Dynamic analysis of fMRI data: technical development and clinical application

2022 Fiscal Year Final Research Report

• Project/Area Number: 18K07707
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: The University of Tokyo
• Principal Investigator: Amemiya Shiori 東京大学, 医学部附属病院, 講師 (90631135)
• Project Period (FY): 2018-04-01 – 2023-03-31
• Keywords: fMRI / neural network / perfusion

Outline of Final Research Achievements

To investigate the origin of the global signals and the local resting-state network signals measured in humans using fMRI and their time lag, we examined the spatiotemporal patterns of the global signals acquired in resting-state as well as under wide-field visual stimulation. The correlation between the extracranial arterial signals and the global signals was also examined. The results showed that resting-state fMRI BOLD signal time lag represents differences in local hemodynamic response functions, rather than time differences derived from stimuli/triggers, whether neurogenic or not, and that the global signals likely reflect physiological changes in blood flow and blood pressure that are almost synchronized in a wide range of intracranial and extracranial regions.

Free Research Field

fMRI

Academic Significance and Societal Importance of the Research Achievements

広域かつ高空間分解能のin vivo神経活動計測を可能とする安静時fMRIは、巨視的神経回路の情報動態解析から、脳高次機能の生理・病理の理解を目指す上で重要な情報をもたらことが期待されている。しかしながらfMRIは神経血管連動を介した間接的計測であり、特に自発性神経活動をターゲットとする安静時fMRIでは信号成分の由来の確認なしには正確な解釈が困難になる。本研究では安静時fMRI信号の時間差解析から広汎性信号成分、局所ネットワーク成分の由来の確認を進めた。結果は非神経性由来の信号分離による、より正確な神経活動計測法開発のための基礎となるものと考えられる。