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评估人类大脑功能图谱中主要静息态网络的空间变异性。

Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases.

机构信息

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.

出版信息

Hum Brain Mapp. 2019 Oct 15;40(15):4577-4587. doi: 10.1002/hbm.24722. Epub 2019 Jul 19.

Abstract

The human brain is intrinsically organized into resting-state networks (RSNs). Currently, several human brain functional atlases are used to define the spatial constituents of these RSNs. However, there are significant concerns about interatlas variability. In response, we undertook a quantitative comparison of the five major RSNs (default mode [DMN], salience, central executive, sensorimotor, and visual networks) across currently available brain functional atlases (n = 6) in which we demonstrated that (a) similarity between atlases was modest and positively linked to the size of the sample used to construct them; (b) across atlases, spatial overlap among major RSNs ranged between 17 and 76% (mean = 39%), which resulted in variability in their functional connectivity; (c) lower order RSNs were generally spatially conserved across atlases; (d) among higher order RSNs, the DMN was the most conserved across atlases; and (e) voxel-wise flexibility (i.e., the likelihood of a voxel to change network assignment across atlases) was high for subcortical regions and low for the sensory, motor and medial prefrontal cortices, and the precuneus. In order to facilitate RSN reproducibility in future studies, we provide a new freely available Consensual Atlas of REsting-state Networks, based on the most reliable atlases.

摘要

人类大脑本质上组织为静息态网络(RSN)。目前,有几种人类大脑功能图谱用于定义这些 RSN 的空间成分。然而,人们对图谱之间的可变性存在重大担忧。有鉴于此,我们对目前可用的五个主要 RSN(默认模式网络[DMN]、突显网络、中央执行网络、感觉运动网络和视觉网络)在大脑功能图谱(n=6)之间进行了定量比较,结果表明:(a)图谱之间的相似性适中,与构建它们所使用的样本大小呈正相关;(b)在不同的图谱中,主要 RSN 之间的空间重叠范围在 17%到 76%之间(平均值为 39%),这导致它们的功能连接存在差异;(c)较低阶的 RSN 在图谱之间通常是空间上保守的;(d)在高阶 RSN 中,DMN 在图谱之间的保守性最高;(e)体素的灵活性(即,体素在图谱之间改变网络分配的可能性)对于皮质下区域较高,对于感觉、运动和内侧前额叶皮质以及楔前叶较低。为了促进未来研究中 RSN 的可重复性,我们提供了一个新的、免费的基于最可靠图谱的静息态网络共识图谱。

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