Neural Basis of Resting-State Functional Connectivity

 描述(申请人提供)：静息状态功能磁共振成像(RsfMRI)已被广泛用于绘制各种脑部疾病下的大规模脑网络及其病理变化图。虽然rsfMRI信号的神经基础已经通过电生理学研究建立起来，但潜在的神经关联的性质在很大程度上仍不清楚。由于缺乏对rsfMRI网络关联的具体神经关联和发生机制的了解，阻碍了对它们的解释。这项拟议研究的总体目标是阐明导致rsfMRI信号关联的特定神经关联和发生机制。本研究的目的将通过两个具体的目标来实现：目标1是利用大规模电生理记录和同时进行的fMRI-电生理测量，阐明静息状态信号中区域相关的神经关联和产生机制。目标2是确定特定的电生理事件，这些事件有助于全局的、空间上非特定的fMRI相关性和 阐明它们与大脑警戒水平的联系，也要使用多模式方法。这项拟议的研究具有创新性，因为它将首次尝试解构两个核心成分之间的rsfMRI相关性，每个核心成分的机制都可以根据电生理基础来表征。这项研究的影响是非常显著的，因为识别不同的电生理贡献对rsfMRI信号将提高对功能网络的理解，并有助于解释网络异常与病理之间的关系。

 DESCRIPTION (provided by applicant): Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to map large-scale brain networks and their pathological changes under with various brain diseases. Although a neural basis for the rsfMRI signals has been established with electrophysiological studies, the nature of the underlying neural correlates remains largely unknown. The lack of understanding of the specific neural correlates and generative mechanisms of rsfMRI network correlations hampers their interpretation. The overall goal of the proposed research is to elucidate specific neural correlates and generative mechanisms that lead to rsfMRI signal correlations. The research objective will be achieved through two specific aims: Aim 1 is to elucidate the neural correlate and generative mechanism of region-specific correlations in resting-state signals with using large-scale electrophysiologica recordings and concurrent fMRI-electrophysiological measurements. Aim 2 is to identify specific electrophysiological events that contribute to global, spatially non-specific fMRI correlations and clarify their link to the brain's vigilance level, also using multi-modal approach. The proposed research is innovative, because it will be the first attempt to deconstruct rsfMRI correlations int two core components, each with a mechanism that can be characterized on electrophysiological grounds. The impact of this research is highly significant, because the identification of distinct electrophysiological contributions to rsfMRI signals will improve the understanding of functional networks and help interpret the relationship between abnormalities in networks and pathology.