Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories
Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories
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DOI：
10.1093/cercor/bhv272
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发表时间：
2017-01-01
期刊：
影响因子：
3.7
通讯作者：
Cabeza, Roberto
中科院分区：
文献类型：
--
作者：
Geib, Benjamin R.;Stanley, Matthew L.;Cabeza, Roberto
记忆研究中的一种常见方法是将单个大脑区域（如海马体）的功能隔离开来，而不解决这些区域如何与更大的网络相互作用。为了研究嵌入大规模网络中的海马体的特性，我们使用功能磁共振成像和图论来表征在生动与暗淡的视觉记忆的主动检索过程中复杂的海马体相互作用。研究得出了4个主要发现。首先，右侧海马显示出更高的通信效率与网络（较短的路径长度），并成为一个更收敛的结构，为信息整合（更高的中心性措施）生动的比暗淡的记忆。第二，在我们感兴趣的图论测量中，右侧海马体的生动减去暗淡的差异在幅度上大于90个区域网络中的任何其他区域。此外，右侧海马显著重组了从模糊到生动记忆提取的直接连接。最后，在海马体之外，整个全脑网络的通信对于生动的记忆比模糊的记忆更有效（更短的全局路径长度）。总之，我们的研究结果说明了多元网络分析可以用来研究大规模网络中特定区域的作用，同时也解释了全球网络的变化。
A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large-scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90-region network. Moreover, the right hippocampus significantly reorganized its set of direct connections fromdim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole-brain networkwas more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate howmultivariate network analyses can be used to investigate the roles of specific regions within the large-scale network, while also accounting for global network changes.