4.8 Article

Multidimensional memory topography in the medial parietal cortex identified from neuroimaging of thousands of daily memory videos

期刊

NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-34075-1

关键词

-

资金

  1. Intramural Research Program of the National Institutes of Health [ZIA-MH-002909]

向作者/读者索取更多资源

This study utilizes a large dataset from social media to investigate the neural substrates of autobiographical memories. The research finds tight interconnections among memory features and identifies a multidimensional topography in the medial parietal cortex that reflects memory content, age, and strength.
Our memories form a tapestry of events, people, and places, woven across the decades of our lives. However, research has often been limited in assessing the nature of episodic memory by using artificial stimuli and short time scales. The explosion of social media enables new ways to examine the neural representations of naturalistic episodic memories, for features like the memory's age, location, memory strength, and emotions. We recruited 23 users of a video diary app (1 s Everyday), who had recorded 9266 daily memory videos spanning up to 7 years. During a 3 T fMRI scan, participants viewed 300 of their memory videos intermixed with 300 from another individual. We find that memory features are tightly interrelated, highlighting the need to test them in conjunction, and discover a multidimensional topography in medial parietal cortex, with subregions sensitive to a memory's age, strength, and the familiarity of the people and places involved. Autobiographical memories are associated with activity in the hippocampus and the parietal cortex. Here the authors characterise the neural substrates for retrieving autobiographical memories from a large dataset, and identify a topography within the medial parietal cortex that reflects memory content, age, and memory strength.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据