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在连接组学中引入轴突髓鞘形成:健康受试者 g 比值分布的初步分析。

Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects.

机构信息

Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy; Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK.

Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy.

出版信息

Neuroimage. 2018 Nov 15;182:351-359. doi: 10.1016/j.neuroimage.2017.09.018. Epub 2017 Sep 14.

Abstract

Microstructural imaging and connectomics are two research areas that hold great potential for investigating brain structure and function. Combining these two approaches can lead to a better and more complete characterization of the brain as a network. The aim of this work is characterizing the connectome from a novel perspective using the myelination measure given by the g-ratio. The g-ratio is the ratio of the inner to the outer diameters of a myelinated axon, whose aggregated value can now be estimated in vivo using MRI. In two different datasets of healthy subjects, we reconstructed the structural connectome and then used the g-ratio estimated from diffusion and magnetization transfer data to characterize the network structure. Significant characteristics of g-ratio weighted graphs emerged. First, the g-ratio distribution across the edges of the graph did not show the power-law distribution observed using the number of streamlines as a weight. Second, connections involving regions related to motor and sensory functions were the highest in myelin content. We also observed significant differences in terms of the hub structure and the rich-club organization suggesting that connections involving hub regions present higher myelination than peripheral connections. Taken together, these findings offer a characterization of g-ratio distribution across the connectome in healthy subjects and lay the foundations for further investigating plasticity and pathology using a similar approach.

摘要

微观结构成像和连接组学是两个具有巨大潜力的研究领域,可以用于研究大脑结构和功能。将这两种方法结合起来,可以更好、更全面地描述大脑作为一个网络的特征。本工作旨在从一个新的角度来描述连接组,使用 g-ratio 给出的髓鞘化测量值。g-ratio 是一个有髓轴的内直径与外直径的比值,其聚合值现在可以使用 MRI 在体内进行估计。在两个不同的健康受试者数据集上,我们重建了结构连接组,然后使用从弥散和磁化传递数据估计的 g-ratio 来描述网络结构。g-ratio 加权图的显著特征出现了。首先,图中边缘的 g-ratio 分布没有显示出幂律分布,而幂律分布是使用流线的数量作为权重观察到的。其次,涉及与运动和感觉功能相关的区域的连接具有最高的髓鞘含量。我们还观察到在枢纽结构和丰富俱乐部组织方面存在显著差异,这表明涉及枢纽区域的连接比外围连接具有更高的髓鞘化。总之,这些发现提供了对健康受试者连接组中 g-ratio 分布的描述,并为使用类似方法进一步研究可塑性和病理学奠定了基础。

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