Hippocampal Signal Complexity and Rate-of-Change Predict Navigational Performance: Evidence from a Two-Week VR Training Program.

The hippocampus is believed to be an important region for spatial navigation, helping to represent the environment and plan routes. Evidence from rodents has suggested that the hippocampus processes information in a graded manner along its long-axis, with anterior regions encoding coarse information and posterior regions encoding fine-grained information. Brunec et al. (2018) demonstrated similar patterns in humans in a navigation paradigm, showing that the anterior-posterior gradient in representational granularity and the rate of signal change exist in the human hippocampus. However, the stability of these signals and their relationship to navigational performance remain unclear. In this study, we conducted a two-week training program where participants learned to navigate through a novel city environment. We investigated inter-voxel similarity (IVS) and temporal auto-correlation hippocampal signals, measures of representational granularity and signal change, respectively. Specifically, we investigated how these signals were influenced by navigational ability (i.e., stronger vs. weaker spatial learners), training session, and navigational dynamics. Our results revealed that stronger learners exhibited a clear anterior-posterior distinction in IVS in the right hippocampus, while weaker learners showed less pronounced distinctions. Additionally, lower general IVS levels in the hippocampus were linked to better early learning. Successful navigation was characterized by faster signal change, particularly in the anterior hippocampus, whereas failed navigation lacked the anterior-posterior distinction in signal change. These findings suggest that signal complexity and signal change in the hippocampus are important factors for successful navigation, with IVS representing information organization and auto-correlation reflecting moment-to-moment updating. These findings support the idea that efficient organization of scales of representation in an environment may be necessary for efficient navigation itself. Understanding the dynamics of these neural signals provides insights into the mechanisms underlying navigational learning in humans.

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

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.

Early Intervention Provider-Reported NDBI Use and Relationships with Provider- to System-Level Implementation Determinants.

An expanding evidence base has advocated for delivery of naturalistic developmental behavioral interventions (NDBIs) within community systems, thus extending the reach of these practices to young autistic children. The current study examined provider-reported use of NBDIs within a Part C Early Intervention (EI) system and the extent to which provider background, attitudes, and perceived organizational support predicted NDBI use. Results from 100 EI providers representing multiple disciplines indicated reported use of NDBI strategies within their practice despite inconsistent reported competency with manualized NDBI programs. Although NDBI strategy use was not predicted by provider experiences or perceived organizational support, provider openness to new interventions predicted the reported use of NDBI strategies. Future directions include mixed methods data collection across and within EI systems to better understand NDBI use and ultimately facilitate NDBI implementation.

Stereotypical hippocampal clustering predicts navigational success in virtualized real-world environments.

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.