Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Medical Informatics, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
Neuroimage. 2018 Dec;183:745-756. doi: 10.1016/j.neuroimage.2018.08.037. Epub 2018 Aug 23.
Previous studies have linked global burden of age-related white matter hyperintensities (WMHs) to cognitive impairment. We aimed to determine how WMHs in individual white matter connections relate to measures of cognitive function relative to measures of connectivity which do not take WMHs into account. Brain connectivity and WMH-related disconnectivity were derived from 3714 participants of the population-based Rotterdam Study. Connectivity was represented by the structural connectome, which was defined using diffusion tensor data, whereas the disconnectome represented disconnectivity due to WMH. The relationship between (dis)connectivity and cognitive measures was estimated using linear regression. We found that lower disconnectivity and higher connectivity corresponded to better cognitive function. There were many more significant associations with cognitive function in the disconnectome than in the connectome. Most connectome associations attenuated when disconnection was included in the model. WMH-related disconnectivity was especially related to worse executive functioning. Better cognitive speed corresponded to higher connectivity in specific connections independent of WMH presence. We conclude that WMH-related disconnectivity explains more variation in cognitive function than does connectivity. Efficient wiring in specific connections is important to information processing speed independent of WMH presence.
先前的研究将与年龄相关的脑白质高信号（WMHs）的全球负担与认知障碍联系起来。我们旨在确定与不考虑 WMH 的连通性测量相比，个体脑白质连接中的 WMH 与认知功能测量之间的关系。脑连接和与 WMH 相关的不连接性是从基于人群的鹿特丹研究的 3714 名参与者中得出的。连通性由结构连接组表示，该连接组是使用扩散张量数据定义的，而不连接组表示由于 WMH 引起的不连接性。使用线性回归估计（不）连通性与认知测量之间的关系。我们发现，较低的不连接性和较高的连接性与更好的认知功能相对应。不连接组中与认知功能相关的关联比连接组多得多。当将断开连接包含在模型中时，大多数连接组的关联都会减弱。与执行功能障碍有关的 WMH 相关不连接性尤其明显。认知速度的提高与特定连接中的更高连通性有关，而与 WMH 的存在无关。我们的结论是，与 WMH 相关的不连接性比连通性更能解释认知功能的变化。特定连接中有效的布线对于独立于 WMH 存在的信息处理速度很重要。
