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将人类大脑局部活动波动与结构和功能网络架构联系起来。

Linking human brain local activity fluctuations to structural and functional network architectures.

机构信息

Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.

出版信息

Neuroimage. 2013 Jun;73:144-55. doi: 10.1016/j.neuroimage.2013.01.072. Epub 2013 Feb 8.

DOI:10.1016/j.neuroimage.2013.01.072
PMID:23396160
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3632346/
Abstract

Activity of cortical local neuronal populations fluctuates continuously, and a large proportion of these fluctuations are shared across populations of neurons. Here we seek organizational rules that link these two phenomena. Using neuronal activity, as identified by functional MRI (fMRI) and for a given voxel or brain region, we derive a single measure of full bandwidth brain-oxygenation-level-dependent (BOLD) fluctuations by calculating the slope, α, for the log-linear power spectrum. For the same voxel or region, we also measure the temporal coherence of its fluctuations to other voxels or regions, based on exceeding a given threshold, Θ, for zero lag correlation, establishing functional connectivity between pairs of neuronal populations. From resting state fMRI, we calculated whole-brain group-averaged maps for α and for functional connectivity. Both maps showed similar spatial organization, with a correlation coefficient of 0.75 between the two parameters across all brain voxels, as well as variability with hodology. A computational model replicated the main results, suggesting that synaptic low-pass filtering can account for these interrelationships. We also investigated the relationship between α and structural connectivity, as determined by diffusion tensor imaging-based tractography. We observe that the correlation between α and connectivity depends on attentional state; specifically, α correlated more highly to structural connectivity during rest than while attending to a task. Overall, these results provide global rules for the dynamics between frequency characteristics of local brain activity and the architecture of underlying brain networks.

摘要

皮质局部神经元群体的活动持续波动,其中很大一部分波动在神经元群体之间共享。在这里,我们寻求将这两种现象联系起来的组织规则。我们使用功能磁共振成像 (fMRI) 识别的神经元活动,对于给定的体素或脑区,通过计算对数线性功率谱的斜率 α,来得出全带宽脑氧合水平依赖 (BOLD) 波动的单一度量。对于相同的体素或区域,我们还根据零滞后相关的给定阈值 Θ 来测量其波动与其他体素或区域的时间相干性,从而在神经元群体之间建立功能连接。我们从静息状态 fMRI 计算了 α 和功能连接的全脑组平均图。这两个图显示出相似的空间组织,两个参数之间的相关系数为 0.75,在所有脑体素上,以及与树突的可变性。一个计算模型复制了主要结果,表明突触低通滤波可以解释这些相互关系。我们还研究了 α 与结构连通性之间的关系,结构连通性由基于扩散张量成像的束追踪确定。我们观察到,α 与连通性之间的相关性取决于注意力状态;具体来说,在休息时,α 与结构连通性的相关性高于在执行任务时。总体而言,这些结果提供了局部脑活动频率特征与基础脑网络结构之间动态关系的全局规则。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/a1ae8b79a437/nihms451570f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/81a0aa11c1a6/nihms451570f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/db296fd50338/nihms451570f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/a1ae8b79a437/nihms451570f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/81a0aa11c1a6/nihms451570f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/db296fd50338/nihms451570f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6d/3632346/a1ae8b79a437/nihms451570f3.jpg

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