Cited by 102Memory and Neural MechanismsSpatial Cognition and NavigationMemory Processes and Influences

The DOI matches its Crossref record; 74 of the 75 references it cites were found in a registry or an index; 4 of the 48 sources its claims cite were checked for support.

Hippocampal and Retrosplenial Goal Distance Coding After Long-term Consolidation of a Real-World Environment

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Published version of a preprint

Abstract

Abstract Recent research indicates the hippocampus may code the distance to the goal during navigation of newly learned environments. It is unclear however, whether this also pertains to highly familiar environments where extensive systems-level consolidation is thought to have transformed mnemonic representations. Here we recorded fMRI while University College London and Imperial College London students navigated virtual simulations of their own familiar campus (> 2 years of exposure) and the other campus learned days before scanning. Posterior hippocampal activity tracked the proximity to the goal in the newly learned campus, but not in the familiar campus. By contrast retrosplenial cortex tracked the distance to the goal in the familiar campus, but not in the recently learned campus. These responses were abolished when participants were guided to their goal by external cues. These results open new avenues of research on navigation and consolidation of spatial information and help advance models of how neural circuits support navigation in novel and highly familiar environments. Significance Statement Historically, research on the hippocampal formation has focused on its role in long-term memory and navigation – often in isolation. No study to date has directly compared realistic navigation within familiar with recently learned environments, nor has it been explored how the neural substrates, along with computational codes, may change. In this study, we show for the first time, a shift from hippocampal to cortical coding of distance to a goal during active navigation. This study bridges the gap between memory consolidation and navigation, and paves the way for more functional and realistic understanding of the hippocampus.

Overview

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TL;DR

In a within-subject fMRI study, students navigated virtual simulations of their own long-familiar university campus and a campus learned days earlier. The right posterior hippocampus tracked goal distance in the recently learned campus and in familiar environments only when many turns lay ahead, whereas retrosplenial cortex tracked goal distance only in the familiar campus.

Key findings

  • During travel in the recently learned campus, right midposterior hippocampal activity showed a significant negative correlation with path distance to the goal, increasing with proximity to the goal; this was absent in the familiar campus and in control routes.
  • In the familiar campus, hippocampal activity showed a significant positive correlation with path distance, but only when the upcoming route contained many turns (≥3); there was no significant correlation with few turns (<3).
  • Retrosplenial cortex showed a significant positive correlation with path distance during travel in the familiar campus, greatest when participants were farthest from the goal; this was absent in the recent campus and control conditions.
  • Behaviorally, participants were more accurate at decision points in the familiar campus (M = 89.9%, SD = 11%) than the recent campus (M = 80.4%, SD = 13%; t[24] = 3.6, P = 0.002) and responded faster in the familiar campus (1.19 ± 0.4 s vs. 1.39 ± 0.5 s; t[24] = −4.7, P < 0.001). They reported more planning at decision points in the recent environment (22%) than the familiar environment (8%; t < −2.9, P < 0.006).

Methods

  • Final sample: 25 students (12 UCL, 13 Imperial; mean age 23 years, range 20–26; 12 males, 13 females) who had studied at their own campus for at least 2 years and had little or no familiarity with the other campus.
  • Participants learned 10 goal locations, 18 streets, and 8 start points in both environments, including a guided real-world tour of both campuses in the days before scanning; fMRI used virtual Google Street View-based routes with active navigation and control “follow” blocks.
  • fMRI analysis used SPM12 with a priori small-volume correction masks for right mid/posterior hippocampus and retrosplenial cortex; path distance, Euclidean distance, and egocentric goal direction were modeled as parametric regressors, with focus on Travel Periods and Decision Points.

Limitations

  • The paper states that scanner coverage made it impossible to assess entorhinal cortex contributions to distance coding, unlike Howard et al. (2014).
  • The paper states that ecological validity required lower trial numbers per condition, though the two main periods of interest had >20 events per condition; control analyses separating travel midpoints replicated the hippocampal effect only with mid-hippocampal small-volume correction and did not replicate the retrosplenial effect, instead finding precuneus/parietal–occipital sulcus effects.
  • Evident from the text: the study used a modest sample (n = 25), a 1.5 T scanner, virtual rather than real-world navigation, and only two campus environments; path distance and Euclidean distance were highly correlated, and the authors note that environment structure, size, and visual detail may influence the direction of hippocampal goal-distance coding.

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Integrity signals

Registry

Crossref · 10.1093/cercor/bhz044

Cites

74 of 74 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1: Campus layouts and training protocol. (A) Participants navigated around either UCL (left) or Imperial College campus (right), finding their way to various goal locations (examples depicted to the right of the maps—note not all goal locations were necessarily famous). The maps show all potential goal locations marked with black circles. The open circle represents an example participant location, the solid and dashed black lines indicate the optimal path to be taken (Path Distance), and Euclidian distance (“as the crow flies”), respectively, to the current goal location. (B) The training protocol consisted of 2 stages. First, a self-guided learning of landmarks and campus layouts with printed training materials. Two days before the fMRI scan participants were taken on an intensive guided tour of one of the campuses, and the next day, they were taken around the other campus. The order was counterbalanced across subjects and could start at either their home campus or the new one. On the day of scanning, participants completed routes in both campuses, in both active navigation and a control condition that involved following directions. During the debrief session, they filled out questionnaires regarding navigation strategies, and performed a behavioral task where they indicated where along the route they engaged in planning while navigating. (C) Excerpt from a “navigation” route at UCL. At the start of every route, participants were shown via text on the screen the street they are on, as well as their cardinal facing direction. Next they were given a New Goal Location, and subsequently asked to indicate the general direction of that landmark. They traveled down the road until they reach a junction (the duration and number of images of this was variable depending on the length of the street), at which point they were asked to indicate the correct turn to take towards the goal (Decision Point). Here, the program automatically advanced on a predefined route, which may have been correct, or it may have been an unplanned detour (occurring less frequently). After a variable interval, a new street was entered and the participant was informed again of their location and facing direction. Note the jittered interval after Decision Points is to allow for separating signals relating to Decision Points and Turns (or Detours). Analysis of fMRI data was constrained to the Travel and Decision Point periods. For fMRI analysis, Travel periods were taken as the midpoint between 2 events, and modeled as a punctate event. For simplicity, the above figure only depicts part of a route, and Travel is shown as 2 frames, but could range between 3 and 48 frames (on average: mean 31 ± 18).
Figure 1: Campus layouts and training protocol. (A) Participants navigated around either UCL (left) or Imperial College campus (right), finding their way to various goal locations (examples depicted to the right of the maps—note not all goal locations were necessarily famous). The maps show all potential goal locations marked with black circles. The open circle represents an example participant location, the solid and dashed black lines indicate the optimal path to be taken (Path Distance), and Euclidian distance (“as the crow flies”), respectively, to the current goal location. (B) The training protocol consisted of 2 stages. First, a self-guided learning of landmarks and campus layouts with printed training materials. Two days before the fMRI scan participants were taken on an intensive guided tour of one of the campuses, and the next day, they were taken around the other campus. The order was counterbalanced across subjects and could start at either their home campus or the new one. On the day of scanning, participants completed routes in both campuses, in both active navigation and a control condition that involved following directions. During the debrief session, they filled out questionnaires regarding navigation strategies, and performed a behavioral task where they indicated where along the route they engaged in planning while navigating. (C) Excerpt from a “navigation” route at UCL. At the start of every route, participants were shown via text on the screen the street they are on, as well as their cardinal facing direction. Next they were given a New Goal Location, and subsequently asked to indicate the general direction of that landmark. They traveled down the road until they reach a junction (the duration and number of images of this was variable depending on the length of the street), at which point they were asked to indicate the correct turn to take towards the goal (Decision Point). Here, the program automatically advanced on a predefined route, which may have been correct, or it may have been an unplanned detour (occurring less frequently). After a variable interval, a new street was entered and the participant was informed again of their location and facing direction. Note the jittered interval after Decision Points is to allow for separating signals relating to Decision Points and Turns (or Detours). Analysis of fMRI data was constrained to the Travel and Decision Point periods. For fMRI analysis, Travel periods were taken as the midpoint between 2 events, and modeled as a punctate event. For simplicity, the above figure only depicts part of a route, and Travel is shown as 2 frames, but could range between 3 and 48 frames (on average: mean 31 ± 18).CC BY · PubMed Central
Figure 2. The hippocampus is involved in processing path distance in novel environments as well as familiar environments when the route is complex. (A) During Travel in recent environments, there was a significant negative correlation with path distance, such that there was higher BOLD activity in the hippocampus when participants were closer to the goal location. (B) During Travel in familiar environments, there was a significant positive correlation with path distance, such that there was higher BOLD activity in the hippocampus when participants were further away from the goal location, but only when the route encompassed many turns (>=3). In each plot, from left to right: parameter estimates (PE) extracted from a categorical model (binned by distance), the BOLD activity for the relevant condition (displayed at P < 0.005, min. 5 contiguous voxels), and the PE from the peak voxel in the ROI for each condition, for illustrative purposes only (note the “*” refers to SVC of the voxels in the SPM; error bars represent s.e.m.). All effects survive small-volume correction, including when ED (Euclidian distance) is added to the models. (C, D) Show the data when the GLM included weighted regressors for the effects seen in A/B, respectively. For example, in C, the contrast was 1 −3 1 1, testing for an overall effect of correlation with path distance during Travel, for the recent navigation condition compared with all other conditions. *P < 0.05 SVC, †P < 0.1 small-volume correction (SVC).
Figure 2. The hippocampus is involved in processing path distance in novel environments as well as familiar environments when the route is complex. (A) During Travel in recent environments, there was a significant negative correlation with path distance, such that there was higher BOLD activity in the hippocampus when participants were closer to the goal location. (B) During Travel in familiar environments, there was a significant positive correlation with path distance, such that there was higher BOLD activity in the hippocampus when participants were further away from the goal location, but only when the route encompassed many turns (>=3). In each plot, from left to right: parameter estimates (PE) extracted from a categorical model (binned by distance), the BOLD activity for the relevant condition (displayed at P < 0.005, min. 5 contiguous voxels), and the PE from the peak voxel in the ROI for each condition, for illustrative purposes only (note the “*” refers to SVC of the voxels in the SPM; error bars represent s.e.m.). All effects survive small-volume correction, including when ED (Euclidian distance) is added to the models. (C, D) Show the data when the GLM included weighted regressors for the effects seen in A/B, respectively. For example, in C, the contrast was 1 −3 1 1, testing for an overall effect of correlation with path distance during Travel, for the recent navigation condition compared with all other conditions. *P < 0.05 SVC, †P < 0.1 small-volume correction (SVC).CC BY · PubMed Central
Figure 3. The retrosplential cortex codes path distance after consolidation. (A) During Travel in familiar environments, there was a significant positive correlation with path distance, such that there was higher BOLD activity in the retrosplenial cortex when participants were further away from the goal location. In each plot, from left to right: parameter estimates (PE) extracted from a categorical model (binned by distance), the BOLD activity for the relevant condition (displayed at P < 0.005, min. 5 contiguous voxels), and the PE from the peak voxel in the ROI for each condition, for illustrative purposes only (note the “*” refers to SVC of the voxels in the SPM; error bars represent s.e.m.). All effects survive small-volume correction, including when ED (Euclidian distance) is added to the models. (B) Shows the data when the GLM included weighted regressors for the effects seen in A, such that the contrast was −3 1 1 1, testing for an overall effect of correlation with path distance during Travel, for the familiar navigation condition compared with all other conditions. †P < 0.1 SVC.
Figure 3. The retrosplential cortex codes path distance after consolidation. (A) During Travel in familiar environments, there was a significant positive correlation with path distance, such that there was higher BOLD activity in the retrosplenial cortex when participants were further away from the goal location. In each plot, from left to right: parameter estimates (PE) extracted from a categorical model (binned by distance), the BOLD activity for the relevant condition (displayed at P < 0.005, min. 5 contiguous voxels), and the PE from the peak voxel in the ROI for each condition, for illustrative purposes only (note the “*” refers to SVC of the voxels in the SPM; error bars represent s.e.m.). All effects survive small-volume correction, including when ED (Euclidian distance) is added to the models. (B) Shows the data when the GLM included weighted regressors for the effects seen in A, such that the contrast was −3 1 1 1, testing for an overall effect of correlation with path distance during Travel, for the familiar navigation condition compared with all other conditions. †P < 0.1 SVC.CC BY · PubMed Central

Verification

Claims the paper makes

36 Own result · 24 Not checked yet · 14 Own method · 13 Own interpretation · 2 No basis given · 1 Supported · 1 Supported so far

Verify all 43 references — free account
  • Probably not a substantial claim. 25
  • It may state two separate claims as one. 28
  • The quote only partly states this claim. 1
46 of 46
  • The paper relies on this without support.

    The crucial difference between the familiar and recent environments was the long-term (>2 year) prior knowledge of one campus, after both campuses were intensively trained in the preceding days.

    “ensuring that the crucial difference between the 2 environments was the long-term (>2 year) prior knowledge of one of them.”
  • The paper relies on this without support.

    The virtual simulation of the campuses using Google Maps Street View panoramas is a valid model of real-world navigation that engages the same spatial representations as real-world navigation.

    “The task in the scanner was designed to simulate walking through the campuses, by using panorama images from Google Maps Street View”
  • The paper relies on this without support.

    Extensive systems-level consolidation has transformed mnemonic representations in highly familiar environments.

    “where extensive systems-level consolidation is thought to have transformed mnemonic representations.”
  • The paper relies on this without support.

    Participants' debriefing reports accurately reflect their planning during scanning.

    “Participants were instructed to report what they remembered thinking during the navigation, not what they should have done”
  • The paper relies on this without support.

    The environment used by Howard et al. (2014) contained more junctions than the environments used in the present study, so most routes in that study likely required many turns.

    “Notably, the environment used by Howard et al. (2014) (Soho, London, UK) contained more junctions to navigate across compared with the environments we used, thus most routes examined would likely have required many turns.”
  • The paper relies on this without support.

    Soho contains many more distinct landmarks than the campuses used in the present study.

    “Soho contains many more distinct landmarks than the campuses we used.”
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Citations

Cites

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Research works printed but not linked: 1

  1. Tulving, 1998, Episodic and declarative memory: role of the hippocampus, Hippocampus, 8, 198, 10.1002/(sici)1098-1063(1998)8:3<198::aid-hipo2>3.0.co;2-g · DOI · Paper page

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Metadata

Authors
Patai, E. Z., Javadi, A.-H., Ozubko, J. D., O’Callaghan, A., Ji, S., Robin, J., Grady, C., Winocur, G., Rosenbaum, R. S., Moscovitch, M., & Spiers, H. J.
Affiliations
  • University College London (GB)
  • University of Kent (GB)
  • SUNY Geneseo (US)
  • University of Toronto (CA)
  • York University (CA)
Corresponding author
Spiers, Hugo J
Venue
Cerebral Cortex
Published
2019-02-20
Volume
29
Issue
6
Pages
2748-2758
ISSN
  • 1047-3211 (ISSN-L)
  • 1460-2199
OpenAlex
W2923481172
Cited by
102
References
75
Citations by year
2026: 10 · 2025: 11 · 2024: 17 · 2023: 14 · 2022: 12 · 2021: 15 · 2020: 9 · 2019: 6
FWCI
6.11
Type
journal article
Language
en
Open access
Locations
  • Cerebral Cortex (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Kent Academic Repository (University of Kent) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • HJS
  • James S. McDonnell Foundation
  • Canadian Institute of Health Research (# 49566)
  • MM and SR
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Spatial Cognition and Navigation
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
Keywords
hippocampus, retrosplenial cortex, spatial navigation, systems consolidation, familiar environment, virtual navigation, external cues, route complexity, spatial memory
MeSH terms
Brain Mapping, Female, Goals, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Temporal Lobe, Young Adult, Spatial Memory, Spatial Learning, Spatial Navigation

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