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Enhanced role of the entorhinal cortex in adapting to increased working memory load.

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Titel: Enhanced role of the entorhinal cortex in adapting to increased working memory load.
Autoren: Yang, Jiayi, Cao, Dan, Guo, Chunyan, Stieglitz, Lennart, Ledergerber, Debora, Sarnthein, Johannes, Li, Jin
Quelle: Nature Communications; 7/1/2025, Vol. 16 Issue 1, p1-12, 12p
Schlagwörter: ENTORHINAL cortex, TEMPORAL lobe, SHORT-term memory, HIPPOCAMPUS (Brain), MACHINE learning
Abstract: In daily life, we frequently encounter varying demands on working memory (WM), yet how the brain adapts to high WM load remains unclear. To address this question, we recorded intracranial EEG from hippocampus, entorhinal cortex (EC), and lateral temporal cortex (LTC) in humans performing a task with varying WM loads (load 4, 6, and 8). Using multivariate machine learning analysis, we decoded WM load using the power from each region as neural features. The results showed that the EC exhibited both higher decoding accuracy on medium-to-high load and superior cross-regional generalization. Further analysis revealed that removing EC-related information significantly reduced residual decoding accuracy in the hippocampus and LTC. Additionally, we found that WM maintenance was associated with enhanced phase synchronization between the EC and other regions. This inter-regional communication increased as WM load rose. These results suggest that under higher WM load, the brain relies more on the EC, a key connector that links and shares information with the hippocampus and LTC. How the brain adapts to rising working memory demands remains unclear. Here, the authors show that entorhinal cortex power features contributed more under medium-to-high loads than hippocampus and lateral temporal cortex, serving as a bridge between these regions. [ABSTRACT FROM AUTHOR]
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  Data: In daily life, we frequently encounter varying demands on working memory (WM), yet how the brain adapts to high WM load remains unclear. To address this question, we recorded intracranial EEG from hippocampus, entorhinal cortex (EC), and lateral temporal cortex (LTC) in humans performing a task with varying WM loads (load 4, 6, and 8). Using multivariate machine learning analysis, we decoded WM load using the power from each region as neural features. The results showed that the EC exhibited both higher decoding accuracy on medium-to-high load and superior cross-regional generalization. Further analysis revealed that removing EC-related information significantly reduced residual decoding accuracy in the hippocampus and LTC. Additionally, we found that WM maintenance was associated with enhanced phase synchronization between the EC and other regions. This inter-regional communication increased as WM load rose. These results suggest that under higher WM load, the brain relies more on the EC, a key connector that links and shares information with the hippocampus and LTC. How the brain adapts to rising working memory demands remains unclear. Here, the authors show that entorhinal cortex power features contributed more under medium-to-high loads than hippocampus and lateral temporal cortex, serving as a bridge between these regions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41467-025-60681-w
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              Text: 7/1/2025
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