Ketamine evoked disruption of entorhinal and hippocampal spatial maps

Francis Kei Masuda; Yanjun Sun; Emily A Aery Jones; Lisa M Giocomo

doi:10.1101/2023.02.05.527227

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[Preprint]. 2023 Feb 6:2023.02.05.527227. [Version 1] doi: 10.1101/2023.02.05.527227

Ketamine evoked disruption of entorhinal and hippocampal spatial maps

Francis Kei Masuda ¹, Yanjun Sun ¹, Emily A Aery Jones ¹, Lisa M Giocomo ¹

PMCID: PMC9934572 PMID: 36798242

Abstract

Ketamine, a rapid-acting anesthetic and acute antidepressant, carries undesirable spatial cognition side effects including out-of-body experiences and spatial memory impairments. The neural substrates that underlie these alterations in spatial cognition however, remain incompletely understood. Here, we used electrophysiology and calcium imaging to examine ketamine’s impacts on the medial entorhinal cortex and hippocampus, which contain neurons that encode an animal’s spatial position, as mice navigated virtual reality and real world environments. Ketamine induced an acute disruption and long-term re-organization of entorhinal spatial representations. This acute ketamine-induced disruption reflected increased excitatory neuron firing rates and degradation of cell-pair temporal firing rate relationships. In the reciprocally connected hippocampus, the activity of neurons that encode the position of the animal was suppressed after ketamine administration. Together, these findings point to disruption in the spatial coding properties of the entorhinal-hippocampal circuit as a potential neural substrate for ketamine-induced changes in spatial cognition.

Introduction

Ketamine has been used in clinical medicine as a rapid-acting dissociative anesthetic for decades ^1,2. Recently, ketamine has received increased clinical attention due to its approval to also rapidly treat depression at sub-anesthetic doses ^3–5. However, despite common clinical uses as an anesthetic and antidepressant, ketamine can induce undesirable side effects associated with spatial cognition, such as dissociation, psychotomimetic effects and spatial memory and navigation impairments ^6–11. Intriguingly, unlike other classes of memory impairing drugs, sub-anesthetic administration of ketamine can also cause people’s internal sense of space to change; patients report floating outside of their body and being able to see the back of their own head ^2,6,12. These effects of ketamine on spatial cognition have also contributed to the abuse of ketamine as a party-drug and as a memory-impairing drug to facilitate assault ^{6–8,12–14}.

While the neural substrates associated with ketamine’s ability to induce dissociation and hallucination-like perception in mice have been recently investigated ^15–17, ketamine’s actions on the neural circuits that support spatial navigation and memory are not fully understood. Here, we focus on the impact of ketamine on neural coding in the medial entorhinal cortex (MEC)-hippocampal circuit, which contains neural substrates for generating an internal spatial map of the external environment. These substrates include spatially-selective neurons that encode the animal’s position relative to environmental features (e.g. grid, border, object-vector, and place cells) and movement through the external environment (e.g. head orientation, head velocity, and speed cells) ^18–24. The spatial firing patterns of many MEC and hippocampal neurons that encode the animal’s spatial position remain relatively stable within a given environment, a coding feature hypothesized to allow these brain regions to provide a spatial representation for navigation within that environment ^19,25–27. Between environments however, the firing patterns of MEC neurons can reconfigure – rotating or moving their spatial position or changing their firing rate ^25–27. Likewise, between environments, the firing patterns of hippocampal place cells, which are active in one or few restricted spatial locations, will change their firing rate, gain or lose firing fields, or move to a new spatial position ^28–30. Collectively, these phenomena are referred to as ‘remapping’ and are proposed to provide a population code for distinct environments ^25–27. Previous in vitro work has demonstrated that ketamine changes the firing rates of MEC and CA1 hippocampal neurons ^31–33. However, whether ketamine affects rate or spatial remapping in MEC or CA1 remains unknown.

One challenge in studying the effects of ketamine on neural circuits in behaving animals has been technological constraints on the number of neurons that can be simultaneously recorded, which increased the difficulty in differentiating between drug effects and individual animal variability. Here, we addressed this challenge by using Neuropixels silicon probes (in MEC) and 1P miniscope imaging (in hippocampus), which allowed us to record simultaneously from hundreds of MEC or hippocampal neurons and thus consider ketamine’s effects at the levels of single neurons, neural populations, and individual animals. Using these approaches, we found that a sub-anesthetic dose of ketamine disrupted stable spatial coding acutely and induced remapping over longer timescales in MEC. This disruption in MEC spatial coding resulted in a loss in the ability of MEC neural activity to predict an animal’s spatial position, possibly due to an acute disruption in MEC cell-pair temporal firing rate relationships — a prerequisite for many network-level computational models capable of generating MEC spatial firing patterns ^34–38. Complementary to these effects of ketamine on MEC, ketamine acutely suppressed the activity of hippocampal place cells. Together, our findings reveal how ketamine disrupts and restructures spatial coding in MEC and the hippocampus, raising the possibility that these circuits may contribute to the effects of ketamine on spatial cognition.

Results

Virtual reality behavior and electrophysiological recordings

To examine the effects of sub-anesthetic ketamine on neural activity in the medial entorhinal cortex (MEC), we recorded neural activity in head-fixed mice as they navigated a virtual reality (VR) linear track (mouse n = 8) (Fig. 1a). For all conditions, the VR linear track consisted of five landmarks (i.e. towers) that repeated every 400 cm. To encourage running, mice received a water reward at the end of the track before seamlessly teleporting back to the start of the track (Fig. 1b). For each recording session, the first 50 trials served as the baseline condition, in which no manipulation was performed (baseline epoch). After trial 50, the VR was paused for ~10 seconds while the mouse received a blank intraperitoneal (IP) injection (control insertion) before proceeding to run a further 50 trials (control epoch). After trial 100, the VR was briefly paused, and the mouse received a 25 mg/kg IP injection of ketamine before proceeding to run 190 trials (ketamine epoch) (Fig. 1c).

Figure 1 | — a. Schematic of Neuropixels probe recording and VR setup. Left: side view. Right: top view. Animals received visual cues from three monitors and received rewards from a front facing lick port.

b. Schematic of the 400 cm linear VR hallway. Five virtual towers spaced 80 cm apart served as landmarks. One traversal through the hallway is equivalent to one trial. The mouse received a water reward for completing the trial before seamlessly teleporting back to the beginning of the hallway.

c. Animals ran 50 baseline trials (gray), 50 trials following a control needle insertion (pink), and 190 trials following a 25 mg/kg ketamine injection (green).

d. Example sagittal slice of a mouse brain showing 3 dye color (DiI, DiD, DiO) tracks of Neuropixels probe recording insertions in MEC. Up to 6 recording sessions were conducted on each mouse (3 insertions per brain hemisphere)

e. Spatial raster plots (top row) and spatial firing rate maps (bottom) of example cells. Raster plots indicate individual spikes (black dots). Bottom panels are color coded for minimum (black) and maximum (red) firing rate values. Each cells’ maximum firing rate is labeled on the top of the cell’s spatial firing rate maps. The left panel shows data from the control session in which an empty needle was inserted intraperitoneally after trial 50 and again after trial 100.

f. As in panel (e), for data from the experimental condition of a control injection after trial 50 and a 25 mg/kg ketamine injection after trial 100.

To record neural activity, we acutely inserted Neuropixels silicon probes ³⁹ into the MEC, with each recording session associated with a unique probe insertion - up to six recording sessions per mouse, three recording sessions per hemisphere (Fig. 1d). Using this approach, we were able to record from thousands of cells (i.e. units) across a large portion of the MEC dorsal-to-ventral axis in individual wildtype mice (n = 3,539 cells; 30 sessions; 8 mice) (Supplementary Fig. 1). During the baseline and control epochs, many of the recorded cells demonstrated ‘spatially stable’ firing patterns, in which cells consistently fired at specific VR track positions (Fig. 1e–f, Methods) (Supplementary Fig. 2).

We first examined the impact of ketamine on running behavior during navigation in the head-fixed VR, as ketamine administration can evoke altered behavior during open field exploration (Supplementary Fig. 3). Demonstrating familiarity with and engagement in the task, mice significantly slowed their running speed near the end of the track in anticipation of the reward in both baseline and control conditions (Fig. 2a–b). Following the ketamine injection however, mice no longer significantly slowed down in anticipation of the reward tower and their mean running speed was less variable when compared to the baseline and control epochs (Fig. 2c). Licking behavior was also altered after the ketamine injection. In the baseline epoch, mice licked near the reward tower with over 80% accuracy (Fig. 2d–f). Licking in the baseline epoch was concentrated around the reward tower, and this behavior remained largely unchanged in the control epoch, with only a small decrease in lick accuracy between the baseline and control epochs (mean accuracy percentage: baseline epoch = 79%±1%, control epoch = 69%±1%) (Fig. 2d–f). In contrast, lick accuracy significantly decreased after ketamine administration (mean accuracy percentage: ketamine epoch = 14% ±1%) (Fig. 2e–f). Lick accuracy never returned to baseline across the rest of the trials (Fig. 2f) (Supplementary Fig. 4a–c). Taken together, these results indicate that while the mice continued to navigate with the VR environment after both the control and the ketamine administration, ketamine altered running and licking behavior.

Fig. 2 | — a. Mean running speed at each position bin during the baseline epoch (n = 30 sessions 8 mice; trials 1–50). Solid lines represent mean running speed and shaded regions represent standard error of the mean (SEM). Gray columns indicate the location of VR landmark towers (spaced by 80 cm). In reward zones (positions 390–400 cm), mice slowed their running speed compared to the main hallway (100–200 cm) demonstrating familiarity with the task (mean difference in running speed ± SEM, 17.9 ± 1.4 cm/s; p < 0.001, unpaired two-sample t-test). Note the tower at 240 cm resembles the reward tower at 400 cm and mice often slowed down briefly around 240 cm before increasing their speed until they reached the reward tower.

b. Mean running speed during the control epoch (n = 30 sessions 8 mice; trials 51–100). In reward zones, mice slowed, demonstrating familiarity with the task (difference in running speed, 13.5 ± 0.7= cm/s; p < 0.001, unpaired two-sample t-test).

c. Mean running speed during the first 50 trials of the ketamine epoch (n = 30 sessions 8 mice; 101–150). There was no difference in running speed between the reward zone and the middle of the track (difference in running speed, −0.6 ± 0.02 cm/s; p = 0.77, unpaired two-sample t-test).

d. Raster plots indicating the spatial positions where the animal licked in two example recording sessions (black dots = lick location).

e. Mean lick accuracy in the baseline, control, and ketamine epochs. Each dot is the mean lick accuracy of a session. The mean lick accuracy was significantly lower in the ketamine epoch than in the baseline and control epochs (p < 0.001, unpaired two-sample t-test). The mean lick accuracy in the control epoch was significantly lower than in the baseline epoch (p < 0.001, unpaired two-sample t-test).

f. Mean lick accuracy per trial, averaged across the 30 sessions (n = 8 mice). Control epoch highlighted in magenta (trials 51–100). Ketamine epoch highlighted in green (trials 101–290). Solid lines represent smoothed lick accuracy and shaded regions represent standard error of the mean.

Ketamine affected firing rates heterogeneously across MEC cell types and impaired spatial coding

To quantify the effect of ketamine administration of the MEC neural population, we examined the firing rate of all MEC neurons across baseline, control, and ketamine epochs. Between baseline and control epochs, the mean firing rate across the MEC population did not differ (control epoch change in firing rate is not significantly different from zero, p=0.12, Student’s one-sample t-test), indicating that injection alone did not cause significant changes in the firing rates of MEC neurons (Fig. 3a–b). However, during the ketamine epoch, we observed a significant increase in the mean firing rate across the MEC population (mean change in firing rate: control epoch = −0.03 Hz, ketamine epoch = 1.52 Hz; p<0.001, two-sided t-test) (Fig. 3a–d). The mean peak of the ketamine-induced increase in the firing rate across sessions occurred 20.69 ± 3.47 minutes (mean ± SEM) after the injection of ketamine (Fig. 3c). The mean time of firing rate increase onset was 3.54 ± 0.20 minutes (mean ± SEM). This coincides with the previously published pharmacodynamics of ketamine (time to onset of immobilization = 2.3 ± 0.5 min, serum half-life of ketamine = 13 min) ⁴⁰ and previously observed effects of ketamine on firing rates in cortical regions ^15,41.

Fig. 3 | — a. Mean firing rate of all recorded neurons in the trials before (5 minutes) and after (10 minutes) either a control (magenta) or a 25 mg/kg ketamine (green) injection. Solid line indicates mean and shaded regions show ± SEM (n = 3539 cells, mouse n = 8). Grey line at 0 indicates the injection time.

b. Boxplot of mean change in firing rate for the 5 minutes before and 5 minutes after the control injection (magenta) or ketamine injection (green) divided by the baseline firing rate. The central mark indicates the median, the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively, and the whiskers extend to the most extreme data points not considered outliers. The mean firing rate of neurons changes significantly in the 5 minutes after a ketamine injection compared to the 5 minutes after a control injection. (p<0.001, n = 3539 neurons, Wilcoxon rank sum test).

c. Change in the firing rate 60 minutes after 25 mg/kg ketamine injection from the mean firing rate 5 minutes prior to the injection. Thin black lines represent the mean firing rate for each individual mouse averaged across sessions (n = 8 mice). Thick red line represents mean firing rate across all cells; shaded region shows ± SEM (n = 3539 cells). Peaks were found in the firing rate for 1306/3233 cells, mean peak time = 2.2 min ± 0.02. It is possible to see the pharmacodynamics of ketamine’s (half-life 13 minutes; onset 1–2 minutes) effect on neural firing rate.

d. Change in firing rate across 290 trials from the mean firing rate in the baseline epoch (trial 1–50). Note that due to variable changes in the running speed of individual mice, effects observed over trials (d) versus time elapsed (c) do not perfectly align. Thin black lines: individual mice averaged across sessions (n = 8 mice); thick red line represents mean firing rate across all neurons; shaded region shows± SEM (n = 3539 cells).

e. Mean firing rate of excitatory neurons 5 minutes before and 10 minutes after either a control (magenta) or a 25 mg/kg ketamine (green) injection. Line indicates mean and shaded region shows SEM (n = 2894 cells, mouse n=8).

f. Mean firing rate of inhibitory neurons 5 minutes before and 10 minutes after either a control (magenta) or a 25 mg/kg ketamine (green) injection. Line indicates mean and shaded region shows SEM (n = 339 cells, mouse n=8).

g. ANOVA with Bonferroni correction comparing the change in the mean firing rate 5 minutes before and 5 minutes after an injection divided by the baseline firing rate of excitatory and inhibitory neurons (p<0.0001 between ketamine excitatory and all other groups). Magenta represents the Δ firing rate/baseline firing rate after the control injection and green represents the Δ firing rate/baseline firing rate after ketamine injection. Interneurons do not meaningfully change their firing rate during the control or ketamine epochs. Excitatory neurons significantly change firing rate during the ketamine epoch but not during the control epoch. The circles and bars represent the means and 95% confidence intervals for each respective group. Gray lines indicate the mean value that is significantly different from the other means.

h. Single example of an MEC spatial cell. Raster plot on the far left, with the equivalent spatial firing rate map in the middle, color coded as in Figure 1e. Animal’s mean running speed (cm/s) per trial shown on the right.

i. Crest factor for the example cell shown in (h) on the left and the average crest factor for all cells on the right (n = 3539 cells, 8 mice). Line indicates mean and shaded region shows SEM (3i-l). Control epoch is highlighted in magenta (trials 51–100). Ketamine epoch is highlighted in green (trials 101–300). Crest factor measures the peak amplitude of the cell’s spatial tuning curve divided by the root mean square of the same spatial tuning curve, providing a measure of the coherency of the spatial tuning curve. Note the average crest factor drops following ketamine injection.

j. Spatial information score (bits/spike) for the example cell shown in (h) on the left and the average spatial information score for all cells on the right (n = 300 trials, 3539 cells, 8 mice). Control epoch is highlighted in magenta (trials 51–100). Ketamine epoch is highlighted in green (trials 101–300). The average spatial information score drops following ketamine injection.

k. Average spatial stability values (spatial correlation of each trial with the trial immediately preceding it and the trial immediately following it) of cells per trial across 300 trials (n = 3539 cells, mouse n = 8). Control epoch is highlighted in magenta (trials 51–100). Ketamine epoch is highlighted in green (trials 101–300). Average stability drops acutely following both the control and the ketamine injection. While stability recovers quickly in the control epoch, stability loss is longer during the ketamine epoch.

l. Mean stability of neurons 30 minutes after baseline (black), control injection (magenta), and the 25 mg/kg ketamine injection (green). There was no significant difference between the baseline and the control stability (p = 0.152, n = 3539 cells, Wilcoxon rank sum test). The post-ketamine stability was significantly less stable than baseline (p = 3.178 × 10–57) and control (p=2.073 × 10–52). Stability rises at the start of any epoch, but within a minute, the mean stability during the ketamine epoch drops, unlike the mean stability during the control epoch.

m. Comparison of the mean stability of neurons in the baseline (black), control (magenta), and ketamine (green) epochs. All violins have the same area, but the width represents the kernel probability density of the data at different values. The central mark of the boxplot indicates the median, the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively, and the whiskers extend to the most extreme data points not considered outliers. There is no significant difference between the spatial stability of neurons in the baseline and the control epochs (p= 0.152, n = 3539 cells, Wilcoxon rank sum test). The spatial stability of neurons in the ketamine epoch (n = 50 trials post ketamine injection) was significantly smaller than in the baseline epoch(p = 3.178 × 10–57) and control epoch (p=2.073 × 10–52).

We next considered ketamine’s effect on the firing rates of different MEC putative-cell types. While the mean rate of excitatory neurons did not change between baseline and the control epochs, their mean firing rates significantly increased after the administration of ketamine (n = 2,894 excitatory neurons, 8 mice) (Fig. 3e). In contrast, the mean firing rate of interneurons (n = 339 interneurons, 8 mice; baseline firing rate > 15hz) did not significantly change between baseline and control epochs, nor between control and ketamine epochs (Fig. 3f–g). Comparisons between excitatory and inhibitory neurons revealed that the mean firing rate of excitatory neurons during the ketamine epoch was significantly different from the mean firing rate of inhibitory neurons in the control and ketamine epochs (ANOVA with Bonferroni correction, mean firing rate 5 minutes prior to injection compared to mean firing rate post injection) (Fig. 3g).

To examine how functionally-defined excitatory MEC cell types responded to ketamine, we leveraged a previously published method for identifying grid cells in 1D linear track virtual reality environments (Methods)⁴² As in the larger excitatory neuron population, the mean firing rate increased by ~2 Hz in putative grid cells after the ketamine injection, while the control injection had no effect on grid cell firing rates. (Supplementary Fig. 4D).

Given that a large portion of MEC neurons encode the spatial position of an animal, we next considered how ketamine impacted spatial coding across the MEC neural population. In individual MEC neurons, we noted that spatial selectivity often degraded after ketamine administration, switching from a firing pattern in which firing activity occurred in one or more discrete spatial positions on the VR track to a firing pattern in which firing activity was distributed across the VR track (Fig. 1f, 3h–j). This was consistently observed in spatial cells across many sessions and across different animals (Supplementary Fig. 2). To quantify this, we binned firing rates into 2 cm spatial bins and classified spatial cells as those with a mean spatial stability score > 0.2 in the baseline condition (cell n = 496) (Methods). For spatial cells, there was no change in the mean spatial stability between baseline and control epochs (Fig. 3k–m). This observation held when comparing spatial stability across time (Fig. 3l) rather than trial numbers (Fig. 3k), although we noted a brief period of decreased spatial stability just after the VR turned on or was paused (Fig. 3k–l). In contrast, the mean spatial stability of spatial cells significantly decreased between the baseline and ketamine epochs, as well as between the control and ketamine epochs (Fig. 3m). This ketamine associated decrease in spatial stability was accompanied by a decrease in the mean spatial crest factor (a measure of how peaked fluctuations were in the spatial firing map, Fig. 3i, see Methods) and a decrease in spatial information (Fig. 3j, Methods). Thus, ketamine affected firing rates heterogeneously across MEC cell types, increasing the firing rates of excitatory neurons while having little effect on the firing rates of interneurons, and broadly impaired spatial coding in the MEC.

Ketamine induced an acute discrete neural activity state characterized by reduced spatial information cell-pair co-activation

To examine the effects of ketamine on MEC neural population activity, we applied an unbiased approach to classify temporal neural activity states. We first estimated the instantaneous firing rates of neurons by smoothing the vector of spike counts across temporal bins with a Gaussian kernel. We then passed these temporally binned firing rates through a linear dimensionality reduction algorithm (PCA), which revealed distinct clusters of population activity (Fig. 4a). Due to the non-linear combination of the latent pharmacodynamics of ketamine (time-based) and the behavior in virtual reality (spatially-based), we applied a non-linear dimensionality reduction (Uniform Manifold Approximation and Projection: UMAP) and a clustering algorithm (Density-based spatial clustering of applications with noise: DBSCAN) to the neural firing rates and compared the resulting clusters. We found that UMAP better revealed the underlying position-tuned neural state space and better clustered the neural data into distinct periods of activity compared to PCA (Fig. 4b, Supplementary Fig. 5).

We then focused on the first significant cluster of activity following the administration of ketamine (Fig. 4c,d. The well-defined activity cluster mapped back onto the period of spatial decoherence seen in the spatially-binned rasters, and we thus defined this as the ‘spatial decoherence period’ (Fig. 4c,d). In 28 of 30 sessions, we observed an acute spatial decoherence period within 2 minutes of the administration of ketamine (Fig. 4e). The decoherence period was associated with an increase in mean firing rate compared to baseline and control epochs and lasted on average 18.6±2.4 minutes (Fig. 4f–g). However, we also noted heterogeneity across individual cells during the decoherence period. While there were, on average, more cells that increased their firing rate during the decoherence period, some cells decreased their firing rate (2.26 cells increased their firing rate for each cell that decreased their firing rate, Fig. 4h). Notably, the spatial information of cells was significantly lowered during the decoherence period (Fig. 4i).

Given ketamine’s disruption of spatial information in MEC, we next considered whether ketamine impacted a cardinal feature of MEC spatial coding. Previous works point to MEC grid cell firing patterns as arising from attractor dynamics that emerge as a result of recurrent connectivity within a network of neurons ^34,43. A key feature of this framework is that pairs of co-active grid cells maintain their temporal firing relationship across environments and experimental conditions, even if their spatial firing relationships are disrupted (Fig. 5a). Indeed, cell-cell pairs have been found to retain their temporal firing relationships across a range of experimental conditions ^35,44,45. Given this, we examined whether cell-pairs maintain their temporal firing relationships across our different experimental conditions.

Fig 5. | — a. Left: Schematic of a spike raster of a stable cell-pair relationship in the baseline epoch. Right: Spiking correlations between stable cell-pairs are well conserved across most perturbations and result in a diagonal one-to-one relationship between spiking correlation coefficients (ρ) across two different conditions (top right schematic). If a test perturbation breaks the relationship between two different neurons, then that results in a non-diagonal relationship between the spiking correlation coefficients in the two conditions (bottom right schematic).

b. Two example sessions (each row is a session) of cell-pair correlations across different experimental epochs(baseline: trials 1–50, control: trials 51–100, acute ketamine: trials 101–150, late ketamine: trials 251–290, gain change: trials 291–300). In order to perturb the relationship between visual cues and locomotion, the animal ran 10 trials with a visual flow where the gain was 0.5x (the real distance the animal had to run to reach the end of the track from 400 cm to 800 cm). Each point on the scatter plot is a cell-pair. We calculated the correlation coefficient of between the smoothed firing rates of the first 50 baseline trials for all pairs of spatially stable (non-interneurons, spatial score > 0.2) cells in a session, then examined cell-pairs with a significant correlation (Pearson correlation coefficient, p < 0.05). Black line indicates the least squares line of the cell pairs in the different conditions. The Pearson correlation (ρ) value for each condition within a session is listed at the bottom of each scatterplot.

c. Violin plots of cell-pair correlations within a session across different experimental states. All violins have the same area, but the width represents the kernel probability density of the data at different values. Each point (gray) indicates the Pearson correlation (calculated as shown in b) of a session. Sessions with >5 stable cell pairs are plotted (n = 20 sessions). Significance values were calculated using ANOVA with a Bonferroni correction for multiple comparisons (F = 16.54, p = 7.81e-09). Significant multiple comparisons highlighted *p<0.05, **p<0.01, and ***p<0.001.

Consistent with previous work, we found that the temporal relationships between cell-pairs were maintained between the baseline and control conditions (Fig. 5b), as well as between late ketamine and a manipulation of the relationship between the virtual reality visual flow and locomotion (i.e. a change in the gain of the visual flow; Methods). In contrast, cell-pair stability was significantly diminished between baseline and acute ketamine conditions (Fig. 5c). This suggests that ketamine acutely disrupted cell-pair connectivity. In an attractor network framework, such a disruption in cell-cell co-activity would lead to a significant disorganization in stable spatial coding (i.e. the spatial position a neuron is most active), which is consistent with our observation that ketamine disrupts spatial information in MEC.

MEC spatial neurons remap following the discrete ketamine-induced neural activity state

Given that ketamine significantly disrupts both the spatial information and temporal relationships between cell-pairs, we next considered whether the spatial representations observed before ketamine administration returned after the acute decoherence period. First, we noted that after the acute decoherence period, the spatial locations in which individual spatial neurons were most active differed from those observed in the baseline condition, a phenomenon often referred to as ‘remapping’ ^26,46 (Fig. 6a). To visualize this, we constructed trial-by-trial population spatial similarity matrices, in which the activity of all neurons for a given trial was correlated to their activity on all other trials ⁴⁷ (Fig. 6b–c). We found that the population activity was spatially stable between the baseline and control period (trial 1–100). This was followed by a period of internal stability during the acute decoherence period. Of note, the trials following the ketamine administration were not spatially correlated with the baseline and control trials. To quantify the post-decoherence remapping of spatial firing patterns, we compared the spatial firing fields of neurons to a spatial template generated from the spatial firing fields of neurons in the baseline condition. Ketamine significantly decreased the correlation score as compared to the control injection (Fig. 6d–g). These results combined with our previous analyses, reveal that ketamine induced an acute decoherence period in which the firing rates of MEC neurons and navigational behavior were significantly altered, followed by a return to stable, albeit remapped, spatial firing patterns.

Fig. 6 | — a. The spatial tuning curve of the firing rate of an example cell split into 50 trial blocks. The control injection was delivered after trial 50 and the 25 mg/kg ketamine injection was delivered following trial 100.

b. Trial-by-trial correlation matrices of spatial firing rates for three example sessions. Each point on the matrix indicates how correlated the firing rate of the neural population is in one trial compared to a different trial.

c. The averaged spatial correlation matrix across 32 recorded sessions (8 mice). The first 100 trials are more internally correlated than the subsequent 200 trials following the ketamine injection.

d. The Pearson correlation score of each trial (x-axis) to the baseline spatial template (calculated from the first 50 baseline trials) averaged across all recorded cells. Control epoch in green, and ketamine epoch in magenta.

e. The spatial correlation score compared to the baseline spatial template for the first 15 recorded minutes in the baseline (gray), control (magenta), and ketamine (green) conditions.

f. Ketamine reduced the cell’s spatial correlation to the baseline spatial template (comparison of the activity of each cell 15 minutes post ketamine injection to the baseline spatial template, Methods). Width represents the kernel probability density of the data at different values. The central mark of the boxplot indicates the median, the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively, and the whiskers extend to the most extreme data points not considered outliers.

g. We calculated a ketamine correlation effect score difference between the average of the spatial correlation score in the 25 trials before the drug and the average of the spatial correlation score in the 25 trials after drug application.

h. Example rasters demonstrating how the logistic decoder predicting mouse position using spatially-binned firing rates was trained on the first 50 baseline trials. The decoder’s performance was then tested with the firing rate from a trial in the control epoch or in the ketamine epoch.

i. Example mouse position predictions over time from the logistic decoder given one trial’s worth of neural data. Examples are a trial from the control epoch trial (top) and a ketamine epoch trial (bottom). Model predictions in orange, and true animal position in blue.

j. The average root mean squared error (RMSE) of the logistic decoder is shown across all 32 sessions. The control period is highlighted in magenta. The post-ketamine period is highlighted in green. Solid lines represent mean RMSE, and shaded regions represent standard error of the mean. Averaged RMSE for control and ketamine epochs significantly differ, with p < 0.001 via the Wilcoxon rank-sum test.

Given the ketamine-induced changes to the firing rates and spatial coding of MEC neurons, we considered how these changes impacted the degree to which spatial information was preserved or disrupted across ketamine-induced neural activity states. We first trained a logistic regression decoder on data from the first 50 baseline trials (Methods, Fig. 6h–j). This decoder was significantly worse at predicting the animal’s position on the VR track during the acute ketamine period compared to the control period (Fig. 6j, p < 0.001 with Wilcoxon rank-sum test). Together with the previous analyses, these results demonstrate that ketamine administration severely disrupts the ability of MEC to encode the spatial position of the animal.

Ketamine disrupts spatial firing patterns in the hippocampus

The MEC is highly reciprocally connected with the hippocampus, which contains neurons called place cells that fire in one or few restricted spatial locations. Inactivating the hippocampus degrades grid cell firing patterns³⁷ , and altering MEC activity impacts place cell firing patterns ^48–50. Thus, given the acute disruption caused by ketamine to neural activity in MEC, in a final set of experiments, we asked how ketamine influences the spatial firing patterns of place cells in the hippocampus. To examine ketamine’s effect across a large population of hippocampal neurons, we performed in vivo 1-photon calcium imaging of CA1 neurons using a miniaturized fluorescence microscope (n = 8 mice, 4146 neurons) (Fig. 7a). Mice freely explored a circular arena for one 15 minute session across four days (days 1–2 served as baseline sessions, on day 3 ketamine was administered immediately before the start of the session and day 4 served as a post-ketamine baseline session) (Fig. 7b). While the timescale of hippocampal experiments differed from those performed in MEC, due to constraints on the amount of time imaging can be performed, this approach allowed us to examine the impact of ketamine on the coding of large populations of hippocampal neurons within individual animals.

Fig. 7 | — a. A schematic illustration of in vivo calcium imaging via a miniaturized fluorescence microscope in the mouse hippocampal CA1. GCaMP6 expression in CA1 pyramidal cells is achieved by using the Ai94;Camk2a-tTA;Camk2a-Cre mouse line.

b. Calcium event rate map of 4 example CA1 cells, from 4 different animals, tracked across the experimental timeline shown on the top. An example raster plot for one cell is also shown, in which black lines denote the animal running trajectory while red dots indicate the location of each calcium event. Scale bar = 10 cm.

c. Mean calcium event rate comparison on baseline 1 (bsl 1) vs. baseline 2 (bsl 2), bsl 2 vs. ketamine (K), and bsl 2 vs. post-baseline (p-bsl) from all recorded neurons (4146 cells from 8 mice). Pairwise comparisons are made based on pairwise occupancy matching from different days. The error bar plots shown in black indicate the median value and 0.25 – 0.75 quantile from all the recorded neurons, while colored circles indicate median values for each animal. Ketamine induces a significant and long-lasting decrease in the mean calcium event rate of CA1 neurons, compared to the baseline, both during (K) and after (p-bsl) drug treatment (p = 0.039 and 0.0078, respectively, two-tailed sign-rank tested on 8 mice). Please note the statistical tests shown in the figure are all based on comparisons within animals, which provide more conservative results than testing based on all the recorded cells.

d. – e. Organized as in (c). Ketamine decreases the peak calcium event rate and spatial information (bits/sec) of CA1 neurons compared to the baseline (p = 0.0078 and 0.0078, respectively, two-tailed sign-rank tested on 8 mice).

f. Number of place cells from each animal across different conditions. Ketamine significantly decreases the number of cells that qualified as place cell only during the drug day (p = 9.51 × 10–5, two-tailed paired t-test).

g. Left, histogram of all the place cells spatial correlation for bsl 2 vs. bsl 1 and bsl 2 vs. K (2536 place cells from 8 mice). Right, comparison of spatial correlation between bsl 2 vs. bsl 1 and bsl 2 vs. K from each animal. There is a significant drop of spatial correlation on bsl 2 vs. K compared to the correlation from the two baseline days (p = 0.0078, two tailed sign-rank test on 8 mice).

h. Organized as in (g). There is no significant difference between the spatial correlation of bsl 2 vs. bsl 1 and bsl 2 vs. p-bsl (p = 0.38, two tailed sign-rank test on 8 mice). Ketamine does not show a long-lasting effect on place field location of CA1 place cells.

i. Matrices of spatial correlation across different days from all the mice. Each matrix is averaged across all the place cells correlation from that animal. Ketamine treatment consistently decreases the spatial correlation of place cells with other baseline sessions.

We observed that the mean and peak calcium event rate and information score were not significantly different between baseline sessions 1 and 2 (4146 cells from 8 mice). However, there was a significant decrease in mean and peak calcium event rate and information score between the baseline (day 2) and ketamine (day 3) sessions (Fig. 7c–e). The peak calcium event rate and information score returned to baseline values on the post-ketamine baseline session (day 4), but the decrease in mean calcium event rate persisted (Fig 7c–e). This indicates that ketamine broadly decreased the activity of hippocampal CA1 neurons. We next considered how ketamine impacted CA1 place cells. First, we found that ketamine significantly decreased the number of cells defined as place cells in all 8 recorded mice (Fig. 7f). In addition, for the place cells identified in the baseline condition (i.e., baseline 2), we followed their activity in the ketamine session and observed significantly different spatial firing patterns between the two conditions (Fig. 7g). This loss of spatial tuning was not due to a complete shutdown of the cell activity (Fig. 7c), suggesting the activity of place cells was dissociated from the current location of the animal after the administration of ketamine, potentially in a manner similar to that observed in MEC during the acute decoherence period. Unlike the spatial cells in MEC, we did not observe place field remapping in the post-baseline after ketamine administration, as we observed similar spatial correlation values between the baseline and post-ketamine sessions as those observed between the two baseline sessions (Fig. 7h–i). One potential reason for this difference however, is that the post-baseline session was performed 1 day after the ketamine administration in the hippocampal experiments, compared to 30 minutes after ketamine administration in the MEC experiments. The possibility remains that over longer time scales (e.g. days), MEC neurons may return to their pre-ketamine baseline spatial firing patterns.

Discussion

Previous clinical work has shown that, while ketamine is both a promising rapid treatment for depression, it can evoke dissociation (i.e. out-of-body experiences) and impair spatial memory ^7,11,14,51. The technical challenge of recording the activity of large numbers of cells during pharmacological manipulations, in which individual animal variability can obfuscate drug effects, has limited our understanding of how ketamine impacts neural coding in regions associated with spatial cognition. By leveraging approaches that support the simultaneous recording of many neurons, we were able to obtain sufficient sampling to reveal the population and single-cell level effects of ketamine on neural activity in MEC and the hippocampus in navigating animals. In MEC, we found that ketamine acutely disrupted spatial coding by increasing firing rates and degrading the temporal firing-rate relationship between cell pairs and caused a longer-term remapping of spatial representations. Complementary to these observations in MEC, hippocampal spatial coding was disrupted during the same period of time as the acute disruption of MEC spatial coding. Taken together, our results point to the MEC-hippocampal circuit as a potential substrate for the effects of ketamine on spatial cognition.

The molecular mechanism by which ketamine exerts its impacts on spatial coding remains to be determined. Ketamine is a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist ^52,53. In MEC, NMDA receptor antagonists cause non-specific increases in excitation, as measured by c-fos mRNA and protein expression ³¹. Ketamine is also a potent inhibitor of HCN1 ion channels and the associated ionic current I(h), with knockout of HCN1 channels reducing the hypnotic and dissociative actions of ketamine ^15,54,55. The loss of HCN1 channels and inhibition of I(h) impacts multiple features of MEC neural activity. At the single cell level, knockout of HCN1 reduces the membrane resting potential, increases input resistance and enhances excitatory post synaptic potentiation summation ^56–58. Complementary to this, ketamine mirrors these single cell effects in MEC neurons in wildtype mice but not in HCN1 knockout mice ⁵⁴. These HCN1 associated effects of ketamine, likely together with ketamine’s antagonistic effects on NMDA receptors, could contribute to the enhanced excitation observed during the acute decoherence period and the longer-term remapping of MEC spatial representations. Future work that combines genetic approaches with large-scale recordings will be required to pinpoint the potential contributions of HCN1 channels versus NMDA channels to ketamine-induced effects on MEC spatial coding.

Another possible mechanism by which ketamine administration might alter MEC spatial coding is via its effects on the hippocampus. While ketamine acutely increased firing rates in MEC, we observed a significant decrease in hippocampal activity during this same time period. In vitro, ketamine transiently decreases population spikes in the hippocampus, due to ketamine-associated increases in the activity of inhibitory γ-aminobutyric acid (GABA) receptors ^33,59. This in vitro decrease in hippocampal population spikes is consistent with our in vivo observation of decreased place cell activity after ketamine administration. Importantly, back-projections from the hippocampus to MEC are important for grid patterns in the MEC, as inactivating the hippocampus deteriorates grid cell firing patterns ³⁷. A role for intact hippocampal input in the emergence of MEC spatial coding is consistent with attractor-network models, a class of computational models capable of generating grid cell firing patterns ^34,38,60. In these models, grid cell firing patterns emerge from a network of neurons with asymmetrically centered inhibitory center-surround synaptic weight profiles. This connectivity profile, in the presence of broad-field excitation, will generate a grid pattern of activity. Individual grid cell responses then emerge when the movement of the pattern of activity across the network is coupled to the velocity of the animal. If the inhibitory neurons compose the inhibitory center-surround synaptic weight profiles of the grid network, a reduction in the excitatory drive from the hippocampus can result in a loss of correlations between grid cell pairs ^35,37. Our finding that hippocampal place cell activity diminishes during the acute ketamine period, combined with the decrease in cell-pair temporal correlations of MEC cells during the same period, is consistent with this framework and points to the degradation in hippocampal place cells as a possible circuit mechanism for ketamine-induced changes in MEC spatial representations. However, any circuit mechanism likely interacts with ketamine’s effects on MEC NMDA receptors or HCN1 channels. For example, previous work has shown that inactivation of the hippocampus does not result in an increase in firing rates of MEC neurons ³⁷. Thus, the effect of ketamine on MEC spatial representations likely reflects an interplay between ketamine-induced circuit, cellular and molecular changes.

The effects of ketamine on MEC and hippocampal spatial representations almost certainly interact with the well-established effect of ketamine on the retrosplenial cortex (Vesuna et al., 2020). As with the hippocampus and MEC, human and animal work point to retrosplenial cortex as a region that supports spatial cognition, route learning and navigation (Claessen and van der Ham, 2017; Hundley et al., 2014; Maguire, 2001; Nelson et al., 2015). Ketamine elicits a slow 1 – 3 Hz rhythm in layer 5 neurons of retrosplenial cortex, with optogenetic activation of these layer 5 neurons producing a dissociation-like state in mice (Vesuna et al., 2020). Retrosplenial cortex also receives input from the hippocampus and MEC, as well as thalamic, sensory, and other neocortical regions (Mitchell et al., 2018; Sugar et al, 2011). Given that ketamine produces a wide repertoire of cognitive changes, ranging from unconsciousness, dissociation, and spatial memory impairments to clinically relevant improvements in depression and anxiety, future work will be needed to disentangle whether ketamine’s effects on specific brain circuits are associated with specific cognitive changes. With the advent of new technologies that can administer drugs such as ketamine with higher anatomical specificity ⁶¹, for example, it may soon be possible to apply pharmacological agents in a way that targets depression without inducing disturbances in spatial cognition.

Methods

Subjects for electrophysiological recordings

All techniques were approved by the Institutional Animal Care and Use Committee at Stanford University School of Medicine. Neural recordings with ketamine injections were made from 8 mice (5 wild type C57Bl/6 and 3 wild type 129S6/SvEvTac) aged 12 weeks to 16 weeks at the time of the first surgery (14.3–24.4 grams). All mice were female except for 2 mice. An additional 2 mice (female wild type 129S6/SvEvTac) received two blank intraperitoneal (IP) injections (control insertion); data from these mice are only shown in Figure 1e. Before surgery, mice were group housed with same-sex littermates, unless separation was required due to water restriction or aggression. After surgery, mice were housed individually. Mice were housed in transparent cages on a 12-h light-dark cycle and experiments were performed during the light phase.

In vivo survival surgeries

For all surgeries, anesthesia was induced with isoflurane (4%; maintained at 0.5–1.5%) followed by an injection of buprenorphine (0.05–0.1 mg/kg). Animals were injected with baytril (10 mg/kg) and rimadyl (5 mg/kg) immediately following both the first and second surgery and for 3 days afterwards. In the first surgery, animals were implanted with a custom-built metal headbar containing two holes for head fixation. The craniotomy sites were exposed and marked during headbar implantation and the surface of the skull was coated in metabond. Additionally, a jewelers’ screw with an attached gold pin, to be used as a ground, was implanted anterior to the metal headbar. After completion of training, a second surgery was performed to make bilateral craniotomies at 3.3 mm lateral to the central suture and immediately anterior of the transverse sinus. A small plastic well was implanted around each craniotomy and affixed with metabond. Craniotomy sites were covered with a drop of sterile saline and with silicone elastomer (Kwik-sil, WPI) in between surgery and recordings.

Virtual reality (VR) environment

The VR recording set-up was based on that described in Cambell et al. 2021 and Low et al. 2022 ^47,62. Head-fixed mice ran on a 15.2-cm-diameter foam roller (ethylene vinyl acetate) constrained to rotate about one axis. The cylinder’s rotation was measured by a high-resolution quadrature encoder (Yumo, 1024 P/R) and processed by a microcontroller (Arduino UNO) using custom software. The virtual environment was displayed on three 24-inch monitors surrounding the mouse and generated using commercial software (Unity 3D) and updated according to the motion signal. The gain of the virtual reality track was calibrated so that the virtual track was 400 cm long. Upon completing the track, the mouse was teleported seamlessly back to the start in a way such that the track seemed infinite to the mouse (visual cues of the next track were repeated and visible in the distance as the animal approached track end). The floor of the virtual environment was a checkerboard texture, and there were 5 pairs of evenly spaced (80 cm apart) visual towers that were the same on the left and right sides of the track (see Fig. 1b). Each of the 5 pairs of towers were different heights, widths, and patterns (all were black and white and had neutral luminance). Water rewards were delivered via Tygon tubing attached to a metal lick spout mounted in front of the mouse, and delivery was triggered via a solenoid valve, which produced an audible clicking sound upon reward delivery. The mouse’s licks were detected with a custom-built infrared light barrier. The Neuropixels probes were mounted on a motorized micromanipulator (UMP Micromanipulator, Sensapex). Probe holders were placed behind the mouse to minimize visual disturbance.

Recording session structure and drug administration

Mice ran 300 trials per recording session. The first 50 trials served as the baseline epoch, in which no manipulation was performed. Trials 51–100 served as the control epoch, which occurred after a blank intraperitoneal (IP) injection (control insertion) was given. Trials 101–290 served as the ketamine epoch which occurred after the administration of a 25 mg/kg IP injection. Trials 290–300 served as the gain manipulation trials where the gain of visual flow was set to 0.5 speed relative to the animal’s movement on the running wheel. IP injections were delivered to the head-fixed animals in about 10 seconds between VR trials. The 25 mg/kg dose of ketamine was determined based on previous literature and an in-house ketamine dose-response curve (Supplementary Fig. 3) (see Ketamine Dose Response Curve section of Methods).

Training and handling

After headbar implantation, mice recovered for three days and then were put on water deprivation. They received 0.8–1.0 mL of water each day and their weights were monitored to ensure that they remained above 80% of baseline. Mice were handled for 15 minutes at least once every 2 days following headbar implantation and given an in-cage running wheel. Training progressed in three stages. In stage one, they were head-fixed on the VR rig and trained to receive water from the lickspout. Water delivery was associated with an audible click of the solenoid. Mice quickly learned this association and began licking upon hearing the click. After stage one, mice progressed to stage two, in which they ran on a training track consisting of a floor with checkerboard texture and evenly spaced, visual landmarks on both sides to receive water rewards at a reward tower. Water rewards (2 μL) were automatically delivered whenever the mouse passed the reward tower. The reward tower spacing started at 40 cm and the track was lengthened daily up to a maximum of 200 cm, such that the reward tower moved further down the track, to encourage running. The reward tower on the habituation track was visually identical to the reward tower on the track used for recording. Once mice ran consistently on the training track (average running speed > 10 cm/s), they progressed to stage three, in which they ran on the same track that would eventually be used for recording, increasing from 50 to 400 trials per day. During this final phase of training, mice developed stereotyped running and licking patterns in which they slowed down and licked prior to the reward tower. Some training sessions were performed on a training rig, but mice were always trained on the recording rig for several days prior to the first recording day to familiarize them with the setup. Mice were deemed fully trained and ready to record when they completed 400 trials within 1 hour for two consecutive days. Mice that never learned the task were excluded from further experiments.

In vivo electrophysiological data collection

All recordings were performed at least 24 -hours after the craniotomy surgery, at which point the mouse was head-fixed on the VR recording rig. The craniotomy site was exposed and rinsed with saline; debris was removed using a syringe tip. Recordings were performed using Phase 3B Neuropixels 1.0 silicon probes³⁹ with 384 active recording sites (out of 960 sites total) located at the bottom ~4 mm of a ~10 mm shank (70 μm wide shank diameter, 24 μm thick, 20 μm electrode spacing), with the reference and ground shorted together. The probe was positioned over the craniotomy site at 10° from vertical and targeted to ~50–300 μm anterior of the transverse sinus using a micromanipulator. On consecutive recording days, probes were targeted medial or lateral of previous recording sites, as permitted by the craniotomy. The reference electrode was then connected to a gold ground pin implanted in the skull. The probe was advanced slowly (~10 μm/s) into the brain until it encountered resistance or until activity quieted on channels near the probe tip, then retracted 100–500 μm and allowed to sit for at least 30 mins prior to recording. While the probe was implanted, the craniotomy site was covered with sterile saline and silicone oil. Signals were sampled at 30 kHz with gain = 200 (7.63 μV/bit at 10 bit resolution) in the action potential band, digitized with a CMOS amplifier and multiplexer built into the electrode array, then written to disk using SpikeGLX software.

Histology and probe localization

In order to identify the probe location after the completion of all experiments, probes were dipped 10 times at 10 second intervals in a fixable lipophilic dye before each probe insertion (1 mM DiI, DiO or DiD, Thermo Fisher). Mice were sacrificed with an overdose of pentobarbital and transcardially perfused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde within 7 days of the first probe insertion. Brains were extracted and stored in 4% paraformaldehyde for at least 24 h before transfer to 30% sucrose in PBS. The brains were then flash frozen, cut into 45-μm sagittal sections with a cryostat, mounted and stained with cresyl violet. Histological sections were examined and the location of the probe tip and entry into the dorsal MEC for each recording were determined based on the reference Allen Brain Atlas ⁶³ (Supplementary Fig. 1). The location of each recording site along the line delineated by the probe tip and entry point was then determined based on each site’s distance from the probe tip. Depth reported is the ventral distance from the location of the dorsal boundary of MEC in the medial section where the probe enters MEC.

Offline spike sorting

Neuropixels probes electrophysiological recordings using SpikeGLX software and Matlab were commo-naverage referenced to the median across channels and high-pass filtered above 150 Hz. Automatic spike sorting was then performed using Kilosort2, a high-throughput spike sorting algorithm that identifies clusters in neural data and is designed to track small amounts of neural drift over time (open source software by Marius Pachitariu, Nick Steinmetz, and Jennifer Colonell, https://github.com/MouseLand/Kilosort2) ⁶⁴. All clusters with peak-to-peak amplitude over noise ratio < 3 (with noise defined as the standard deviation of voltage traces in a 10ms window preceding detected spike times), total number of spikes < 100, and repeated refractory period violations (0–1 ms autocorrelogram bin > 20% of maximum autocorrelation) were excluded after automatic spike-sorting. All remaining clusters were manually examined using Phy (an open-source Python graphical user interface for visualization and manual curation of large-scale electrophysiological data) and labeled as “good” (i.e. stable and likely belonging to a single, well-isolated neural unit), “MUA” (i.e. likely to represent multi-unit activity), or “noise.” This paper only analyzed well-isolated “good” units from within MEC with greater than 100 spikes from sessions with >10 cells.

Virtual reality behavioral data preprocessing

Virtual position and time stamps were recorded on each frame of the VR scene, and a synchronizing TTL pulse was generated from an Arduino UNO and recorded in SpikeGLX using an auxiliary National Instruments data acquisition card (NI PXIe-6341 with NI BNC-2110). The time of each lick (identified by the breaking of an IR beam by the mouse tongue) was also synchronized to the neurophysiological data. Time stamps were adjusted to start at 0 and all behavioral data was interpolated to convert the variable VR frame rate to a constant frame rate of 50 Hz. Since the virtual 400 cm track was effectively infinite, recorded positions less than 0 or greater than 400 cm were converted to the appropriate position on the circular track. Trial transitions were identified as timepoints where the difference in position across time bins was less than −100 cm (i.e. a transition from ~400 cm to ~0 cm) and a trial number was accordingly assigned to each time point. Running speed for each time point was computed by calculating the difference in position between that time point and the previous, divided by the framerate (speed at the first time point was assigned to be equal to that at the second time point). Speed was then interpolated to fill removed time points and smoothed with a Gaussian filter (standard deviation 0.2 time bins).

Statistics

Analysis was conducted in Matlab and Python. All tests were two-sided unless otherwise noted, values are presented as mean ± standard error of the mean (SEM), and correlation coefficients are Pearson’s correlations unless otherwise noted. Wilcoxon signed-rank tests and Wilcoxon rank-sum tests were used to assess significance for paired and unpaired data respectively. Experimenters were not blinded during data collection and experimental analysis. Sample sizes were consistent with previous similar studies and not predetermined.

Spatial information, stability and crest factor score in VR.

Spatial information calculations were based on the procedures in Skaggs et al. 1993 ⁶⁵. Spatial information was calculated in bits per second over 2 cm position bins. Firing rates were computed empirically (number of spikes in position bin i divided by occupancy time). Spatial firing rate vectors were generated by dividing the linear VR track into 2 cm bins and dividing the number of spikes in each bin by the dwell time in that bin. Time periods when the mouse was moving at less than 2 cm/s were omitted from the analysis. A spatial stability score was calculated by smoothing the firing rate vector with a Gaussian filter (standard deviation = 2 cm), normalizing the data, correlating each trial to the preceding trial and the subsequent trial, and finding the mean between those two values. The stability of the first and last trial were calculated by correlating with only the subsequent and preceding trial respectively. ‘Crest factor’ scores for each trial measuring the ‘peakiness’ of each cell’s signal were calculated from a cell’s spatial firing rate vectors by calculating the peak value divided by the root mean square of the spatial tuning curve.

x_{c r e s t} = \frac{x_{p e a k}}{\sqrt{\frac{1}{N} \sum_{i = 1}^{N} x_{i}^{2}}}

Lick accuracy and running speed

Licking behavior was measured with an infrared light barrier. Breaking the IR light barrier resulted in photodiode voltage output drops, which were monitored by an Arduino UNO. The VR computer queried the voltage on each frame and individual licks were defined as the voltage dropping below a predefined threshold. Accurate licks were defined as occurring within 50 cm of the reward tower placed at 400 cm. VR lick accuracy percentage was calculated by dividing the number of accurate licks by the total number of licks. Trials where the animal did not lick were considered to have a lick accuracy of 0. To calculate the running speed of the mouse, we calculated the difference in VR position between consecutive VR frames. The resulting running speed trace was smoothed with a Gaussian kernel (sigma = 0.2 s).

Functional cell type identification

Spatially stable cells were classified by first generating spatial firing rate vectors by dividing the linear VR track into 2 cm bins and dividing the number of spikes in each bin by the dwell time in that bin. Time periods when the mouse was moving at less than 2 cm/s were omitted from the analysis. To identify spatially stable cells at baseline, we examined the firing rate vectors for each neuron across the 50 baseline trials. The firing rate vectors were smoothed with a Gaussian filter (standard deviation = 2 cm), the data were normalized, and baseline spatial stability scores were generated calculating the mean spatial stability score (see above) for the baseline epoch. Cells with a baseline spatial stability > 0.2 were classified as spatially stable cells.

Putative high-firing rate interneurons were identified by finding cells with a mean firing rate > 15Hz (putative high-firing interneurons). Excitatory neurons had mean firing rates < 15Hz. Putative grid cells were identified based on cells whose spatial tuning curves were modulated by changing the visual gain. Animals ran 10 trials following the completion of the recording session (trials 291–300) where the correlation between the VR visual environment flow and locomotion was manipulated to have a gain of 0.5x, such that the animal had to travel two times the normal distance to cover the same amount of visual track. Previous work has shown disassociating the visual flow from locomotion with a gain < 1 can be used to identify putative grid cells - as grid cells in MEC are more sensitive to gain change^42,62. We computed the spatial tuning curves for the last 10 normal trials and the 10 gain manipulation trials and calculated the Pearson correlation between these tuning curves for each cell. Putative grid cells were then classified as neurons with a normal-gain manipulation correlation less than 0.2 (Supplementary Fig. 4d). Note this method is a rough classification of grid cells that likely includes some false positives and may falsely reject some true grid cells.

Identifying the decoherence period using UMAP/DBSCAN

Temporally binned firing vectors for each recorded cell were generated by binning spikes into 0.02 second bins. The population of recorded cells per session was then stacked together to produce a matrix C of size (number of cells, number of time bins). UMAP and DBSCAN were performed using corresponding MATLAB packages ^66–68 . UMAP dimensionality reduction was then performed on matrix C to get embedding coordinates for each cell. Parameters for UMAP for this embedding were n_neighbors=15, n_componenents=3, distance metric=euclidean, min_dist=0.3, embedding dimension=2. Clustering with DBSCAN was performed in the three-dimensional embedding space to identify clusters of neural activity corresponding to the main manifolds. The first major cluster of neural activity (duration > 1 min), which started between 2 – 15 min after the ketamine administration, was labeled the ‘decoherence period’.

Cell-pair connectivity analysis

To calculate cell-pair connectivity, we first calculated the Pearson correlation coefficient between the smoothed temporally-binned firing rate of the first 50 baseline trials for all spatially stable pairs of cells in a session. We then identified cell-pairs as those with significant Pearson correlations (p<0.05) during the baseline epoch. We then compared the correlation coefficients of these cell-pairs across five different epochs: baseline (trial 1–50), control (trials 51–100), acute ketamine (trials 101–150), late ketamine (trials 251–290), and the visual-gain change (291–300) epochs. During the 10 trial visual-gain change epoch, the correlation between the VR visual environment flow and locomotion was manipulated to have a gain of 0.5x, such that the animal had to travel two times the normal distance to cover the same amount of visual track. We chose to focus on gain values < 1, as these tended to show more strongly the influence of path integration on spatial firing in MEC ^42,62. We calculated the least squares line of the cell-pairs in the different conditions and a Pearson correlation (ρ) value for each the following comparisons: baseline versus control, baseline versus acute ketamine, control versus late ketamine, late ketamine versus visual-gain change.

Trial x trial population similarity matrices and correlation scores

Spatially binned firing matrices A_i (trial x position) for each recorded cell i = 1…N from a single session were concatenated to form the population matrix P = [A₁ |A₂|...|A_N], where N is the number of cells recorded in that session. We computed the trial x trial population similarity matrix S for each session by calculating the Pearson’s correlation coefficient between each row (trial) in the population matrix P (S_ij = corr(P_i^T, P_j^T)). To generate the averaged spatial correlation matrix $\underline{S}$ , we found the mean of the 30 recorded session’s trial x trial population similarity matrices. To calculate the correlation score which quantified the post-decoherence remapping of spatial firing patterns, we first created a ‘baseline template’ for each cell in a session by computing the mean spatial firing rate tuning curve across the baseline epoch (trials 1–50), denoted as b (b_i = mean(A_i [a₁^T…a₅₀^T], where a_i^T is the i^th row of A_i). We then calculated Pearson’s correlation coefficient between the baseline spatial template b and the remaining trials of the spatial firing fields (corr(b_i, A_i [a_j^T]) for j = 51…290).

Logistic decoder

We used a logistic regression model(penalty=‘l2’,random_state=0, solver=‘lbfgs’, multi_class=‘multinomial’, max_iter=10000000, C = 0.03) to predict the animal’s position in VR from the spiking activity of all recorded neurons in a session, and we referred to the optimized model as a ‘‘decoder’’ following common terminology and practice69 . We used the baseline epoch (trials 1–50) as the training (encoding) trials. The z-scored firing rate was used to compute spatial tuning curves for encoding trials by averaging the population activity within each spatial bin (2 cm). This defined an average N-dimensional trial trajectory during encoding trials, where N is the number of neurons. The model was trained 50 times dropping 1 to find the baseline RMSE. We then tested the model with the neural population activity from each subsequent trial during both the control epoch (trials 51–100) and ketamine epoch (trial 101–290). For each time point during the decoding (test) trials, we determined the closest point on the average encoding trajectory by minimizing the Euclidean distance. The decoded position then corresponded to the VR position for this point on the average trajectory. We calculated the decoding error as the root mean squared error (RMSE) between decoded position and actual position of the animal respecting the circularity of the track. We then found the mean RMSE curve by averaging the RMSE curves (trials 51–290) across the 30 recorded sessions (n = 8 mice).

Ketamine dose-response curve and open-field behavior

To test spatial mobility, 5 female and 5 male (C57BL/6 mice, aged 12 weeks to 24 weeks) were allowed to freely explore a 60 cm×60 cm open field. In order to minimize external visual cues, the open-field was surrounded by black walls. A single white cue was located on one wall to orient the animal. In order to minimize the effect of external noise, the open field arena was be placed in a 49”(l) × 49”(w) × 102”(h) soundproof studio room (reduces noise above 1000 Hz by greater than 60dB, and reduces noise from 250 Hz to 100Hz by ~ 50dB). A HEPA filter was run constantly inside the behavioral box to both provide a constant source of white noise and to clean the air of particulates and odors. The open-field behavioral tasks were recorded via a ceiling mounted infrared camera. Ethovision’s behavioral analysis software was used to analyze the behavior including animal tracking, animal speed, and animal sniffs of the objects (Supplementary Fig. 3b–e).

To examine spatial memory consolidation, we tested 14 female mice (C57BL/6 mice, aged aged 12 weeks to 24 weeks) on the Object-Location Memory task (Supplementary Fig. 3a–b). This task specifically tested hippocampal-dependent spatial memory without needing an aversive stimulus ^70,71. This task leveraged the fact that mice naturally spend more time exploring novel objects and the fact that mice notice when objects have been moved to a new location. Mice were exposed to an open field arena with two distinct objects for 10 minutes. Half the mice (n = 7) were given 25 mg/kg ketamine and the other half (n = 7) were given a control injection. The following day, one of the objects was moved to a new spatial location and the mice were allowed to explore freely for 5 minutes. If the animal remembered the previous day, it would spend more time investigating the object that had moved. The animals were recorded using an infrared camera mounted above the arena, and the amount of time spent sniffing the objects was scored using Ethovision’s behavior tracking software. The videos were analyzed and the discrimination index (DI = {[time spent exploring object in novel location – time spent exploring object in familiar location]/total time exploring both objects} during the test phase was calculated as a measure of spatial memory.

In order to test the dose-response curve of ketamine, mice were given ketamine at the following doses: 0 mg/ kg, 10 mg/kg, 12.5 mg/kg, 15 mg/kg, 20 mg/kg, and 25 mg/kg once a day intraperitoneally (IP) (Supplementary Fig. 3d). The drug was delivered to mice through an IP injection and then immediately placed in the open field arena to be tracked for 30 minutes afterwards using Ethovision’s behavior tracking software. Different doses of ketamine (1 – 25 mg/kg) were used to generate a dose-response curve of the total distance the animal traveled during a recording session (Supplementary Fig. 3). Angular acceleration (radians/s²) was also calculated to quantify the stereotypic post-ketamine spinning behavior. The mouse underwent only a single injection with a single concentration of the drug on any given day. The number of lifetime injections was limited to 20 per animal and no more than 5 injections per week (one per day).

Imaging of the hippocampus in the open field arena

5 male and 3 female Ai94;Camk2a-tTA;Camk2a-Cre (JAX id: 024115 and 005359) mice were used for imaging experiments in region CA1 of the hippocampus. Mice were 8–12 weeks old at the time of surgery. Detailed surgical procedures are described in Sun and Giocomo, 2022 ⁷². Briefly, after fully anesthetizing each mouse, a gradient refractive index (GRIN) lens (0.25 pitch, 0.55 NA, 1.8 mm diameter and 4.31 mm in length, Edmund Optics) was implanted above the CA1 region of the hippocampus after aspirating the overlying cortical tissue. The implantation coordinates were centered at −2.30 mm anterior/posterior, +1.75 mm medial/lateral and −1.53 mm dorsal/ventral relative to bregma. Two weeks after the implantation of the GRIN lens, a small aluminum base plate was cemented to the animal’s head on top of the existing dental cement. After the installation of the baseplate, the imaging window was fixed for long-term use with respect to the miniscope used during installation. For all imaging experiments, each mouse had a dedicated miniscope. When not imaging, a plastic cap was placed in the baseplate to protect the GRIN lens from dust and dirt. After mice had fully recovered from the baseplate surgery, they were handled and allowed to habituate to wearing the head-mounted miniscope by freely exploring an open arena (a clean cage bottom) in a different room for 20 minutes every day for one week. Animals were also habituated to mock intraperitoneal injections (needle poking) once a day for four days.

Before the imaging experiment, mice were further habituated in the circular open field (36 cm in diameter) 20 minutes/day for 2 days in the experimental room. The imaging experiment then followed an AABA paradigm over four days, in which A represents two baseline sessions and a post-baseline session on days 1, 2 and 4, respectively; and B represents the ketamine session on day 3. Each session was 20 minutes long and mice were allowed to freely explore the circular open field arena. Ketamine (25 mg/kg) was injected intraperitoneally on day 3 just before imaging began.

Miniscope imaging data acquisition and initial batch processing

Details of the general processing steps for the customized miniscope acquired data are described in Sun and Giocomo, 2022 ⁷². Briefly, miniscope videos of individual sessions were first concatenated and down-sampled by a factor of 2 using custom MATLAB scripts, then motion corrected using the NoRMCorre MATLAB package. To align miniscope videos across different sessions for the entire experiment, we applied an automatic 2D image registration method (github.com/fordanic/image-registration) with rigid x-y translations according to the maximum intensity projection images for each session. The registered videos for each animal were then concatenated together in chronological order to generate a combined data set for extracting calcium activity. This process made it unnecessary to perform individual footprint alignment or cell registration across sessions. We then used the CNMF-E package to extract an individual neuron’s calcium activity for the concatenated video data. CNMF-E is based on the CNMF framework, which enables simultaneous denoising, deconvolving and demixing of calcium imaging data. These extracted calcium signals for the combined data set were then split back into each session according to their individual frame numbers.

Place cell analyses

To calculate spatial rate maps, the position and speed of the animal was determined by applying a custom MATLAB script to the animal’s behavioral tracking video. Time points at which the speed of the animal was lower than 2 cm/second were identified and excluded from further analysis. We then used linear interpolation to temporally align the position data to the calcium imaging data. After we obtained the deconvolved spiking activity of neurons, we extracted and binarized the effective neuronal calcium events from the deconvolved spiking activity by applying a threshold (3 × standard deviation of all the deconvolved spiking activity for each neuron). We then treated the binarized deconvolved spikes as calcium events. The position data was sorted into 1.75 × 1.75 cm non-overlapping spatial bins. The spatial rate map for each neuron was constructed by dividing the total number of calcium events by the animal’s total occupancy in a given spatial bin. The rate maps were smoothed using a 2D convolution with a Gaussian filter that had a standard deviation of 2.

To identify place cells, and quantify the information content of a given neuron’s activity, we calculated spatial information scores in bits/sec (each calcium event is treated as a spike here) for each neuron according to the previously published formula⁶⁵. Bins with total occupancy time of less than 0.1 second were excluded from the calculation. To identify place cells, the timing of calcium events for each neuron was circularly shuffled 1000 times and spatial information (bits/sec) recalculated for each shuffle. This generated a distribution of shuffled information scores for each individual neuron. The value at the 95^th percentile of each shuffled distribution was used as the threshold for classifying a given neuron as a place cell, and we excluded cells with an overall mean calcium event rate less than 0.1 Hz. This threshold was roughly equal to the 5^th percentile of the mean event rate distribution for all neurons.

Position-matching for comparisons of cell activity across sessions

Analyses that compare hippocampal neuronal activity across different sessions (longitudinal comparisons) can be influenced by biases in the animal’s spatial occupancy, which can arise due to the effects of ketamine. To circumvent the effect of differences in occupancy on our analyses, we implemented a position-matched down-sampling protocol when performing longitudinal comparisons of place cell activity. For down-sampling, we first binned the spatial arena into 1.75 × 1.75 cm non-overlapping bins. We then computed the number of position samples (frames) observed in each spatial bin for the to-be-matched sessions. Finally, the number of samples in each corresponding spatial bin were downsampled by randomly removing position samples, and the corresponding neural activity, from the session with greater occupancy. Due to the stochastic nature of the down-sampling process, we repeated this procedure 50 times (unless otherwise specified) for each cell, and the final value for each cell was calculated as the average of all 50 iterations. This final value was then used to obtain the reported means or perform statistical comparisons. This protocol was applied to our analyses for all the within subject comparisons including mean and peak calcium event rates, spatial information, and spatial correlations. To avoid over-downsampling using many sessions simultaneously, we only performed pairwise downsampling with two sessions at a time.

Supplementary Material

Supplement 1

media-1.pdf^{(2.9MB, pdf)}

Acknowledgements

We thank A. Diaz for histology assistance and animal care; J. Maturi for assistance with open field behavioral training; J. Wen for assistance with virtual reality behavioral training and Giocomo Lab members for discussions and feedback. This work was supported by Stanford University Medical Scientist Training Program grant T32-GM007365 and T32-GM145402 (to FKM), an A.P. Giannini Foundation Fellowship (to EAAJ); NIH Brain Initiatives (U19NS118284), NIMH (MH126904 and MH130452), the Simons Foundation (SCGB 542987SPI), NIDA (DA042012), the Vallee Foundation, and the James S. McDonnell Foundation (to LMG).

Footnotes

Competing interests

The authors declare no competing interests

Data availability

All data will be made available before publication on figshare, mendeley, or DANDI

Code will be made available on github

References

1.Dundee J. et al. Ketamine as an induction agent in anaesthetics. Lancet 295, 1370–1371 (1970). [DOI] [PubMed] [Google Scholar]
2.Krystal J. H. et al. Subanesthetic Effects of the Noncompetitive NMDA Antagonist, Ketamine, in Humans. Arch Gen Psychiat 51, 199 (1994). [DOI] [PubMed] [Google Scholar]
3.Zanos P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Cui Y. et al. Astroglial Kir4.1 in the lateral habenula drives neuronal bursts in depression. Nature 554, 323–327 (2018). [DOI] [PubMed] [Google Scholar]
5.Yang Y. et al. Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554, 317–322 (2018). [DOI] [PubMed] [Google Scholar]
6.Malhotra A. K. et al. NMDA Receptor Function and Human Cognition: The Effects of Ketamine in Healthy Volunteers. Neuropsychopharmacology 14, 301–307 (1996). [DOI] [PubMed] [Google Scholar]
7.Morgan C. J. & Curran V. H. Acute and chronic effects of ketamine upon human memory: a review. Psychopharmacology 188, 408–424 (2006). [DOI] [PubMed] [Google Scholar]
8.Morgan C. J., Mofeez A., Brandner B., Bromley L. & Curran V. H. Acute Effects of Ketamine on Memory Systems and Psychotic Symptoms in Healthy Volunteers. Neuropsychopharmacology 29, 1300342 (2003). [DOI] [PubMed] [Google Scholar]
9.Newcomer J. W. et al. Ketamine-Induced NMDA Receptor Hypofunction as a Model of Memory Impairment and Psychosis. Neuropsychopharmacology 20, 106–118 (1999). [DOI] [PubMed] [Google Scholar]
10.Morgan C. J. A. et al. Long-Term Heavy Ketamine Use is Associated with Spatial Memory Impairment and Altered Hippocampal Activation. Frontiers Psychiatry 5, 149 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Rowland L. M. et al. Selective Cognitive Impairments Associated with NMDA Receptor Blockade in Humans. Neuropsychopharmacology 30, 633–639 (2005). [DOI] [PubMed] [Google Scholar]
12.Wilkins L. K., Girard T. A. & Cheyne J. A. Anomalous bodily-self experiences among recreational ketamine users. Cogn Neuropsychiatry 17, 415–430 (2012). [DOI] [PubMed] [Google Scholar]
13.Hindmarch I. & Brinkmann R. Trends in the use of alcohol and other drugs in cases of sexual assault. Hum Psychopharmacol Clin Exp 14, 225–231 (1999). [Google Scholar]
14.Venâncio C., Magalhães A., Antunes L. & Summavielle T. Impaired Spatial Memory after Ketamine Administration in Chronic Low Doses. Current Neuropharmacology 9, 251–255 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Vesuna S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 87–94 (2020) doi: 10.1038/s41586-020-2731-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Schmack K., Bosc M., Ott T., Sturgill J. F. & Kepecs A. Striatal dopamine mediates hallucination-like perception in mice. Science 372, eabf4740 (2021). [DOI] [PubMed] [Google Scholar]
17.Cichon J. et al. Ketamine triggers a switch in excitatory neuronal activity across neocortex. Nat Neurosci 26(1), 39–52 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.O’Keefe J. Place units in the hippocampus of the freely moving rat. Experimental Neurology 51, 78–109 (1976). [DOI] [PubMed] [Google Scholar]
19.Hafting T., Fyhn M., Molden S., Moser M.-B. & Moser E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005). [DOI] [PubMed] [Google Scholar]
20.Sargolini F. et al. Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex. Science 312, 758–762 (2006). [DOI] [PubMed] [Google Scholar]
21.Solstad T., Boccara C. N., Kropff E., Moser M.-B. & Moser E. I. Representation of geometric borders in the entorhinal cortex. Sci New York N Y 322, 1865–8 (2008). [DOI] [PubMed] [Google Scholar]
22.Høydal Ø. A., Skytøen E. R., Andersson S. O., Moser M.-B. & Moser E. I. Object-vector coding in the medial entorhinal cortex. Nature 568, 400–404 (2019). [DOI] [PubMed] [Google Scholar]
23.Kropff E., Carmichael J. E., Moser M.-B. & Moser E. I. Speed cells in the medial entorhinal cortex. Nature 523, 419 (2015). [DOI] [PubMed] [Google Scholar]
24.Mallory C. S. et al. Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals. Nat Commun 12, 671 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Colgin L., Moser E. & Moser M. Understanding memory through hippocampal remapping. Trends Neurosci 31, 469–477(2008). [DOI] [PubMed] [Google Scholar]
26.Fyhn M., Hafting T., Treves A., Moser M.-B. & Moser E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007). [DOI] [PubMed] [Google Scholar]
27.Barry C., Hayman R., Burgess N. & Jeffery K. J. Experience-dependent rescaling of entorhinal grids. Nat Neurosci 10, 682–684 (2007). [DOI] [PubMed] [Google Scholar]
28.Bostock E., Muller R. U. & Kubie J. L. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1, 193–205 (1991). [DOI] [PubMed] [Google Scholar]
29.Muller R. & Kubie J. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. Journal of Neuroscience 7(7), 1951–68 (1987). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Knierim J. J., Kudrimoti H. S. & McNaughton B. L. Interactions Between Idiothetic Cues and External Landmarks in the Control of Place Cells and Head Direction Cells. J Neurophysiol 80, 425–446 (1998). [DOI] [PubMed] [Google Scholar]
31.Väisänen J. et al. Excitatory Actions of NMDA Receptor Antagonists in Rat Entorhinal Cortex and Cultured Entorhinal Cortical Neurons. Neuropsychopharmacology 21, 1395330 (1999). [DOI] [PubMed] [Google Scholar]
32.Väisänen J., Ihalainen J., Tanila H. & Castrén E. Effects of NMDA-Receptor Antagonist Treatment on c-fos Expression in Rat Brain Areas Implicated in Schizophrenia. Cell Mol Neurobiol 24, 769–780 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Jang G. & MacIver M. B. Ketamine Produces a Long-Lasting Enhancement of CA1 Neuron Excitability. Int J Mol Sci 22, 8091 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Burak Y. & Fiete I. R. Accurate Path Integration in Continuous Attractor Network Models of Grid Cells. Plos Comput Biol 5, e1000291 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Yoon K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat Neurosci 16, 1077–1084 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Gardner R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Bonnevie T. et al. Grid cells require excitatory drive from the hippocampus. Nat Neurosci 16, 309–317 (2013). [DOI] [PubMed] [Google Scholar]
38.Couey J. J. et al. Recurrent inhibitory circuitry as a mechanism for grid formation. Nat Neurosci 16, 318–324 (2013). [DOI] [PubMed] [Google Scholar]
39.Jun J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Maxwell C. R. et al. Ketamine Produces Lasting Disruptions in Encoding of Sensory Stimuli. J Pharmacol Exp Ther 316, 315–324 (2005). [DOI] [PubMed] [Google Scholar]
41.Levin-Arama M. et al. Subcutaneous Compared with Intraperitoneal KetamineXylazine for Anesthesia of Mice. J Am Assoc Laboratory Animal Sci Jaalas 55, 794–800 (2016). [PMC free article] [PubMed] [Google Scholar]
42.Campbell M. G. et al. Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nature Neuroscience 21, (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Fuhs M. C. & Touretzky D. S. A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex. J Neurosci 26, 4266–4276 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Trettel S. G., Trimper J. B., Hwaun E., Fiete I. R. & Colgin L. L. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nat Neurosci 22, 609–617 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Chaudhuri R. & Fiete I. Computational principles of memory. Nature Neuroscience 19, 394–403 (2016). [DOI] [PubMed] [Google Scholar]
46.Colgin L. L., Moser E. I. & Moser M.-B. Understanding memory through hippocampal remapping. Trends Neurosci 31, 469–477 (2008). [DOI] [PubMed] [Google Scholar]
47.Low I. I. C., Williams A. H., Campbell M. G., Linderman S. W. & Giocomo L. M. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 110, 903 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Kanter B. R. et al. A Novel Mechanism for the Grid-to-Place Cell Transformation Revealed by Transgenic Depolarization of Medial Entorhinal Cortex Layer II. Neuron 93, 1480–1492.e6 (2017). [DOI] [PubMed] [Google Scholar]
49.Mallory C. S., Hardcastle K., Bant J. S. & Giocomo L. M. Grid scale drives the scale and long-term stability of place maps. Nature Neuroscience 21, 270–282 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Miao C. et al. Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex. Neuron 88, 590–603 (2015). [DOI] [PubMed] [Google Scholar]
51.Morgan H. L. et al. Exploring the impact of ketamine on the experience of illusory body ownership. Biol Psychiat 69, 35–41 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Nosyreva E. et al. Acute Suppression of Spontaneous Neurotransmission Drives Synaptic Potentiation. J Neurosci 33, 6990–7002 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Yamakura T., Mori H., Masaki H., Shimoji K. & Mishina M. Different sensitivities of NMDA receptor channel subtypes to non-competitive antagonists. Neuroreport 4, 687–690 (1993). [DOI] [PubMed] [Google Scholar]
54.Chen X., Shu S. & Bayliss D. A. HCN1 channel subunits are a molecular substrate for hypnotic actions of ketamine. The Journal of neuroscience: the official journal of the Society for Neuroscience 29, 600–9 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Zhou C. et al. Forebrain HCN1 Channels Contribute to Hypnotic Actions of Ketamine. Anesthesiology 118, 785 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Giocomo L. & Hasselmo M. Knock-out of HCN1 subunit flattens dorsal–ventral frequency gradient of medial entorhinal neurons in adult mice. J Neurosci (2009) doi: 10.1523/JNEUROSCI.0609-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Magee J. C. Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. Journal of Neuroscience 18, 7613–7624 (1998). [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Nolan M. F., Dudman J. T., Dodson P. D. & Santoro B. HCN1 channels control resting and active integrative properties of stellate cells from layer II of the entorhinal cortex. J Neurosci Official J Soc Neurosci 27, 12440–51 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Wang D.-S., Penna A. & Orser B. A. Ketamine Increases the Function of γ-Aminobutyric Acid Type A Receptors in Hippocampal and Cortical Neurons. Anesthesiology 126, 666–677 (2017). [DOI] [PubMed] [Google Scholar]
60.McNaughton B. L., Battaglia F. P., Jensen O., Moser E. I. & Moser M.-B. Path integration and the neural basis of the “cognitive map.” Nat Rev Neurosci 7, 663–678 (2006). [DOI] [PubMed] [Google Scholar]
61.Wang J. B., Aryal M., Zhong Q., Vyas D. B. & Airan R. D. Noninvasive Ultrasonic Drug Uncaging Maps Whole-Brain Functional Networks. Neuron 100, 728–738.e7 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Campbell M. G., Attinger A., Ocko S. A., Ganguli S. & Giocomo L. M. Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex. Cell Reports 36, 109669 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Wang Q. et al. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 181, 936–953.e20 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Pachitariu M., Sridhar S. & Stringer C. Solving the spike sorting problem with Kilosort. (2023) doi: 10.1101/2023.01.07.523036. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Skaggs W. E., McNaughton B. L., Wilson M. A. & Barnes C. A. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6, (1996). [DOI] [PubMed] [Google Scholar]
66.Xu X., Ester M., Kriegel H.-P. & Sander J. A distribution-based clustering algorithm for mining in large spatial databases. Proc 14th Int Conf Data Eng 324–331 (1998) doi: 10.1109/icde.1998.655795. [DOI] [Google Scholar]
67.McInnes L., Healy J., Saul N. & Großberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw 3, 861 (2018). [Google Scholar]
68.Meehan C., Ebrahimian J., Moore W. & and S. M. (2022). U. M. A. and P. (UMAP). Uniform Manifold Approximation and Projection (UMAP). MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/71902) (2022).
69.Kriegeskorte N. & Douglas P. K. Interpreting encoding and decoding models. Curr Opin Neurobiol 55, 167–179 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Maroso M. et al. Cannabinoid Control of Learning and Memory through HCN Channels. Neuron 89, 1059–73 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Assini F., Duzzioni M. & Takahashi R. Object location memory in mice: Pharmacological validation and further evidence of hippocampal CA1 participation. Behavioural Brain Research 204, 206–211 (2009). [DOI] [PubMed] [Google Scholar]
72.Sun Y. & Giocomo L. M. Neural circuit dynamics of drug-context associative learning in the mouse hippocampus. Nat Commun 13, 6721 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1

media-1.pdf^{(2.9MB, pdf)}

Data Availability Statement

All data will be made available before publication on figshare, mendeley, or DANDI

Code will be made available on github

[R1] 1.Dundee J. et al. Ketamine as an induction agent in anaesthetics. Lancet 295, 1370–1371 (1970). [DOI] [PubMed] [Google Scholar]

[R2] 2.Krystal J. H. et al. Subanesthetic Effects of the Noncompetitive NMDA Antagonist, Ketamine, in Humans. Arch Gen Psychiat 51, 199 (1994). [DOI] [PubMed] [Google Scholar]

[R3] 3.Zanos P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Cui Y. et al. Astroglial Kir4.1 in the lateral habenula drives neuronal bursts in depression. Nature 554, 323–327 (2018). [DOI] [PubMed] [Google Scholar]

[R5] 5.Yang Y. et al. Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554, 317–322 (2018). [DOI] [PubMed] [Google Scholar]

[R6] 6.Malhotra A. K. et al. NMDA Receptor Function and Human Cognition: The Effects of Ketamine in Healthy Volunteers. Neuropsychopharmacology 14, 301–307 (1996). [DOI] [PubMed] [Google Scholar]

[R7] 7.Morgan C. J. & Curran V. H. Acute and chronic effects of ketamine upon human memory: a review. Psychopharmacology 188, 408–424 (2006). [DOI] [PubMed] [Google Scholar]

[R8] 8.Morgan C. J., Mofeez A., Brandner B., Bromley L. & Curran V. H. Acute Effects of Ketamine on Memory Systems and Psychotic Symptoms in Healthy Volunteers. Neuropsychopharmacology 29, 1300342 (2003). [DOI] [PubMed] [Google Scholar]

[R9] 9.Newcomer J. W. et al. Ketamine-Induced NMDA Receptor Hypofunction as a Model of Memory Impairment and Psychosis. Neuropsychopharmacology 20, 106–118 (1999). [DOI] [PubMed] [Google Scholar]

[R10] 10.Morgan C. J. A. et al. Long-Term Heavy Ketamine Use is Associated with Spatial Memory Impairment and Altered Hippocampal Activation. Frontiers Psychiatry 5, 149 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Rowland L. M. et al. Selective Cognitive Impairments Associated with NMDA Receptor Blockade in Humans. Neuropsychopharmacology 30, 633–639 (2005). [DOI] [PubMed] [Google Scholar]

[R12] 12.Wilkins L. K., Girard T. A. & Cheyne J. A. Anomalous bodily-self experiences among recreational ketamine users. Cogn Neuropsychiatry 17, 415–430 (2012). [DOI] [PubMed] [Google Scholar]

[R13] 13.Hindmarch I. & Brinkmann R. Trends in the use of alcohol and other drugs in cases of sexual assault. Hum Psychopharmacol Clin Exp 14, 225–231 (1999). [Google Scholar]

[R14] 14.Venâncio C., Magalhães A., Antunes L. & Summavielle T. Impaired Spatial Memory after Ketamine Administration in Chronic Low Doses. Current Neuropharmacology 9, 251–255 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Vesuna S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 87–94 (2020) doi: 10.1038/s41586-020-2731-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Schmack K., Bosc M., Ott T., Sturgill J. F. & Kepecs A. Striatal dopamine mediates hallucination-like perception in mice. Science 372, eabf4740 (2021). [DOI] [PubMed] [Google Scholar]

[R17] 17.Cichon J. et al. Ketamine triggers a switch in excitatory neuronal activity across neocortex. Nat Neurosci 26(1), 39–52 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.O’Keefe J. Place units in the hippocampus of the freely moving rat. Experimental Neurology 51, 78–109 (1976). [DOI] [PubMed] [Google Scholar]

[R19] 19.Hafting T., Fyhn M., Molden S., Moser M.-B. & Moser E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005). [DOI] [PubMed] [Google Scholar]

[R20] 20.Sargolini F. et al. Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex. Science 312, 758–762 (2006). [DOI] [PubMed] [Google Scholar]

[R21] 21.Solstad T., Boccara C. N., Kropff E., Moser M.-B. & Moser E. I. Representation of geometric borders in the entorhinal cortex. Sci New York N Y 322, 1865–8 (2008). [DOI] [PubMed] [Google Scholar]

[R22] 22.Høydal Ø. A., Skytøen E. R., Andersson S. O., Moser M.-B. & Moser E. I. Object-vector coding in the medial entorhinal cortex. Nature 568, 400–404 (2019). [DOI] [PubMed] [Google Scholar]

[R23] 23.Kropff E., Carmichael J. E., Moser M.-B. & Moser E. I. Speed cells in the medial entorhinal cortex. Nature 523, 419 (2015). [DOI] [PubMed] [Google Scholar]

[R24] 24.Mallory C. S. et al. Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals. Nat Commun 12, 671 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Colgin L., Moser E. & Moser M. Understanding memory through hippocampal remapping. Trends Neurosci 31, 469–477(2008). [DOI] [PubMed] [Google Scholar]

[R26] 26.Fyhn M., Hafting T., Treves A., Moser M.-B. & Moser E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007). [DOI] [PubMed] [Google Scholar]

[R27] 27.Barry C., Hayman R., Burgess N. & Jeffery K. J. Experience-dependent rescaling of entorhinal grids. Nat Neurosci 10, 682–684 (2007). [DOI] [PubMed] [Google Scholar]

[R28] 28.Bostock E., Muller R. U. & Kubie J. L. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1, 193–205 (1991). [DOI] [PubMed] [Google Scholar]

[R29] 29.Muller R. & Kubie J. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. Journal of Neuroscience 7(7), 1951–68 (1987). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Knierim J. J., Kudrimoti H. S. & McNaughton B. L. Interactions Between Idiothetic Cues and External Landmarks in the Control of Place Cells and Head Direction Cells. J Neurophysiol 80, 425–446 (1998). [DOI] [PubMed] [Google Scholar]

[R31] 31.Väisänen J. et al. Excitatory Actions of NMDA Receptor Antagonists in Rat Entorhinal Cortex and Cultured Entorhinal Cortical Neurons. Neuropsychopharmacology 21, 1395330 (1999). [DOI] [PubMed] [Google Scholar]

[R32] 32.Väisänen J., Ihalainen J., Tanila H. & Castrén E. Effects of NMDA-Receptor Antagonist Treatment on c-fos Expression in Rat Brain Areas Implicated in Schizophrenia. Cell Mol Neurobiol 24, 769–780 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Jang G. & MacIver M. B. Ketamine Produces a Long-Lasting Enhancement of CA1 Neuron Excitability. Int J Mol Sci 22, 8091 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Burak Y. & Fiete I. R. Accurate Path Integration in Continuous Attractor Network Models of Grid Cells. Plos Comput Biol 5, e1000291 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Yoon K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat Neurosci 16, 1077–1084 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Gardner R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Bonnevie T. et al. Grid cells require excitatory drive from the hippocampus. Nat Neurosci 16, 309–317 (2013). [DOI] [PubMed] [Google Scholar]

[R38] 38.Couey J. J. et al. Recurrent inhibitory circuitry as a mechanism for grid formation. Nat Neurosci 16, 318–324 (2013). [DOI] [PubMed] [Google Scholar]

[R39] 39.Jun J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Maxwell C. R. et al. Ketamine Produces Lasting Disruptions in Encoding of Sensory Stimuli. J Pharmacol Exp Ther 316, 315–324 (2005). [DOI] [PubMed] [Google Scholar]

[R41] 41.Levin-Arama M. et al. Subcutaneous Compared with Intraperitoneal KetamineXylazine for Anesthesia of Mice. J Am Assoc Laboratory Animal Sci Jaalas 55, 794–800 (2016). [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Campbell M. G. et al. Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nature Neuroscience 21, (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Fuhs M. C. & Touretzky D. S. A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex. J Neurosci 26, 4266–4276 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Trettel S. G., Trimper J. B., Hwaun E., Fiete I. R. & Colgin L. L. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nat Neurosci 22, 609–617 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Chaudhuri R. & Fiete I. Computational principles of memory. Nature Neuroscience 19, 394–403 (2016). [DOI] [PubMed] [Google Scholar]

[R46] 46.Colgin L. L., Moser E. I. & Moser M.-B. Understanding memory through hippocampal remapping. Trends Neurosci 31, 469–477 (2008). [DOI] [PubMed] [Google Scholar]

[R47] 47.Low I. I. C., Williams A. H., Campbell M. G., Linderman S. W. & Giocomo L. M. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 110, 903 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Kanter B. R. et al. A Novel Mechanism for the Grid-to-Place Cell Transformation Revealed by Transgenic Depolarization of Medial Entorhinal Cortex Layer II. Neuron 93, 1480–1492.e6 (2017). [DOI] [PubMed] [Google Scholar]

[R49] 49.Mallory C. S., Hardcastle K., Bant J. S. & Giocomo L. M. Grid scale drives the scale and long-term stability of place maps. Nature Neuroscience 21, 270–282 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Miao C. et al. Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex. Neuron 88, 590–603 (2015). [DOI] [PubMed] [Google Scholar]

[R51] 51.Morgan H. L. et al. Exploring the impact of ketamine on the experience of illusory body ownership. Biol Psychiat 69, 35–41 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Nosyreva E. et al. Acute Suppression of Spontaneous Neurotransmission Drives Synaptic Potentiation. J Neurosci 33, 6990–7002 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Yamakura T., Mori H., Masaki H., Shimoji K. & Mishina M. Different sensitivities of NMDA receptor channel subtypes to non-competitive antagonists. Neuroreport 4, 687–690 (1993). [DOI] [PubMed] [Google Scholar]

[R54] 54.Chen X., Shu S. & Bayliss D. A. HCN1 channel subunits are a molecular substrate for hypnotic actions of ketamine. The Journal of neuroscience: the official journal of the Society for Neuroscience 29, 600–9 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Zhou C. et al. Forebrain HCN1 Channels Contribute to Hypnotic Actions of Ketamine. Anesthesiology 118, 785 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Giocomo L. & Hasselmo M. Knock-out of HCN1 subunit flattens dorsal–ventral frequency gradient of medial entorhinal neurons in adult mice. J Neurosci (2009) doi: 10.1523/JNEUROSCI.0609-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Magee J. C. Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. Journal of Neuroscience 18, 7613–7624 (1998). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Nolan M. F., Dudman J. T., Dodson P. D. & Santoro B. HCN1 channels control resting and active integrative properties of stellate cells from layer II of the entorhinal cortex. J Neurosci Official J Soc Neurosci 27, 12440–51 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Wang D.-S., Penna A. & Orser B. A. Ketamine Increases the Function of γ-Aminobutyric Acid Type A Receptors in Hippocampal and Cortical Neurons. Anesthesiology 126, 666–677 (2017). [DOI] [PubMed] [Google Scholar]

[R60] 60.McNaughton B. L., Battaglia F. P., Jensen O., Moser E. I. & Moser M.-B. Path integration and the neural basis of the “cognitive map.” Nat Rev Neurosci 7, 663–678 (2006). [DOI] [PubMed] [Google Scholar]

[R61] 61.Wang J. B., Aryal M., Zhong Q., Vyas D. B. & Airan R. D. Noninvasive Ultrasonic Drug Uncaging Maps Whole-Brain Functional Networks. Neuron 100, 728–738.e7 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Campbell M. G., Attinger A., Ocko S. A., Ganguli S. & Giocomo L. M. Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex. Cell Reports 36, 109669 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Wang Q. et al. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 181, 936–953.e20 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Pachitariu M., Sridhar S. & Stringer C. Solving the spike sorting problem with Kilosort. (2023) doi: 10.1101/2023.01.07.523036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Skaggs W. E., McNaughton B. L., Wilson M. A. & Barnes C. A. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6, (1996). [DOI] [PubMed] [Google Scholar]

[R66] 66.Xu X., Ester M., Kriegel H.-P. & Sander J. A distribution-based clustering algorithm for mining in large spatial databases. Proc 14th Int Conf Data Eng 324–331 (1998) doi: 10.1109/icde.1998.655795. [DOI] [Google Scholar]

[R67] 67.McInnes L., Healy J., Saul N. & Großberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw 3, 861 (2018). [Google Scholar]

[R68] 68.Meehan C., Ebrahimian J., Moore W. & and S. M. (2022). U. M. A. and P. (UMAP). Uniform Manifold Approximation and Projection (UMAP). MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/71902) (2022).

[R69] 69.Kriegeskorte N. & Douglas P. K. Interpreting encoding and decoding models. Curr Opin Neurobiol 55, 167–179 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R70] 70.Maroso M. et al. Cannabinoid Control of Learning and Memory through HCN Channels. Neuron 89, 1059–73 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R71] 71.Assini F., Duzzioni M. & Takahashi R. Object location memory in mice: Pharmacological validation and further evidence of hippocampal CA1 participation. Behavioural Brain Research 204, 206–211 (2009). [DOI] [PubMed] [Google Scholar]

[R72] 72.Sun Y. & Giocomo L. M. Neural circuit dynamics of drug-context associative learning in the mouse hippocampus. Nat Commun 13, 6721 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

This is a preprint.

Ketamine evoked disruption of entorhinal and hippocampal spatial maps

Francis Kei Masuda

Yanjun Sun

Emily A Aery Jones

Lisa M Giocomo

Abstract

Introduction

Results

Virtual reality behavior and electrophysiological recordings

Figure 1 |. Ketamine alters the coding features of MEC neurons in navigating mice.

Fig. 2 |. Ketamine disrupts VR task behavior.

Ketamine affected firing rates heterogeneously across MEC cell types and impaired spatial coding

Fig. 3 |. Ketamine acutely affects firing rates and disrupts single cell spatial scores.

Ketamine induced an acute discrete neural activity state characterized by reduced spatial information cell-pair co-activation

Fig. 4 |. Unbiased identification of the acute decoherence period evoked by ketamine.

Fig 5. |. Ketamine acutely disrupts cell-pair connectivity.

MEC spatial neurons remap following the discrete ketamine-induced neural activity state

Fig. 6 |. Ketamine induces a long-term remapping following the acute decoherence period.

Ketamine disrupts spatial firing patterns in the hippocampus

Fig. 7 |. Ketamine’s effect on the spatial coding of hippocampal place.

Discussion

Methods

Subjects for electrophysiological recordings

In vivo survival surgeries

Virtual reality (VR) environment

Recording session structure and drug administration

Training and handling

In vivo electrophysiological data collection

Histology and probe localization

Offline spike sorting

Virtual reality behavioral data preprocessing

Statistics

Spatial information, stability and crest factor score in VR.

Lick accuracy and running speed

Functional cell type identification

Identifying the decoherence period using UMAP/DBSCAN

Cell-pair connectivity analysis

Trial x trial population similarity matrices and correlation scores

Logistic decoder

Ketamine dose-response curve and open-field behavior

Imaging of the hippocampus in the open field arena

Miniscope imaging data acquisition and initial batch processing

Place cell analyses

Position-matching for comparisons of cell activity across sessions

Supplementary Material

Acknowledgements

Footnotes

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

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