Abstract
During goal-directed navigation, ‘what’ information, describing the experiences occurring in periods surrounding a reward, can be combined with spatial ‘where’ information to guide behavior and form episodic memories. This integrative process likely occurs in the hippocampus, which receives spatial information from the medial entorhinal cortex; however, the source of the ‘what’ information is largely unknown. Here, we show that mouse lateral entorhinal cortex (LEC) represents key experiential epochs during reward-based navigation tasks. We discover separate populations of neurons that signal goal approach and goal departure and a third population signaling reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning the new reward location. Therefore, the LEC contains a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the medial entorhinal cortex.
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Data availability
Datasets generated in this study have been deposited online and are publicly available (https://doi.org/10.5281/zenodo.10160991). Source data are provided with this paper.
Code availability
Original code used for source extraction of two-photon imaging is available at GitHub (https://github.com/DombeckLab/Issa2023) and has been deposited on Zenodo (https://doi.org/10.5281/zenodo.10162356).
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Acknowledgements
We thank P. Dayan, J. Heys, J. Mellor, A. Miri and members of the Dombeck laboratory for helpful comments and discussion on the manuscript. This work was supported by the National Institutes of Health (NIMH R01-MH101297 to DAD; T32MH067564 to FX), a Hartwell Fellowship from the Hartwell Foundation (to J.B.I.), and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation (to J.B.I.). rAAV2-Retro-CAG-tdTomato was a gift from the Janelia Research Campus and the GENIE project. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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J.B.I., B.A.R. and D.A.D. conceptualized the study. J.B.I. and D.A.D. analyzed the data. J.B.I. performed the investigation, with CA1 cannulation by J.B.I. and F.X. D.A.D. supervised the study. J.B.I. and D.A.D. wrote the manuscript with input from all coauthors.
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Extended data
Extended Data Fig. 1 Histological confirmation of imaging window over LEC.
a) Histology of lateral entorhinal cortex (LEC). Injection of retrograde tracer in CA1 labels CA1-projecting LIII pyramidal cells of entorhinal cortex in a mouse with GCaMP6s expression. Horizontal section is taken after PFA fixation. Tracer injection and histology was performed on n=3 mice with similar results across mice; data here is shown for a representative exemplar. b) Zoomed in images of tdTomato and GCaMP6s labeling of the LEC in this horizontal slice. Histogram quantifies the amount of fluorescence from each of these channels as a function of depth from the dural surface. The cell-free zone separating layers II and III is approximately 150 to 200 µm beneath dura. c) To validate the prism placement, CA1-projecting LIII cells in LEC were again labeled with a retrograde tracer and two-photon imaging was performed on an implanted mouse. Labeled cell bodies were only seen at depths of at least 200 µm. This validation experiment was performed on n=1 mice. d) Simplified diagram of connectivity between superficial layers of entorhinal cortex and regions of the hippocampus along with dopaminergic inputs from the ventral tegmental area (VTA).
Extended Data Fig. 2 LEC firing peaks around reward and differs by layer.
a) Histogram of spatial cell peaks and mean transient rates relative to reward location. Datasets were combined across days with reward at 2.3 m (as in Fig. 2a-d), 0.7 m, and 1.5 m. Histograms binned every 10 cm; transient rate sampled every 1 cm. Mean ± SEM shown as dark line with light shading. Green LEC+MEC trace is average of LEC and MEC traces. b) Comparison of Ca2+ transients and cell firing properties from LII (n=23 FOVs) and LIII (n=9 FOVs) of LEC and LII (n=22 FOVs) and LIII (n=10 FOVs) of MEC. Data quantified for each imaging session; black cross represents mean ± SEM across sessions. Statistical tests between pairs (2-sided 2-sample t-test, Bonferroni correction); significant p-values (p < 0.05) shown. Imaging depths for all LEC and MEC fields (47 and 44 total, respectively, which include some sessions with tasks not included here) shown on left. c) Bayesian decoding of spatial position for sessions with reward at 2.3 m. Decoder trained with data from odd laps for a random subset of 40 non-reward spatial cells for each FOV and tested on even laps. Only sessions with ≥40 non-reward spatial cells were used. Mean ± SEM shown as dark line with light shading. Decoded error taken as the mean absolute difference between most likely decoded position and the actual position in 10-cm bins. Data quantified for each imaging session; black cross represents mean ± SEM across sessions. p-value from 2-sided 2-sample t-test shown. Shuffle (shown as dashed lines) achieved by randomly permuting position data for test set (even laps) and performing the same decoding analysis. n = 12, 17, 22 FOVs for LEC, MEC, and CA1, respectively. d) Unsupervised k-means clustering of spatial firing patterns for LEC with reward location at 2.3 m (data shown in Fig. 2c). Calinski-Harabasz criterion identified the optimal number of clusters as 2. Cluster centroids shown in purple and green, along with fraction of prereward and postreward cells that identify as each cluster. Dot raster: relative distance to each, taken as (d2-d1)/(d2+d1), where di is the squared Euclidean distance to centroid i. e) Quantification of spatial field widths in each region, computed as distance of the track with firing rate >30% of each cell’s maximum. Each point represents mean of spatial cells from one imaging session. f) Fraction of active cells that are spatial cells and fraction of spatial cells that are reward cells in each region. Each point represents one imaging session. Chance was calculated assuming uniform distribution of spatial cell peaks along the track. Since the reward zone is 80 cm total and the track length in 310 cm, chance is 80/310, or ~0.26. g) Reward clustering ratio, calculated as number of cells that peak in each reward zone divided by expected number of cells if peaks were uniformly distributed along the track. Ratio greater than one (chance) indicates an increased number of cells in that reward zone. h) Reward clustering ratio stratified by imaging depth. For LEC: ≤150 µm below dura (layer II) and ≥200 µm below dura (layer III); for MEC: ≤150 µm below dura (nominal layer II) and >150 µm below dura (nominal layer III). e–h) One-sided 1-sample t-tests used for individual regions compared to chance and 2-sided 2-sample t-tests for comparisons between regions (indicated by horizontal line). p-values indicated on panels. n = 32 (23+9), 32 (22+10), 39 FOVs for LEC, MEC, CA1, respectively, with LII+LIII breakdown in parentheses; black cross represents mean ± SEM across sessions.
Extended Data Fig. 3 Additional details for pre- and postreward populations across reward and environment switches.
a) Spatial firing patterns along the track and histogram of firing peaks for spatial cells sorted by their firing peaks with the familiar reward location for MEC and CA1. Same format as Fig. 3c. b) Distribution of difference in peak location for MEC and CA1 spatial cells across the two conditions. Dashed line is the distribution for cross-validated data with the familiar reward location (even versus odd laps). c) Paradigm for environment switches. Four different environments were visited in the same session, with ~20 laps run in each environment before switching to the next one. d) Fraction of reward cells that remain reward cells after environment switch, quantified as fraction of reward cells that maintain their peak location within 40 cm of the new reward location. Data quantified for each environment switch in an imaging session. e) Peak locations shown relative to reward locations with first environment on the x-axis and second environment on the y-axis. Each point represents one LEC neuron that was a spatial cell in both. f) Peak locations for second environment (rew2) for cells that are spatial in both conditions and prereward relative to first environment (rew1). Histograms calculated for each environment switch in an imaging session; mean ± SEM shown as dark line with light shading. g) Same as panel f but for postreward cells. h) Quantification of stable pre- or postreward cells for both environments, either as fraction of all spatial cells or of pre- or postreward cells for first environment. d,h) Two-sided 2-sample t-tests for comparisons between regions, indicated by horizontal line with p-values. n = 5, 5, 6 FOVs for LEC, MEC, CA1, respectively, each of which has 9 pairs of environments that are used for assessing stability of reward cells; each dot represents average for one pair of environment switches from one imaging session and black cross represents mean ± SEM (black rectangle represents chance).
Extended Data Fig. 4 Additional details on hidden Markov model for LEC prereward population firing.
a) A hidden Markov model (HMM) was used to detect transitions in the prereward population activity. The 10 seconds before reward delivery were considered. The system begins in an inactive state and can transition to an absorbing active state on each lap. ‘Number of cells active’ is the observed state. The HMM is trained to learn the emission probabilities for each state (number of cells active) and the transition probability matrix. b) Deceleration times and position relative to reward are highly correlated, thus indicating that running velocity is fairly consistent during approach to reward. Analysis in main text uses time because it was more reliable in practice. c) Transition from high running speed to lower running speed during reward approach was measured in two ways: deceleration time and velocity HMM time (see Methods). For exemplar session (top left: points represent transition times for individual laps; top right: exemplar laps where velocity HMM time agreed or disagreed with the deceleration time, indicated by green or red highlight, respectively) and across sessions (bottom left: each point represents correlation between the two measures for one imaging session; black cross represents mean ± SEM across sessions), the two measures were highly correlated. Both measures were also similarly highly correlated to the HMM-detected state change in the prereward cell firing (bottom right: Pearson correlation coefficients for each session, calculated as in Fig. 4g; each point represents one imaging session; black cross represents mean ± SEM across sessions). b-c) Statistical tests performed against zero (1-sided 1-sample t-test) and between each pair (2-sided Wilcoxon signed-rank test); p-values indicated on figure. n = 23 FOVs.
Extended Data Fig. 5 Pre- and postreward firing in LEC is specific to a virtual navigation task.
a) Mean running velocity at track positions surrounding reward are similar (pre: 40 cm before reward, mean of 22.8 cm/s; post: 40 cm after reward, mean of 25.0 cm/s) but lower than running speed along the rest of the track (other: all track positions excluding 40 cm around reward, mean of 37.6 cm/s). Each point represents one imaging session in LEC for reward at 2.3 m; black cross represents mean ± SEM across sessions. Statistical tests performed between each group (2-sided Wilcoxon signed-rank test; p-values indicated on figure). n=17 sessions. b) VR (left column): mean velocity, lick rate, and transient rate across all active cells in LEC, as a function of time relative to reward. LEC transient rate plot is reproduced from Fig. 5a. Inset shows mean transient rate for prereward neurons and postreward neurons relative to reward time. Gray boxes highlight periods when prereward and postreward cells are highly active outside of the immediate reward consumption period. No VR (right column): same measures but for naïve head-fixed mice (7 FOVs in 3 mice) receiving random (unpredictable) rewards. We only included rewards with immediate consumption (first lick within 0.5 s of reward delivery) and with some treadmill movement in the 2.5 s preceding reward. Mean ± SEM shown as dark line with light shading, respectively. c) After imaging LEC during delivery of random rewards (‘no VR’ condition), the same exemplar mouse was trained in virtual reality over a few days and imaging was performed during a reward navigation task (‘VR’ condition). Transient rate averaged across all active neurons for each session. Mean ± SEM shown as dark line with light shading. After normalizing transient rate (by dividing by the mean transient rate in the period from −10 s to −5 s), difference in bulk firing between ‘VR’ and ‘no VR’ conditions calculated, which captured two periods of large difference: prereward and postreward, relieved by a brief moment of equality for 0.5-1 s immediately after reward delivery.
Extended Data Fig. 6 Trajectory of LEC population firing using state space analysis.
a) Population LEC firing with respect to behavioral epochs is reduced to two dimensions using principal component analysis. The first two components (PC1 and PC2) explained 20% and 10% of the variance, respectively. The mean trajectory is plotted, color-coded by the behavioral epoch as established in Fig. 4 (yellow: postreward/running, gray: reward approach, blue: reward consumption). Open circles highlight the points in the trajectory that correspond to the transition points between epochs. The same components are used to plot the population firing for familiar reward (rew1) and novel reward (rew2). The trajectory for familiar reward is reproduced on the right plot using a thin blue line for comparison. b) Magnitude of difference between successive points (1-norm of the derivative) using first 10 principal components. Mean ± SEM (across the 10 components) are shown as dark line with light shading. c) Quantification of difference between trajectories for familiar and novel rewards using the first two principal component dimensions, taken as the 2-norm of the difference normalized by the 2-norm of the familiar reward trajectory. For reference, we compared this quantity to the same measure applied to MEC and CA1. Unlike LEC, the trajectories in MEC and CA1 differed more after the reward location was moved.
Extended Data Fig. 7 Pairwise correlations between LEC neurons across trials decrease near rewards.
a) Correlation matrices were formed for the activity patterns of a neural population in a given imaging session at a given position. We calculated the correlation between the activity of each pair of cells at a given position on the track across all the laps for that session. b) Example correlation matrices at two track positions are shown. c) Correlations as a function of track position or time relative to reward. At a given position (or time), the correlation matrix was averaged across all cell-cell pairs (excluding the diagonal) and computed for each imaging session. Mean ± SEM shown as dark line with light shading, respectively. a–c) Cell-cell correlations were calculated at a given position (or time point relative to reward) across laps. For an imaging session, the mean population correlation was then taken as the average cell-cell correlation across all cell pairs (autocorrelations excluded). Positions were binned in 20 cm intervals and time in 1 s intervals.
Supplementary information
Supplementary Video 1
Sample two-photon imaging field in LEC (from Fig. 1c), 20× real time.
Source data
Source Data Fig. 2
Source data for Fig. 2c heatmaps.
Source Data Fig. 3
Source data for Fig. 3c heatmaps and Fig. 3e,i statistics.
Source Data Fig. 4
Statistical source data.
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Issa, J.B., Radvansky, B.A., Xuan, F. et al. Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward. Nat Neurosci 27, 536–546 (2024). https://doi.org/10.1038/s41593-023-01557-4
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DOI: https://doi.org/10.1038/s41593-023-01557-4
