Hamilton Arthur P, Sotebeer Kaiah N, Grundy John G, Chadwick Katherine, Morrison Cassandra, Dadar Mahsa, Bialystok Ellen, Anderson John A E
Carleton University, Department of Cognitive Science.
Iowa State University, Department of Psychology.
medRxiv. 2025 Aug 19:2025.08.15.25331829. doi: 10.1101/2025.08.15.25331829.
Previous research examining the contribution of white matter hyperintensities (WMHs) to cognitive decline has focused on overall lesion burden. A new approach, afforded by the Lesion Quantification Toolkit (LQT), measures localized connectivity disruption from WMHs to better estimate their impact on cognition. This methodology shifts the focus from lesion volume to the level of network disruption between brain regions. In this novel study, we applied the LQT approach to healthy aging and linked the degree of disconnection of gray matter by WMHs to both cognitive impairment and resilience via cognitive reserve. Using three pre-existing MRI datasets of older adults (total N = 259), we used the LQT to examine localized disruptions to brain connectivity due to WMHs. We then used partial least-squares path modeling to examine the relationships between this disruption, cognitive performance, age, and cognitive reserve. The results support a link between connectivity disruption and reduced cognitive performance. An analysis of one of the three datasets, which included a detailed measure of cognitive reserve, showed a link between cognitive reserve and higher cognitive performance, suggesting cognitive reserve offsets the negative impact of WMHs.
先前研究白质高信号(WMHs)对认知衰退的影响时,主要关注的是整体病变负担。病变量化工具包(LQT)提供了一种新方法,可测量白质高信号导致的局部连接中断,从而更好地估计其对认知的影响。这种方法将关注点从病变体积转移到脑区之间的网络中断程度。在这项新研究中,我们将LQT方法应用于健康老龄化研究,并通过认知储备将白质高信号导致的灰质断开程度与认知障碍和恢复力联系起来。我们使用了三个现有的老年人MRI数据集(总样本量N = 259),利用LQT来研究白质高信号对脑连接的局部破坏。然后,我们使用偏最小二乘路径模型来研究这种破坏、认知表现、年龄和认知储备之间的关系。结果支持了连接中断与认知表现下降之间的联系。对三个数据集中之一进行的分析,其中包括对认知储备的详细测量,结果显示认知储备与较高的认知表现之间存在联系,这表明认知储备抵消了白质高信号的负面影响。