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全球脑血流波动表明存在一个独立于全身因素的大脑全局网络。

Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors.

机构信息

1 Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

2 Department of Neurology and Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

J Cereb Blood Flow Metab. 2019 Feb;39(2):302-312. doi: 10.1177/0271678X17726625. Epub 2017 Aug 17.

Abstract

Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.

摘要

全局同步是网络模型和体内电测量的共同特征。虽然血氧敏感静息态功能磁共振成像(rsfMRI)可用于专门化脑网络的成像,但 rsfMRI 中对全局相关波动的研究受到来自全身生理因素和其他噪声源的对全局信号的虚假贡献的干扰。在这里,我们使用替代 rsfMRI 方法动脉自旋标记灌注 MRI 来描述全局相关性及其与区域网络相关性和反相关性的关系。不能用全身因素解释的全局波动主导了脑血流的波动。这些波动的功率谱被限制在低于 0.05 Hz,类似于先前对大脑区域网络波动的测量。在测量区域网络之前,去除这些全局波动会降低所有区域网络波动幅度,使其低于全局波动幅度,并改变网络间相关性的强度和符号。我们的发现支持跨网络的大振幅、全局同步活动,这需要重新评估区域网络幅度和相关性测量。

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