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使用正电子发射断层扫描评估大脑中的功能连接性:一篇综述短文

Evaluation of Functional Connectivity in the Brain Using Positron Emission Tomography: A Mini-Review.

作者信息

Watabe Tadashi, Hatazawa Jun

机构信息

Department of Nuclear Medicine and Tracer Kinetics, Graduate School of Medicine, Osaka University, Osaka, Japan.

Institute for Radiation Sciences, Osaka University, Osaka, Japan.

出版信息

Front Neurosci. 2019 Jul 26;13:775. doi: 10.3389/fnins.2019.00775. eCollection 2019.

Abstract

Resting-state networks (RSNs) exhibit spontaneous functional connectivity in the resting state. Previous studies have evaluated RSNs mainly based on spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signals during functional magnetic resonance imaging (fMRI). However, separation between regional increases in cerebral blood flow (CBF) and oxygen consumption is theoretically difficult using BOLD-fMRI. Such separation can be achieved using quantitative O-gas and water positron emission tomography (PET). In addition, F-FDG PET can be used to investigate functional connectivity based on changes in glucose metabolism, which reflects local brain activity. Previous studies have highlighted the feasibility and clinical usefulness of F-FDG-PET for the analysis of RSNs, and recent studies have utilized simultaneous PET/fMRI for such analyses. While PET provides seed information regarding the focus of the abnormalities (e.g., hypometabolism and reduced target binding), fMRI is used for the analysis of functional connectivity. Thus, as PET and fMRI provide different types of information, integrating these modalities may aid in elucidating the pathological mechanisms underlying certain diseases, and in characterizing individual patients.

摘要

静息态网络(RSNs)在静息状态下表现出自发的功能连接性。以往的研究主要基于功能磁共振成像(fMRI)期间血氧水平依赖(BOLD)信号的自发波动来评估RSNs。然而,使用BOLD-fMRI理论上很难区分脑血流量(CBF)的区域增加和氧消耗。使用定量氧气体和水正电子发射断层扫描(PET)可以实现这种区分。此外,F-FDG PET可用于基于反映局部脑活动的葡萄糖代谢变化来研究功能连接性。以往的研究强调了F-FDG-PET用于分析RSNs的可行性和临床实用性,最近的研究已将同时进行的PET/fMRI用于此类分析。虽然PET提供有关异常焦点的种子信息(例如,代谢减低和靶标结合减少),但fMRI用于功能连接性分析。因此,由于PET和fMRI提供不同类型的信息,整合这些模式可能有助于阐明某些疾病的病理机制,并对个体患者进行特征描述。

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