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刻板的海马体聚类可预测在虚拟化现实环境中的导航成功。

Stereotypical Hippocampal Clustering Predicts Navigational Success in Virtualized Real-World Environments.

机构信息

Psychology Department, SUNY Geneseo, Geneseo, New York 14454

Psychology Department, SUNY Geneseo, Geneseo, New York 14454.

出版信息

J Neurosci. 2024 Jun 12;44(24):e1057232024. doi: 10.1523/JNEUROSCI.1057-23.2024.

Abstract

Structural differences along the hippocampal long axis are believed to underlie meaningful functional differences. Yet, recent data-driven parcellations of the hippocampus subdivide the hippocampus into a 10-cluster map with anterior-medial, anterior-lateral, and posteroanterior-lateral, middle, and posterior components. We tested whether task and experience could modulate this clustering using a spatial learning experiment where male and female participants were trained to virtually navigate a novel neighborhood in a Google Street View-like environment. Participants were scanned while navigating routes early in training and after a 2 week training period. Using the 10-cluster map as the ideal template, we found that participants who eventually learn the neighborhood well have hippocampal cluster maps consistent with the ideal-even on their second day of learning-and their cluster mappings do not deviate over the 2 week training period. However, participants who eventually learn the neighborhood poorly begin with hippocampal cluster maps inconsistent with the ideal template, though their cluster mappings may become more stereotypical after the 2 week training. Interestingly this improvement seems to be route specific: after some early improvement, when a new route is navigated, participants' hippocampal maps revert back to less stereotypical organization. We conclude that hippocampal clustering is not dependent solely on anatomical structure and instead is driven by a combination of anatomy, task, and, importantly, experience. Nonetheless, while hippocampal clustering can change with experience, efficient navigation depends on functional hippocampal activity clustering in a stereotypical manner, highlighting optimal divisions of processing along the hippocampal anterior-posterior and medial-lateral axes.

摘要

人们认为,沿着海马体长轴的结构差异是导致其具有重要功能差异的原因。然而,最近基于数据的海马体分割将海马体细分为 10 个聚类图谱,包括前内侧、前外侧和后外侧、中间和后部分。我们通过一项空间学习实验来测试任务和经验是否可以调节这种聚类,在该实验中,男性和女性参与者被训练在类似于谷歌街景的虚拟环境中导航一个新的街区。参与者在训练早期和 2 周的训练期后进行导航路径扫描。使用 10 个聚类图谱作为理想模板,我们发现最终很好地学习了街区的参与者的海马体聚类图谱与理想图谱一致——即使在学习的第二天——并且他们的聚类图谱在 2 周的训练期间不会偏离。然而,最终学习不好的参与者开始时的海马体聚类图谱与理想模板不一致,尽管他们的聚类图谱在 2 周的训练后可能会变得更加典型。有趣的是,这种改善似乎是特定于路线的:在早期有一些改善后,当导航新路线时,参与者的海马图谱又回到不太典型的组织。我们得出结论,海马体聚类不仅取决于解剖结构,还取决于解剖结构、任务,以及重要的是经验的组合。尽管如此,虽然海马体聚类可以随着经验而改变,但有效的导航依赖于功能上以刻板方式聚类的海马体活动,突出了沿着海马体前后和内外侧轴进行处理的最佳划分。

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