PDF(Pubmed)
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：
长期以来，人们一直认为沿海马长轴的结构差异是有意义的功能差异的基础。最近的发现表明，海马的数据驱动分割将海马分为10个簇的图，其中包括前内侧，前外侧,和后前外侧，中间,和后部组件。我们使用空间学习实验测试了任务和经验是否可以调节这种聚类，在该实验中，男性和女性参与者接受了训练，可以在类似Google街景的环境中虚拟导航一个新的社区。在培训初期和为期两周的培训期结束时，参与者在导航路线时进行了扫描。使用10簇地图作为理想模板，我们发现,最终很好地学习邻域的参与者的海马簇图与理想状态一致-即使在学习的第二天-而且他们的簇图在两周的训练期内没有偏离.然而,最终学习邻居的参与者开始时海马聚类图与理想模板不一致，尽管经过两周的培训，它们的簇映射可能会变得更加刻板。有趣的是，这种改进似乎是特定于路线的：经过一些早期改进，当一条新路线被导航时，参与者的海马图恢复到不那么刻板的组织。我们得出的结论是，海马聚集并不仅仅依赖于解剖结构，而是由解剖学的组合驱动，任务,而且重要的是,经验。尽管如此，而海马集群可以随着经验而改变，有效的导航依赖于功能性海马活动以刻板的方式聚集，突出沿海马前后轴和内侧外侧轴的最佳处理划分。意义声明海马是对记忆和导航重要的大脑区域。最近的研究表明，当人们休息时，海马体内的加工活动模式可以揭示海马体内的不同加工区域。我们通过在个体学习如何在新的虚拟现实环境中导航时检查海马体中的处理来扩展这项工作。我们的发现表明，不仅海马的活动模式可靠地将海马分为子组件，而且海马的干净功能分割与更强的导航性能有关。因此，虽然个人可以使用他们的海马体以不同的方式处理信息，可能有一个理想的模板来支持有效的空间学习。
