Exploring the brain network: A review on resting-state fMRI functional connectivity

静息状态功能磁共振成像 功能连接 神经科学 职能组织 神经功能成像 功能集成 认知 功能磁共振成像 动态功能连接 神经影像学 心理学 计算机科学 人脑 积分方程 数学 数学分析
作者
Martijn P. van den Heuvel,Hilleke E. Hulshoff Pol
出处
期刊:European Neuropsychopharmacology [Elsevier]
卷期号:20 (8): 519-534 被引量:3087
标识
DOI:10.1016/j.euroneuro.2010.03.008
摘要

Our brain is a network. It consists of spatially distributed, but functionally linked regions that continuously share information with each other. Interestingly, recent advances in the acquisition and analysis of functional neuroimaging data have catalyzed the exploration of functional connectivity in the human brain. Functional connectivity is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions and in the past years an increasing body of neuroimaging studies has started to explore functional connectivity by measuring the level of co-activation of resting-state fMRI time-series between brain regions. These studies have revealed interesting new findings about the functional connections of specific brain regions and local networks, as well as important new insights in the overall organization of functional communication in the brain network. Here we present an overview of these new methods and discuss how they have led to new insights in core aspects of the human brain, providing an overview of these novel imaging techniques and their implication to neuroscience. We discuss the use of spontaneous resting-state fMRI in determining functional connectivity, discuss suggested origins of these signals, how functional connections tend to be related to structural connections in the brain network and how functional brain communication may form a key role in cognitive performance. Furthermore, we will discuss the upcoming field of examining functional connectivity patterns using graph theory, focusing on the overall organization of the functional brain network. Specifically, we will discuss the value of these new functional connectivity tools in examining believed connectivity diseases, like Alzheimer's disease, dementia, schizophrenia and multiple sclerosis.
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