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脑白质高信号负荷调节健康成年人的脑形态和脑连接:神经重塑机制?

White Matter Hyperintensity Load Modulates Brain Morphometry and Brain Connectivity in Healthy Adults: A Neuroplastic Mechanism?

机构信息

Department of Neuroscience, University of Sheffield, Sheffield, UK.

Functional MR, S.Orsola-Malpighi Hospital, Department of Biomedical and Neuromotor Science (DIBINEM), Bologna, Italy.

出版信息

Neural Plast. 2017;2017:4050536. doi: 10.1155/2017/4050536. Epub 2017 Aug 3.

Abstract

White matter hyperintensities (WMHs) are acquired lesions that accumulate and disrupt neuron-to-neuron connectivity. We tested the associations between WMH load and (1) regional grey matter volumes and (2) functional connectivity of resting-state networks, in a sample of 51 healthy adults. Specifically, we focused on the positive associations (more damage, more volume/connectivity) to investigate a potential route of adaptive plasticity. WMHs were quantified with an automated procedure. Voxel-based morphometry was carried out to model grey matter. An independent component analysis was run to extract the anterior and posterior default-mode network, the salience network, the left and right frontoparietal networks, and the visual network. Each model was corrected for age, global levels of atrophy, and indices of brain and cognitive reserve. Positive associations were found with morphometry and functional connectivity of the anterior default-mode network and salience network. Within the anterior default-mode network, an association was found in the left mediotemporal-limbic complex. Within the salience network, an association was found in the right parietal cortex. The findings support the suggestion that, even in the absence of overt disease, the brain actuates a compensatory (neuroplastic) response to the accumulation of WMH, leading to increases in regional grey matter and modified functional connectivity.

摘要

脑白质高信号(WMHs)是累积并破坏神经元间连接的获得性病变。我们在 51 名健康成年人样本中测试了 WMH 负荷与(1)局部灰质体积和(2)静息状态网络功能连接之间的关联。具体而言,我们专注于正相关(更多损伤,更多体积/连接),以研究潜在的适应性可塑性途径。WMHs 采用自动程序进行量化。进行基于体素的形态计量学以模拟灰质。进行独立成分分析以提取前、后默认模式网络、突显网络、左、右额顶网络和视觉网络。每个模型均针对年龄、整体萎缩水平以及脑和认知储备指数进行校正。在前默认模式网络和突显网络的形态计量学和功能连接中发现了正相关。在前默认模式网络中,在左颞叶边缘复合体中发现了关联。在突显网络中,在右顶叶皮层中发现了关联。研究结果支持了以下观点,即即使在没有明显疾病的情况下,大脑也会对 WMH 的积累做出代偿性(神经可塑性)反应,导致局部灰质增加和功能连接改变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6eb/5560090/af95f6fd80a6/NP2017-4050536.001.jpg

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