Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion

Abstract

Hippocampal place cells represent the cellular substrate of episodic memory. Place cell ensembles reorganize to support learning but must also maintain stable representations to facilitate memory recall. Despite extensive research, the learning-related role of place cell dynamics in health and disease remains elusive. Using chronic two-photon Ca²⁺ imaging in hippocampal area CA1 of wild-type and Df(16)A^+/- mice, an animal model of 22q11.2 deletion syndrome, one of the most common genetic risk factors for cognitive dysfunction and schizophrenia, we found that goal-oriented learning in wild-type mice was supported by stable spatial maps and robust remapping of place fields toward the goal location. Df(16)A^+/- mice showed a significant learning deficit accompanied by reduced spatial map stability and the absence of goal-directed place cell reorganization. These results expand our understanding of the hippocampal ensemble dynamics supporting cognitive flexibility and demonstrate their importance in a model of 22q11.2-associated cognitive dysfunction.

Figure 1

Differences in learning performance between Df(16)A^+/− and WT mice in GOL task. (a) The three conditions of the GOL task. Mice spend 3 d in each condition. Contexts A and A′ are composed of different auditory, visual, olfactory and tactile cues (Online Methods), varied between Condition I and Condition II. The location of the hidden reward (blue circles, Rew 1 and Rew 2) is switched between Condition II and Condition III. Water-deprived mice trained to run on a linear treadmill were introduced to a novel environmental context (Context A) consisting of a feature-rich fabric belt and specific background with nonspatial odor, tones and blinking light patterns (Context A) on the first day of the experiment. Operant water rewards were available at a single unmarked location on the belt (Rew 1 in Conditions I and II; Rew 2 in Condition III); if the mouse licked in the correct location they received a water reward, but no water was administered if they did not lick in the reward location or if they licked outside the reward location (Condition I, 3 d and 3 sessions per d). The time of each lick as well as the position of the mouse on the treadmill were recorded both to determine when to deliver water rewards and to provide a readout of learning. To test the ability of mice to adjust to changes in the task conditions, mice were exposed to an altered context (Context A′: same sequence of belt materials, shuffled local cues, different nonspatial odor, tone and light; Online Methods), while maintaining the same reward location relative to the belt fabric sequence (Condition II, 3 d and 3 sessions per d). During the last part of the task, the location of the hidden reward was changed while maintaining the familiar context from Condition II (Condition III, 3 d and 3 sessions per d). (b) Example histograms of lick counts by position for a WT mouse (blue) and a Df(16)A^+/− mouse (red) on the first and last days of each condition. Green bars, reward locations. As the mice learned the reward location they switched from exploratory licking along the entire belt to focused licking only at the reward location, suppressing licking at other locations. Hence licking became increasingly specific for the reward location. (c) Learning performance of WT and Df(16)A^+/− mice based on fraction of licks in the reward zone (n = 6 mice per genotype, main effects described in Results section, post hoc tests with Benjamini-Hochberg correction, Condition I, two-way mixed-design RM ANOVA, main effect of day: F_2,20 = 28.235, P < 0.0001; Condition II, two-way mixed-design RM ANOVA, main effect of genotype: F_1,10 = 6.297, P = 0.031; main effect of day: F_2,20 = 4.076, P = 0.033; Day 1: P = 0.015 ; Condition III, two-way mixed-design RM ANOVA, main effects of day: F_2,20 = 15.762, P < 0.0001; main effect of genotype: F_1,10 = 7.768, P = 0.019; day × genotype interaction P = 0.932: n.s.; Day 3: P = 0.022). Error bars represent s.e.m. (d) Learning performance for WT and Df(16)A^+/− mice on the first and last session of each day by Condition. Across all conditions, both genotypes performed better at the end of the day. During Condition I, WT and Df(16)A^+/− mice performed similarly throughout the day (main effects described in Results section), while in Condition II, Df(16)A^+/− mice were more impaired at the start of the day (post hoc tests with Benjamini-Hochberg correction: two-way mixed-design RM ANOVA, main effect of session: F_1,10 = 40.506, P < 0.0001; genotype × session interaction: F_1,10 = 6.404, P = 0.030; main effect of genotype, P = 0.213: n.s.), and in Condition III they additionally never reached WT levels (post hoc tests with Benjamini-Hochberg correction: two-way mixed-design RM ANOVA, main effect of genotype: F_1,10 = 6.433, P = 0.030; main effect of session: F_1,10 = 53.237, P < 0.0001; genotype × session interaction P = 0.085: n.s.). Center line in box plot is the median, the top and bottom of the box denote the 1st and 3rd quartile of the data, respectively, and the whiskers mark the full range of the data. *P < 0.05.

Figure 2

Altered place cell properties in Df(16)A^+/− mice. (a) Schematic of head-fixed behavioral setup. Two-photon objective, 2-p obj. (b) Mice were injected with AAV1/2(Synapsin-GCaMP6f) (rAAV(GCaPM6f)) in dorsal hippocampal area CA1 to express the genetically encoded Ca²⁺ indicator GCaMP6f in neurons located in the CA1 pyramidal layer. Mice were then implanted with a head-post and imaging window to provide long-term optical access to the CA1 pyramidal layer. Left: schematic of two-photon Ca²⁺ imaging in the CA1 pyramidal layer. Right: representative two-photon fields of view across the pyramidal layer showing cross-sections of GCaMP6f-expressing cell bodies from a WT mouse (middle) and a mouse Df(16)A^+/− (right). We chronically imaged 179–621 regions of interest (ROIs; Online Methods) corresponding to cell bodies in each field of view. (c) Left: GCaMP6f Ca²⁺ fluorescence (ΔF/F) traces from two example spatially tuned CA1 place cells in WT and Df(16)A^+/− mice during 10-min sessions. Significant Ca²⁺ transients are highlighted in blue or red, and treadmill position is shown below the traces. Middle: polar trajectory plots showing significant running-related transients for the same example cells. Animals’ position (angle) over time (radius), gray; onset times of significant running-related calcium transients, colored circles. Shaded slices denote place fields. Right: transient vector plots showing the position (angle) and occupancy-normalized weight of each running-related transient (radius), as used to calculate occupancy-normalized transient rate histograms and transient circular variance. Green lines, transient resultant vector (magnitude = 1 – circular variance). (d–g) Compared to WT, Df(16)A^+/− mice had (d) a smaller fraction of cells per experiment with significant spatial information (place cell fraction: WT: 0.2553 ± 0.0109, n = 124 sessions; Df(16)A^+/−: 0.1924 ± 0.0079, n = 98 sessions; P < 0.0001; inset: averaged by mouse, independent samples t test, t = 1.620, P = 0.140; linear mixed-effects model with mouse as random factor: F_1,10.917 = 3.086, P = 0.107), (e) fewer multipeaked place cells (place fields per place cell; WT: 1.180 ± 0.004, n = 12,571 PC × sessions; Df(16)A^+/−: 1.110 ± 0.004, n = 7,683 PC × sessions; linear mixed-effects model with number of place fields and genotype as fixed factors and mouse as random factor: number of place fields × genotype interaction: F_3,38.000 = 5.054, P = 0.005 ; genotype effect for single place field, P = 0.0037; for two fields per PC, P = 0.010; for three fields per PC, P = 0.755), (f) narrower place fields (place field width; WT: 32.531 ± 0.135, n = 12,571 PC × sessions; Df(16)A^+/−: 29.532 ± 0.144, n = 7,683 PC × sessions; linear mixed-effects model with mouse as random factor: F_1,11.164 = 4.371, P = 0.060; dashed vertical lines indicate means) and (g) lower circular variance (WT: 0.310 ± 0.0013, n = 43,068 cells × sessions; Df(16)A^+/−: 0.189 ± 0.0014, n = 27,397 cells × sessions, linear mixed-effects model with mouse as random factor: F_1,11.006 = 5.695, P = 0.036; inset: averaged by mouse, Welch’s t test, t = 2.327, P = 0.0491). *P < 0.05, **P < 0.01.

Figure 3

Disrupted stability of place cell population in Df(16)A^+/− compared to WT mice. (a) Top: example of place cell recurrence. In a given field of view, a subset of all cells has significant spatial tuning each day (place cells, green). The overlap in this population is the recurrence probability (40% in this example). Bottom: distribution of recurrence fractions from day to day for WT and Df(16)A^+/− mice for all sessions (dotted line is cell-identity shuffle distribution: WT: 0.456 ± 0.015, n = 74 sessions, Df(16)A^+/−: 0.327 ± 0.017, n = 59 sessions, shuffle: 0.229 ± 0.009, n = 133 sessions; WT vs. shuffle: Welch’s t test, t = 12.64, P < 0.0001; Df(16)A^+/− vs. shuffle: Welch’s t test, t = 5.124, P < 0.0001; WT vs. Df(16)A^+/−: independent samples t test, t = 5.72, P < 0.0001) and aggregated by mouse (inset; horizontal dotted line is cell-identity shuffle: WT vs. Df(16)A^+/−: independent samples t test, t = 2.611, P = 0.028). (b) Mean fraction of cells that reoccur as place cells from session to session (S-S) or day to day (D-D) for WT and Df(16)A^+/− mice (dotted line is mean place cell fraction; linear mixed-effects model with genotype and elapsed time as fixed effects and mouse ID as random effect; genotype × elapsed time interaction: F_1,145.754 = 5.858, P = 0.017; post hoc analysis, WT vs. Df(16)A^+/−, S-S: F_1,10.659 = 0.664, P = 0.433; D-D: F_1,10.086 = 20.534, P = 0.001, significant after Benjamini-Hochberg correction). (c) Correlation of place cell recurrence with performance throughout the task. Solid lines, linear regression fit; shaded regions, 95% confidence intervals calculated from bootstrap resampling (Pearson’s correlation coefficient, WT: 0.288, P = 0.013; Df(16)A^+/−: 0.416, P = 0.001; WT correlation vs. Df(16)A^+/− correlation, Fisher z-transformation of correlations, general linear model (GLM), univariate ANOVA: genotype × z recurrence probability interaction: F_1,132 = 0.599, P = 0.440; alternatively: linear mixed effects model with genotype as fixed effect, recurrence probability as covariate and mouse ID as random effect: genotype × recurrence probability interaction: F_1,129 = 1.083, P = 0.300; recurrence effect: F_1,129.000 = 18.197, P < 0.0001). (d) Top: preferred spatial tuning is represented as vectors where the angle is the position on the treadmill of maximal activity. Across three sessions (green, blue and orange lines), spatial preference is generally stable (green to blue sessions), though salient events or changes to the environment can induce remapping (blue to orange sessions). The centroid shift is the angle between these vectors, represented as the fraction of the belt. Bottom: distribution of mean centroid shift from day to day per session (dotted line is cell-identity shuffled distribution: WT: 0.204 ± 0.003, n = 74 sessions, Df(16)A^+/−: 0.224 ± 0.003, n = 59 sessions, shuffle: 0.242 ± 0.002, n = 133; WT vs. shuffle: independent sample t test, t = −9.42, P < 0.0001; Df(16)A^+/− vs. shuffle: independent samples t test, t = −4.25, P < 0.0001; WT vs. Df(16)A^+/−: independent samples t test, t = −4.71, P < 0.0001) and aggregated by mouse (inset; horizontal dashed line is cell-identity shuffle; independent samples t test, t = 2.58, P = 0.0295). (e) Mean centroid shift from S-S or D-D for WT and Df(16)A^+/− mice (dotted line is mean centroid shift, linear mixed-effects model with genotype and elapsed time as fixed effects and mouse ID as random effect, genotype × elapsed time interaction: F_{1, 38,078.993} = 15.042, P < 0.0001; post hoc analysis, WT vs. Df(16)A^+/−, S-S: F_1,11.137 = 0.303, P = 0.593; D-D, F_1,10.577 = 8.724, P = 0.014, significant after Benjamini-Hochberg correction). (f) Correlation of mean day-today stability with performance throughout the task. Solid line and shaded regions as in d (Pearson’s correlation coefficient, WT: −0.306, P = 0.008; Df(16)A^+/−: −0.218, P = 0.097; WT correlation vs. Df(16)A^+/− correlation, Fisher z transformation of correlations, GLM, univariate ANOVA: genotype × R stability–probability interaction: F_1,132 = 0.268, P = 0.605; linear mixed effects model with genotype as fixed effect, centroid shift as covariate and mouse ID as random effect: genotype × centroid shift: F_1,133.000 = 0.001, P = 0.982; centroid shift: F_1,133.000 = 8.804, P = 0.004). In b and e, center line in box plot is the median, the top and bottom of the box denote the 1st and 3rd quartile of the data, respectively, and the whiskers mark the full range of the data. (g–i) Task performance and population stability by genotype follow similar trajectories across conditions. Error bars represent s.e.m. of total number of sessions by mouse. Bonferroni-corrected post hoc tests comparing genotype per condition. (g) Fraction of licks in the reward zone by condition (two-way ANOVA, main effect of genotype P < 0.0001, main effect of condition P < 0.0001, genotype × condition interaction: P < 0.0001; post hoc comparisons: Condition II, P = 0.011; Condition III, P < 0.001). (h) Recurrence probability by condition (linear mixed-effects model with condition and genotype as fixed effects and mouse as random effect, genotype effect: F_1,11.084 = 7.293, P = 0.021, genotype × condition interaction: P = 0.083; post hoc comparisons: Condition III, P = 0.004). (i) Mean centroid shift by condition (linear mixed-effects model as before, genotype effect: F_1,10.107 = 6.771, P = 0.026). *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 4

Lack of context change and task-dependent stability of spatial maps in Df(16)A^+/− mice. (a) Mean centroid shift from the last day of Condition I to the first day of Condition II (WT: 0.195 ± 0.008, n = 6; Df(16)A^+/−: 0.227 ±0.002, n = 5; independent samples t test: t = −3.626, P = 0.0055). Horizontal dashed line represents shuffled data. Error bars represent s.e.m. for mice. (b) Fraction of all cells classified as place-preferring (position), cue-preferring (cue) or neither (pooled across mice) for WT, Df(16)A^+/− and shuffled data (Pearson chi-square test: ±² = 85.7776, P < 0.0001). (c) Ratio of the number of cue-preferring to place-preferring cells per mouse for WT mice, Df(16)A^+/− mice (independent samples t test: t = −3.172, P = 0.0131) and shuffled data (horizontal dashed line). Error bars represent s.e.m. for mice. (d) Schematic of RF task. Rewards (blue circles) are presented randomly throughout the belt in the same context (Context B): same belt fabric sequence but different auditory, visual, olfactory and tactile cues. (e) Distribution of mean centroid shift per session from day to day during RF task (dotted line is cell-identity shuffled distribution; WT: 0.222 ± 0.004, n = 30 session pairs; Df(16)A^+/−: 0.220 ± 0.004, n = 42 session pairs; shuffle: 0.244 ± 0.002, n = 72; WT vs. shuffle: independent sample t test: t = −5.05, P < 0.0001; Df(16)A^+/− vs. shuffle: Welch’s t test, t = −5.12, P < 0.0001; WT vs. Df(16)A^+/−: independent samples t test: t = 0.451, P = 0.653) and aggregated by mouse (inset; horizontal dashed line is cell-identity shuffle; WT vs. Df(16)A^+/−: independent samples t test: t = 0.799, P = 0.448). (f) Comparison of mean centroid shift per mouse in the RF and GOL tasks (GOL data replotted from Fig. 3e; linear mixed-effects model, genotype × task interaction: F_1,19.471 = 4.316, P = 0.051; effect of task: F_1,19.471 = 4.924, P = 0.039; post hoc analysis: WT, GOL vs. RF: F_1,11.285 = 10.472, P = 0.008, significant after B Benjamini-Hochberg correction; Df(16)A^+/−, GOL vs. RF: F_1,8.562 = 0.006, P = 0.940). In box plots, center line represents the median, the top and bottom of the box denote the 1st and 3rd quartile of the data, respectively, and the whiskers mark the full range of the data. *P < 0.05, **P < 0.01

Figure 5

Place field enrichment of goal location. (a) Tuning profiles for all place cells in WT and Df(16)A^+/− mice on the first and last days of Condition III. Each row is an individual place cell. The intensity corresponds to the normalized transient rate in each spatial bin along the x axis. Goal location is between dotted lines. WT mice show more place cells near the reward by Day 3, an enrichment lacking in Df(16)A^+/− mice. (b) Fraction of place cells near the goal location (within 1/16 of the belt length) across all days of the experiment. Horizontal dotted line is uniformly distributed fraction (linear mixed-effects model, condition and genotype as fixed effects and day nested under condition as covariate, with mouse ID as random factor: genotype × condition × interaction: F_2,174.406 = 4.257, P = 0.016; genotype × day (nested under condition) interaction: F_3,112.789 = 3.257, P = 0.024; post hoc analysis with Benjamini-Hochberg correction for multiple comparisons, Conditions I and II: no significant effect of genotype; Condition III, genotype effect: F_1,71.243 = 8.776, P = 0.004; genotype × day interaction: F_1,64.041 = 12.307, P = 0.001; Day 3: t = 4.669, P < 0.0001). Error bars are s.e.m. of place cell number. (c) Place cell goal-zone enrichment is correlated with task performance during Condition III in WT but not Df(16)A^+/− mice (WT: Pearson correlation: 0.362, P = 0.023; fraction of licks in the reward zone: F_1,31.460 = 11.436, P = 0.002, significant after Benjamini-Hochberg correction; Df(16)A^+/−: Pearson correlation: −0.068, P = 0.791). Linear regression and confidence intervals as in Figure 3c. ***P < 0.001.

Figure 6

Session-to-session place field shift dynamics. (a) Example fields of view from two consecutive sessions. Background is time-averaged GCaMP6f movie. Place cells are colored corresponding to their spatial tuning within each session. The color bar shows the mapping of place field location on the belt. The reward zone for these sessions was between the dotted lines. Place fields are generally stable (red arrow), but some shift their place field (yellow arrow), while others stop being spatially active (blue arrow). (b) Recurrence probability as a function of original distance from the reward. For all pairs of consecutive sessions during Condition III, each place cell during the first session is plotted with the centroid of its place field along the x axis and whether or not it was also a place cell in the second session on the y axis (top cluster is place cell in second session; bottom cluster is not a place cell; random y-axis jitter within each cluster for visualization). Cyclic logistic regression fit with 95% confidence interval from cross-validation plotted on left axis. (c) Session-to-session place field shift as a function of original distance from the reward. For all pairs of consecutive sessions during Condition III, each place cell during the first session is plotted with the centroid of its place field along the x axis and the change in centroid position in the second session along the y axis. Data is fit as a continuous series of von Mises distributions for each position, with the offset (solid purple line) and variance (shaded band, 1/κ, where κ is the concentration parameter) shown. Green dotted line denotes cells that move directly to the reward position in the second session. While it is possible that a subset of the goal-enriching cells are reward cells that directly follow the reward, the larger effect is the gradual drift of the entire place cell population toward the reward, not the active recruitment of reward cells directly remapping to the reward location (for example, lack of cells clustered around green dotted line). (d) Same offset curve (solid line, shaded region is 90% confidence interval calculated from refitting bootstrap resampled data) as in c. Positive values to the left of the zero-crossing and negative values to the right correspond to drift toward the reward position. (e) Same variance fit as in c, plotted independently. Shaded region represents 90% confidence interval calculated from refitting bootstrap resampled data. Place field shift is most consistent (minimum variance) at a position that corresponds to the most stable place field location from d, just after the goal location.

Figure 7

Place field drift toward reward drives enrichment in the place field dynamics model. (a) Schematic of place cell recurrence (left) and stability (right) model including the four parameters that were fit from our data: non-place cell to place cell transition probability (P_on), place cell recurrence probability by position (P_recur), session-to-session place field shift variance and session-to-session place field shift offset. (b) Mean population enrichment by simulated iteration (solid lines) for WT and flat parameter sets (dashed lines: 90% confidence intervals from 100 simulations). WT parameters reproduce the enrichment observed during Condition III. (c) Final distribution of place fields after eight iterations for WT and flat-model parameters. Vertical dashed line denotes reward location. (d) Mean population enrichment after eight iterations with true-fit parameters and then with swapping each set of position-dependent parameters individually between WT and the flat model: recurrence probability (P_recur), place field shift variance and place field shift offset. (e) Final WT place field distributions after eight iterations with the same parameter swaps as in d. Mean place field shift (offset) toward the reward is revealed as the main factor underlying enrichment in GOL.

Figure 8

Df(16)A^+/− mice place fields do not drift toward goal and the model produces no enrichment. (a) Place cell recurrence by distance from reward, as in Figure 6b. (b) Session-to-session place field shift as a function of original distance from the reward, as in Figure 6c. (c,d) Place field shift and variance fits from b, as in Figure 7c,d (shaded region is 90% confidence interval calculated from refitting bootstrap resampled data). (e) Unlike the WT model, the enrichment model with Df(16)A^+/− parameters shows no enrichment (see Fig. 7b). (f) Final distribution of place fields after eight iterations for Df(16)A^+/− parameters.