Ozubko Jason D, Campbell Madelyn, Verhayden Abigail, Demetri Brooke, Brady Molly, Brunec Iva
bioRxiv. 2023 Mar 25:2023.03.23.533994. doi: 10.1101/2023.03.23.533994.
Structural differences along the long-axis of the hippocampus have long been believed to underlie meaningful functional differences, such as the granularity of information processing. Recent findings show that data-driven parcellations of the hippocampus sub-divide 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 subjects were trained to virtually navigate a novel neighborhood in a Google Street View-like environment over a two-week period. Subjects were scanned while navigating routes early in training and at the end of their two-week training. Using the 10-cluster map as the ideal template, we find that subjects 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 change over the two week training period. However, subjects who eventually learn the neighborhood poorly begin with hippocampal cluster-maps inconsistent with the ideal, though their cluster mappings become more stereotypical by the end of the two week training. Interestingly this improvement seems to be route specific as even 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.
长期以来,人们一直认为海马体长轴上的结构差异是有意义的功能差异(如信息处理的精细程度)的基础。最近的研究结果表明,数据驱动的海马体分割将海马体细分为一个包含前内侧、前外侧、后前外侧、中间和后部成分的10簇图谱。我们进行了一项空间学习实验,让受试者在类似谷歌街景的环境中,在两周时间内训练虚拟导航一个新的街区,以此来测试任务和经验是否能调节这种聚类。在训练初期和两周训练结束时,受试者在导航路线时接受扫描。以10簇图谱作为理想模板,我们发现,最终能很好掌握街区的受试者,其海马体簇图谱从学习的第二天起就与理想图谱一致,并且在两周的训练期内其簇映射没有变化。然而,最终对街区掌握较差的受试者,其海马体簇图谱一开始与理想图谱不一致,不过在两周训练结束时,他们的簇映射变得更具刻板性。有趣的是,这种改善似乎是特定于路线的,因为即使在早期有所改善之后,当导航新路线时,参与者的海马体图谱又会恢复到不太刻板的组织状态。我们得出结论,海马体聚类并非仅取决于解剖结构,而是由解剖结构、任务以及重要的经验共同驱动。尽管如此,虽然海马体聚类会随着经验而变化,但高效导航依赖于以刻板方式进行的功能性海马体活动聚类,这突出了沿海马体前后轴和内侧-外侧轴的最佳处理划分。