A Shared Hippocampal Network in Retrieving Science-related Semantic Memories

Author:

She Hsiao-Ching1,Huang Li-Yu2,Duann Jeng-Ren13

Affiliation:

1. Institute of Education, National Yang Ming Chiao Tung University, Taiwan, ROC

2. Graduate Institute of Science Education, National Changhua University of Education, Changhua, Taiwan, ROC

3. Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA

Abstract

In responding to the calls for revisiting the role that hippocampus (HIP) plays in semantic memory retrieval, this study used functional neuroimaging-based connectivity technique to elucidate the functional brain network involved in retrieving the correct and incorrect science-related semantic memories. Unlike episodic memory retrieval, the 40 scientific concepts learned during middle and high school were selected to assess 46 science majors’ semantic memory retrieval and correctness monitoring, which requires neither the support of spatial information nor events to retrieve the memory. Our results demonstrated that HIP was significantly and robustly engaged in the semantic memory retrieval of correct scientific concepts than incorrect ones. Importantly, the Granger causality analysis indicated that effective connectivity of [Formula: see text] and [Formula: see text] was shared by the semantic memory retrieval of both correct and incorrect scientific concepts. On the other hand, the strengths of connectivity in the [Formula: see text] and [Formula: see text] brain networks appeared more pronounced during the processing of correct scientific concepts than of incorrect ones. The shared hippocampal networks highlight the role of the HIP as a hub to coordinate the INS, ACC, and MTG, in turn, support the semantic memory retrieval of scientific concepts.

Funder

Ministry of Science and Technology, Taiwan

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A rendering‐based lightweight network for segmentation of high‐resolution crack images;Computer-Aided Civil and Infrastructure Engineering;2024-06-23

2. Multi-Semantic Decoding of Visual Perception with Graph Neural Networks;International Journal of Neural Systems;2024-02-17

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