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Theta rhythm supports hippocampus-dependent integrative encoding in schematic/semantic memory networks
NeuroImage ( IF 4.7 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.neuroimage.2020.117558
Berta Nicolás , Jacint Sala-Padró , David Cucurell , Mila Santurino , Mercè Falip , Lluís Fuentemilla

Integrating new information into existing schematic/semantic structures of knowledge is the basis of learning in our everyday life as it enables structured representation of information and goal-directed behaviour in an ever-changing environment. However, how schematic/semantic mnemonic structures aid the integration of novel elements remains poorly understood. Here, we showed that the ability to integrate novel picture information into learned structures of picture associations that overlapped by the same picture scene (i.e., simple network) or by a conceptually related picture scene (i.e., schematic/semantic network) is hippocampus-dependent, as patients with lesions at the medial temporal lobe (including the hippocampus) were impaired in inferring novel relations between pictures within these memory networks. We also found more persistent and widespread scalp EEG theta oscillations (3-5Hz) while participants integrated novel pictures into schematic/semantic memory networks than into simple networks. On the other hand, greater neural similarity was observed between EEG patterns elicited by novel and related events within simple networks than between novel and related events within schematic/semantic memory networks. These findings have important implications for our understanding of the neural mechanisms that support the development and organization of structures of knowledge.

中文翻译:

Theta 节律支持图解/语义记忆网络中依赖海马体的综合编码

将新信息整合到现有的知识图解/语义结构中是我们日常生活中学习的基础,因为它可以在不断变化的环境中结构化地表示信息和目标导向的行为。然而,示意图/语义助记符结构如何帮助新元素的整合仍然知之甚少。在这里,我们展示了将新图片信息整合到与相同图片场景(即简单网络)或概念相关图片场景(即示意图/语义网络)重叠的图片关联的学习结构中的能力是海马依赖的,因为内侧颞叶(包括海马体)病变的患者在推断这些记忆网络中图片之间的新关系时受到损害。我们还发现了更持久和广泛的头皮 EEG theta 振荡(3-5Hz),而参与者将新图片集成到示意图/语义记忆网络中而不是简单网络中。另一方面,与示意图/语义记忆网络中的新事件和相关事件之间相比,在简单网络中由新事件和相关事件引发的 EEG 模式之间观察到更大的神经相似性。这些发现对我们理解支持知识结构发展和组织的神经机制具有重要意义。在简单网络中由新事件和相关事件引发的 EEG 模式之间观察到比在示意图/语义记忆网络中的新事件和相关事件之间具有更大的神经相似性。这些发现对我们理解支持知识结构发展和组织的神经机制具有重要意义。在简单网络中由新事件和相关事件引发的 EEG 模式之间观察到比在示意图/语义记忆网络中的新事件和相关事件之间具有更大的神经相似性。这些发现对我们理解支持知识结构发展和组织的神经机制具有重要意义。
更新日期:2021-02-01
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