The Robustness of White Matter Brain Networks Decreases with Aging.

IF 2.5 4区 医学 Q3 NEUROSCIENCES
Chenye Huang, Xie Wang, Daojun Xie
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引用次数: 0

Abstract

Background: White matter (WM) is a principal component of the human brain, forming the structural basis for neural transmission between cortico-cortical and subcortical structures. The impairment of WM integrity is closely associated with the aging process, manifesting as the reorganization of brain networks based on graph theoretical analysis of complex networks and increased volume of white matter hyperintensities (WMHs) in imaging studies.

Methods: This study investigated changes in the robustness of WM brain networks during aging and assessed their correlation with WMHs. We constructed WM brain networks for 159 volunteers from a community sample dataset using diffusion tensor imaging (DTI). We then calculated the robustness of these networks by simulating neurodegeneration based on network attack analysis, and studied the correlations between WM network robustness, age, and the proportion of WMHs.

Results: The analysis revealed a moderate, negative correlation between WM network robustness and age, and a weak and negative correlation between WM network robustness and the proportion of WMHs.

Conclusions: These findings suggest that WM pathologies are associated with aging and offer new insights into the imaging characteristics of the aging brain.

脑白质网络的稳健性随着年龄的增长而下降。
背景:白质(White matter, WM)是人脑的主要组成部分,是皮层-皮层和皮层下结构之间神经传递的结构基础。WM完整性的损害与衰老过程密切相关,表现为基于复杂网络图理论分析的脑网络重组和成像研究中白质高强度(WMHs)体积的增加。方法:研究WM脑网络在衰老过程中鲁棒性的变化,并评估其与wmh的相关性。我们利用弥散张量成像(diffusion tensor imaging, DTI)技术构建了159名志愿者的WM脑网络。在网络攻击分析的基础上,通过模拟神经退行性变来计算这些网络的鲁棒性,并研究WM网络鲁棒性、年龄和wmh比例之间的相关性。结果:分析发现,WM网络稳健性与年龄呈中度负相关,WM网络稳健性与wmh比例呈弱负相关。结论:这些发现提示WM病理与衰老有关,并为衰老大脑的影像学特征提供了新的见解。
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来源期刊
CiteScore
2.80
自引率
5.60%
发文量
173
审稿时长
2 months
期刊介绍: JIN is an international peer-reviewed, open access journal. JIN publishes leading-edge research at the interface of theoretical and experimental neuroscience, focusing across hierarchical levels of brain organization to better understand how diverse functions are integrated. We encourage submissions from scientists of all specialties that relate to brain functioning.
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