Wang Qing, He Cancan, Wang Zan, Zhang Zhijun, Xie Chunming
Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Neuropsychiatric Institute, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China.
Brain Connect. 2021 Apr;11(3):213-224. doi: 10.1089/brain.2020.0823. Epub 2021 Feb 1.
It is unknown the alterations in the dynamic networks of the brain and the underlying molecular pathological mechanism of Alzheimer's disease (AD) spectrum. Here, we aim to explore the association between alterations in the dynamic brain networks' trajectory and cognitive decline in the AD spectrum. One hundred sixty subjects were recruited from the ADNI database, including 49 early mild cognitive impairment, 28 late mild cognitive impairment, 24 AD patients, and 59 cognitively normal. All participants completed the resting-state functional magnetic resonance imaging scan and neuropsychological tests. We integrated a new method combining large-scale network analysis and canonical correlation analysis to explore the dynamic spatiotemporal patterns within- and between resting-state networks (RSNs) and their significance in the AD spectrum. All RSNs represented an increase in connectivity within networks by enhancing inner cohesive ability, while 7 out of 10 RSNs were characterized by a decrease in connectivity between networks, which indicated a weakened connector among networks from the early stage to dementia. This dichotomous mode presenting large-scale dynamic network abnormality was significantly correlated with the levels of molecular biomarkers of AD, and cognitive performance, as well as with the accumulating effects of 10 identified AD-related genetic risk factors. These findings deepen our understanding of the associated mechanism underlying large-scale network disruption, linking known molecular biomarkers and phenotypic variations in the AD spectrum.
目前尚不清楚阿尔茨海默病(AD)谱系中大脑动态网络的改变及其潜在的分子病理机制。在此,我们旨在探讨AD谱系中动态脑网络轨迹改变与认知衰退之间的关联。从ADNI数据库招募了160名受试者,包括49名早期轻度认知障碍者、28名晚期轻度认知障碍者、24名AD患者和59名认知正常者。所有参与者均完成了静息态功能磁共振成像扫描和神经心理学测试。我们整合了一种结合大规模网络分析和典型相关分析的新方法,以探索静息态网络(RSN)内部和之间的动态时空模式及其在AD谱系中的意义。所有RSN均通过增强内部凝聚能力表现出网络内连通性增加,而10个RSN中有7个的特征是网络间连通性降低,这表明从早期到痴呆阶段网络间的连接物减弱。这种呈现大规模动态网络异常的二分模式与AD分子生物标志物水平、认知表现以及10个已确定的AD相关遗传风险因素的累积效应显著相关。这些发现加深了我们对大规模网络破坏相关机制的理解,将已知的分子生物标志物与AD谱系中的表型变异联系起来。