Department of Medical Social Sciences and Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
Hum Brain Mapp. 2018 Nov;39(11):4312-4321. doi: 10.1002/hbm.24248. Epub 2018 Jun 28.
Autobiographical memory retrieval is associated with activity of a distributed network that is similar to the default-mode network (DMN) identified via activity correlations measured during rest. We tested whether activity correlations could be used to identify the autobiographical network during extended bouts of retrieval. Global-correlativity analysis identified regions with activity correlation differences between autobiographical-retrieval and resting states. Increased correlations were identified for retrieval versus resting states within a distributed network that included regions prototypical for autobiographical memory. This network segregated into two subnetworks comprised of regions related to memory versus cognitive control, suggesting greater functional segregation during autobiographical retrieval than rest. DMN regions were important drivers of these effects, with increased correlations between DMN and non-DMN regions and segregation of the DMN into distinct subnetworks during retrieval. Thus, the autobiographical network can be robustly identified via activity correlations and retrieval is associated with network functional organization distinct from rest.
自传体记忆检索与一个分布式网络的活动有关,该网络与在休息期间通过测量活动相关性识别的默认模式网络 (DMN) 相似。我们测试了活动相关性是否可用于在长时间的检索过程中识别自传体网络。全局相关性分析确定了自传体检索与静息状态之间活动相关性差异的区域。在一个包括自传体记忆典型区域的分布式网络中,检索与静息状态相比,相关性增加。该网络分为两个子网,由与记忆相关的区域和认知控制相关的区域组成,这表明在自传体检索期间比在静息状态下功能分离度更高。DMN 区域是这些影响的重要驱动因素,DMN 与非 DMN 区域之间的相关性增加,并且在检索期间 DMN 分为不同的子网。因此,自传体网络可以通过活动相关性来可靠地识别,并且检索与与静息状态不同的网络功能组织相关联。