As we learn, dynamic memory processes build structured knowledge across our experiences. Such knowledge enables the formation of internal models of the world that we use to plan, make decisions, and act. Recent theorizing posits that mnemonic mechanisms of differentiation and integration – which at one level may seem to be at odds – both contribute to the emergence of structured knowledge. We tested this possibility using fMRI as human participants learned to navigate within local and global virtual environments over the course of 3 days. Pattern similarity analyses on entorhinal cortical and hippocampal patterns revealed evidence that differentiation and integration work concurrently to build local and global environmental representations, and that variability in integration relates to differences in navigation efficiency. These results offer new insights into the neural machinery and the underlying mechanisms that translate experiences into structured knowledge that allows us to navigate to achieve goals. Memory is central to who we are, providing the foundation for our sense of self, understanding of the world, and predictions about the future. While we experience life as a series of events, our ability to build knowledge across events – to abstract common themes and infer relationships between events – enables us to construct internal models of the world, or ‘cognitive maps’, that we use to plan, make decisions, and act . One goaInteractions between a network of brain regions in the medial temporal lobe and neocortex are known to support episodic memory, or memory for events. Central to this network is the hippocampus, whose computations allow us to encode and recall specific episodes from the past and to abstract relationships across experiences that share common elements . In subsOne hypothesized hippocampal mechanism is pattern separation, in which the hippocampus creates distinct neural representations for highly similar events . ThroughDespite evidence supporting the hippocampus’s role in forming distinct memories for events, decades of work also suggest it is essential for building structured knowledge and forming cognitive maps that capture relations between spatial, perceptual, and conceptual features that were encountered in separate events that share common elements . For exaHow do overlapping features from separate events lead to integration and generalization? Prior work suggests that retrieval-based learning, recurrent mechanisms within the episodic memory network, and memory replay enable representational integration and allow for the encoding of relationships that have never been directly experienced . For exaDespite a large and rapidly expanding theoretical and empirical literature, our understanding of how differentiation and integration occur across discrete experiences and contribute to the building of coherent, multi-level structured representations is still far from comprehensive. Additional insights may come from examining these processes during the building of structured spatial knowledge, or maps of the external environment. For example, if one moves to a new city, one often first learns individual routes between navigational goals . Over time, the episodic memory network links individual routes, building a spatial map that enables planning of novel routes, detours, and shortcuts. Spatial knowledge acquired across learning consists of both local and global representations that are used to different extents based on navigational demands , and theSpatial learning is rich with structure and thus well-suited for exploring the role of differentiation and integration in the formation of local and global knowledge across experiences. Indeed, a previous study in which participants viewed first-person trajectories to real-world destinations found that representations of routes with overlapping sections were differentiated in the hippocampus following learning . AnotherTowards this goal, we developed an experimental paradigm that leverages immersive virtual navigation and fMRI to investigate how neural representations of items encountered during goal-directed navigation evolve across learning. Specifically, pattern similarity analyses and linear mixed-effects models characterized experience-driven changes in EC and hippocampus following ‘local’ and ‘global’ navigation. We hypothesized that we would find evidence for both integration and differentiation emerging at the same time points across learning, as participants build local and global representations of the virtual environment. Moreover, we predicted that early evidence of global map learning during local navigation would depend on integration and predict participants’ ability to subsequently navigate across the environment.Participants (n = 23) completed two behavioral navigation tasks interspersed between 3 consecutive days of fMRI . Partici22 = 82.831, one-sample t-test; p < 2.2e–16, d = 17.27) and did not differ across Day 1 test runs . To assess whether knowledge of goal locations within local environments was retained overnight, participants completed two additional test runs . Accuracy on Day 2 (mean ± SEM: 0.985 ± 0.004) did not differ from that on the final Day 1 test run .Across Day 1 test trials, participants navigated to the correct location on 96% of trials, with performance close to ceiling from the beginning of testing , top. Thβ = –8.99 ± 1.262; t = –7.125; p < 1.34e–12; d = 1.49), was lower on the first test run relative to the first learning run , and was consistently low across Day 1 test trials . As with accuracy, path inefficiency did not differ between Day 2 (mean ± SEM: 6.405 ± 1.681%) and the final Day 1 test run . Together, these data demonstrate that participants quickly acquired precise knowledge of goal locations on the three tracks and the relations between locations within a track, as evidenced by their ability to successfully navigate between goals using nearly the shortest possible path.Participants were instructed to take the shortest path to a goal. To assess learning of spatial relationships between goals in local environments, we computed a path inefficiency metric that represents a participant’s path length relative to the length of the shortest possible path (see Methods). During Local Navigation , bottom,20 = –3.396; p = 0.003, paired t-test; d = 0.74, 20 = 27.029, one-sample t-test; p < 2.2e–16, d = 5.90; across-track trials: t20 = 23.600, one-sample t-test; p < 2.2e–16, d = 5.15). During Global Navigation, within-track accuracy was higher than across-track accuracy . However, across-track accuracy showed a trend towards increasing across test runs , such that accuracy did not differ between trial types on the final Day 2 test run . Before fMRI on Day 3, participants completed four additional test runs of Global Navigation. Accuracy significantly improved overnight for both trial types , and did not differ between them on Day 3 .Next, 21 of 23 participants completed a Global Navigation Task where the separately learned tracks were connected and participants were required to navigate to goals both within and across tracks. While within-track accuracy on Day 2 was lower in the Global Task . Similarly, on Day 2 test runs, navigation was significantly more inefficient on across-track (mean ± SEM: 37.86 ± 6.389%) vs. within-track trials . However, performance on both trial types improved over the course of Day 2 test runs , such that efficiency did not significantly differ between them during the final Day 2 test run . As with accuracy, efficiency tended to improve overnight for both trial types .During Day 2, navigation was less efficient on the Global Task , bottom We also visually examined participants’ routes on each across-track Global Navigation Task trial at the start (first four test runs) and end (last two test runs) of the task on Day 2, and at the end (last two test runs) of Day 3, noting whether participants (a) initially switched to the correct track on the trial and (b) navigated in the correct direction after switching. At the start of the Global Task on Day 2, participants switched to the correct track on 65.78% (SD = 21.35%) of across-track trials. Of those trials, participants navigated in the correct direction after switching 77.75% (SD = 22.52%) of the time. By the end of Day 2, those numbers increased to 75.09% (SD = 20.33%) and 85.64% (SD = 16.91%) respectively. Performance continued to improve on Day 3, with participants switching to the correct track on 78.36% (SD = 21.77%) of across-track trials during the last two runs of the Global Navigation Task and navigating in the correct direction 90.48% (SD = 10.63%) of the time after switching tracks.Taken together, these data show that participants learned both the local environments (as evidenced by within-track navigation) and the global environment (as evidenced by across-track navigation), achieving high accuracy and efficiency on within- and across-track trials prior to the final scan on Day 3.Hippocampal representations predict later navigation performance and Figure 6).Examination of performance at the participant level revealed striking individual differences in path inefficiency when navigating across tracks in the Global Navigation Task on Day 2 . Some participants were highly efficient on across-track trials from the beginning, suggesting they had acquired much of the requisite global knowledge during performance of the preceding Local Task. By contrast, others became more efficient at across-track navigation over the course of the Global Task . This vaHippocampus and entorhinal cortex learn to separate the three tracks; distance within and across tracks, see The hippocampus represents both local and global distance) and (b) behavioral differences in when knowledge of the global environment was evident . To test neural hypotheses, we fit linear mixed-effects models against pattern similarity, and included fixed effects of interest, nuisance regressors corresponding to the average univariate activation for each stimulus as well as estimates of perceptual similarity, and a standard set of random effects. Complete details of modeling procedures can be found in the Methods. In the following sections, we report results from both planned and exploratory analyses. For planned analyses, we interpret a priori effects when significant at p < 0.05 uncorrected, but for completeness we note whether all reported effects survive FDR correction (see Methods). Given theoretical interest in ventromedial prefrontal cortex (vmPFC) and the representation of structured knowledge, we report exploratory analyses on data from this region in the Appendix.Our primary objectives were to characterize representational changes in the human memory network driven by local and global environmental learning. Participants underwent fMRI at three timepoints across the study—Pre-Learning, Post Local Navigation, and Post Global Navigation . During EC and hippocampus encode a spatial map that may underlie the formation of a cognitive map . To examβ = 0.006 ± 0.002; t = 2.756; p = 0.006, survived FDR correction; d = 0.60; β = –0.011 ± 0.003, t = –3.477, p = 0.0006, survived FDR correction; d = 0.76; Day 3 > Day 1 × hemisphere, β = 0.012 ± 0.003, t = 3.795, p = 0.0001, survived FDR correction; d = 0.83). The model also demonstrated a main effect of stimulus type  and an interaction between stimulus type and scan . The latter interaction reflected a difference in similarity between landmarks and fractals Pre-Learning  which was no longer present following Global Navigation .In the hippocampus, an initial model that included hemisphere revealed a main effect of hemisphere . This model also revealed an interaction between context and scan , such that there was no difference in similarity between within-track and across-track items Pre-Learning , nor were within-track items less similar to each other (i.e. differentiated) following Local Navigation . No main effect or interactions were observed with stimulus type.Turning to EC, an initial model with left and right EC revealed a main effect of hemisphere . A similar pattern of findings was observed in right EC, but did not reach statistical significance . Importantly, there was no interaction between region, scan, and context , d = 0.56; Day 3 > Day 1 × distance, β = 0.034 ± 0.015; t = 2.259; p = 0.024; did not survive FDR correction; d = 0.49; An initial model including hemisphere revealed no main effect or interactions with hemisphere . As a prIn addition to encoding local (within-track) spatial representations, navigation may result in the acquisition of a global map of the virtual environment. Although participants performed the Local Task separately on each track, it is possible that the landmarks shared across tracks trigger integration of the tracks, contributing to the encoding of (at least some dimensions of) a global environmental map. We were particularly interested in whether there would be evidence of global knowledge of the environment  after the Local Task, prior to any experience navigating across the connected tracks.To this end, we examined pattern similarity for landmarks located on different tracks at increasing distances, predicting it would scale with distance such that pattern similarity would be greatest for proximal vs. distal landmarks . To testβ = –0.02 ± 0.007; t = –2.891; p = 0.004, survived FDR correction; d = 0.60), but not the Global Task  and global (across-track) distance. An initial model of local distance that included hemisphere (left and right EC) and link distance (1 and 2) revealed interactions between scan session and hemisphere  and trial type  significantly predicted navigation performance in the landmark model. There was no interaction between hippocampal pattern similarity for landmark pairs and trial type .Next, we examined single-trial navigation data in relation to pairwise neural similarity in the hippocampus , using mixed-effects models to predict path inefficiency for each trial across the first four test runs of the Global Navigation Task (Day 2). The models included (a) neural similarity  for a given pair of fractals or the nearby landmarks and (b) length of the optimal path for each trial as predictors, and a regressor indicating whether the trial was a within-track or across-track trial. Models were run for landmarks and fractals separately. Here, we observed trend-level evidence that hippocampal pattern similarity  for landmark pairs predicted trial-level subsequent Global Navigation performance  and trial type .The length of the optimal path  learning restructures representations in the hippocampus and EC, reflecting the structure of the virtual environment; (b) the hippocampus begins to build a representational structure extending beyond directly experienced transitions; and (c) changes in the similarity structure of hippocampal representations relate to subsequent navigation performance.We first characterized learning on two behavioral navigation tasks. In the Local Navigation Task, participants quickly learned goal locations along individual tracks and navigated between goals using the most efficient paths . Prior tAt the neural level, we investigated whether mnemonic regions come to represent the three local tracks following navigation by comparing pattern similarity for items located on the same track vs. different tracks. Motivated by recent studies finding lower hippocampal pattern similarity for events with overlapping vs. distinct features , we expeSeveral factors may explain why we did not find stronger hippocampal differentiation effects. The demands of our navigation tasks differ substantially from those of prior tasks eliciting strong differentiation effects . In prioWhile we did not observe particularly strong differentiation of hippocampal patterns for all items located on the same track, we hypothesized that participants would need to form distinct representations of local environmental landmarks to perform well. Accordingly, we examined local spatial representations by comparing the similarity between nearest neighboring landmarks to second nearest neighbors on the same track, and found that hippocampal patterns for nearest neighboring landmarks were differentiated following both Local and Global Navigation . This fiWe next investigated global representations of map knowledge within the virtual environment. Here, we examined hippocampal pattern similarity for landmarks encountered on different tracks at increasing distances in the virtual environment, expecting to see the emergence of a relationship between hippocampal pattern similarity and distance. We were particularly interested in whether this relationship would be observed after the Local Task, which would suggest that participants were starting to build a global map of the environment prior to direct experience navigating the connected tracks.less efficient at the outset of Global Navigation, whereas we predicted a negative slope would be more apparent in highly efficient navigators. We initially hypothesized a negative distance-related similarity function would reflect global map learning, and thus expected to observe such a function in efficient navigators at the start of the Global Task on Day 2 and across all participants on Day 3. Such a function was absent following Global Navigation . While the present study was not designed to optimize power to detect trial-level effects, here we found trend-level evidence that hippocampal pattern similarity  for landmark pairs predicted trial-level subsequent Global Navigation performance , providiReplay and recurrence within the hippocampal network is thought to enable the hippocampus to learn relationships between items that have never been directly experienced together . The preThe present findings appear to diverge from work finding hippocampal pattern similarity scaled with perceived spatiotemporal distance in a virtual environment, such that patterns for nearby items were integrated . In thatWhile we expected similar results for both fractal and landmark stimuli throughout our analyses, the null findings we observed across ROIs when local and global distance models were run with fractal stimuli were not completely surprising. Fractal and landmark stimuli differ in several key ways, which we believe explain the observed pattern of findings. For example, fractals are only visible in the environment for a minority of trials. During the majority of navigation, participants must rely on the landmark buildings to guide them. Fractals serve as pointers to the goal location to which a participant must navigate on a particular trial, but fractal information may not be used during route planning. Further, fractals are not necessary for participants to learn the layout of the environment; local and global maps can be built from landmarks alone.a priori analyses focused on the hippocampus and EC, but we conducted exploratory analyses in to determine whether and how vmPFC would come to represent the virtual environment, given its proposed role as a storage site for integrated memory representations , and data from one participant were excluded for failing to respond during the fMRI task. The final sample consisted of 23 right-handed participants  with normal or corrected-to-normal vision and no self-reported history of psychiatric or neurological disorders. Day 3 data were excluded for two of these participants . Thus, Global Navigation Task/Day 3 analyses were conducted with 21 participants. Participants received compensation for their participation ($20/hr).Participants completed an experimental paradigm where they learned to navigate to goal locations within a virtual environment. The 3-day study included two behavioral navigation tasks—the Local Navigation Task and Global Navigation Task—and three fMRI scan sessions  that assParticipants first completed the Local Navigation Task, where they learned to navigate to goals on each of the three tracks separately, with fog serving to hide others from view . The next day, participants completed the Global Navigation Task, where the separately learned tracks were connected and participants were required to navigate to goals both within and across tracks , right. https://www.lughertexture.com and used to create landmarks in the virtual environment. Facades were selected to have a variety of features and be of different architectural styles and colors. After resizing, removing signs and reflections, and adding roofs in Adobe Photoshop , buildings were rendered onto 3D models of equal shape and size. Twelve buildings were randomly selected to be incorporated into each participant’s unique environment. Fractal images marked goal locations within the virtual environment. Twenty-three fractals were drawn from the Dryad Digital Repository  radius of each landmark (1 a.u. = ~3 m/10 ft); thus, goal locations differed slightly across participants.All 3D models for the virtual environment were created in Maya , a 3D graphics program. Textures were acquired via a Google search and The navigation tasks were developed with Panda3D (1.9.4), a python-based open-source gaming engine, and the PandaEPL library . For allDuring the Local Navigation Task, participants learned to navigate to goals on the individual tracks separately, with the other tracks hidden from view. After fMRI scanning on Day 1 (see fMRI task below), the Local Task began with four runs of learning trials on track 1. On learning trials, fractals were visible at goals to allow for learning of their locations within the track environment. The participants then completed two runs of test trials, where fractals were no longer visible and participants had to rely on memory to navigate successfully. This procedure was repeated for track 2 and track 3. After completing two runs of test trials on each track, participants began additional interleaved blocks of test trials, switching between tracks until six test runs  were completed for each of the individual tracks. In total, participants navigated from every goal on a track to every other goal on that track, several times. Track order was randomized and counterbalanced across participants. After Day 1, participants returned 24 hr (range = 21–31.5 hr) later for Day 2, which started with two additional runs of Local Navigation Task test trials on each track prior to fMRI on Day 2. These two runs provided an assay of whether knowledge of the individual tracks had been retained overnight.After fMRI on Day 2, participants began the Global Navigation Task. Here, the three tracks were connected and participants were required to navigate to goals both within and across the different tracks. In this task, common landmarks served as linking points within the larger environment . ParticiTo assay whether participants successfully learned goal locations, we computed percent correct as the ratio of the number of trials where participants ended navigation within 8 a.u. of the virtual goal location (defined as a correct response) over the number of trials attempted. To assay whether participants had learned spatial relationships between the locations in the environment, we computed a path inefficiency metric by dividing path length by the length of the shortest possible path to the goal, subtracting 1, and multiplying by 100 to express as a percent. Thus, a path inefficiency of 0% would indicate that the participant took the shortest path possible, and a path inefficiency of 100% would indicate that their path was twice the length of the shortest possible path to the goal.Participants underwent fMRI scanning prior to any learning (Day 1), after the Local Navigation Task (Day 2), and after the Global Navigation Task (Day 3). During each fMRI session, participants viewed images of the 12 landmarks and 15 fractals that were used in their unique virtual environment. Stimuli were presented 12 times per scan session (3 repetitions/run x 4 runs). Within each scan, the stimuli appeared in mini-blocks such that participants saw all 27 images prior to seeing any repeated, with third repeats appearing only after all stimuli had appeared twice. Images were pseudo-randomized within each mini-block such that the same image could not appear within three steps of itself at the block transitions. Moreover, no first order stimulus-to-stimulus transitions were repeated within a scan session.On each trial, the stimulus appeared on a gray background for 1.8 s, followed by a fixation cross of 5.4 s . To correct for distortions of the B0 field that may occur with EPI, two B0 field maps were acquired before every functional run, one in each phase encoding direction, with the same slice prescription as the functional runs. Structural images were acquired using a T1-weighted (T1w) spoiled gradient recalled echo structural sequence . The MR data collection techniques closely mirrored procedures in Whole-brain imaging data were acquired on a 3T GE Discovery MR750 MRI scanner  using a 32-channel radiofrequency receive-only head coil . Functional data were acquired using a three-band echo planar imaging (EPI) sequence (acceleration factor = 2) consisting of 63 oblique axial slices parallel to the long axis of the hippocampus  (Nipype 1.3.1 (RRID:SCR_002502) (SCR_004757) (Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as the target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w-reference using FAST  , which i_002502) . The T1-_002502) , distrib_004757) , and useFor each of the 12 functional runs per participant (4 per scan session), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. A B0-nonuniformity map (or fieldmap) was estimated based on the two EPI references with opposing phase-encoding directions, with 3dQwarp (AFNI) . Based oPrior to fMRI analyses, we removed the first 6 TRs in each run. For each scan session, we built general linear models (GLMs) for even and odd runs, which were regressed against preprocessed fMRI data at the voxel level. To obtain stimulus-level beta estimates for brain activity, each stimulus  was represented by a single regressor, time-locked to when it appeared onscreen in odd or in even runs. Each event was modeled as an epoch lasting for the duration of stimulus presentation (1.8 s) and convolved with a canonical hemodynamic response function. A separate regressor was included for response trials to exclude them from further analysis. Response trials were modeled as epochs lasting for the duration of the stimulus presentation and subsequent response period (7.2 s). GLMs also included nuisance regressors marking outlier TRs (DVARS > 5 or FD > 0.9 mm from previous TR), a run regressor, and regressors generated by fMRIPREP representing TR-level six-dimensional head movement estimates, framewise displacement (mm), and low frequency components for temporal high-pass filtering. GLMs yielded estimates of voxel-level brain activity for each stimulus during odd and even runs of each scan session. Modeling was performed with SPM12 (Wellcome Trust Centre for Neuroimaging) and custom Matlab (vR2017b) routines.a priori region-of-interest (ROI) approach targeting the hippocampus and EC. Bilateral hippocampal, entorhinal, parahippocampal, and perirhinal cortical ROIs were manually delineated on each participant’s high-resolution T1-weighted structural image using established procedures  is comparable to that of prior fMRI studies examining mnemonic representations that did not scan across three consecutive days e.g. .Statistical analyses were implemented in R (v3.6.3). For neural analyses described above, we implemented linear mixed-effects models using the lme4 and lmerTest statistical packages . Correlahttps://codeocean.com/capsule/9570390/tree/v1.To control for perceptual similarity between task stimuli, we input the stimuli into vNet, a deep neural network model. vNet was trained on ecoset, a large-scale image set containing images from 565 basic level categories . We extrFor all mixed-effects models, the standard set of random effects was chosen by taking a maximal model with all random effects indicated by the experiment setup, then incrementally removing effects and testing the nested model fits using the likelihood ratio test. This procedure was performed for all ROIs. The final standard set of random effects was composed of the minimum necessary effects to achieve the best fit in all regions, and included a random intercept modeling the mean subject-specific outcome value, as well as random slope terms modeling subject-specific effects of independent variables of interest . Scan session was dummy-coded with Pre-Learning (Day 1) as the baseline. Stimulus type and context were sum-coded with the contrasts of landmark > fractal and same track > different tracks, respectively. For the trial-level model relating performance to hippocampal pattern similarity and distance, separate models were run for landmarks and fractals. Models predicted path inefficiency for each trial in the first four test runs of the Global Navigation Task (Day 2). Fixed effects of interest included the length of the optimal path for that trial, the estimated hippocampal pattern similarity for the pair of fractal goals or nearby landmarks associated with the start and end locations the trial, and a regressor indicating whether the trial was a within- or across-track trial. Models were estimated using a restricted maximum likelihood (REML) approach. Model convergence issues were resolved by changing from the default optimizer to ‘bobyqa’ and not having the model calculate derivatives.m, a number representing the number of tests conducted for a given hypothesis multiplied by the number of ROIs we examined . For example, to examine learning-driven changes in pattern similarity for items located at different distances on the same track, we tested for two interactions: distance and scan (Day 2 > Day 1), and distance and scan (Day 3 > Day 1). ROIs tested were hippocampus, left and right EC, vmPFC, and the visual control region . m thus represented 2 interactions * 5 ROIs = 10. We then controlled for the false discovery rate (FDR) by ordering the p-values for each hypothesis test from smallest to largest (P(min)...P(max)), and checking if the following was satisfied for each ordered p-value ≤α ×a priori predicted effects at this level. For completeness, we also note whether the reported effects survived FDR correction throughout the text and in the Appendix tables. Cohen’s d effect sizes were computed for t-values as Unless otherwise specified, all p-values reported were uncorrected and we interpret https://github.com/coreyfernandez/RID,  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Chris Baker as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Kenneth A Norman (Reviewer #1); Jacob L S Bellmund (Reviewer #3).Thank you for submitting your article "Representational integration and differentiation in the human hippocampus following goal-directed navigation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.As you will see from the detailed comments below, the reviewers all think this study addresses an interesting and timely question with careful design and analysis all leading to a high-quality manuscript.However, the reviewers also highlight areas where the manuscript could be improved, including additional analyses. Below, I summarize the essential revisions you will need to make, although we would like you to respond in detail to all of the comments and recommendations of the reviewers.Essential revisions:1) Given the exploratory nature of many of the analyses, you should provide greater clarity on the specific predictions, highlight what alternative predictions/outcomes would mean, and provide a clearer link between the conceptual prediction and analytic outcomes. In this context, you should also provide greater clarity on how multiple comparisons were applied for the different analyses.2) Please present the results in a clear and consistent way throughout the manuscript .3) Consider an alternate analytic analysis to the median split applied to the performance data. We would recommend a mixed effects model (see Reviewer 2).4) The reviewers were skeptical of the explanation of the results linking behavior to neural similarity . The additional behavioral and neural analyses suggested in the reviews might help you arrive at a better-substantiated account of these results.5) Fractals and landmarks should be analyzed in a more consistent way .6) Present additional behavioral measures to more precisely examine navigational performance (see comments from Reviewer 3).7) Reconsider the hippocampus-vmPFC "connectivity" analysis (see comments from Reviewer 2).Reviewer #1 (Recommendations for the authors):In addition to addressing the recommendations noted in the public review, here are some other points for the authors to consider:1. In the Introduction, the authors say, "we predicted that early evidence of global map learning during local navigation would depend on the integration and predict participants' ability to subsequently navigate across the environment". In the paper, it is not clear what the basis for this prediction was: Was this an a priori prediction arising from existing results and/or theories, or did the prediction arise after the authors observed the slope across the distance 2, 3, 4 conditions post-local-learning? It is also unclear whether the authors predicted the specific direction of this relationship . Lastly, it is unclear whether the authors originally planned to operationalize "global map learning" using the slope across the 2, 3, and 4 conditions, and whether they also considered other ways of relating neural data to navigation behavior. Specifying the answers more clearly in the paper will help readers to evaluate the results.2. Issues with results presentation: The way that the results are presented makes it very challenging to do apples-to-apples comparisons of different brain regions. For example, Figure 3 shows the "within – across context similarity" measure for EC and Figure 4 shows the "link distance 1 – link distance 2 similarity" measure for the hippocampus. Featuring different measures for different areas makes it hard to compare them. I know that the corresponding apples-to-apples measures are in various supplements but readers should not have to dig for them. If the authors had a priori reasons to feature these different dependent measures for different regions, that might justify the results presentation strategy used here, but – as things stand – it is unclear why some results were featured in the main text and others were relegated to the supplement. In the absence of an a priori justification for the present way of displaying results, I think it would be useful if the authors showed corresponding results for hippocampus and EC right next to each other in the main paper, regardless of significance.3. Additional analyses to connect to other findings in the literature: I think it would be useful for the authors to consider whether there are additional exploratory analyses they could run to connect better to other findings in the literature. For example, many of the extant studies on MTL integration and differentiation separately investigate hippocampal subfields. If there are reasons to expect different results in different subfields, it might be worth doing this . Also, the "hippocampal-vmPFC interaction" analysis is unconventional. Usually, this involves some kind of functional connectivity but here it was a second-order similarity analysis. Is there a reason that the authors took this approach? Do the authors think that their paradigm lends itself to interesting predictions relating to the more standard functional connectivity approach? Lastly, there are other ROIs that are known to be involved in spatial learning, e.g., RSC. Why did the authors focus on vmPFC instead of RSC in their analyses?4. I think that it might be useful to show the similarity values for the 1, 2 within-track conditions alongside the 2, 3, and 4 (across-track) conditions. This may not be enough to resolve the question about whether integration is taking place, but looking at these results together  might provide a more clear picture of what the regions are doing.Reviewer #2 (Recommendations for the authors):I have a few more suggestions to help with readability/interpretation.1. It was difficult to keep track of the results as they related to the different ROIs, within vs. across tracks, distances, and time (related to my comment #5 above). It would be helpful to display all relevant plots in the same way throughout the manuscript, especially as the same mixed-effects models were applied . Even if the findings are not significant, it is helpful to see the results in the same figure rather than comparing them with supplementary figures.For example, Figure 4B shows data for same-track landmarks collapsed across link distance, and Figure 4C shows data for different-track landmarks split by link distance. This makes it difficult to directly compare these values, even though they were entered into the same model.2. More broadly, the reporting of the results was somewhat difficult to follow. While I appreciate the importance of accounting for all factors in the same model, the order in which different factors were reported sometimes varied across models, making it difficult to keep in mind how the different models compared.3. As I was reading the results, I made a note to suggest a distance analysis only to realize it was carried out later on (p. 16) – it would be helpful to include more signposting to motivate and help the reader anticipate this analysis.4. Figure 5B was difficult to follow. The solid lines represent similarity prior to the global navigation task for both groups, but the dashed line only represents similarity post-global navigation for the less efficient navigators.Reviewer #3 (Recommendations for the authors):1. The path inefficiency metric conflates the effects of local and global knowledge of the environment. For example, when participants switch tracks correctly in the global condition but fail to respond precisely on the goal track, this could result in a relatively high error despite efficient global navigation performance. While the comparison to the local condition is helpful of course, the report of additional behavioral measures would be desirable in my view. Relevant measures could quantify how frequently participants (a) manage to switch tracks correctly and (b) how frequently they choose to navigate in the correct direction after switching tracks.2. It is unclear to me why the distance-based analyses  are based on the landmark images only. I would expect a similar effect to be present for the fractal stimuli as participants were asked to memorize the fractal locations. Thus, I think demonstrating a similar effect for the fractals as for the landmarks would substantially strengthen the finding of a hippocampal representation of local and global distances.3. Again related to the analysis of distance representations in the hippocampus, I am wondering why the authors chose the link distance as their distance measure. I think the paper would benefit from a justification for why this measure was chosen.eLife, 2016). I would be curious to see if the authors can detect similar representations of Euclidean distances in their data in an additional exploratory analysis.4. With respect to the formation of cognitive maps of space there is also evidence for the representation of (remembered) Euclidean distances in the hippocampus , I would like to know whether the effect also holds when using an approach based on correlation/regression.7. I tried to access the analysis code, but the GitHub repository referred to in the Data availability statement is currently empty. Essential Revisions (for the authors):1) Given the exploratory nature of many of the analyses, you should provide greater clarity on the specific predictions, highlight what alternative predictions/outcomes would mean, and provide a clearer link between the conceptual prediction and analytic outcomes. In this context, you should also provide greater clarity on how multiple comparisons were applied for the different analyses.We thank the Editors and Reviewers for these helpful directions. Throughout the revised manuscript, we added text that we believe more clearly links predictions with outcomes. For example:“We hypothesized that there would be a change in hippocampal and entorhinal pattern similarity for items located on the same track vs. items located on different tracks. An increase in pattern similarity would suggest that within-track item representations are integrated, while a decrease would suggest these representations are differentiated following learning.”Other edits made to address this concern are marked in the manuscript using track changes.Regarding corrections for multiple comparisons, we have added the following to the Results:“We report results from both planned and exploratory analyses. For planned analyses, we interpret a priori effects when significant at p <.05 uncorrected, but for completeness we note whether all reported effects survive FDR correction (see Methods).”Further, we have added the following to the Methods section:97: “To correct for multiple comparisons, we first computed Further, as per Reviewer 1 :m for each model in the Appendix tables and clearly indicate which findings survive FDR correction. For example, we added the following text to the caption for Appendix – Table 1:– We now report the denominator “To correct for multiple comparisons, we adjusted the – We updated text throughout the manuscript to further clarify which results survived corrections for multiple comparisons.– We emphasize in the Discussion that a number of our findings do not survive FDR correction and encourage the reader to interpret our results with caution:“Moreover, some of our findings do not survive correction for multiple comparisons, and thus should be interpreted with caution.”2) Please present the results in a clear and consistent way throughout the manuscript .We are grateful for the feedback we received from several reviewers with suggestions about how to improve the clarity and consistency of the presentation of our findings. Accordingly, we revised the figures to present findings from hippocampus and EC next to each other (regardless of significance) in the main text. To be consistent in reporting our findings for the local and global distance analyses, we now report similarity values (as opposed to model contrasts) for the link distance 1 and 2 (within-track) conditions in Figure 4.3) Consider an alternate analytic analysis to the median split applied to the performance data. We would recommend a mixed effects model (see Reviewer 2).We greatly appreciate the reviewers’ suggestions regarding alternative analytic approaches. We removed this section of the original manuscript and ran two models to address comments related to the median split: (1) a mixed-effects model predicting neural pattern similarity Post Local Navigation, with a continuous metric of task performance  and link distance as predictors (as suggested by Reviewer 3); and (2) a mixed-effects model relating trial-wise navigation data to pairwise similarity values for each given pair of landmarks and fractals (as suggested by Reviewer 2). We report findings for both; the following has been added to the main text:“The variability in the observed across-track hippocampal distance effect may reflect that some participants encoded global map knowledge during Local Navigation, whereas others did not . To the extent that this is the case, this would predict that the distance-related hippocampal pattern similarity effect Post Local Navigation should relate to navigational efficiency at the outset of performing the Global Task. Specifically, we predicted that more efficient navigators would have a negative distance function, such that pattern similarity would be greatest for the most proximal across-track landmarks and decrease with distance. To test this hypothesis, we first ran a mixed effects model predicting neural pattern similarity Post Local Navigation (Day 2), with path inefficiency  and link distance as predictors. Indeed, we observed a significant interaction between path inefficiency and link distance , but the direction of the effect was unexpected. Participants who did well from the beginning of the Global Task showed no effect of distance in hippocampal pattern similarity, whereas less efficient navigators showed a negative slope . A similar interaction between path inefficiency and link distance was not observed when the model was fit to data from Day 1  or to data from Day 3 .Next, we examined single-trial navigation data in relation to pairwise neural similarity in the hippocampus , using mixed-effects models to predict path inefficiency for each trial across the first four test runs of the Global Navigation Task (Day 2). The models included (a) neural similarity  for a given pair of fractals or the nearby landmarks and (b) length of the optimal path for each trial as predictors, and a regressor indicating whether the trial was a within-track or across-track trial. Models were run for landmarks and fractals separately. Here we observed trend-level evidence that hippocampal pattern similarity  for landmark pairs predicted trial-level subsequent Global Navigation performance , such that greater hippocampal pattern similarity was predictive of a more efficient path . The length of the optimal path  and trial type  significantly predicted navigation performance in the landmark model. There was no interaction between hippocampal pattern similarity for landmark pairs and trial type .-5, survived FDR correction; d = 0.96; Appendix – Table 28) were also significant predictors of navigation performance in the model relating pattern similarity for fractal pairs to subsequent performance on the Global Task. However, hippocampal pattern similarity for fractal pairs did not significantly predict subsequent performance .”The length of the optimal path  and trial type  The reviewers were skeptical of the explanation of the results linking behavior to neural similarity . The additional behavioral and neural analyses suggested in the reviews might help you arrive at a better-substantiated account of these results.Based on the additional behavioral and neural analyses suggested by reviewers, we added the following to the Discussion section:less efficient at the outset of Global Navigation, whereas we predicted a negative slope would be more apparent in highly efficient navigators. We initially hypothesized a negative distance-related similarity function would reflect global map learning, and thus expected to observe such a function in efficient navigators at the start of the Global Task on Day 2 and across all participants on Day 3. Such a function was absent following Global Navigation .“We hypothesized that this variability in the neural data might relate to the behavioral variability we observed on the subsequent Global Navigation Task, and explored this relationship in two ways. First, we ran a mixed effects model predicting hippocampal pattern similarity Post Local Navigation, with across-track path inefficiency at the start of Global Navigation and link distance as predictors. Here we observed a significant interaction between path inefficiency and link distance, but the effect was not in the direction we expected. Our results indicate that the interaction between distance and scan  was driven by participants who were 61. Hippocampal pattern similarity differed, however, between items that crossed an event boundary, such that nearby items had increased pattern similarity compared to items located further apart in time. While this study examined temporal events, spatial event boundaries may function similarly in that during retrieval, representations of other within-event items are reinstated. The flat slope observed on Day 3 could thus be a signature of an integrated map.Why do we observe a flat distance-related similarity function after global map learning? It is possible that individual differences in navigational strategy or the particular learning processes utilized by efficient navigators in the present task organized the map in such a way. It is also possible our findings reflect a change in event boundaries. Evidence from a study examining temporal context found that hippocampal pattern similarity did not differ between items located nearby vs. further apart within a temporal eventOur second approach to relating variability in the neural data to behavioral variability used a model to predict path inefficiency for each trial across the first four test runs of the Global Navigation Task (Day 2). While the present study was not designed to optimize power to detect trial-level effects, here we found trend-level evidence that hippocampal pattern similarity  for landmark pairs predicted trial-level subsequent Global Navigation performance , providing additional suggestive evidence for a relationship between hippocampal pattern similarity and subsequent behavior.”5) Fractals and landmarks should be analyzed in a more consistent way .In our original submission, we included findings with respect to fractals in all analyses except for the distance analyses. In the revision, we updated the relevant sections of the manuscript to include results from fractals. No significant interactions between distance and scan were observed when local and global distance models were run for fractal stimuli in the hippocampus, EC, and vmPFC. These findings have been added throughout the main text and the Appendix with variations of the following language:“There were no significant interactions between distance and scan when similar models were run for fractal stimuli.”Full model results are reported in the Appendix .In addition, based on feedback from Reviewers 2 and 3, we added the following to the discussion:“While we expected similar results for both fractal and landmark stimuli throughout our analyses, the null findings we observed across ROIs when local and global distance models were run with fractal stimuli were not completely surprising. Fractal and landmark stimuli differ in several key ways, which we believe explain the observed pattern of findings. For example, fractals are only visible in the environment for a minority of trials. During the majority of navigation, participants must rely on the landmark buildings to guide them. Fractals serve as pointers to the goal location to which a participant must navigate on a particular trial, but fractal information may not be used during route planning. Further, fractals are not necessary for participants to learn the layout of the environment; local and global maps can be built from landmarks alone.”6) Present additional behavioral measures to more precisely examine navigational performance (see comments from Reviewer 3).Following the guidance from Reviewer 3, we determined whether participants (a) switched to the correct track and (b) navigated in the correct direction once switching on each Global Navigation trial at the start (first four test runs) and end (last two test runs) of the task on Day 2, and at the end of Day 3 (last two test runs).We added the following to the Results section:“We also visually examined participants’ routes on each across-track Global Navigation Task trial at the start (first four test runs) and end (last two test runs) of the task on Day 2, and at the end (last two test runs) of Day 3, noting whether participants (a) initially switched to the correct track on the trial and (b) navigated in the correct direction after switching. At the start of the Global Task on Day 2, participants switched to the correct track on 65.78% (SD = 21.35%) of across-track trials. Of those trials, participants navigated in the correct direction after switching 77.75% (SD = 22.52%) of the time. By the end of Day 2, those numbers increased to 75.09% (SD = 20.33%) and 85.64% (SD = 16.91%) respectively. Performance continued to improve on Day 3, with participants switching to the correct track on 78.36% (SD = 21.77%) of across-track trials during the last two runs of the Global Navigation Task and navigating in the correct direction 90.48% (SD = 10.63%) of the time after switching tracks.”7) Reconsider the hippocampus-vmPFC "connectivity" analysis (see comments from Reviewer 2).We could not undertake a more traditional univariate functional connectivity analysis because we did not scan during navigation. We agree with the reviewers that the null results reported for the second-order similarity analysis in the initial manuscript are confusing and difficult to interpret. Based on the reviewer comments, we replaced the second-order similarity analysis with the following:-8, survived FDR correction; d = 1.25) and Global Navigation , but not in the visual control region . This pattern of findings suggests that functional connectivity between the hippocampus and vmPFC weakens over time.”“While context- and distance-related effects on vmPFC and hippocampal pattern similarity did not significantly differ, such effects were significant in hippocampus but not in vmPFC. To further test whether distance-related similarity structures were similar between the regions, we modeled the relationship between vmPFC pattern similarity and hippocampal pattern similarity. Specifically, we ran a linear mixed-effects model predicting pairwise similarity values in vmPFC, with scan session, pairwise similarity values in the hippocampus (averaged across hemispheres) and pairwise similarity values in a visual control region as predictors. Here, we observed significant interactions between scan session and pairwise similarity values in the hippocampus Post Local :Here are some other points for the authors to consider:1. In the Introduction, the authors say, "we predicted that early evidence of global map learning during local navigation would depend on the integration and predict participants' ability to subsequently navigate across the environment". In the paper, it is not clear what the basis for this prediction was: Was this an a priori prediction arising from existing results and/or theories, or did the prediction arise after the authors observed the slope across the distance 2, 3, 4 conditions post-local-learning? It is also unclear whether the authors predicted the specific direction of this relationship . Lastly, it is unclear whether the authors originally planned to operationalize "global map learning" using the slope across the 2, 3, and 4 conditions, and whether they also considered other ways of relating neural data to navigation behavior. Specifying the answers more clearly in the paper will help readers to evaluate the results.Thank you for this recommendation. We followed it by adding the following to the Results section so as to clarify our prediction with respect to the brain-behavior analysis:“Specifically, we predicted that more efficient navigators would have a negative distance function, such that pattern similarity would be greatest for the most proximal across-track landmarks and decrease with distance. To test this hypothesis, we first ran a mixed effects model predicting neural pattern similarity Post Local Navigation (Day 2), with path inefficiency  and link distance as predictors. Indeed, we observed a significant interaction between path inefficiency and link distance , but the direction of the effect was unexpected. Participants who did well from the beginning of the Global Task showed no effect of distance in hippocampal pattern similarity, whereas less efficient navigators showed a negative slope . A similar interaction between path inefficiency and link distance was not observed when the model was fit to data from Day 1  or to data from Day 3 .”We found the direction of this effect to be counterintuitive as well. Additional follow-up analyses provided further suggestive evidence that across-track path inefficiency relates to trial-level hippocampal pattern similarity . We offer the following interpretation of these analyses in the Discussion:less efficient at the outset of Global Navigation, whereas we predicted a negative slope would be more apparent in highly efficient navigators. We initially hypothesized a negative distance-related similarity function would reflect global map learning, and thus expected to observe such a function in efficient navigators at the start of the Global Task on Day 2 and across all participants on Day 3. Such a function was absent following Global Navigation .“Our results indicate that the interaction between distance and scan  was driven by participants who were 61. Hippocampal pattern similarity differed, however, between items that crossed an event boundary, such that nearby items had increased pattern similarity compared to items located further apart in time. While this study examined temporal events, spatial event boundaries may function similarly in that during retrieval, representations of other within-event items are reinstated. The flat slope observed on Day 3 could thus be a signature of an integrated map.”Why do we observe a flat distance-related similarity function after global map learning? It is possible that individual differences in navigational strategy or the particular learning processes utilized by efficient navigators in the present task organized the map in such a way. It is also possible our findings reflect a change in event boundaries. Evidence from a study examining temporal context found that hippocampal pattern similarity did not differ between items located nearby vs. further apart within a temporal event2. Issues with results presentation: The way that the results are presented makes it very challenging to do apples-to-apples comparisons of different brain regions. For example, Figure 3 shows the "within – across context similarity" measure for EC and Figure 4 shows the "link distance 1 – link distance 2 similarity" measure for the hippocampus. Featuring different measures for different areas makes it hard to compare them. I know that the corresponding apples-to-apples measures are in various supplements but readers should not have to dig for them. If the authors had a priori reasons to feature these different dependent measures for different regions, that might justify the results presentation strategy used here, but – as things stand – it is unclear why some results were featured in the main text and others were relegated to the supplement. In the absence of an a priori justification for the present way of displaying results, I think it would be useful if the authors showed corresponding results for hippocampus and EC right next to each other in the main paper, regardless of significance.We appreciate this comment and agree that the organization of the paper makes it difficult to compare results between regions. Accordingly, we revised the figures to present findings from the hippocampus and EC next to each other. Please see our above response to Essential Revisions General comment #2 for additions to the revised manuscript.3. Additional analyses to connect to other findings in the literature: I think it would be useful for the authors to consider whether there are additional exploratory analyses they could run to connect better to other findings in the literature. For example, many of the extant studies on MTL integration and differentiation separately investigate hippocampal subfields. If there are reasons to expect different results in different subfields, it might be worth doing this . Also, the "hippocampal-vmPFC interaction" analysis is unconventional. Usually, this involves some kind of functional connectivity but here it was a second-order similarity analysis. Is there a reason that the authors took this approach? Do the authors think that their paradigm lends itself to interesting predictions relating to the more standard functional connectivity approach? Lastly, there are other ROIs that are known to be involved in spatial learning, e.g., RSC. Why did the authors focus on vmPFC instead of RSC in their analyses?The exploratory analysis in vmPFC was flagged by multiple reviewers, and we now take an alternative approach in the resubmission. Please see our response above to Essential Revisions General comment #7 for full details. Because we cannot undertake a more traditional univariate functional connectivity analysis , we have moved the vmPFC section to the Appendix.Finally, we agree that additional exploratory analyses  have merit. We are currently undertaking an analysis in RSC and hope to publish these findings in the future; however, in this work we focus on a priori hypotheses in EC and hippocampus. Unfortunately, our scanning protocol was not optimized for subfield segmentation.4. I think that it might be useful to show the similarity values for the 1, 2 within-track conditions alongside the 2, 3, and 4 (across-track) conditions. This may not be enough to resolve the question about whether integration is taking place, but looking at these results together  might provide a more clear picture of what the regions are doing.Following this guidance and other related comments, we revised the figures in the main text to show similarity values for the 1 and 2 within-track conditions , in addition to the 2, 3, and 4 across-track conditions . Please see our above response to Essential Revisions General comment #2 for additions to the revised manuscript.Reviewer #2 (Recommendations for the authors):I have a few more suggestions to help with readability/interpretation.1. It was difficult to keep track of the results as they related to the different ROIs, within vs. across tracks, distances, and time (related to my comment #5 above). It would be helpful to display all relevant plots in the same way throughout the manuscript, especially as the same mixed-effects models were applied . Even if the findings are not significant, it is helpful to see the results in the same figure rather than comparing them with supplementary figures.For example, Figure 4B shows data for same-track landmarks collapsed across link distance, and Figure 4C shows data for different-track landmarks split by link distance. This makes it difficult to directly compare these values, even though they were entered into the same model.We appreciate this comment and agree that the organization of the paper makes it difficult to compare results across regions. Accordingly, we revised the figures to present findings from the hippocampus and EC next to each other (regardless of significance). Please see our above response to Essential Revisions General comment #2 for additions to the revised manuscript.2. More broadly, the reporting of the results was somewhat difficult to follow. While I appreciate the importance of accounting for all factors in the same model, the order in which different factors were reported sometimes varied across models, making it difficult to keep in mind how the different models compared.We appreciate this guidance and revised the manuscript with a focus on increasing the clarity of our results, with an emphasis on how the reported results relate to our a priori hypotheses.3. As I was reading the results, I made a note to suggest a distance analysis only to realize it was carried out later on (p. 16) – it would be helpful to include more signposting to motivate and help the reader anticipate this analysis.We appreciate this feedback and added additional language to the beginning of the Results, under the section entitled “fMRI assays of learning-driven representational change”:“we extracted voxel-level estimates of neural activity for each stimulus from EC and hippocampus regions-of-interest  and used pattern similarity analysis to probe whether learning resulted in representational differentiation or integration as a function of (a) the experienced relations between stimuli in the environment  and (b)…”4. Figure 5B was difficult to follow. The solid lines represent similarity prior to the global navigation task for both groups, but the dashed line only represents similarity post-global navigation for the less efficient navigators.We removed the median-split analysis from the manuscript and the dashed line from this figure . As noted above, our analysis of the across-participant brain-behavior relationship was now conducted using a linear mixed-effects model. However, to qualitatively visualize the findings from the linear model, we retained presentation of the split-half data in Figure 6, and we revised the caption as follows:“To qualitatively visualize the relationship between pattern similarity, link distance, and path inefficiency, we split participants into two groups – More Efficient and Less Efficient – based on their median path inefficiency on across-track trials in the first four test runs of the Global Task on Day 2. (A) Path inefficiency (%) for each across-track trial during the first four test runs of the Global Navigation Task, plotted for each participant and colored by performance group. (B) We used a linear mixed-effects model to formally test this relationship (see main text for details). The linear model revealed a significant interaction between path inefficiency and link distance, with the direction of the effect being unexpected. To qualitatively depict this effect, we plot hippocampal pattern similarity for landmarks on different tracks prior to the Global Navigation Task for the Less Efficient and More Efficient median-split data. Data are split by participants’ subsequent navigation performance as shown in (A). .”Reviewer #3 (Recommendations for the authors):1. The path inefficiency metric conflates the effects of local and global knowledge of the environment. For example, when participants switch tracks correctly in the global condition but fail to respond precisely on the goal track, this could result in a relatively high error despite efficient global navigation performance. While the comparison to the local condition is helpful of course, the report of additional behavioral measures would be desirable in my view. Relevant measures could quantify how frequently participants (a) manage to switch tracks correctly and (b) how frequently they choose to navigate in the correct direction after switching tracks.We thank Dr. Bellmund for the proposed suggestions. In the revised manuscript, we determined whether participants (a) switched to the correct track and (b) navigated in the correct direction once switching on each Global Navigation trial at the start (first four test runs) and end (last two test runs) of the task on Day 2, as well as at the end of Day 3 (last two test runs). We agree that the addition of these data provides a more complete window onto task performance. Please see the above response to Essential Revisions General comment #6 for full details.2. It is unclear to me why the distance-based analyses  are based on the landmark images only. I would expect a similar effect to be present for the fractal stimuli as participants were asked to memorize the fractal locations. Thus, I think demonstrating a similar effect for the fractals as for the landmarks would substantially strengthen the finding of a hippocampal representation of local and global distances.Again, we are grateful for the feedback on how to improve the consistency of results reporting. In the revision, we now include results for fractal stimuli in the revised manuscript; please see our above response to Essential Revisions General comment #5 for full details.3. Again related to the analysis of distance representations in the hippocampus, I am wondering why the authors chose the link distance as their distance measure. I think the paper would benefit from a justification for why this measure was chosen.Please see our above response to Dr. Bellmund’s General Comment #3.4. With respect to the formation of cognitive maps of space there is also evidence for the representation of (remembered) Euclidean distances in the hippocampus . I would be curious to see if the authors can detect similar representations of Euclidean distances in their data in an additional exploratory analysis.We appreciate Dr. Bellmund’s suggestion; however, our task was not designed with hypotheses regarding Euclidean distances in mind . We ran a small behavioral pilot study that asked participants to draw a map of the environment following Global Navigation, with mixed success. While some participants were able to do this, others were unable to conceptualize a top-down view of the environment. Further, a number of participants thought the tracks were circular instead of oval-shaped, which would distort their estimates of Euclidean distances. For these reasons, we ultimately decided against conducting the proposed exploratory analyses.5. A surprising and somewhat puzzling aspect of the reported results is that the global distance effect can only be detected after the local, but not after the global navigation task. Further, this effect seems to be driven by participants who take less efficient paths. I cannot quite follow the authors' interpretation in the Results section that the "acquisition of a more fully integrated global map" would lead to this pattern of results. Further, the authors state in the Discussion section that a "negative distance-related similarity function reflects restricted learning of the global map, hindering performance on the Global Task". In my view, a negative relationship between distance and representational similarity could rather point towards the formation of an integrated, map-like representation of the environment, where nearby landmarks share more similar representations than those that are separated by larger distances. I would appreciate some clarification on why the authors think such an effect would hinder task performance and why it might disappear with the "building of a more global cognitive map".As noted above, we appreciate Dr. Bellmund’s input here. We revised and clarified the Discussion based on reviewer comments. Please see our above response to Essential Revisions General comment #4.6. I am wondering about why the authors chose a median split to analyze interindividual differences. As dichotomizing a continuous variable such as navigation performance is often problematic , I would like to know whether the effect also holds when using an approach based on correlation/regression.As noted above, we are grateful for this input. Following this guidance, we replaced the median-split analysis with a mixed-effects model predicting neural pattern similarity Post Local Navigation, with a continuous metric of task performance  and link distance as predictors. Please see our above response to Dr. Bellmund’s General Comment 5.7. I tried to access the analysis code, but the GitHub repository referred to in the Data availability statement is currently empty.We remedied this oversight. The analysis code is now available at https://github.com/coreyfernandez/RID. fMRI data will be uploaded to Open Neuro by the time of publication.


Vaccine hesitancy – a delay in acceptance or refusal of vaccines despite availability – has the potential to threaten the successful roll-out of SARS-CoV-2 vaccines globally. In this study, we aim to understand the likely impact of vaccine hesitancy on the control of the COVID-19 pandemic.We modelled the potential impact of vaccine hesitancy on the control of the pandemic and the relaxation of non-pharmaceutical interventions (NPIs) by combining an epidemiological model of SARS-CoV-2 transmission with data on vaccine hesitancy from population surveys.Our simulations suggest that the mortality over a 2-year period could be up to 7.6 times higher in countries with high vaccine hesitancy compared to an ideal vaccination uptake if NPIs are relaxed. Alternatively, high vaccine hesitancy could prolong the need for NPIs to remain in place.While vaccination is an individual choice, vaccine-hesitant individuals have a substantial impact on the pandemic trajectory, which may challenge current efforts to control COVID-19. In order to prevent such outcomes, addressing vaccine hesitancy with behavioural interventions is an important priority in the control of the COVID-19 pandemic. People refusing or delaying COVID-19 vaccination might impact current efforts to control the pandemic caused by SARS-CoV-2. Here, we have examined the effects of low vaccine uptake due to vaccine hesitancy on the need to prolong other public health measures to control the pandemic. We used mathematical modelling and data on vaccine hesitancy from population surveys across different countries. Our results suggest that when there is vaccine hesitancy and relaxation of other public health measures, mortality could increase by up to seven times compared with ideal vaccination coverage of the population. Furthermore, for some scenarios analysed, longer and more stringent public health measures would be required to compensate for lower vaccine uptake. Our work demonstrates that vaccine hesitancy might have a substantial health impact on the population, and therefore, it is a public health priority to increase trust in vaccines. Olivera Mesa et al. model the potential impact of vaccine hesitancy in prolonging the need for non-pharmaceutical interventions (NPIs) to control the COVID-19 pandemic. They show that vaccine hesitancy could prolong the need for NPIs to remain in place and lead to increased mortality if NPIs are relaxed. As a control measure, non-pharmaceutical interventions (NPIs) such as social distancing and mobility restrictions have been put in place worldwide and have successfully reduced transmission of the virus. However, these interventions are unsustainable in the long-termhttps://vac-lshtm.shinyapps.io/ncov_vaccine_landscape/) and more than 1.3 billion vaccination doses administered worldwide (https://ourworldindata.org/covid-vaccinations#). Their reported efficacy against symptomatic disease ranges from 50% to over 95%6. Given the high basic reproduction number for SARS-CoV-2 (estimates range between 3–4)1 high levels of vaccine uptake will be required to achieve herd immunity7, particularly if children are not vaccinated during the first phase of roll-out.In December 2020, the first vaccine against SARS-CoV-2 was approved; by May 2021, 14 vaccines had been licensed  of the population cannot be reached for vaccination. This value is based on maximum vaccination uptake reported for England’s current COVID-19 vaccine rollout . We model each scenario with both a high and a moderate vaccine efficacy profile that represents the range of efficacies of currently approved vaccines. Informed by current vaccine roll-out in high-income countries, we assume that vaccination started in January 2021 and is implemented at a rate that results in a total campaign of 10 months to fully vaccinate the population above 15 years old.In the present study, we aim to understand the likely impact of vaccine hesitancy on future control of the pandemic, using a mathematical model of SARS-CoV-2 transmissionOur simulations suggest that mortality could be higher in countries with high vaccine hesitancy compared to an ideal vaccination and this could prolong the need for NPIs to remain in place. We show that to reduce this impact, vaccination campaigns could include less vulnerable groups, like children. Vaccine hesitancy is an important public health priority that needs to be addressed in order to control the current pandemic.10. This data set includes weekly surveys about people’s behaviours in response to COVID-19 (including vaccines) as well as standard demographic questions on age, gender and household structure. Ethics approval and informed consent were not required given that all data was publicly available and de-identified.Attitudes towards COVID-19 vaccination were obtained from the Imperial College London YouGov Covid 19 Behaviour Tracker DataWe extracted the survey results from 8th to 15th February 2021 for 10 European countries. To assess vaccine hesitancy, we used data from one question pertaining to COVID-19 vaccine acceptance in which participants were asked to what extent they would definitely get a COVID-19 vaccine, if it became available to them next week. Answers were obtained on a numeric scale ranging from “Strongly agree – 1” to “Strongly disagree – 5”. To capture survey uncertainty, answers per age group were used to parameterise a multinomial distribution, from which we drew 100 replicates. To capture further uncertainty associated with the translation of survey response to vaccine uptake, for each replicate, coverage per age group was estimated assuming the probability of vaccination as a beta distribution with means: 0.98, 0.75, 0.50, 0.25 and 0.02 for survey responses 1, 2, 3, 4 and 5, respectively. Coverage distributions per age group, median as well as the 10% and 90% quantiles are shown in Table 7 , those who have received the vaccine but are not yet protected (this stage represents the two-dose vaccine schedule and the need to wait ~28 days from dose 1 for protection to develop) (v1 and v2), those who have received the vaccine and are protected (v3 and v4) and those who have received the vaccine but are no-longer protected (v5) (if vaccine-derived immunity is not life-long). In this model, only those who are currently infected do not receive the vaccine. Protection due to vaccination is modelled at two stages in the model; (1) reducing the probability of infection upon exposure (efficacy against infection) and (2) reducing the probability of hospitalisation being indicated after developing disease .13  and one with moderate efficacy (63% efficacy against infection3). For both vaccines we assume an additional 60% efficacy against hospitalisation for breakthrough infections, resulting in an overall vaccine efficacy against hospitalisation and death of 98% for the high efficacy vaccine and 85% for the moderate efficacy vaccine. A summary of key parameters is given in Tables S1–https://github.com/mrc-ide/nimue15.Parameters for SARS-CoV-2 infection, health care capacity, age-distribution and contact patterns are based on previous work3 Tables  and S4. κ), at which all individuals aged 15 years and above (~78% of the population) will be vaccinated over a 10-month period. This rate is implemented for all scenarios modelled, since we assume vaccination rate is constrained not by vaccine uptake but by the supply and delivery of vaccines. Therefore, lower levels of coverage, result in shorter vaccination campaigns; given that in the model, once coverage targets are met, vaccination is ceased. To illustrate the effect including children vaccination, vaccination rate was maintained constant and vaccination period was extended such that all individuals age 5–15 years could be vaccinated.To mimic current vaccine rollout plans, vaccination is introduced in the population at the beginning of January 2021. We assumed a constant vaccination rate  up to April 2020, subsequently decreased to 1 to represent the impact of NPIs against the first wave, and then rose to 1.5 during the latter half of 2020 to represent a second wave. Following the introduction of vaccination in January 2021, we set Rt to increase in 10 fixed steps. Each step representing the lifting of NPIs. The time for each step increase was determined by estimating when vaccination coverage had reached levels such that the herd immunity threshold due to vaccine immunity was reached. At the end of the vaccination period, Rt remained at a value such that the herd immunity threshold was maintained, given final vaccination coverage and vaccine efficacy against infection.To simulate a representative pre-vaccination scenario, we generated a reproductive number profile in which Rt was the same as To estimate the coverage needed for each Rt step, the following herd immunity threshold equation was used:We consider two potential scenarios for vaccine coverage target per age group: an ideal scenario 2020 with 20 cases. A simulation was run for each vaccine coverage scenario for both adult-only vaccination campaign and vaccination campaign including children. As an output for each simulation, we estimated the number of deaths and hospitalisations associated with COVID-19 over the 2-year period from 1 January 2021 to 31 December 2022.https://population.un.org/wpp/) and representative contact matrices obtained from a systematic review of social contact surveys through the socialmixR package . The model was then fitted to reported daily cases and deaths up to 31 December 2020 by varying three parameters - the start date of the epidemic, the initial R0 and the effect size of changes in mobility on transmission (using mobility data from Google (https://www.google.com/covid19/mobility)). Model fitting was performed using a Metropolis Hastings MCMC based sampling scheme as previously described16. The resulting fit generates a fitted R0 as baseline, an Rt trajectory up to the introduction vaccination in January 2021, after which, Rt was set to increase by 10 fixed steps, up to the theoretical herd immunity threshold based on an ideal vaccination schedule (as described above). The pandemic trajectory was evaluated using country-specific data on vaccine hesitancy and demography for the two coverage scenarios described above and assuming vaccination for individuals aged 15 years and above only.To generate country-specific simulations, we parameterise the model with data on the population size and age distribution of the country  for a vaccine with high efficacy  Fig. . For eacFor a vaccine with high efficacy, we project 1.2 (1.1–1.3), 5.0 (4.0- 6.3)- and 6.6 (5.7–7.6) times more deaths in 2021/2022 in a scenario with hesitancy compared to an ideal scenario in the UK, Germany and France respectively Fig.  and S6.19. However, the control of the pandemic as reduction of severe cases  and mortality, does not only depend on vaccine uptake but vaccine efficacy and stringency levels of NPIs21, which we have represented as underlying transmissibility (Rt). Our simulations confirm, that vaccination alone is unlikely to control the current pandemic and NPIs still have a large impact on the epidemic trajectories, until sufficient coverage is reached22. In a scenario with lower vaccine efficacy and vaccine hesitancy, longer and more stringent NPIs would be required to compensate lower efficacy as higher coverage levels are required to achieve herd immunity19.We have examined the effects of low vaccine uptake due to vaccine hesitancy for the current COVID-19 pandemic and have shown the impact of vaccine hesitancy, detailing the considerable mortality that could be averted with increased vaccine coverage. Our results have demonstrated that including less vulnerable groups, like children, can reduce the impact of vaccine hesitancy for current vaccination campaigns. These results further support the idea of the indirect benefits of vaccination, which are necessary to achieve herd immunity11 and analyse its effect in current vaccine rollout plans, which are prioritising older individuals. We have shown that even though older age groups have higher vaccine acceptance levels, these groups have higher mortality in a vaccine hesitancy scenario. As our model does not capture differential risk within sub-populations, it was not possible to assess the effect of vaccine hesitancy in other prioritised populations like health care workers. In which high levels of vaccine hesitancy have been reported despite having higher risk of infection23.Our model structure allowed us to capture vaccine hesitancy heterogeneity between age groups1. It is still unknown how transmission levels will develop in the long term as more transmissible variants are emerging and NPIs behaviour may persist after the pandemic. Here we have assumed a staged release of NPIs with a step-wise increase of Rt, representing governments’ easing of restrictions. This step function is a simplification to illustrate the process of balancing the relaxation of NPIs whilst continuing to suppress transmission. Nonetheless, the evaluation approaches introduced in this study can be adjusted to include complex Rt dynamics as more information on COVID-19 transmissibility evolution become available.Country fitting showed a higher initial Rt compared to our illustrative example. These values are consistent with those estimated for other European countries, where initial Rt values have been estimated as high as ~4.5, which may be due to possible under-ascertainment in deaths in early periods of the pandemics24. Transmission is more likely to be sustained within clusters with low vaccine coverage26 and therefore future outbreaks may be limited to these sub-populations. Secondly, we have modelled hesitancy levels constant over the time frame analysed; yet, self-reported attitudes to COVID-19 vaccines are changing over time10 as the perceived risk for both disease and vaccines keeps varying21. Thirdly, we have assumed vaccination rate remains constant over the vaccination period. However, vaccination logistics depend on multidisciplinary factors27 and both vaccine availably and uptake can be dynamic. Finally, our model does not account for immune escape from the vaccine due to new variants arising. Whilst second-generation vaccines will likely become available to address this issue, it is currently unclear whether some of the high levels of vaccine uptake observed in early vaccine rollouts would be sustained in subsequent booster programmes.Our analysis necessarily makes many simplifying assumptions, and it is important to note that the future trajectory of the epidemic will depend on the complex interactions between vaccination uptake, behaviour and government interventions. First, we have assumed homogenous mixing between vaccine-hesitant individuals. However, as has been seen for other diseases, COVID-19 vaccine hesitancy is heterogenous and clustered within population subgroups29. Reducing vaccine hesitancy is therefore an important public health priority. Interventions that aim to build trust, for example with community-based public education or via positive role-models, are proven efficacious approaches to address hesitancy30. There is an ongoing debate about vaccine passports as a condition to travel, or a vaccination requirement for employees31. Such interventions may be effective because they incentivize individuals to get vaccinated, but they are controversial in libertarian democracies because they curtail personal freedom and individual choice about medical treatments. The alternative will be to accept some level of disease, hospitalisation and deaths given the level of vaccine coverage achieved whilst allowing NPIs to be lifted, given that NPIs are not a sustainable long-term method for control.Getting vaccinated is an individual choice, but these individual choices have population wide effects that are likely to challenge current efforts to control COVID-19. Our findings suggest that vaccine hesitancy may have a substantial impact on the pandemic trajectory, deaths and hospitalisation. To prevent such adverse outcomes, NPIs would need to stay in place longer, or possibly indefinitely, resulting in high economic and social costsSupplementary DataSupplementary MaterialsPeer Review FileReporting SummaryDescription of Additional Supplementary Files


This review explains the origin of the LIV-1 family of zinc transporters, paying attention to how this family of nine human proteins was originally discovered. Structural and functional differences between these nine human LIV-1 family members and the five other ZIP transporters are examined. These differences are both related to aspects of the protein sequence, the conservation of important motifs and to the effect this may have on their overall function. The LIV-1 family are dependent on various post-translational modifications, such as phosphorylation and cleavage, which play an important role in their ability to transport zinc. These modifications and their implications are discussed in detail. Some of these proteins have been implicated in cancer which is examined. Furthermore, some additional areas of potential fruitful discovery are discussed and suggested as worthy of examination in the future. This review describes features of the LIV-1 family of zinc transporters from a historical perspective. It mentions how the LIV-1 family was originally discovered from the conserved zinc-binding motif seen in the LIV-1 sequence, now known as SLC39A6 or ZIP6. It then describes the main differences between the LIV-1 family of proteins and the other ZIP family transporters. This review then considers aspects of post-translational modification and how these have a role in the location and activity of these transporters. Initial investigations of this family of proteins often used recombinant proteins and the failures and successes of this technique are discussed, pointing out some peculiarities in ZIP6 and ZIP10 expression. As more is discovered about the function of these zinc transporters, a number of the LIV-1 family members are now implicated in cancer and details of their roles in this disease is mentioned. This review finishes by mentioning areas where further work and new discoveries may be especially fruitful in the future.The LIV-1 gene has been recognized as an estrogen-regulated gene since 1994 . LIV-1 wThe protein sequence of ZIP6 was shown to contain many histidine residues, known to bind zinc, as well as be predicted to contain eight transmembrane domains (TM) . FurtherAnalysis of the protein sequences of these zinc transporters has allowed much information to be obtained about the LIV-1 family which may be relevant to their function in cells. The nine human members of this LIV-1 subfamily of ZIP transporters are ZIP4-8, ZIP10 and ZIP12-14 , althougPHEXGD  which have the ability to cleave other proteins especially within their TM domains . These pAdditionally, the signal peptide peptidase (SPP) family of proteases also contain TM motifs although they do not have the HEXXH motif. Interestingly, these proteases also contain a motif in their C-terminal region which has similarities to the CPALLY motif in ZIP transporters. The ZIP transporters all contain motifs within different TM domains which appear to be organized in the same direction to each other  and contThe LIV-1 family members reside in the plasma membrane or in intracellular membranes, bringing zinc into the cytoplasm from these locations . The intIn an effort to investigate whether ZIP transporters were in fact activated by phosphorylation we used ZIP7 as an example. ZIP7 is located on the endoplasmic reticulum and is responsible for releasing zinc out of that store . Having These data indicate that ZIP7 acts as a channel to transport zinc from intracellular stores to the cytosol and the channel gate can be opened by phosphorylation to allow zinc to move down a concentration gradient. The cytoplasmic loop region between TM3–4 contains mixed charged residues and is not conserved between family members, suggesting the potential for differential regulation between transporters, especially as there are numerous predicted phosphorylation sites between TM3–4 for most of the other LIV-1 family members . Initial investigations with ZIP6 and ZIP10 have suggested that there are indeed multiple kinases involved in their activation which now need further study. It seems likely that other LIV-1 family members also require phosphorylation to open the channel gate and definition in this area will enhance future investigations. The kinase predictions for these sites are extremely diverse and consistent with each transporter having a different and independent role. Interestingly, apart from serine and threonine predictions there are also tyrosine predictions for ZIP6, ZIP10 and ZIP14. One predicted tyrosine site in ZIP6 has been observed 126 times in mass spectrometry studies, suggesting that tyrosine phosphorylation may also be important in channel function. However, to date there are no studies investigating the role of tyrosine phosphorylation on zinc transporter function.Interestingly, histidine residues have also been demonstrated more recently to have a phosphorylation capability . HistidiIt is well known that zinc is essential for normal cell growth and proliferation . The zinRecently, several LIV-1 family zinc transporters have been connected to driving the growth of cancer cells. Some of these zinc transporters have more of a defined role than others and the following discussion is restricted to three LIV-1 family members that have been investigated in detail. Many estrogen receptor-positive breast cancers are often treated with anti-hormones. One commonly used anti-estrogen agent is tamoxifen. Unfortunately, although tamoxifen treatment can prevent or slow the growth of the cancers this is not always permanent and with time most cancers become resistant to tamoxifen by developing more aggressive phenotypes. Our wider group has developed a tamoxifen-resistant cell model from MCF-7 breast cancer cells that we have previously characterized for zinc signaling mechanisms . During ZIP7 requires phosphorylation on residues S275 and S276 before it can release zinc from stores . As a re705STAT3, the transcription active form of STAT3, and changes it to pS727STAT3 which continues to bind to the ZIP6/ZIP10 heteromer throughout mitosis [727STAT3 form also binds to pStathmin during mitosis [ZIP6 and ZIP10 transporters have always behaved differently to the other LIV-1 family transporters that we have investigated. Primarily this has been due to the fact that they both play an important role in initiating migration through cell rounding and detachment . This th mitosis . The lar mitosis , the for mitosis . At the This important discovery can be used to inhibit the growth of cancer cells and, in the laboratory, the growth of many different types of cancer cells have already been inhibited using a ZIP6 or ZIP10 antibody which binds to the extracellular N-terminus of the ZIP6/ZIP10 heteromer, thus preventing the influx of zinc required for mitosis . This woThere are many useful databases emerging now that allow gene searching in clinical cancer samples. We have used Gepia  to searcMeasuring zinc levels in the blood of cancer patients has proven to be very unreliable but the general consensus suggests that cancer tissues, with the exception of prostate cancer, do have elevated zinc levels. ZIP4 is known to be increased when cells are zinc-deficient due to its role in zinc uptake from food, suggesting the presence of a zinc deficiency in breast cancer cells causes an elevation in ZIP4 levels. This is an interesting result as both lower zinc serum levels and increased ZIP4 tissue expression have already been linked to a poorer prognosis in colon cancer , suggestDespite the information above there are some areas where zinc transporters may play an important role yet they have not been thoroughly investigated and merits consideration in the future. These are mentioned below.ZIP7 is known to release zinc into cells from the ER and this cytoplasmic zinc can be used to drive multiple growth and proliferation pathways . Zinc isThe LIV-1 protein family contains nine human members and individual transporters are often genetically manipulated to provide functional information. However, because there are nine family members, this may provide an opportunity for individual family members to be altered in cells to compensate when others are deleted. For example, we have used a mouse breast cell line that has had ZIP6 removed by CRISPR technology . We haveThis is a good example of when using genetic manipulation to alter a zinc transporter protein, it is important to assess any effects that this may have had on the other family members, to rule out any compensation by other transporters, before drawing premature conclusions of the effects of removing that individual protein. Increasingly, calcium channels have been shown to be able to transport zinc into cells. There is good evidence that zinc can be transported into cells by acetylcholine channels, glutamatergic receptors, voltage-gated calcium channels  and alsoOther robust data exist which demonstrates that zinc levels in the endoplasmic reticulum can stimulate the ryanodine receptor to release zinc ,77, provAlthough the LIV-1 family of SLC39A zinc transporters was originally documented over 20 years ago, there is still much to discover about the functions of these individual transporters. Furthermore, as there are nine human members it is likely that each transporter has independent functions, each regulated by different agents, providing different endpoints. This is certainly true for what has been discovered for ZIP7, ZIP6 and ZIP10 to date. Continued efforts in this area are likely to produce important information, especially as aberrant levels of these zinc transporters are increasingly being implicated in different disease states.Some of the work reported in this manuscript is covered by a patent entitled ‘anti-mitotic composition comprising antibodies against ZIP6 and/or ZIP10’ with an international publication number of WO 2020/249483.