-networks in the hippocampus using magnetic resonance imaging (MRI) is challenging. Here we employed a high spatial resolution of conventional structural imaging and incident component analysis (ICA) to investigate structural covariance intra-networks in the hippocampus. We extracted intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. Further, we investigated the abnormality of the intra-networks in major depressive disorders (MDD). The ICA extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with MDD and HS. In logistic analysis, all four bilateral networks were significant predictors of MDD, with an accuracy of 78.1%.
Free Research Field	放射線科学
Academic Significance and Societal Importance of the Research Achievements	今回の研究では、脳構造画像の高い空間分解能を利用して、海馬内のネットワークを描出および定量化する手法を開発した。脳内のネットワーク解析は脳機能画像や拡散テンソル画像が主流であるが、フラッグシップモデルのMRIを使用する必要がある。今回開発した手法では、一般的なMRIでも評価が可能な上により詳細なネットワーク解析が行えると考えている。この手法は日常診療で行われるMRI検査を含めて、幅広く応用が可能である。また、本手法で計測する海馬内ネットワークの指標は、大うつ病の診断や重症度の評価において客観的なバイオマーカーとなり得る可能性が示唆された。