Functional engagement of white matter in resting-state brain networks.
Functional engagement of white matter in resting-state brain networks.
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DOI：
10.1016/j.neuroimage.2020.117096
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发表时间：
2020-10-15
期刊：
影响因子：
5.7
通讯作者：
Gore JC
中科院分区：
文献类型：
--
作者：
Li M;Gao Y;Gao F;Anderson AW;Ding Z;Gore JC
功能网络的拓扑特征源自灰质 (GM) 静息态连接的测量，与个体认知能力或特定功能障碍相关。然而，白质 (WM) 中的血氧水平依赖性 (BOLD) 信号通常被忽略，甚至被视为网络模型基础数据分析中的干扰因素。最近的研究证明了 WM BOLD 信号的可靠检测，并意味着这些信号反映了相关的神经活动。在这里，我们定量评估单个 WM 体素对功能网络识别的贡献，我们将其称为它们的参与（或概念上，它们的重要性）。我们通过测量连接性的减少来量化参与度，连接性的减少是通过忽略每个 WM 体素内的信号波动而产生的，无论是整个网络（全局）还是单个 GM 节点（本地）。我们观察到全球参与地图的高度可重复的空间分布，以及延迟时间深层 WM 体素相关性增加的趋势。局部参与图相对于构成公认的子功能网络的内部节点表现出均匀的空间分布，但相对于其他节点表现出不均匀的分布。 WM 体素根据其解剖位置显示出不同的参与分布。这些发现证明了 WM 在网络建模中的重要作用，从而支持了改变 WM 信号变化仅代表生理噪声的传统观点的必要性。
The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise.
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影响因子：
3.3
作者：
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DOI：
10.1196/annals.1440.011
发表时间：
2008-01-01
期刊：
YEAR IN COGNITIVE NEUROSCIENCE 2008
影响因子：
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作者：
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通讯作者：
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