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人类大脑皮层的跨群体髓鞘形成协方差

Cross-population myelination covariance of human cerebral cortex.

作者信息

Ma Zhiwei, Zhang Nanyin

机构信息

Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania.

出版信息

Hum Brain Mapp. 2017 Sep;38(9):4730-4743. doi: 10.1002/hbm.23698. Epub 2017 Jun 20.

Abstract

Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc.

摘要

脑形态学量的跨群体协方差提供了一种区域间连通性的度量,因为人们认为它是由相连脑区的协调神经发育所决定的。尽管结构协方差分析很有用,但它主要采用的是包含混合成分的庞大形态学测量方法,而关于髓磷脂等任何特定细分结构协方差的研究却很少。表征髓鞘形成协方差很有意义,因为它将揭示由脑区之间髓鞘结构的协调发育所决定的连通性模式。利用人类连接组计划的髓磷脂含量MRI图谱,我们在此表明皮质髓鞘形成协方差具有高度可重复性,并且呈现出与先前其他连通性测量方法所揭示的类似的脑组织结构。此外,髓鞘形成协方差网络具有人类脑网络的共同拓扑特征,如小世界特性。此外,我们发现髓鞘形成协方差与静息态功能连通性(RSFC)之间的相关性在每个静息态网络(RSN)内是一致的,但在不同的RSN之间可能有很大差异。有趣的是,这种髓鞘形成协方差 - RSFC相关性在感觉和运动网络中比在认知和多模态联合网络中明显更强,这可能是由于它们不同的电路结构。本研究建立了一种专门与轴突相关的新的脑连通性测量方法,这种测量方法对于研究髓鞘结构的协调发育可能很有价值。《人类大脑图谱》38:4730 - 4743,2017年。© 2017威利期刊公司。

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