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Impaired spatial memory codes in a mouse model of Rett syndrome

  1. Sara E Kee
  2. Xiang Mou
  3. Huda Y Zoghbi  Is a corresponding author
  4. Daoyun Ji  Is a corresponding author
  1. Baylor College of Medicine, United States
  2. Howard Hughes Medical Institute, Baylor College of Medicine, United States
  3. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, United States
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Cite this article as: eLife 2018;7:e31451 doi: 10.7554/eLife.31451

Abstract

The Mecp2+/- mouse model recapitulates many phenotypes of patients with Rett syndrome (RTT), including learning and memory deficits. It is unknown, however, how the disease state alters memory circuit functions in vivo in RTT mice. Here we recorded from hippocampal place cells, which are thought to encode spatial memories, in freely moving RTT mice and littermate controls. We found that place cells in RTT mice are impaired in their experience-dependent increase of spatial information. This impairment is accompanied by an enhanced baseline firing synchrony of place cells within ripple oscillations during rest, which consequently occludes the increase in synchrony after a novel experience. Behaviorally, contextual memory is normal at short but not long time scale in RTT mice. Our results suggest that hypersynchrony interferes with memory consolidation and leads to impaired spatial memory codes in RTT mice, providing a possible circuit mechanism for memory deficits in Rett Syndrome.

https://doi.org/10.7554/eLife.31451.001

Introduction

Rett syndrome, a postnatal neurodevelopmental disorder, is characterized by a period of normal development lasting until 6–18 months of life, followed by a period of regression and development of motor deficits, autonomic dysfunction, and intellectual disability (Chahrour and Zoghbi, 2007). Rett syndrome is caused by loss-of- function mutations in the X-linked gene MECP2, which encodes Methyl-CpG Binding Protein 2, a protein proposed to regulate chromatin states (Amir et al., 1999). Rett syndrome is primarily seen in heterozygous females (Chahrour and Zoghbi, 2007). Accordingly, female mice heterozygous for the deletion of Mecp2 (Mecp2+/-; hereafter referred to as RTT mice) have been used to model the syndrome (Guy et al., 2001). RTT mice recapitulate many features of the human disorder, including the cognitive phenotypes (Guy et al., 2001). For example, RTT mice display deficits in contextual fear memory (Samaco et al., 2013) and spatial reference memory (Hao et al., 2015). However, how changes in the learning and memory neural circuits in RTT mice give rise to these deficits in vivo is not understood.

The hippocampus, a key area in the brain’s learning and memory circuits, is critical for both contextual fear memory (Phillips and LeDoux, 1992; Fanselow, 2000) and spatial memory (Morris et al., 1982). In particular, spatial memory is thought to be encoded by hippocampal ‘place cells’, which fire when the animal enters one or a few locations (place fields) in an environment (O'Keefe and Dostrovsky, 1971; Wilson and McNaughton, 1993). Place field locations are mostly stable over time and become more refined as the animal’s experience in the environment increases (Thompson and Best, 1990; Frank et al., 2004; Cacucci et al., 2007). This experience-dependent refinement of spatial memory code is thought to involve memory consolidation in rest periods and sleep (Diekelmann and Born, 2010; Walker and Stickgold, 2004), during which place cells are activated synchronously within short (~100 ms) time windows of high-frequency (100–250 Hz) oscillations, called sharp-wave ripples, in the local-field potentials (LFPs) of the hippocampal CA1 area (Buzsáki et al., 1992; Csicsvari et al., 2000; Buzsáki, 1989; Wilson and McNaughton, 1994). The synchronous firing in ripples is believed to induce synaptic plasticity, which transforms short-term memories to long-term memories (Buzsáki, 1989; Wilson and McNaughton, 1994; Tatsuno et al., 2007; Ji and Wilson, 2007; Sirota et al., 2003). Consistent with this idea, disrupting ripples is shown to impair experience-dependent learning (Girardeau et al., 2009; Ego-Stengel and Wilson, 2010). Despite the importance of place cells and their activities during ripple oscillations in hippocampus-dependent learning and memory, how these cells contribute to cognitive deficits in animal models of neurodevelopmental disorders is unknown.

To understand the neural circuit basis of memory deficits in RTT mice, we set out to study how hippocampal place cell activities are altered in RTT mice. In particular, our previous study shows that hippocampal cells in RTT mice tend to display an exaggerated level of synchrony (hypersynchrony) in vitro and in vivo (Lu et al., 2016). We therefore focused on the impact of hypersynchrony on spatial memory codes in freely behaving RTT mice. Specifically, we set out to test the hypothesis that hypersynchrony interferes with ripple-dependent memory processing and consequently impairs the long-term refinement of spatial memory codes in RTT mice.

Results

To test the hypothesis we recorded place cells and LFPs using tetrodes (Cheng and Ji, 2013), from the hippocampal CA1 of adult RTT mice and their wildtype (WT) littermates that were at least 3 months of age (Figure 1A). The recordings were made as animals ran back and forth (two trajectories) for food rewards on either a novel or familiar linear track, repeatedly for two sessions (Run 1, Run 2). There was also a rest session before (Pre-run) and after (Post-run) the first run session in a majority of the recordings (Figure 1B). Each of these run and rest sessions was 10–15 min. Animals (N = 10 RTT, 9 WT mice) were first trained to perform the task on the familiar track in a familiar room for at least 2 weeks before the recording on the same track began. Then, a subset of these animals (N = 9 RTT, 7 WT mice) was placed in a new room and recorded following the same procedure on a novel track, which had a similar shape to the familiar track. The animals had never been exposed to the novel room or the novel track before. During the recording on both the familiar and novel tracks, RTT and WT mice displayed similar performance, as measured by the number of times (laps) an animal traversed a trajectory in a run session and the animal’s average speed of running (Figure 1C,D). We did not observe any seizures behaviorally and did not detect any epileptic-like activities in any of the recorded LFPs.

Recording of CA1 cells in WT and RTT mice during track-running and during rest.

(A) Coronal brain sections of a WT and a RTT mouse stained with cresyl violet. Arrow: tetrode location. (B) Recording sessions and schematics of a track and a rest box. Animals ran back and forth on the track for food reward (R: reward sites) for two sessions (Run 1 and Run 2) and rested in the box before (Pre-run) and after (Post-run) the first run session. (C) Number of laps and running speed of WT and RTT mice on the novel track. Number of animals: N = 7 (WT), 9 (RTT). Wilcoxon ranksum tests: p=0.66 for number of laps; p=0.65 for speed. (D) Same as in (C), but on the familiar track. Number of animals: N = 7 (WT), 8 (RTT). Wilcoxon ranksum tests: p=0.93 for number of laps, p=0.96 for speed.

https://doi.org/10.7554/eLife.31451.002

Place cells in RTT mice contained less spatial information in the familiar, but not the novel, environment

We first analyzed place cell activities on the novel track. A total of 47 cells from 9 RTT mice (RTT cells) and 93 cells from 7 WT mice (WT cells) were recorded on the novel track. Of these, 39 RTT cells and 84 WT cells were classified as putative pyramidal cells (with mean firing rate <7 Hz; Table 1). We defined a run-active cell as a putative pyramidal cell active (with mean firing rate >0.5 Hz) on at least one trajectory during at least one run session. The percentage of run-active cells among all putative pyramidal cells in RTT mice (79%, N = 31) did not significantly differ from that in WT (63%, N = 53) mice (p=0.069, binomial test). The median firing rate of the run-active cells in RTT mice (median [25–75%] range: 1.8 [0.71 3.2] Hz, N = 31) was also not significantly different from that in WT mice (1.1 [0.57 2.3] Hz, N = 53; p=0.10, Wilcoxon ranksum test) on the novel track.

Table 1
WT and RTT animals used in the experiment and number of putative pyramidal cells recorded from each animal on the novel and familiar track.

Putative interneurons were not included. x: no recording was made, -: behavioral and neural data not analyzed because of insufficient number of laps during running.

https://doi.org/10.7554/eLife.31451.003
GenotypeNovel track cellsFamiliar track cells
Putative pyramidalActivePutative pyramidalActive
WT23123627
WT--11
WT211585
WT8664
WT1062611
WT1176
WT941811
WT129xx
RTT--3629
RTT5554
RTT6586
RTT653427
RTT731914
RTT1083023
RTT1130
RTT1100
RTT00xx
RTT33xx
WT total845310265
RTT total3931135103

To examine place fields of run-active cells on the novel track, we linearized an animal’s locations on a track trajectory. For each cell active on a trajectory in a run session, we plotted the cell’s spike raster over the linearized locations during every running lap and computed a firing rate curve (average firing rates over all laps versus linearized locations of the trajectory). On the novel track, raster plots and rate curves show that RTT and WT cells fired spikes at specific locations (place fields) along a trajectory (Figure 2A). To quantify firing specificity, we computed a cell’s spatial information (Skaggs et al., 1993), which measures, in bits per spike, the amount of information the cell’s firing provides about the animal’s location on a trajectory. We found that the spatial information of RTT cells was similar to that of WT cells (Figure 2B) on the novel track. We then quantified how consistent a cell’s firing on a trajectory was between Run 1 and Run 2 by firing stability, which is the Pearson correlation between the rate curves of a cell in the two sessions (Ciupek et al., 2015). We found that the stability of RTT cells on the novel track was significantly reduced from that of WT cells (Figure 2B). We then identified individual place fields of each cell and determined whether place field properties were altered in RTT mice. We found a greater number of place fields per cell per active trajectory in RTT (median [25–75%] range: 2 [1 3]; N = 31 cells) than that in WT cells (1 [1 2], N = 53; p=0.049, Wilcoxon ranksum test), but the place field length in RTT mice was shorter (Figure 2B). The median peak firing rate within place fields was comparable between RTT and WT cells on the novel track (Figure 2B). These quantifications show that place cells in RTT mice contained similar information on the novel track as those in WT mice, but encoded space by more place fields with shorter lengths.

Figure 2 with 2 supplements see all
Place cells in RTT mice had less firing specificity in the familiar, but not in the novel, environment.

(A) Firing activities of an example WT and RTT cell during running on the novel track in two sessions (Run 1, Run 2). For each panel, the top shows spike raster of a cell during every running lap on a linearized trajectory (running direction: from left to right). Each row is a lap and each tick is a spike. The bottom trace is the firing rate curve averaged over all laps in a session. (B) Distributions of spatial information, stability, peak firing rate within place fields, and place field length for WT and RTT place cells on the novel track. Spatial information: p=0.67, Wilcoxon ranksum test; number of cells by active trajectories: N = 79 (WT), 51 (RTT). Stability: ***p=0.0008; number of cells by active trajectory in both run sessions: N = 37 (WT), 25 (RTT). Peak rate: p=0.25; place field length: ***p=0.0001; number of place fields: N = 151 (WT), 115 (RTT). (C) Same as in (A), but for example cells on the familiar track. (D) Same as in (B), but for place cells on the familiar track. Spatial information: *p=0.016, number of cells by active trajectories: N = 87 (WT), 120 (RTT). Stability: p=0.33; number of cells by active trajectories in both run sessions: N = 52 (WT), 61 (RTT). Peak rate: p=0.26; place field length: ***p=2.1×10−8; number of place fields: N = 206 (WT), 280 (RTT).

https://doi.org/10.7554/eLife.31451.004

We next performed the same analyses on RTT and WT cells active on the familiar track (Figure 2C), where we recorded 171 cells from 8 RTT mice and 136 cells from 7 WT mice. Among these, 135 RTT cells and 102 WT cells were putative pyramidal cells (Table 1). The percentage of run-active cells among all putative pyramidal neurons in RTT mice (77%, N = 103) was greater than that in WT (64%, N = 65) mice (p=0.035). The median firing rate of these run-active neurons in RTT (1.8 [1.1 3.0] Hz, N = 103) mice was significantly increased from that in WT mice (1.1 [0.6 2.1] Hz, N = 65; p=0.0012). However, the spatial information of run-active RTT cells was significantly reduced (by 63%), compared to that of WT run-active cells, while the stability was similar (Figure 2D). The number of place fields per cell was similar between RTT (2 [1 2], N = 103 cells) and WT (1 [1 2], N = 65) cells (p=0.16, Wilcoxon ranksum test) on the familiar track, but the place field length of RTT cells was significantly larger than that of WT cells (Figure 2D). The median peak firing rate within place fields was comparable between RTT and WT cells (Figure 2D). The results suggest that the reduced specificity of RTT cells on the familiar track was due to larger place fields.

To gain more understanding of this abnormality, we compared the firing specificity and place field properties of WT or RTT cells between the novel and familiar tracks. In WT mice, the median spatial information of place cells was greatly increased (300%) from the novel to familiar track (p=3.3×10−16, Wilcoxon ranksum test), indicating an experience-dependent refinement of firing specificity in WT cells. The number of place fields remained similar between the novel and familiar track, but the field length became significantly smaller (p=0.00056) and median peak firing rate within place fields was significantly increased (p=1.5×10−7). The result suggests that experience refines place cells by narrowing place field sizes and increasing firing rates. In RTT mice, the spatial information of RTT cells was only modestly increased between novel and familiar tracks (48%, p=0.008), indicating a reduction in experience-dependent refinement of firing specificity in RTT mice, compared to that in WT mice. The number of place fields was similar between the novel and familiar track and the peak rate increased (p=4.5×10−5), as in WT mice. However, place fields in RTT mice were relatively narrow on the novel track and became significantly widened on the familiar track (p=2.4×10−8). The comparison indicates that, although place cells in RTT mice still increased spatial information with experience, likely due to higher peak firing rates, this increase was much less than that in WT mice mainly because of larger place fields.

We need to point out that there were fewer cells recorded on the novel track than on the familiar track. The smaller sample size could render a true effect involving the novel track statistically insignificant. Therefore, we performed a downsampling analysis, in which we re-sampled down the number of cells active on the familiar track in both WT and RTT mice, as well as the number of WT cells active on the novel track, to the same number of RTT cells (31) on the novel track. We repeated the downsampling 200 iterations and examined a key effect involving place cells on the familiar track, spatial information, for each iteration. We found that 42% of the iterations produced a significant difference in spatial information between WT and RTT cells on the familiar track and 3% in spatial information on the novel track (Figure 2—figure supplement 1). The numbers suggest that, although the difference on the familiar track was more frequently observed than that on the novel track, the downsampling did reduce the statistical power for identifying a significant difference between WT and RTT cells on the familiar track. On the other hand, 98% of the iterations produced a significant difference in spatial information of WT cells between the novel and familiar tracks, but only 25% in RTT cells, suggesting that the experience-dependent refinement was robust in WT mice, but largely disappeared in RTT mice, with the downsampled cells. Therefore, although the downsampling led to less statistical power, the reduced experience-dependent refinement in RTT cells, compared to that in WT cells, was reproduced even with the downsampled cells.

We then examined the dynamic change in spatial information at a shorter time scale of within a recording day. We computed the lap-by-lap spatial information during both Run 1 and Run 2 on the novel and familiar track (Figure 2—figure supplement 2). On the novel track, WT and RTT cells showed similar spatial information in early and late laps during Run 1 and throughout Run 2. On the familiar track, WT cells started both Run 1 and Run 2 with already high spatial information in early laps, but RTT cells started with a low level of spatial information similar to that on the novel track. This suggests that place cells in RTT mice could produce specific firing from ongoing sensory experience on the track. However, the specificity was not maintained between running sessions or across days.

Finally, to understand whether place fields of RTT cells were impaired at a fine level, we examined theta phase precession, which establishes a fine correlation between place cell firing and location (O'Keefe and Recce, 1993). We found that theta phases of both RTT and WT place cells precessed as animals passed through their place fields (Figure 3A). We quantified theta phase precession by an optimal linear regression (O'Keefe and Recce, 1993) and a circular-linear correlation (Ravassard et al., 2013) between spike theta phases and positions within a place field. The linear correlation and the slope of the linear regression did not significantly differ between WT and RTT cells on either the novel or the familiar track (Figure 3B). However, there was a small, yet significant, reduction in the circular-linear correlation of RTT cells in the familiar environment (11%), compared to that of WT cells, but not in the novel environment (Figure 3C). The analysis suggests that the fine level of correlation between firing activity and location within place fields was present in RTT mice, but could be slightly weaker in the familiar environment, possibly related to their larger place fields.

Place cells in RTT mice displayed largely normal phase precession on the novel and familiar track.

(A) Example cells with theta phase precession from a WT and RTT animal on the novel and familiar track. For each panel, the top depicts theta phases of a cell versus positions on the track plotted three cycles and the bottom is the cell’s mean firing rate. Solid line: linear regression of the theta phases versus positions. (B) Cumulative distributions of linear correlations and slopes of theta phase and position regressions for place fields in the WT and RTT mice on the novel and familiar tracks. Novel correlation: p=0.11, Wilcoxon ranksum test; slope: p=0.16; number of place fields: N = 160 (WT), 103 (RTT). Familiar correlation: p=0.15; slope: p=0.053; N = 237 (WT), 293 (RTT). (C) Cumulative distributions of circular-linear correlations of theta phase and position regressions for place fields in the WT and RTT mice on the novel and familiar tracks. Novel: p=0.77, Wilcoxon ranksum test; number of place fields: N = 160 (WT), 103 (RTT). Familiar: *p=0.016; N = 237 (WT), 293 (RTT).

https://doi.org/10.7554/eLife.31451.007

Place cell synchrony failed to increase after novel experience in RTT mice

Next, we examined ripples in the rest session before (Pre-run) and after (Post-run) the first spatial experience (Run 1) on a given day in either the novel or familiar room. For each animal and each rest session, we identified individual ripple events in LFPs (Figure 4A) and quantified ripple properties by occurrence rate (number of ripples per second), duration, amplitude, and frequency. Each of these parameters was averaged over all ripple events in a rest session. In the novel environment, ANOVA analysis shows that there was no significant effect of genotype or significant interaction between genotype and rest session in ripple rate, duration, amplitude, or frequency (Figure 4B). Similar findings were made in the familiar environment (Figure 4C). The data suggest that ripples in RTT and WT mice were qualitatively similar in rest sessions.

Figure 4 with 3 supplements see all
Ripple parameters were similar between WT and RTT mice during resting before (Pre-run) and after (Post-run) running on the novel and familiar track.

(A) Raw and filtered (pass band: 100–250 Hz) LFP traces from a WT and RTT animal. (B) Ripple occurrence rate, duration, amplitude, and frequency during resting before (Pre-run) and after (Post-run) running on the novel track. Each dot is the parameter averaged over all ripple events in an animal. Number of animals: N = 5 (WT), 7 (RTT) for Pre-run and 7 (WT), 9 (RTT) for Post-run. ANOVA analysis reveals no significant effect of genotype on occurrence rate (F(1, 26)=2.0, p=0.17), duration (F(1, 26)=1.8, p=0.19), amplitude (F(1, 26)=0.04, p=0.84), or frequency (F(1, 26)=0, p=1.0). There was a significant effect of session on occurrence rate (F(1, 26)=6.8, *p=0.015), but not on duration (F(1, 26)=1.6, p=0.22), amplitude (F(1, 26)=0.14, p=0.71), or frequency (F(1, 26)=0.33, p=0.57). There was no significant effect of the interaction between genotypes and resting sessions (Pre- and Post-run) on occurrence rate (F(1, 24)=1.8, p=0.20), duration (F(1, 24)=0.54, p=0.47), amplitude (F(1, 24)=0.04 P=0.84), or frequency (F(1, 24)=0.41, p=0.53). (C) Same as in (B), but during resting before (Pre-run) and after (Post-run) running on the familiar track. Number of animals: N = 5 (WT), 5 (RTT) for Pre-run and 7 (WT), 8 (RTT) for Post-run. Effect of genotype on occurrence rate (F(1, 23)=0.15, p=0.70), duration (F(1, 23)=0.49, p=0.49), amplitude (F(1, 23)=0.12, p=0.73, and frequency (F(1, 23)=0.06, p=0.81). The main effect of session on occurrence rate was no longer significant (F(1, 26)=4.2, p=0.053), neither was on duration (F(1, 26)=0.02, p=0.88), amplitude (F(1, 26)=1.0, p=0.31), or frequency (F(1, 26)=0.33, p=0.57). There was also no significant effect of the interaction between genotypes and resting sessions (Pre- and Post-run) on occurrence rate (F(1, 21)=0.02, p=0.88), duration (F(1, 21)=0, p=0.96), amplitude (F(1, 21)=0.57, p=0.46), or frequency (F(1, 21)=0.14, p=0.71).

https://doi.org/10.7554/eLife.31451.008

We then examined firing rates of CA1 cells within ripple events (Figure 4—figure supplement 1). In the novel room, the firing rates of all putative pyramidal cells or run-active cells were not significantly different between WT and RTT in either Pre- or Post-run. In the familiar room, the median firing rate of all putative pyramidal cells in RTT mice was similar to that in WT mice in both Pre-run and Post-run, but run-active cells had higher firing rate in RTT mice than in WT mice, especially in Pre-run. This firing rate increase suggests that run-active cells might fire in ripples more often in RTT mice than in WT mice in the familiar room. Indeed, run-active cells in RTT mice participated in ripples with a higher rate and fired more spikes per ripple event in the familiar room, but not in the novel room (Figure 4—figure supplement 2). In contrast, run-inactive cells did not differ between WT and RTT mice in either of these parameters in either the novel or familiar room (Figure 4—figure supplement 3), providing a control that the difference in ripple participation between WT and RTT mice was not due to differences in resting behavior or ripple events per se.

It is known that place cells active in a new spatial experience tend to fire synchronously during ripples immediately after the experience more frequently than during ripples before (Wilson and McNaughton, 1994; Skaggs and McNaughton, 1996). We therefore asked whether this experience-dependent increase in firing synchrony was altered in RTT mice. We examined firing activities of run-active cells within ripples in Pre- and Post-run in the novel room. In WT mice, run-active cells rarely fired together during ripples in Pre-run, but did so more frequently in Post-run. However, in RTT mice run-active cells already fired together in Pre-run and they appeared not to do so more frequently in Post-run (Figure 5A). To quantify this, we computed a normalized pair-wise cross-correlogram (Sirota et al., 2003; Ciupek et al., 2015) within ripples in Pre-/Post-run for each pair of run-active cells that were also recorded in Pre-run and Post-run. Cross-correlograms of these cell pairs in WT mice showed low correlation around the time lag 0 in Pre-run and relatively high correlation in Post-run (Figure 5B). However, cross-correlograms in RTT mice showed high correlation around the time lag 0 already in Pre-run, without further increase in Post-run (Figure 5B). We used the average correlation value around time lag 0 (within [−50 50] ms) as a measure of ‘coactivity’ for each pair of cells (Wilson and McNaughton, 1994). We then compared the coactivity values of all run-active pairs between RTT and WT mice to evaluate their difference in firing synchrony at the cell population level. We found that the coactivity in WT mice was significantly increased from Pre- to Post-run (Figure 5C), indicating an experience-dependent increase in synchrony following running on the novel track, as expected (Wilson and McNaughton, 1994). In RTT mice, however, the coactivity was not significantly different between Pre-run and Post-run, indicating a lack of increase in synchrony after the novel track experience (Figure 5C). Moreover, the Pre-run coactivity was significantly higher in RTT mice than that in WT mice, whereas the Post-run coactivity was not significantly different. Therefore, run-active cells in RTT mice failed to increase their synchrony following a novel experience and this failure appeared to be due to their higher than normal synchrony (hypersynchrony) prior to the experience, consistent with our previous study (Lu et al., 2016).

Figure 5 with 3 supplements see all
Run-active cells in RTT mice failed to increase their firing synchrony following running on the novel track.

(A) Spiking activities of run-active cells in an example WT and RTT mouse within a ripple event in Pre-run and Post-run and within a running lap in Run 1. Each row represents a cell and each tick is a spike. Bottom trace: LFP filtered within the ripple band (100–250 Hz). (B) Cross-correlograms of pairs of run-active cells within ripples in Pre-run and Post-run. Pairs are ordered by their peak correlations. 25 pairs in RTT mice and a random subset of 25 pairs out of 34 in WT mice are plotted. All these pairs were recorded throughout all sessions of Pre-run, Run 1 and Post-run. (C) Coactivities of all run-active cell pairs in WT and RTT mice within ripple events in Pre-run and Post-run. Solid lines: median values. Number of pairs: N = 34 (WT), 25 (RTT). WT coactivity between Pre-run and Post-run: *p=0.044, paired Student’s t-test; RTT: p=0.47. Pre-run coactivity between WT and RTT mice: *p=0.039, Wilcoxon ranksum test; Post-run: p=0.65.

https://doi.org/10.7554/eLife.31451.012

The finding of hypersynchrony among run-active cells in Pre-run leads to the question of whether a general hypersynchrony within ripples existed among all putative pyramidal cells, unrelated to any track-running experience. We first re-analyzed the correlograms in Pre-run before the novel-track running, but for all pairs of putative pyramidal cells in RTT and WT mice. Indeed, we found that the coactivity in RTT mice was significantly higher than that in WT mice (Figure 5—figure supplement 1). Second, we analyzed the coactivity of run-active cells in the familiar environment. We found a similar hypersynchrony among run-active cells in RTT mice, relative to those in WT mice, within ripples in both Pre- and Post-run (Figure 5—figure supplement 2), although the coactivity of RTT cells in Post-run now became significantly higher than that in Pre-run, likely because the repetitive experience on the familiar track eventually further increased the cells’ coactivity (see Discussion). Finally, we computed the coactivity of run-silent cells within ripples (Pre- and Post-run in novel and familiar environments combined) and found that the coactivity was higher in RTT mice than that in WT mice (Figure 5—figure supplement 3). The results suggest a general hypersynchrony among CA1 neurons in RTT mice.

RTT mice displayed normal contextual fear memory at short, but not at long, time scales

Our analysis so far indicates that place cell synchrony failed to increase after novel experience, likely due to baseline hypersynchrony, and place cell firing specificity was impaired on the familiar track. These results suggest that impaired memory consolidation contributes to memory deficits in RTT mice. We attempted to provide behavioral evidence for this hypothesis. Previous studies have shown that RTT mice are impaired in contextual fear memory when they are tested 3 or 24 hr after learning (Samaco et al., 2013; Hao et al., 2015). Here we examined the contextual fear memory of our RTT and WT mice at a shorter time scale, 20 min after learning, when presumably only minimal consolidation had occurred and memory should be relatively normal, according to our hypothesis. After exploring a novel fear-conditioning box and receiving two mild foot shocks, animals were returned to their home cages. We measured their freezing level 20 min later, at which time the animals were placed back to the conditioning box for 5 min. We found that the freezing level at 20 min was comparable between RTT and WT mice (Figure 6A). We also examined the freezing level of the mice at 1 and 24 hr later after the shocks and used the 24 hr freezing level as a measure of long-term memory, when presumably substantial ripple events and thus memory consolidation had occurred. ANOVA analysis revealed a significant effect of genotype and post-hoc Wilcoxon ranksum test showed that there was a significant difference at 24 hr, but not at 20 min, between WT and RTT mice. The memory of a subset of mice (N = 12 WT, 14 RTT) was examined at all three time points. However, ANOVA analysis for these mice failed to identify a significant interaction of genotype by time (F(2,72) = 0.5; p=0.61), probably due to memory extinction of these animals that were tested repeatedly. Nevertheless, our data did show that the contextual memory at a short time scale (20 min) was relatively normal in RTT mice. This result, together with previous studies (Samaco et al., 2013; Hao et al., 2015) showing contextual fear memory deficits at longer time scales, is consistent with a memory consolidation impairment in RTT mice.

Dynamics of memory deficits paralleled those of neurophysiological deficits.

(A) Change in memory deficit from short- to long-term memory. Percentages of freezing time in a fear conditioning box, tested 20 min and 24 hr after WT and RTT animals were foot-shocked in the box, are plotted. Each dot represents an animal. Lines are median values. Two-way ANOVA for the effect of genotype: F(1,66) = 4.2; p=0.044. Post-hoc Wilcoxon ranksum test at 20 min: p=0.78, N = 12 WT, 14 RTT; at 24 hr: *p=0.034, number of animals: N = 20 WT, 22 RTT. (B) Change in spatial information from novel to familiar track in WT and RTT mice. Data are shown as median and [25–75%] range values (same below, see Figure 2). (C) Change in coactivity of run-active cells before (Pre-run) and after (Post-run) running on a novel track in WT and RTT mice (see Figure 5).

https://doi.org/10.7554/eLife.31451.016

Discussion

To understand how genetic deficiencies underlying Rett syndrome alter the function of memory circuits in vivo, we have examined CA1 place cells in freely behaving RTT and WT mice as they ran a linear track and as they rested in both a novel and familiar environment. We found that the firing specificity of place cells in RTT mice does not increase from the novel to the familiar environment as much as that of place cells in WT mice (Figure 6B). In parallel to this dynamic change of place cells, contextual fear memory in RTT mice is normal at a short time scale (20 min), but is impaired at longer time scales (Figure 6A). These deficits point to a possible impairment in memory consolidation in RTT mice. Indeed, we found that run-active cells fail to increase their firing synchrony within ripples following the novel-track running (Figure 6C), likely due to an already high baseline synchrony (hypersynchrony) among CA1 cells in RTT mice. These results are consistent with our hypothesis that hippocampal spatial memory codes are abnormally refined with experience, due to impaired memory consolidation. Our finding may provide a neural circuit mechanism for memory deficits of Rett syndrome.

It is believed that spatial memories are initially encoded by a population of place cells active in a novel environment, driven by sensory input from the environment (O'Keefe and Burgess, 1996; Knierim and Hamilton, 2011). As novel environments become familiar, spatial memories are consolidated and their memory codes become refined, which reflect not only ongoing sensory input from the familiar environments, but also reflect the consolidated memories (Frank et al., 2004; Cacucci et al., 2007; Lever et al., 2002). Our data show that CA1 place cells in RTT mice contain a similar amount of spatial information as those in WT mice on the novel track, but form more place fields with relatively shorter field lengths. This suggests that, although certain aspects of the encoding are changed, ongoing sensory input is capable of driving place cells to encode novel spatial experience in RTT mice. Indeed, our data show that contextual fear memory in RTT mice is normal shortly after learning, presumably because place cells in RTT mice can initially encode the spatial context of a conditioning box. Our data also show that place cells in WT mice are more specific on the familiar track with narrower place fields and increased peak firing rates, compared to those on the novel track. In contrast, place cells in RTT mice increase their spatial specificity from the novel to the familiar track much less than those in WT mice. Although the smaller number of recorded cells on the novel track might not produce sufficient statistical power to identify possible significant differences between WT and RTT cells in parameters such as firing rate or ripple participation rate, our key finding of impaired increase in spatial information of RTT cells in the familiar environment is robust even with downsampled cells. Consistent with this observation, RTT mice are impaired in contextual fear memories tested 3 or 24 hr after learning in previous studies (Samaco et al., 2013; Hao et al., 2015). Therefore, abnormal spatial memory code refinement may underlie behavioral deficits in RTT mice.

It is known that ripple evens are important in memory processing (Girardeau et al., 2009; Ego-Stengel and Wilson, 2010; Wu et al., 2017). Within ripple events during Pre-run prior to a novel spatial experience, our data show an enhanced correlation among place cells in RTT mice. This hypersynchrony may be related to the greater number of place fields or shorter field size seen on the novel track, but appears not sufficient to impact overall spatial information or the behavioral performance. Importantly, following the running on the novel track, run-active cells in RTT mice fail to increase their synchrony within ripples. This is in contrast to the known phenomenon that place cells active in a novel experience normally tend to fire together within ripples after the experience, as shown in previous studies (Wilson and McNaughton, 1994; Ji and Wilson, 2007) and in our data from WT mice. This experience-dependent increase in firing synchrony is hypothesized to be a neural mechanism for memory consolidation, because it likely results from synaptic plasticity during the novel experience and leads to further experience-specific synaptic plasticity for memory consolidation (Buzsáki, 1989; Wilson and McNaughton, 1994), via precise ‘replay’ of the activity patterns associated with the experience (Skaggs and McNaughton, 1996; Lee and Wilson, 2002). Our finding that this experience-dependent increase in synchrony is reduced in RTT mice suggests an impairment in memory consolidation in these animals. In addition, we also found that place fields are relatively unstable between two running sessions of the novel track, providing further evidence that spatial memory codes fail to consolidate following the first running session. Given that there is hypersynchony during Pre-run in RTT mice, it is likely that this baseline hypersynchrony occludes further experience-dependent increases in firing synchrony and thus prevents the experience-specific synaptic plasticity underlying memory consolidation, possibly due to saturation or other homeostatic mechanisms. Our previous study reported the hypersynchrony in hippocampal slices and when animals are quietly awake on an elevated resting platform (Lu et al., 2016). Our previous study also showed that the hypersynchrony, as well as memory deficits, in RTT mice can be rescued by deep-brain stimulation that promotes synaptic plasticity (Hao et al., 2015). Taken together with these previous findings, our data support a model that, although sensory input can drive the initial formation of spatial memory codes in novel environments, the baseline hypersynchrony leads to reduced experience-dependent increase in firing synchrony within ripples and consequently impairs memory consolidation and memory code refinement.

One issue that arises from our data is how to understand the change in firing synchrony within ripples in familiar environment. In WT mice, although there is still an increase in firing synchrony after running on a familiar track, the increase is less dramatic than that following running on a novel track. This is likely because experience-induced firing synchrony among run-active cells gets weaker when memories no longer need to be consolidated. In RTT mice, although hypersynchrony is still present in the rest session before running on the familiar track, the hypersynchrony among run-active cells becomes less prominent than that before running on the novel track. Also, running on the familiar track induces a larger increase in the firing synchrony than running on the novel track. It seems that run-active cells in RTT mice exhibit a change following the running on the familiar track similar to the cells in WT mice following the running on the novel track. One possibility is that this is caused by the repetitive overtraining experience on the familiar track, which may induce some long-term synaptic plasticity that ‘breaks’ the baseline hypersynchrony. The low baseline may now permit some experience-dependent increase in firing synchrony within ripples.

Our notion of abnormal memory consolidation caused by abnormal firing synchrony within ripples in RTT mice is consistent with a previous study on a mouse model of schizophrenia, which found that stronger correlations of place cell pairs within ripples lead to impaired reactivation of place cell patterns following an experience (Suh et al., 2013). However, in our study the abnormally high correlation occurs prior to any experience, and therefore the abnormal memory consolidation appears to be caused by a baseline hypersynchrony within ripples during resting. In addition, our idea that abnormal place field refinement in RTT mice is linked to abnormal neuronal activities within ripples is supported by a recent study that silencing place cells within ripples impairs refinement of their place fields (Roux et al., 2017). Finally, our finding of unstable place fields on the novel track in the face of hypersynchrony within ripples is consistent with several papers that study how manipulating neuronal activities within ripples affects place field stability. Silencing place cells within ripples leads to unstable place fields of these cells (Roux et al., 2017). Disrupting ripples altogether did not impact place field stability in one study (Kovács et al., 2016), but did reduce stability of a portion of place cells in another study (van de Ven et al., 2016). In any case, place cell silencing or ripple disruption is a much more severe alteration than the hypersynchrony in RTT mice, yet the hypersynchrony in RTT mice seems sufficient to impact place field stability.

Although we need to be cautious when interpreting data from animal studies, our study may provide insights into the cognitive deficits in human patients. The RTT mouse model shares the same genetic deficiencies that cause Rett syndrome and recapitulates key behavioral deficits in Rett patients, including impaired learning and memory (Samaco et al., 2013). It is likely that the in vivo neural circuit deficits identified here in RTT mice may also occur in human patients. Indeed, there is evidence that the increased synchrony described here is paralleled by human studies of Rett patients using fMRI or EEG recordings, which have shown that hypersynchronous neural activity at baseline is a hallmark of the disease (Naidu et al., 2001; Garofalo et al., 1988; Glaze, 2005). Our data suggest that baseline hypersynchrony in RTT mice reduces experience-dependent increase in firing synchrony for memory consolidation and consequently leads to impaired spatial memory codes. Our animal study on RTT mice thus suggests that abnormal memory codes caused by impaired memory consolidation underlie the cognitive deficits in Rett syndrome.

Materials and methods

Key resources table
Reagent typeDesignationSource or referenceIdentifiersAdditional information
Gene (mouse)Mecp2DOI: 10.1038/85899RRID:MGI:3624717
Strain (mouse)Wild-Type controlSource (public (Taconic Farms,
Inc., Germantown, NY, USA));
Source (DOI: 10.1038/85899)
129SvEv: RRID:MGI:5653381;
FVB: RRID:IMSR_TAC:fvb
Strain (mouse)RTTPMID: 11242117; DOI: 10.1038/85899RRID:IMSR_JAX:003890
SoftwareNeuralynx Cheetahhttps://neuralynx.com/software/cheetah
SoftwareMatLabhttps://www.mathworks.com/products/matlab.html
Softwarexclust2https://github.com/wilsonlab/mwsoft64/tree/master/src/xclust
SoftwareDataManagerhttps://github.com/DaoyunJiLab/DataManagerCopy archived at
https://github.com/elifesciences-publications/DataManager

Animals

A total of 9 wild-type (WT) and 10 Rett (RTT) mice were used for electrophysiological recordings. Among them, 8 WT and 9 RTT mice yielded electrophysiological data during at least one behavioral procedure (see Table 1 for details) and were used for the subsequent fear conditioning task. Given the large amount of effort in tetrode recording of each individual animal, to reduce unnecessary effort, the animals were genotyped before the recording experiment and the genotype was not blind to the experimenter. However, to ensure that WT and RTT animals followed the same experimental procedure, we always conducted experiments on a pair of WT and RTT mice at the same time. The number of animals was decided based on existing literature, where a typical tetrode recording experiment uses 3–10 animals per group. Additionally, two non-recorded cohorts totaling 11 WT and 12 RTT mice were used for fear conditioning only. F1 hybrid of FVB/N x 129S6/SvEv (FVB.129F1) animals were generated by mating female Mecp2+/- mice which were maintained on a pure FVB/N background (RRID:IMSR_TAC:fvb) to male WT mice of a pure 129SvEv background (RRID:MGI:5653381). These mice were maintained on a 12 hr light:12 hr dark cycle, fed standard mouse chow and water ad libitum until the experiment began. The Mecp2tm1.1Bird allele (RRID:IMSR_JAX:003890), lacking exons 3 and 4, was used in the Mecp2+/- mice, and it generated a true Mecp2-null allele (Guy et al., 2001). Adult mice at ages of at least 3 months were used for electrophysiological recording and fear conditioning. Only female mice were used. Animals were housed 4 to 5 per cage until tetrode hyperdrives were implanted, then mice were single housed. All research and animal care procedures followed the recommendations in the ‘Guide for the Care and Use of Laboratory Animals’ of the National Institute of Health and were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee.

Surgery

Mice were implanted with a tetrode hyperdrive containing nine tetrodes (eight for recording, and one as a reference). Mice were anesthetized with 0.5–2% isoflurane and fixed using a stereotaxic device throughout the surgery. The implantation was made at 2.0 mm antereoposterior and 1.5 mm mediolateral from Bregma, targeting CA1 cells in the right hemisphere. The cannulae of the hyperdrive was affixed to the skull using dental cement and stainless steel anchoring screws. Supportive care of ketoprofen (5 mg/kg) and saline (1 ml) were injected prior the beginning of surgery.

Tetrode recording

Tetrode recordings were made as previously described (Cheng and Ji, 2013; Ciupek et al., 2015). In 2–4 weeks following the surgery, the tetrodes were lowered slowly into the CA1 pyramidal cell layer. Identification of the CA1 pyramidal cell layer was made by visual inspection of the spiking activity and sharp-wave ripples in the local field potentials (LFPs) while the animals were at rest. A Neuralynx Digital Lynx system was used to record both LFPs and neuronal spiking activity. Recordings were made while animals ran on a rectangular shaped linear track, or were on an elevated platform (Figure 1B). Spiking activity was identified with a pre-set threshold of 50–70 µV, sampled at 32 kHz, and was band-pass filtered from 0.6 to 6 kHz. LFPs were band-pass filtered from 0.1 to 1 kHz and sampled at 2 kHz. Animals’ positions were monitored by two color diodes mounted over the tetrode hyperdrive and sampled at 33 Hz. Data was analyzed offline.

Behavioral apparatuses and tasks

Track-running and resting tasks

Animals (N = 10 RTT, 9 WT mice) were food deprived to ≥85% of their ad libitum weight ~2 weeks after recovery from surgery. They were first trained to run back and forth (two trajectories) on a familiar, rectangular-shaped track of ~2 m length for a food reward (condensed milk) at each end of the track in a familiar room for at least 2 weeks. Recordings started when the mice were able to run consistently back and forth on the track. On each of the recording days, the animal had two track-running sessions (Run 1, Run 2), each lasting ~15 min (Figure 1B). Then, a subset of animals (N = 9 RTT, 7 WT mice) was also recorded while they ran on a novel track for two sessions in a novel room, following the same daily procedure as before. The novel track had a similar shape to the familiar one, but with different tactile and visual cues. The animals had never been exposed to the novel room or the novel track before the recording. Some of the animals were also recorded while they were resting for 10–15 min on an elevated platform elevated platform before (Pre-run) and after (Post-run) the first track session on either the familiar or the novel track. For the familiar track, 5 RTT and 5 WT mice were recorded during Pre-run and 8 RTT and 7 WT mice recorded during Post-run. For the novel track, 7 RTT and 5 WT mice were recorded in Pre-run and 9 RTT and 7 WT mice recorded in Post-run.

Fear Conditioning

22 RTT and 20 WT mice were studied, including those implanted, which were tested in fear conditioning after all recordings were completed. The animals explored a novel fear-conditioning box for 2 min, then a 30 s tone (80 dB, 5 kHz) was played with a coinciding mild footshock (2 s, 0.7 mA) at the end of the tone. After 1 min, the tone/footshock pairing was repeated. The animals stayed in the box for one more minute before returned to their home cages. To test contextual fear memory, 1 hr and 24 hr later, the animals were placed back to the conditioning box for 5 min and their freezing behavior was measured. A subset of animals (12 WT, 14 RTT) were also tested at an additional time point of 20 min after conditioning, prior to the testing at other two time points. Data at 20 min and 24 hr were plotted to assess contextual memories before and after memory consolidation.

Histology

After all recordings, each recorded animal was euthanized using a pentobarbital overdose (50 mg/kg). Current of 30 µA was passed through each tetrode for 10 s to generate a small lesion at each recording site. The brain was then dissected out, fixed in 10% formaldehyde solution for at least 24 hr, cryoprotected in 30% sucrose, and then sectioned at 50 µm thickness. Slices were placed on slides and dried. They were then stained using 0.2% Cresyl violet and cover-slipped. Recording sites were then identified by matching lesion sites with tetrode depths and relative positions. Only tetrodes in the CA1 pyramidal cell layer were used for analysis.

Data analysis

For all recording days, single units were sorted offline using a manual clustering software (https://github.com/wilsonlab/mwsoft64/tree/master/src/xclust) (Wilson, 2008). Only well-sorted clusters (with isolation distance >9.5) were included in the analysis. Although animals were recorded across several days on the familiar track, only one day’s data was used in the analysis for these behavioral conditions, to avoid possible repeated sampling of same cells. Only putative pyramidal cells (average firing rate ≤7 Hz) were included in the analysis. Behavioral and neural spike data were analyzed using a custom Matlab package DataManager (https://github.com/DaoyunJiLab/DataManager) (Ji, 2018; copy archived at https://github.com/elifesciences-publications/DataManager). Results are presented with median and quartile [25–75%] values unless otherwise indicated. Statistical comparisons were completed using the non-parametric Wilcoxon ranksum test or ANOVA, unless otherwise specified.

Firing rate curve and place field quantification

We identified every time period (lap) an animal traveled through one of two trajectories on a track. Each trajectory was separately linearized and divided into 2 cm spatial bins. Run-active cells, defined as those with average rate >0.5 Hz on at least one trajectory in one track-running session, were analyzed on each trajectory they were active on. For each cell on a trajectory, we computed its firing rate (xi) within each spatial bin (i), as the total number of spikes divided by the total occupancy time (ti) across all laps on that trajectory in a session. The cell’s firing rate curve (firing rate at each bin: [x1, x2, …, xN]) was smoothed by a Gaussian kernel with a sigma of 2 bins. For this analysis, we excluded the spatial bins at the reward site and those stopping periods, when animal did not move (speed <3 cm/s for more than 1 s). Spatial information was calculated from a rate curve as previously described (Sirota et al., 2003). Spatial information was computed on each active trajectory and averaged between the two running sessions (Run 1, Run 2). Stability was the Pearson correlation between a cell’s rate curves on the same active trajectory across two running sessions (Ciupek et al., 2015). Place fields were defined as peaks of a firing rate curve with a peak rate ≥1 Hz. A threshold of 10% of peak rate was used to detect place field boundaries (Mehta et al., 2002). Place fields with a gap of ≤6 cm were combined into one.

Downsampling analysis

For downsampling analysis, the 4 groups of cells, WT and RTT cells active on the familiar and novel tracks, were equalized to have the same number of cells, which was the number of RTT cells on the novel track (31). To do so, 31 cells were randomly chosen among each of the other three groups. This random downsampling was repeated 200 iterations. Since the downsampling largely did not change the cells on the novel track, it mostly affected the results involving cells on the familiar track. Therefore, we subjected a key variable, spatial information, to the downsampling analysis. For each iteration, the spatial information of the chosen cells was statistically compared between WT and RTT cells or between the novel and familiar tracks, using the Wilcoxon ranksum test. For each comparison, the distribution of resulting P values was plotted and the proportion of iterations that produced a significant P value (<0.05) was reported (Figure 2—figure supplement 1).

Theta phase precession

LFPs were filtered through the theta frequency range of 6–10 Hz. For all spikes within a place field, we computed the regression between their phases relative to the filtered theta LFPs and their firing locations. Two types of regression were used. The first was the optimal linear regression as in the original phase precession paper (O'Keefe and Recce, 1993), where a linear regression was computed with spike phases shifted by every 1° among a 360° range and the regression with the best fit was used. The second was the circular-linear regression between the circular phase values and spike locations, with the formula given in a previous study (Ravassard et al., 2013).

Ripple event detection and quantification

Individual ripple events were detected via CA1 LFPs recorded while mice were resting before (Pre-run) and after (Post-run) track-running, as in previous studies (Csicsvari et al., 2000; Lee and Wilson, 2002). A LFP trace was first filtered through the ripple band (100–250 Hz) and its standard deviation (std) was computed. A (peak) threshold of 6 stds was used to identify potential events. Start and end times of ripple events were determined by a threshold of 2.5 stds. Neighboring events with a gap of <30 ms were combined into single events. We defined those identified events with durations between 30 to 500 ms as ripple events. Ripple parameters, including occurrence rate (number of ripples per second), amplitude (the absolute value of the most negative trough), duration, frequency, were quantified for each ripple event and averaged across all ripple events in an animal for each rest session. For each putative pyramidal cell, we quantified its ripple participation rate as the ratio of number of ripples when the cell fired at least one spike over the total number of ripples in a session. The numbers of spikes within all ripple events in a session were also averaged and reported as number of spikes per ripple event.

Pairwise cross-correlation and coactivity

We computed pairwise spike cross-correlation within ripple events, similarly to previous studies (Sirota et al., 2003; Ciupek et al., 2015). For each ripple event in a session, we identified its start and end times as described above and defined a time period of 150 ms before the start time and 150 ms after the end time. For each pair of neurons, we computed a spike-count cross-correlation correlogram for spikes within these ripple time periods (Terada et al., 2017; Diba et al., 2014), with a bin size of 20 ms, which was then smoothed by 5-bin nearest neighbor averaging. The spike-count cross-correlation was then subtracted by the average number of spikes among the baseline time lags ([−220–180] ms and [180 220] ms). Finally, the cross-correlograms were re-scaled by the square root of the number of spikes expected from two random Poisson spike trains with identical firing rates (Sirota et al., 2003; Ciupek et al., 2015). The normalization renders the cross-correlation insensitive to firing rates of the two neurons. For a pair of neurons, their coactivity within ripples events was defined as the average of the normalized cross-correlogram values at lag bins within [−50 50] ms (Wilson and McNaughton, 1994).

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Decision letter

  1. Timothy E Behrens
    Reviewing Editor; University of Oxford, United Kingdom
  2. Fabian Kloosterman
    Reviewer; KU Leuven, Belgium

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Spatial memory codes are unrefined in a mouse model of Rett syndrome" for consideration by eLife. Your article has been reviewed by Timothy Behrens as the Reviewing Editor and Senior Editor, and two reviewers. One of the reviewers, Fabian Kloosterman, has agreed to reveal his identity.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We have had a very clear discussion of the manuscript, the upshot of which is that the reviewers both find the manuscript interesting, but both have reservations that might be addressed with additional analyses, which are detailed in the reviews below.

There is one point that I should mention that emerged from the discussion. You will note that reviewer #2 has a major concern that would require extra data. This is that in many analyses on the novel track there is a paucity of cells and many of the cells in key analyses are from a single animal. This is noted in R2 point 1. In the discussion, it was agreed that this was a weakness, but it was also agreed that the situation is substantially better in the familiar track. The reviewers remained firm that they would like to see more data to support the claims as currently made in the manuscript, and they thought it possible that you already have the data available. However, if this not possible, the reviewers noted that they were open to a toning down of the interpretation of the data from the novel track, and a stronger focus on the data from the familiar track.

Summary:

Kee et al., use female Mecp2 +/- mice, a transgenic model of Rett syndrome, to examine changes in place coding and synchrony of CA1 pyramidal cells. This work follows published data from some of the same authors which identified hypersynchrony in the CA1 of these model mice. They report that on novel tracks CA1 place cells are indistinguishable between genotypes, however in familiar environments Mecp2+/- place cells showed higher firing rates, broader place fields and reduced spatial information compared to those in control mice. The authors then go on to characterize pyramidal cell activity during sharp-wave ripples and attribute the changes to a lack of experience dependent increases in synchrony between exposures, with wildtypes showing an increase but Mecp2+/- mice having an elevated baseline synchrony that fails to increase further following experience. Finally, they use fear conditioning to demonstrate that these changes while important for long term memory are dispensable for short term retrieval of contextual fear memory.

Reviewer #1:

Kee et al., use female Mecp2 +/- mice, a transgenic model of Rett syndrome, to examine changes in place coding and synchrony of CA1 pyramidal cells. This work follows published data from some of the same authors which identified hypersynchrony in the CA1 of these model mice. They report that on novel tracks CA1 place cells are indistinguishable between genotypes, however in familiar environments Mecp2+/- place cells showed higher firing rates, broader place fields and reduced spatial information compared to those in control mice. The authors then go on to characterize pyramidal cell activity during sharp-wave ripples and attribute the changes to a lack of experience dependent increases in synchrony between exposures, with wildtypes showing an increase but Mecp2+/- mice having an elevated baseline synchrony that fails to increase further following experience. Finally, they use fear conditioning to demonstrate that these changes while important for long term memory are dispensable for short term retrieval of contextual fear memory.

While the data and their interpretations are interesting and are consistent with existing publications in the field, I have several issues with the manuscript and underlying data set that would preclude publication at this stage. I would suggest the authors include significantly more data and use a more appropriate method for the cell-pair analysis, detailed below.

Essential revisions:

1) My primary concern with these data is the small sample size and the outsized contribution of individual animals to the overall data. For example, the authors only analyze 29 place cells in the RTT mice on the novel track and 66 on the familiar. These low overall numbers make the analyses of the data very susceptible to outliers. Further, while I appreciate the breakdown of data in Table 1, it reveals that in several cases one or two mice of a given genotype contribute the vast majority of data to a given analyses. 2 wildtype mice contribute nearly 70% of data to novel track place field analyses and 85% of the pairwise analyses data. Similarly, a single RTT mouse contributes 76% of the pairs analyzed during slow-wave sleep. Given that all these measures vary with the behavior, experience and state of a single mouse it is crucial that a more distributed and representative data set be collected. I would urge the authors to add additional animals to their dataset to more robustly prove their findings. In line with this, I think that several of their animals need to be excluded completely from their analyses. Recording from CA1 with eight tetrodes and having a total yield of 5 or less place cells suggests that something has gone very wrong, and the data are most likely not reliable. I would ask the authors to add several good mice to their dataset (for example, comparable to the first two wildtype mice in Table 1) for both genotypes and confirm that their results stand or, are indeed strengthened by additional data.

2) The main focus of the paper is on place cell activity during exploration and their pre and post activity during ripples in the rest sessions. I therefore don't really understand the inclusion of non-track active putative pyramidal cell data and think this would only act to confuse the reader.

3) I am confused with the place cell analyses as presented in the manuscript and believe that several additional restrictions in the analysis may produce more robust results. Subsection “Less specific place fields in the familiar, but not novel, environment”, do the authors think that a place cell having an average of 4.9 fields (or 4.6 for wildtypes) is consistent with place cell literature? Obviously in that population (with a mean of 4.9) I would expect some would have close to 10 fields, which on an approximately 1.5m track is not believable. I understand and respect that there is variability across labs on how to define this measure and what is important is the between group comparisons, nonetheless this should be addressed.

4) Continuing from point 3, several key pieces of information are missing from the methods section which may clarify these data. First, the authors should state the linearized length of the tracks used. Next, there is no mention of any cluster quality metrics being calculated and cells excluded if they do/don't exceed a threshold such as: interspike interval, minimum spike number, complex spike index and isolation distance. Likewise, I see no mention of a lower velocity threshold for spike data, while running the linear track something that is routinely done to avoid false fields generated by discharge during rest/ripple periods on the track. The authors should check the number of spikes contributing to each 'field' and disregard any field containing less than 50 spikes or a similar sensible threshold. The authors may also want to increase their rather low 1 Hz threshold for initial place cell detection, to further avoid spurious place field detection. I would expect the mean number of place fields per cell to reflect the example shown in figure 3 A and C where most cells have 1, or 2 fields at most. As a result, the statement in Subsection “Less specific place fields in the familiar, but not novel, environment” about broader place fields in RTT cells should be re-evaluated following the changes outlined to cluster quality and place field detection used in the study.

5) A fundamental claim of this work is that there is not an experience-dependent increase in pairwise synchrony in the RTT mice following exploration of a novel track. Previous work however has shown this increase is contingent on co-activity of those place cells during running and/or a function of the physical distance between the centers of their place fields (Wilson and McNaughton 1994, Karlsson and Frank 2009, Suh et al., 2013). The conclusions of this paper as currently presented would require a similar approach to the analyses. The sample size and data quality issues would probably prevent this, thus without further data the conclusions would need to be softened and qualified.

6) I am confused by the authors approach to calculating correlations between spike trains. From the Materials and methods section and their references I understand they are applying a normalization step to correct for differences in firing rate, however in their previous work employing this method (Ciupek et al., 2015) and the reference they provide (Sirota et al., 2003) the resulting correlations were all positive values. Intuitively, cross correlating spike counts shouldn't give you any negative numbers, as the minimum number of spikes you can have is zero. In Figure 5—figure supplement 1B in this paper in the pretrack condition there is a large negative component evident- how is this to be interpreted? Moreover, in subsection “Lack of experience-dependent increase in place cell synchrony within ripples” the authors reference this figure as evidence that RTT cells show hypersynchrony in the pre-track condition compared to WT cells, however the medians are identical, both at zero.

7) While it appears the cross-correlation results (Figure 5B) is highly smoothed, possibly along both axes. This needs to be reported in the legend and/or methods, as smoothing along the y-axis should be avoided. Further regarding Figure 5B, subsection “Lack of experience-dependent increase in place cell synchrony within ripples”,the increased correlation at time zero in WT mice post-track is not at all clear from the plot, nor is the increased synchrony in RTT pre-track cell pairs compared to controls.

8) Regarding the correlation methods. The Diba et al., 2014 (Figure 2) and Terada et al., 2017 (Figure 5B right column) papers show what I would expect spike pair CCGs to look like for hippocampal neurons, consisting of positive values. The authors should consider replicating the findings with one of these accepted methods to demonstrate differences in synchrony during ripples.

9) I am also concerned that there are different numbers of mice contributing to the pre and post track session data. The changes the authors report between these periods in terms of CA1 firing rates and pairwise correlation should be shown to exist in the subset of mice recorded in both sessions.

10) I am puzzled by the difference in pretrack synchrony in the RTT mice between the novel and familiar recording session (Figure 5C and Figure 5—figure supplement 1B) The axes on these 2 figures, which could be thought of as equivalent baseline recording sessions- differ by a factor of 4. Again, the most reasonable explanation I can think of for this discrepancy is the sparseness of the data set.

11) Regarding the behavior – as written in the methods section, implies all mice were tested at 20 minutes and 24 hours, however the n is quite different in Figure 7A. Does the behavioral phenotype hold up if the analyses is restricted to the subset of mice tested at both time points and analyzed with a 2-way repeated measure ANOVA?

Reviewer #2:

In this manuscript, the authors characterize the activity of hippocampal place cells in a mouse model of Rett Syndrome. The main finding is that experience-dependent changes in the spatial firing properties of hippocampal place cells are diminished in the Rett (RTT) mouse model, a phenotype that is accompanied by hypersynchronous coactivation of hippocampal cells during ripple events that have been implicated in offline memory consolidation.

Overall, the results are interesting and – at the surface – the distinct findings reported in this study, i.e. higher firing rates (on track and during ripple events) in RTT mice, absence of spatial tuning refinement in RTT mice, increased baseline coactivity during ripple events without an experience-dependent increase, and abnormal contextual fear memory at 24 hours, support the conclusion that hypersynchronous hippocampal activity interferes with memory consolidation in this mouse model.

Still, I have a number of questions and some major concerns regarding the link between the different findings, the depth of the analyses that were performed and a few inconsistencies between the reported results and the data visualization. I believe most major concerns can be properly addressed with additional data analyses and clarifications in the text.

1) The authors report increased average firing rate (on the track and in ripple events) as well as hypersynchrony among CA1 cells during ripple events in RTT mice. How are these two phenomena related? Are cells active in more ripple events, or are cells emitting more spikes in the same number of ripple events? According to Figure 2, the firing rate changes during ripple events are observed in the population of all cells, but not (or much less so) in the subset of "track-active" cells. Could the authors confirm that in particular the "track-inactive" cells have an enhanced participation in ripple events?

2) The authors report increased baseline coactivity in RTT mice, which also expresses itself in the pre-track period before the mice ran on the novel track. However, according to the methods, the mice were trained on the familiar track before being introduced to the novel track. Do hippocampal cell populations in RTT mice show the same level of orthogonalization between the familiar and novel tracks (i.e. global remapping) as in WT mice? If not, could this explain the increased coactivity of novel track cells prior to the experience? Relatedly, the increase in synchrony/coactivity could reflect a stereotyped subset of cells that is more active than other cells, but not specifically tied to any given spatial environment.

3) The authors conclude that the observed hypersynchrony in RTT mice "interferes" with memory consolidation, possibly because the higher baseline hypersynchrony during ripple events in RTT mice may "occlude" experience-dependent increases in firing synchrony (Discussion section). The authors should clarify what they mean by "interfere" and "occlude" – e.g. following the classic model that spike sequence reactivation induces synaptic plasticity, do they mean that the higher baseline coactivation leads to a saturation of connection strengths between participating cells? Alternatively, if "track-inactive" cells participate more in ripple events (see point 1), it is conceivable that the deficits in memory consolidation and spatial tuning refinements are due to "imprecise" spike sequence reactivation that includes cells unrelated to the original experience.

4) The authors suggest that "spatial memory encoding is normal in RTT mice" (Discussion section) mainly based on the lack of clear differences in coarse spatial tuning properties between WT and RTT cells when mice run on a novel track. However, apart from the possibility that stereotyped cell populations are active in RTT mice (see above), spatial memory encoding may also be abnormal when studied at a more detailed level. For example, the authors should address the possibility that lap-to-lap refinements of place cell firing within a single recording session and/or theta sequences (which share some similarities with reactivation sequences during ripple events) are affected.

5) There are discrepancies and possible visualization errors in the reporting of the coactivity/correlation data (subsection “Baseline hypersynchrony within ripples during rest and sleep”,Figure 5 and Figure 6). The text and legend of Figure 6 mention a significantly higher coactivity in RTT as compared to WT, but Figure 6A and the summary statistics in the figure legend do not support this statement. Possibly related to this: there appears to be an error in the correlation plots in Figure 5—figure supplement 1 and Figure 6—figure supplement 1, as several traces appear to be "clipped" to zero (even though the legend claims that there is a significant difference between WT and RTT). Please double check the data – it is currently difficult to assess the validity of the results.

6) In the fear conditioning experiment, the same mice were tested three times at 20 min, 1 hour and 24 hours after acquisition. This introduces the possibility that reduced freezing at 24 hours that was observed in the RTT mice is due to enhanced extinction, rather than impaired consolidation. One way the authors could address this is by testing a group of mice only at 24 hours.

7) One of the main results is that WT cells, but not RTT cells, refine their spatial tuning properties in the familiar track (as indicated a spatial information increase – figure 3D). However, the place field length of WT cells appears to be larger in familiar as compared to novel track (figure 3B vs 3D), without an apparent decrease in the number of fields (subsection “Less specific place fields in the familiar, but not novel, environment”). Could the authors explain these seemingly opposing changes? They should also extend the comparison between novel and familiar spatial tuning properties in subsection “Less specific place fields in the familiar, but not novel, environment” to include place field length and peak rate measures.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for submitting your article "Impaired spatial memory codes in a mouse model of Rett syndrome" for consideration by eLife. Your article has been reviewed by Timothy Behrens as the Reviewing Editor and Senior Editor, and two reviewers.. One of the reviewers, Fabian Kloosterman, has agreed to reveal this identity.

The reviewers agree that the manuscript is nearly ready but there are a few outstanding comments below.

Reviewer #1:

Overall, I find the manuscript of Kee et al. to be much improved and appreciate the additional data and analyses the authors have provided. I have a few remaining points:

1) While the current manuscript is improved, I do still suspect that some of the differences in the novel and familiar track data related to the small sample size on the novel track. For example, median firing rate of run-active neurons is 1.1 Hz in WT on both types of track and 1.8 Hz in RTT mice in both types of track, yet only significant on the familiar due to the much more robust sample size. A similar trend is obvious in the pre-run ripple participation data. I would be more comfortable if the authors briefly acknowledge this possibility in the discussion.

2) I am confused by Figure 2—figure supplement 1. In the text the authors state that down sampling the WT cells still resulted in significantly lower spatial information in the RTT cohort. First, it's unclear in the text and methods how this was done- was down sampling repeated many times as is the norm, then each individual sample compared to the RTT distribution to find if a significant difference was present? If so, how many iterations were run and what fraction were significantly different? Also, the graph in panel A shows the opposite effect of what the authors conclude, with the distribution of the RTT population in red shifted to the right of the WT- please clarify.

3) The lap-by-lap data in Figure 2—figure supplement 2 is a nice addition, however I am not convinced the authors applied the correct statistical measure. I understand this is non-parametric data, hence their choice of the Wilcoxon ranksum, however this is clearly a repeated comparison between the groups, hence I believe the α must be adjusted for the number of comparisons (8) when claiming significance.

4) I still am a bit troubled by the behavioral data. The authors now include a 2-way ANOVA but fail to find the significant interaction between genotype and time which their conclusion would require. This should be made clear and the conclusions adjusted appropriately.

Reviewer #2:

The authors have made substantial changes to the manuscript, most notably adding extra mice to increase to number of recorded cells and performing additional analyses. As a result, the manuscript has improved considerably.

As the authors acknowledge, the discrepancy in the number of cells between novel and familiar conditions remains, and their explanation is reasonable. More importantly, the authors address the imbalance by subsampling the groups with high cell number (and limiting the contribution of individual mice) and show that main effect on spatial information remains.

Overall, I am very positive about the revised manuscript. I have a few minor remarks and a number of comments about the statistical tests that are applied.

https://doi.org/10.7554/eLife.31451.021

Author response

There is one point that I should mention that emerged from the discussion. You will note that reviewer #2 has a major concern that would require extra data. This is that in many analyses on the novel track there is a paucity of cells and many of the cells in key analyses are from a single animal. This is noted in R2 point 1. In the discussion, it was agreed that this was a weakness, but it was also agreed that the situation is substantially better in the familiar track. The reviewers remained firm that they would like to see more data to support the claims as currently made in the MS, and they thought it possible that you already have the data available. However, if this not possible, the reviewers noted that they were open to a toning down of the interpretation of the data from the novel track, and a stronger focus on the data from the familiar track.

We conducted additional experiments on 2 mutant (RTT) and 1 wildtype (WT) mice and recorded a total of 24 neurons on the novel track. However, we have also applied a more stringent criterion for cell selection, as one of the reviewers requested, and thus lost 11 cells on the novel track. In the end, we have added 13 cells on the novel track experiment (and lost 16 cells on the familiar track). The difference in number of cells between novel and familiar tracks is improved, but still present. We have further addressed this difference by resampling down and plotting data using similar number of cells (see details in response to point #1 below). We would like to point out that recording on the novel track was harder than that on the familiar track, because we only had one chance on the novel track per animal, whereas on the familiar track we recorded repeatedly for a few days and then analyzed the one day with most cells.

In addition, we did tone down the writing: We changed the title and changed the description of the place cells on the novel track as having normal spatial information, but with some parameters of place fields altered. We have also revised the Discussion section accordingly.

Reviewer #1:

[…] Essential revisions:

1) My primary concern with these data is the small sample size and the outsized contribution of individual animals to the overall data. For example, the authors only analyze 29 place cells in the RTT mice on the novel track and 66 on the familiar. These low overall numbers make the analyses of the data very susceptible to outliers. Further, while I appreciate the breakdown of data in Table 1, it reveals that in several cases one or two mice of a given genotype contribute the vast majority of data to a given analyses. 2 wildtype mice contribute nearly 70% of data to novel track place field analyses and 85% of the pairwise analyses data. Similarly, a single RTT mouse contributes 76% of the pairs analyzed during slow-wave sleep. Given that all these measures vary with the behavior, experience and state of a single mouse it is crucial that a more distributed and representative data set be collected. I would urge the authors to add additional animals to their dataset to more robustly prove their findings. In line with this, I think that several of their animals need to be excluded completely from their analyses. Recording from CA1 with eight tetrodes and having a total yield of 5 or less place cells suggests that something has gone very wrong, and the data are most likely not reliable. I would ask the authors to add several good mice to their dataset (for example, comparable to the first two wildtype mice in table 1) for both genotypes and confirm that their results stand or, are indeed strengthened by additional data.

1) We conducted additional experiments on 2 mutant (RTT) and 1 wildtype (WT) mice and recorded a total of 24 neurons on the novel track. However, we have also applied a more stringent criterion for cell selection, as one of the reviewer requested, and thus lost 13 cells. The main conclusions remain the same.

2) The issue of some animals yielding more cells than others remains. The recorded number of neurons from an animal depends on how many tetrodes are placed exactly at the thin pyramidal layer of the CA1, which in many cases is accidental. This is harder on the novel track since we had only one chance (one day) for recording, while on the familiar track we recorded for several days and analyzed the one day with most cells. In our hands, the yield per tetrode in mice is lower than in rats.

3) We agree with the reviewer that the issue of over-representation is a concern. To address this, we have repeated a key result with down-sampling to limit the contribution of animal to less than 30% (Figure 2—figure supplement 1).

We have used more stringent criteria for cell selection including cluster quality measures and others as suggested below. However, we still include the cells from animals with low yields in our analyses. If the cells met the criteria for inclusion and the animals were healthy and performed normally, we believe we need to report these cells. As we stated above, the yield was largely accidental, and we could not identify any valid behavioral or procedural reasons for excluding these animals. Also, only a very small number of cells (~7) were included this way and their inclusion does not affect the results.

2) The main focus of the paper is on place cell activity during exploration and their pre and post activity during ripples in the rest sessions. I therefore don't really understand the inclusion of non-track active putative pyramidal cell data and think this would only act to confuse the reader.

The point is to show that the hypersynchrony in Pre-run is not just specific to later run-active cells, but present in all cells. We believe this is useful for readers to know. However, to follow the reviewer’s suggestion and to keep the paper tight, we have moved the data related this point to a supplementary figure (Figure 5—figure supplement 1).

In addition, we do agree with the reviewer that we need to focus on track running behavior.

We have removed the non-specific analysis on cell activity during slow-wave sleep (SWS). The cells recorded during SWS were removed from Table 1.

3) I am confused with the place cell analyses as presented in the manuscript and believe that several additional restrictions in the analysis may produce more robust results. Subsection “Less specific place fields in the familiar, but not novel, environment”, do the authors think that a place cell having an average of 4.9 fields (or 4.6 for wildtypes) is consistent with place cell literature? Obviously in that population (with a mean of 4.9) I would expect some would have close to 10 fields, which on an approximately 1.5m track is not believable. I understand and respect that there is variability across labs on how to define this measure and what is important is the between group comparisons, nonetheless this should be addressed.

We apologize for the confusion. Place fields were computed independently on each of the 2 trajectories across 2 sessions. The previous numbers of place fields were the total numbers of place fields per cell identified over the 4 trajectories across 2 sessions. We have now revised the number as the number of place fields per active trajectory. This number computed from the updated data after imposing additional restrictions (see below) is 1.2 – 2.0 per cell per active trajectory, comparable with the existing literature.

4) Continuing from point 3, several key pieces of information are missing from the methods section which may clarify these data. First, the authors should state the linearized length of the tracks used. Next, there is no mention of any cluster quality metrics being calculated and cells excluded if they do/don't exceed a threshold such as: interspike interval, minimum spike number, complex spike index and isolation distance. Likewise, I see no mention of a lower velocity threshold for spike data, while running the linear track something that is routinely done to avoid false fields generated by discharge during rest/ripple periods on the track. The authors should check the number of spikes contributing to each 'field' and disregard any field containing less than 50 spikes or a similar sensible threshold. The authors may also want to increase their rather low 1 Hz threshold for initial place cell detection, to further avoid spurious place field detection. I would expect the mean number of place fields per cell to reflect the example shown in figure 3 A and C where most cells have 1, or 2 fields at most. As a result, the statement in Subsection “Less specific place fields in the familiar, but not novel, environment” about broader place fields in RTT cells should be re-evaluated following the changes outlined to cluster quality and place field detection used in the study.

We are sorry for the missing information. We have now revised the method section to clarify the points the reviewer raised here.

1) We specifically stated the length of the tracks (~2 m).

2) We have now removed all the stopping time periods on the track for our place field analysis. This is a very important point and we thank the reviewer for this. This step changed some of our reported numbers in place field parameters, including place field length and place field stability, but the main conclusion of less spatial information on the familiar track, but not on the novel track, remains.

3) Regarding cluster quality measures, we added a threshold for isolation distance (> 9.5) for unit selection and a threshold for number of spikes in a session (> 100). We did not use interspike interval, because it offers little selection. Hippocampal units from putative pyramidal neurons almost always produce 0 or few spikes within the refractory spiking period, whether they were contaminated by other spikes or not, due to their sparse firing. We also think complex spike index is a measurement of the property of a sorted unit and is biased against cells with lower rates. We believe it may not be the best as criterion for data selection.

4) We clarified the criteria for place field detection: (i) First, the overall average firing rate of a cell on a trajectory > 0.5 Hz; (ii) Second, the peak firing rate of the smoothed (σ = 4 cm) firing rate curve >= 1 Hz, which is not low in our opinion given that the rates were smoothed; (iii) field length > 6 cm. These criteria essentially exclude unreliable fields with small number of spikes per running lap. These criteria yielded ~1.6 fields per trajectory and a median field size of ~35 cm on the familiar track, which are comparable to previous studies in other types of WT mice and even rats.

5) A fundamental claim of this work is that there is not an experience-dependent increase in pairwise synchrony in the RTT mice following exploration of a novel track. Previous work however has shown this increase is contingent on co-activity of those place cells during running and/or a function of the physical distance between the centers of their place fields (Wilson and McNaughton 1994, Karlsson and Frank 2009, Suh et al., 2013). The conclusions of this paper as currently presented would require a similar approach to the analyses. The sample size and data quality issues would probably prevent this, thus without further data the conclusions would need to be softened and qualified.

We have checked our data. Despite additional new experiments, indeed there were not sufficient pairs of cells with overlapping place fields for this analysis. However, we would like to point out that here we study the broader co-activity of cell pairs within ripples than the previous studies the reviewer mentioned. Our focus is to measure broadly to what extent cells active together within a track tend to be active together in a ripple (as in replay events, during which the first and last cells may have non-overlapping place fields on the track yet tend to fire together in ripple replays).

6) I am confused by the authors approach to calculating correlations between spike trains. From the Materials and methods section and their references I understand they are applying a normalization step to correct for differences in firing rate, however in their previous work employing this method (Ciupek et al., 2015) and the reference they provide (Sirota et al., 2003) the resulting correlations were all positive values. Intuitively, cross correlating spike counts shouldn't give you any negative numbers, as the minimum number of spikes you can have is zero. In Figure 5—figure supplement 1B in this paper in the pretrack condition there is a large negative component evident- how is this to be interpreted? Moreover, in subsection “Lack of experience-dependent increase in place cell synchrony within ripples” the authors reference this figure as evidence that RTT cells show hypersynchrony in the pre-track condition compared to WT cells, however the medians are identical, both at zero.

We thank both reviewers for raising this important point and we have modified the analysis (see additional details in point #8 below).

1) Our method applies a normalization step to the regular spike count crosscorrelation. The normalization is necessary, because we need to average and compare correlation (coactivity) values among groups of cell pairs, but spike counts greatly depend on firing rates (total spikes) of a pair of cells. In our normalization scheme (see response to point #8 below), 0 represents baseline chance-level correlation predicted from firing rates, and negative values mean fewer spikes found than the baseline level (anti-correlation). Anti-correlation occurs obviously in time bins when two cells with non-zero firing rates do not fire together.

2) Our previous paper (Ciupek et al., 2015) and the referred paper (Sirota et al., 2013, which used a similar but not identical normalization scheme) computed cross-correlations during SWS, which have much longer time periods and thus contains many more spikes. The large number of spikes renders correlation values in large time lags to fluctuate smoothly around the baseline value of 0 (but not exactly 0, with some small positive/negative values). The correlation values of smaller time lags are positive due to the synchronized firing in SWS. This is also consistent with the SWS correlation plots in our previous submission (no longer present in the revised manuscript in order to focus on runactive cells).

3) In this study, our Pre-run and Post-run were short resting sessions (~15 minutes) and contained a much smaller number of ripple events than SWS. Some cell pairs did not fire at all during the ripple events. More importantly, with small bin size (<10 ms), many pairs had no spikes within a given time bin. As a result, the correlation values, and therefore medial values, are dominated by zeros and small negative values. To address this issue, we have made 2 modifications. (i) We used a longer time bin size (20 ms) and (ii) we smoothed cross-correlation values by 5-point nearest neighbor averaging.

7) While it appears the cross-correlation results (Figure 5B) is highly smoothed, possibly along both axes. This needs to be reported in the legend and/or methods, as smoothing along the y-axis should be avoided. Further regarding Figure 5B, line 186, the increased correlation at time zero in WT mice post-track is not at all clear from the plot, nor is the increased synchrony in RTT pre-track cell pairs compared to controls.

We agree with the reviewer and now the y-axis smoothing has been removed. We have also replotted the data by ordering peak correlation values. The differences between WT and RTT pairs are now visually obvious.

8) Regarding the correlation methods. The Diba et al., 2014 (Figure 2) and Terada et al., 2017 (Figure 5B right column) papers show what I would expect spike pair CCGs to look like for hippocampal neurons, consisting of positive values. The authors should consider replicating the findings with one of these accepted methods to demonstrate differences in synchrony during ripples.

3) Our method consists of two steps. The first step is the same as in Diba et al., 2014 and Terada et al., 2017, which computes a spike count correlation value at each time bin for two spike trains. (We have now added the papers in the references). Because cell counts greatly depend on firing rates (total spikes) of a pair of cells, here we need to apply a second, normalization step in order to average and compare correlation values among groups of cell pairs with different firing rates.

This normalization step basically subtracts a baseline spike count computed at large time lags ([180 – 220] ms and [-220 -180] ms for within-ripple correlations) from the spike count values. Then, we re-scale the spike count by the square root of the chance-level expected spike count per bin, given the firing rates of the two cells. This method removes the effect of firing rates and allows comparison of correlation values of cell pairs with different firing rates, as long as the same bin size is used, and the pairs are recorded with similar time duration.

However, we did recognize that the small bin size we used caused too many zeros and negative values at many bins for sparsely firing cells in a short period of time. We have revised the bin size from the previous 5 ms to 20 ms and applied a 5-point average smoothing to the computed cross-correlation values.

9) I am also concerned that there are different numbers of mice contributing to the pre and post track session data. The changes the authors report between these periods in terms of CA1 firing rates and pairwise correlation should be shown to exist in the subset of mice recorded in both sessions.

We have now reanalyzed and mainly reported the coactivity data restricted to those cell pairs recorded in both Pre and Post. The key results remain similar (Figure 5). When we addressed this concern, we realized that some of the cells recorded on the track did not have a Pre-run or Post-run session. Therefore, we removed the number of pairs from Table 1, because it did not reflect the pair numbers in Pre-run or Post-run session. The number of pairs is reported in each of the relevant figures (Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2).

10) I am puzzled by the difference in pretrack synchrony in the RTT mice between the novel and familiar recording session (Figure 5c and Figure 5—figure supplement 1B) The axes on these 2 figures, which could be thought of as equivalent baseline recording sessions- differ by a factor of 4. Again, the most reasonable explanation I can think of for this discrepancy is the sparseness of the data set.

Part of the confusion is due to presenting median values for correlation values with a small bin size, which are dominated by zeros. We have modified the quantification and data presentation. The results are more consistent after using the revised methods.

11) Regarding the behavior- as written in the methods section, implies all mice were tested at 20 minutes and 24 hours, however the n is quite different in Figure 7A. Does the behavioral phenotype hold up if the analyses is restricted to the subset of mice tested at both time points and analyzed with a 2-way repeated measure ANOVA?

The reviewer is correct in pointing out that not all animals were tested at 20 minutes, but all tested at 24 hours (and at the 1 hour time point). We have now clarified the issue and have added a few more animals. We have now reported the 2-way ANOVA statistics.

When we restricted the analysis to a subset of animals that were tested at both 20minute and at 24-hour time points (12 WT, 14 RTT), the 2-way ANOVA showed a similar genotype effect, but the difference at 24-hours between WT and RTT mice did not reach the significant level, possibly due to extra fear extinction by testing at 3 time points. However, the response at 20-minute had no extinction issue and should be valid. At 24hour time points, several previous studies (Samaco et al., 2013; Hao et al., 2016) already show the contextual fear memory deficit and our data including all animals confirm this result. Given the time-consuming nature of breeding these animals, we believe we should report the results with all animals we had at this time.

Reviewer #2:

In this manuscript, the authors characterize the activity of hippocampal place cells in a mouse model of Rett Syndrome. The main finding is that experience-dependent changes in the spatial firing properties of hippocampal place cells are diminished in the Rett (RTT) mouse model, a phenotype that is accompanied by hypersynchronous coactivation of hippocampal cells during ripple events that have been implicated in offline memory consolidation.

Overall, the results are interesting and – at the surface – the distinct findings reported in this study, i.e. higher firing rates (on track and during ripple events) in RTT mice, absence of spatial tuning refinement in RTT mice, increased baseline coactivity during ripple events without an experience-dependent increase, and abnormal contextual fear memory at 24 hours, support the conclusion that hypersynchronous hippocampal activity interferes with memory consolidation in this mouse model.

Still, I have a number of questions and some major concerns regarding the link between the different findings, the depth of the analyses that were performed and a few inconsistencies between the reported results and the data visualization. I believe most major concerns can be properly addressed with additional data analyses and clarifications in the text.

1) The authors report increased average firing rate (on the track and in ripple events) as well as hypersynchrony among CA1 cells during ripple events in RTT mice. How are these two phenomena related? Are cells active in more ripple events, or are cells emitting more spikes in the same number of ripple events? According to Figure 2, the firing rate changes during ripple events are observed in the population of all cells, but not (or much less so) in the subset of "track-active" cells. Could the authors confirm that in particular the "track-inactive" cells have an enhanced participation in ripple events?

We thank the reviewer for the suggestion. We have compared the number of spikes and participation rate of run-active and run-inactive cells in ripples between WT and RTT cells. The activity of run-inactive cells in ripples was similar in WT and RTT mice. The run-active cells tend to participate more in ripples, consistent with their tendency to fire more during ripples, especially during Post-run sessions.

2) The authors report increased baseline coactivity in RTT mice, which also expresses itself in the pre-track period before the mice ran on the novel track. However, according to the methods, the mice were trained on the familiar track before being introduced to the novel track. Do hippocampal cell populations in RTT mice show the same level of orthogonalization between the familiar and novel tracks (i.e. global remapping) as in WT mice? If not, could this explain the increased coactivity of novel track cells prior to the experience? Relatedly, the increase in synchrony/coactivity could reflect a stereotyped subset of cells that is more active than other cells, but not specifically tied to any given spatial environment.

This is a great point. Unfortunately, we did not track same cells in both the familiar and novel environments. The recordings on the familiar track and novel track were conducted on different days with only one type of environment each day. Since we had two running sessions on the same track (to monitor stability), we could not do this on both tracks because mice could only run two or three sessions per day. More sessions would saturate their food intake and they would not be motivated enough to complete all sessions.

Our focus here is on the hypersynchrony and its impact on consolidation and place cells. The issue of remapping is interesting but can be answered by future experiments.

3) The authors conclude that the observed hypersynchrony in RTT mice "interferes" with memory consolidation, possibly because the higher baseline hypersynchrony during ripple events in RTT mice may "occlude" experience-dependent increases in firing synchrony (Discussion section). The authors should clarify what they mean by "interfere" and "occlude" – e.g. following the classic model that spike sequence reactivation induces synaptic plasticity, do they mean that the higher baseline coactivation leads to a saturation of connection strengths between participating cells? Alternatively, if "track-inactive" cells participate more in ripple events (see point 1), it is conceivable that the deficits in memory consolidation and spatial tuning refinements are due to "imprecise" spike sequence reactivation that includes cells unrelated to the original experience.

Again, we thank the reviewer for this point. Our additional analysis found no difference in the participation rate of run-inactive cells in ripples, suggesting that the higher baseline coactivity of CA1 cells somehow makes the connections among the hippocampal cells (presumably CA3 cells) less plastic, which is supported by our previous study (Hao et al., 2016). This is consistent with the reviewer’s saturation hypothesis. We have added this point to the Discussion section.

4) The authors suggest that "spatial memory encoding is normal in RTT mice" (Discussion section) mainly based on the lack of clear differences in coarse spatial tuning properties between WT and RTT cells when mice run on a novel track. However, apart from the possibility that stereotyped cell populations are active in RTT mice (see above), spatial memory encoding may also be abnormal when studied at a more detailed level. For example, the authors should address the possibility that lap-to-lap refinements of place cell firing within a single recording session and/or theta sequences (which share some similarities with reactivation sequences during ripple events) are affected.

We appreciate the comment. Unfortunately, the small number of simultaneously recorded cells with active place fields on the same trajectory prevents us analyzing theta sequences. Nevertheless, we have added additional analyses on the lap-bylap changes in spatial information (Figure 2—figure supplement 2) and theta phase precession (Figure 3). While theta phase precession was largely normal, the lap-by-lap refinement revealed that cells in RTT started with lower spatial information than those in WT mice on the familiar track but started with similar spatial information on the novel track.

5) There are discrepancies and possible visualization errors in the reporting of the coactivity/correlation data (subsection “Baseline hypersynchrony within ripples during rest and sleep”,Figure 5 and Figure 6). The text and legend of Figure 6 mention a significantly higher coactivity in RTT as compared to WT, but Figure 6A and the summary statistics in the figure legend do not support this statement. Possibly related to this: there appears to be an error in the correlation plots in Figure 5—figure supplement 1 and Figure 6—figure supplement 1, as several traces appear to be "clipped" to zero (even though the legend claims that there is a significant difference between WT and RTT). Please double check the data – it is currently difficult to assess the validity of the results.

Please see our response to points #6,8 of the reviewer #2. The problem was due to presenting median correlation values with small bin size, which are dominated by zeros. We have modified the bin size and data presentation in these figures and other relevant figures. The results are now visually obvious.

6) In the fear conditioning experiment, the same mice were tested three times at 20 min, 1 hour and 24 hours after acquisition. This introduces the possibility that reduced freezing at 24 hours that was observed in the RTT mice is due to enhanced extinction, rather than impaired consolidation. One way the authors could address this is by testing a group of mice only at 24 hours.

This is a valid point. Our previous experiments (Samaco et al., 2013; Hao et al., 2016) have already shown that there is a significant impairment in contextual fear memory at 24 hours.

7) One of the main results is that WT cells, but not RTT cells, refine their spatial tuning properties in the familiar track (as indicated a spatial information increase – figure 3D). However, the place field length of WT cells appears to be larger in familiar as compared to novel track (figure 3B vs 3D), without an apparent decrease in the number of fields (subsection “Less specific place fields in the familiar, but not novel, environment”). Could the authors explain these seemingly opposing changes? They should also extend the comparison between novel and familiar spatial tuning properties in subsection “Less specific place fields in the familiar, but not novel, environment” to include place field length and peak rate measures.

We thank the reviewer for pointing this out. In our last submission, we did not exclude stopping time periods on the track, which produced many seemingly narrow place fields. We have made the correction, which have changed some of our conclusions regarding place field properties. Nevertheless, the main conclusion that spatial information was no different between WT and RTT mice on the novel track, but became so on the familiar track, remains. As the reviewer predicted, the place field length in WT mice on the familiar track was significantly shorter than that on the novel track.

We have also extended the comparison between novel and familiar place fields to include place field length and peak rate. Our result shows that the smaller spatial information in RTT mice on the familiar track was largely due to larger place field sizes.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The reviewers agree that the manuscript is nearly ready but there are a few outstanding comments below.

Reviewer #1:

Overall, I find the manuscript of Kee et al. to be much improved and appreciate the additional data and analyses the authors have provided. I have a few remaining points:

1) While the current manuscript is improved, I do still suspect that some of the differences in the novel and familiar track data related to the small sample size on the novel track. For example, median firing rate of run-active neurons is 1.1 Hz in WT on both types of track and 1.8 Hz in RTT mice in both types of track, yet only significant on the familiar due to the much more robust sample size. A similar trend is obvious in the pre-run ripple participation data. I would be more comfortable if the authors briefly acknowledge this possibility in the discussion.

We agree with the reviewer. We have added a sentence in the Results section and one in the Discussion section.

2) I am confused by Figure 2—figure supplement 1. In the text the authors state that down sampling the WT cells still resulted in significantly lower spatial information in the RTT cohort. First, it's unclear in the text and methods how this was done- was down sampling repeated many times as is the norm, then each individual sample compared to the RTT distribution to find if a significant difference was present? If so, how many iterations were run and what fraction were significantly different? Also, the graph in panel A shows the opposite effect of what the authors conclude, with the distribution of the RTT population in red shifted to the right of the WT- please clarify.

(i) We have added a paragraph in the Materials and methods section on downsampling. (ii) We thank the reviewer for pointing out the discrepancy in Panel A of this figure. It was a mistake in assigning plot colors. We have made the correction.

(iii) We thank the reviewer for the comment, which made us realize single iteration of downsampling is not that meaningful. We have now provided distributions of significant level (P values) among 200 downsampling iterations in Figure 2—figure supplement 1 and added a paragraph in the result section. The new results show that about half of the iterations produced significant P values for comparing spatial information between WT and RTT cells in the familiar environment, as expected from reduced statistical power. However, about 98% of iterations produced significant P values for comparing spatial information of WT cells between novel and familiar tracks, while only 25% for comparing spatial information of RTT cells. The result supports our key conclusion that WT cells show strong experience-dependent refinement in spatial memory codes, but not RTT cells, which is the emphasis throughout the manuscript (e.g. the conclusion Figure 6). We have also revised the text to recognize the sample size issue as in the response to #1 above and toned down the difference between WT and RTT cells.

3) The lap-by-lap data in Figure 2—figure supplement 2 is a nice addition, however I am not convinced the authors applied the correct statistical measure. I understand this is non-parametric data, hence their choice of the Wilcoxon ranksum, however this is clearly a repeated comparison between the groups, hence I believe the α must be adjusted for the number of comparisons (8) when claiming significance.

We thank the reviewer for pointing this out. We have adjusted the significance level to 0.00625 and marked those laps only with P values lower than this level. Also, as suggested by reviewer #3, we combined data in the first 3 laps and those in the last 3 laps and did statistical comparisons for the groups separately with a significance level of 0.025.

4) I still am a bit troubled by the behavioral data. The authors now include a 2-way ANOVA but fail to find the significant interaction between genotype and time which their conclusion would require. This should be made clear and the conclusions adjusted appropriately.

Given that the deficit in contextual fear memory of RTT mice at 24 hours has been studied in multiple previous studies (e.g., Samaco et al., 2013; Hao et al., 2016), our purpose here is to show that the contextual memory at 20 minutes in RTT mice was relatively normal, which our data are valid to support. Since the memory in the mice for the 2-way ANOVA was tested at 3 time points, it is likely the weaker memory deficit at 24-hours in these mice was due to memory extinction. Therefore, we believe our behavioral data together with previous studies are consistent with our conclusion. Nevertheless, we agree with the reviewer that the negative 2-way ANOVA test for these animals need to be made clear and the conclusion needs to be adjusted. We have revised the text to do so and have toned down the conclusion accordingly.

https://doi.org/10.7554/eLife.31451.022

Article and author information

Author details

  1. Sara E Kee

    1. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States
    2. Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
  2. Xiang Mou

    Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States
    Contribution
    Software, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8579-7316
  3. Huda Y Zoghbi

    1. Department of Neuroscience, Baylor College of Medicine, Houston, United States
    2. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    3. Department of Neurology, Baylor College of Medicine, Houston, United States
    4. Department of Pediatrics, Baylor College of Medicine, Houston, United States
    5. Howard Hughes Medical Institute, Baylor College of Medicine, Houston, United States
    6. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - original draft, Writing—review and editing
    For correspondence
    hzoghbi@bcm.edu
    Competing interests
    Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0700-3349
  4. Daoyun Ji

    1. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States
    2. Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Wriitng - original draft, Writing—review and editing
    For correspondence
    dji@bcm.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4115-5888

Funding

W. M. Keck Foundation

  • Huda Y Zoghbi
  • Daoyun Ji

National Institutes of Health (5R01NS057819)

  • Huda Y Zoghbi

Howard Hughes Medical Institute

  • Huda Y Zoghbi

Intellectual and Developmental Disabilities Research Center (1U54HD083092)

  • Huda Y Zoghbi

Simons Foundation (273886)

  • Daoyun Ji

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the W M Keck Foundation (HYZ, DJ), NIH/NINDS grant 5R01NS057819 (HYZ), Simons Foundation (DJ), and partly by the Neuroconnectivity Core and Neurobehavior Core of IDDRC at Baylor College of Medicine (1U54HD083092). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development or the National Institutes of Health. HYZ is an investigator with the Howard Hughes Medical Institute.

Ethics

Animal experimentation: All research and animal care procedures followed the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institute of Health and were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee (protocol #AN-5134).

Reviewing Editor

  1. Timothy E Behrens, University of Oxford, United Kingdom

Reviewer

  1. Fabian Kloosterman, KU Leuven, Belgium

Publication history

  1. Received: August 24, 2017
  2. Accepted: July 1, 2018
  3. Version of Record published: July 20, 2018 (version 1)

Copyright

© 2018, Kee et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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