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1篇 您的检索式:作者名="Juanwei Ma"
    题名 作者 年代 出处 被引量
1Selective Aberrant Functional–Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment显示文摘Subcortical vascular mild cognitive impairment(svMCI)is a common prodromal stage of vascular dementia.Although mounting evidence has suggested abnormalities in several single brain network metrics,few studies have explored the consistency between functional and structural connectivity networks in svMCI.Here,we constructed such networks using resting-state f MRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls.The functional networks were then parcellated into topological modules,corresponding to several well-defined functional domains.The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level(whole brain and modular level).We found no significant intergroup differences in the functional–structural coupling within the whole brain;however,there was significantly increased functional–structural coupling within the dorsal attention module and decreased functional–structural coupling within the ventral attention module in the svMCI group.In addition,the svMCI patients demonstrated decreased intramodular connectivity strength in the visual,somatomotor,and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network,mainly linking the visual,somatomotor,dorsal attention,ventral attention,and frontoparietal control modules.There was no significant correlation between the altered module-level functional–structural coupling and cognitive performance in patients with svMCI.These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.Juanwei Ma Feng Liu Bingbing Yang Kaizhong Xue Pinxiao Wang Jian Zhou Yang Wang Yali Niu Jing Zhang 2021Neuroscience Bulletin2021,37,3:4
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