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杏仁核-海马回路在工作记忆处理过程中的功能特化和相互作用。

Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing.

机构信息

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.

School of Artificial Intelligence, University of Chinese Academy of Sciences, 100049, Beijing, China.

出版信息

Nat Commun. 2023 May 22;14(1):2921. doi: 10.1038/s41467-023-38571-w.

DOI:10.1038/s41467-023-38571-w
PMID:37217494
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10203226/
Abstract

Both the hippocampus and amygdala are involved in working memory (WM) processing. However, their specific role in WM is still an open question. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task, and compared their representation patterns during the encoding and maintenance periods. By combining multivariate representational analysis and connectivity analyses with machine learning methods, our results revealed a functional specialization of the amygdala-hippocampal circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were more similar across different items but remained stable in the absence of the stimulus. WM encoding and maintenance were associated with bidirectional information flow between the amygdala and the hippocampus in low-frequency bands (1-40 Hz). Furthermore, the decoding accuracy on WM load was higher by using representational features in the amygdala during encoding and in the hippocampus during maintenance, and by using information flow from the amygdala during encoding and that from the hippocampus during maintenance, respectively. Taken together, our study reveals that WM processing is associated with functional specialization and interaction within the amygdala-hippocampus circuit.

摘要

海马体和杏仁核都参与了工作记忆 (WM) 处理。然而,它们在 WM 中的具体作用仍然是一个悬而未决的问题。在这里,我们同时记录了癫痫患者杏仁核和海马体的颅内 EEG,在执行 WM 任务时,比较了它们在编码和维持期间的表示模式。通过将多变量表示分析和连接分析与机器学习方法相结合,我们的结果揭示了杏仁核-海马体回路的功能专业化:杏仁核中的记忆表示高度独特,从编码到维持逐渐减少。然而,海马体的表示在不同项目之间更相似,但在没有刺激的情况下保持稳定。WM 编码和维持与低频带(1-40 Hz)中杏仁核和海马体之间的双向信息流有关。此外,在编码期间使用杏仁核中的表示特征以及在维持期间使用海马体中的表示特征,以及在编码期间使用来自杏仁核的信息流和在维持期间使用来自海马体的信息流,都可以提高 WM 负荷的解码准确性。总的来说,我们的研究表明,WM 处理与杏仁核-海马体回路中的功能专业化和相互作用有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/27f6fad2e194/41467_2023_38571_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/0fc614f30940/41467_2023_38571_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/dbb8c5f651a4/41467_2023_38571_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/430dfa1addf1/41467_2023_38571_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/27f6fad2e194/41467_2023_38571_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/0fc614f30940/41467_2023_38571_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/dbb8c5f651a4/41467_2023_38571_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/430dfa1addf1/41467_2023_38571_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbc4/10203226/27f6fad2e194/41467_2023_38571_Fig4_HTML.jpg

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