A Shared Hippocampal Network in Retrieving Science-related Semantic Memories.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hsiao-Ching She, Li-Yu Huang, Jeng-Ren Duann
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引用次数: 0

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.

共享海马网络在检索科学相关语义记忆中的作用。
为了响应重新审视海马体(HIP)在语义记忆检索中的作用的呼吁,本研究使用基于功能神经成像的连接技术来阐明涉及检索正确和错误科学相关语义记忆的功能脑网络。与情景记忆检索不同,本研究选择初高中阶段学习的40个科学概念,对46名科学专业学生在不需要空间信息和事件支持的情况下进行语义记忆检索和正确性监测。结果表明,脑电对正确科学概念的语义记忆检索比错误科学概念的语义记忆检索具有显著性和稳健性。重要的是,格兰杰因果分析表明[Formula: see text]和[Formula: see text]的有效连通性被正确和错误的科学概念的语义记忆检索所共享。另一方面,在处理正确的科学概念时,[公式:见文]和[公式:见文]大脑网络的连通性优势比处理不正确的科学概念时更为明显。共享的海马体网络突出了HIP作为协调INS、ACC和MTG的枢纽的作用,反过来,支持科学概念的语义记忆检索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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