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

Nature Neuroscience volume 20, pages 1612–1623 (2017)Cite this article

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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.

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Figure 1: Differences in learning performance between Df(16)A+/− and WT mice in GOL task.
Figure 2: Altered place cell properties in Df(16)A+/− mice.
Figure 3: Disrupted stability of place cell population in Df(16)A+/− compared to WT mice.
Figure 4: Lack of context change and task-dependent stability of spatial maps in Df(16)A+/− mice.
Figure 5: Place field enrichment of goal location.
Figure 6: Session-to-session place field shift dynamics.
Figure 7: Place field drift toward reward drives enrichment in the place field dynamics model.
Figure 8: Df(16)A+/− mice place fields do not drift toward goal and the model produces no enrichment.

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Acknowledgements

We thank Z. He in the laboratory of I. Ionita-Laza for helpful discussions on the statistical analysis. J.D.Z. is supported by NIMH 1F31MH105169. N.B.D. is supported by NINDS F30NS090819. P.W.K. is supported by the Howard Hughes Medical Institute. F.T.S is supported by the Canadian Institutes of Health Research. J.A.G. is supported by NIMH MH097879 and NIMH MH096274. A.L. is supported by NIMH 1R01MH100631, NINDS 1U01NS090583, NINDS 1R01NS094668, the Searle Scholars Program, the Human Frontier Science Program, and the McKnight Memory and Cognitive Disorders Award.

Author information

Author notes

  1. Jeffrey D Zaremba and Anastasia Diamantopoulou: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Neuroscience, Columbia University, New York, New York, USA

    Jeffrey D Zaremba, Nathan B Danielson, Andres D Grosmark, Patrick W Kaifosh, John C Bowler, Zhenrui Liao, Fraser T Sparks, Joseph A Gogos & Attila Losonczy

  2. Department of Psychiatry, Columbia University, New York, New York, USA

    Anastasia Diamantopoulou

  3. Department of Physiology and Cellular Biophysics, Columbia University, New York, New York, USA

    Anastasia Diamantopoulou & Joseph A Gogos

  4. Center for Theoretical Neuroscience, Columbia University, New York, New York, USA

    Patrick W Kaifosh

  5. Kavli Institute for Brain Science, Columbia University, New York, New York, USA

    Attila Losonczy

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  1. Jeffrey D Zaremba
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Contributions

A.L. and J.A.G. conceived the project. J.D.Z. and A.D. designed and performed experiments with the help of F.T.S. J.D.Z. and A.D. analyzed data with the help of N.B.D., A.D.G., P.W.K., J.C.B. and Z.L. in analysis and technical infrastructure. J.D.Z., A.D., J.A.G., F.T.S. and A.L. wrote the manuscript.

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Correspondence to Joseph A Gogos or Attila Losonczy.

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Integrated supplementary information

Supplementary Figure 1 Task performance by mouse.

Behavioral performance per mouse across conditions of the task for (a) WT and (b) Df(16)A± mice. Each line is an individual mouse showing the overall task performance each day.

Supplementary Figure 2 Comparison of behavior during initial learning in GOL.

a. Mean velocity (excluding stationary time) during Condition I for WT and Df(16)A± mice. (WT: 7.123 ± 1.002, n=6 mice; Df(16)A±: 6.778 ± 0.312, n=5 mice; independent samples T-test, t=0.302, p=0.769). b. Velocity during Condition I separated by session within each day (two-way ANOVA for session and genotype, all n.s.). c,d. Lap rate (as in a,b) (WT: 0.947 ± 0.147, n=6 mice; Df(16)A±: 1.164 ± 0.128, n=5 mice; independent samples T-test, t=-1.087, p=0.305; two-way ANOVA for session and genotype, all n.s.). e,f. Lick rate (as in a,b) (WT: 1.100 ± 0.101, n=6 mice; Df(16)A±: 1.362 ± 0.312, n=5 mice; independent samples T-test, t=-1.102, p=0.299; two-way ANOVA for session and genotype, all n.s.). Both WT and Df(16)A± mice show similar levels of activity during the initial learning period, as well as across sessions within each day.

Supplementary Figure 3 Anticipatory licking.

a. Task performance across all conditions of the task (Two-way mixed-effects RM ANOVA, genotype*condition interaction, p=0.060). b. Task performance and centroid shift correlation (centroid shift vs. fraction of licks in reward zone, Pearson’s correlation coefficient, WT: -0.282, p=0.015; Df(16)A±: -0.343, p=0.008). c. Task performance and goal zone place cell enrichment during Condition III (fraction of place cells near reward vs. fraction of licks in reward zone, Pearson’s correlation coefficient, WT: 0.418, p=0.008; Df(16)A±: -0.119, p=0.503).

Supplementary Figure 4 Comparison of lifetime place coding and place cell metrics between Df(16)A+/− and WT mice.

a. Lifetime place coding percentage, the fraction of ROIs that were ever identified as a place cell by the nth session imaged (lifetime place coding, Cox Regression, B=0.222, p=0.244). b. Fraction of all sessions imaged that an ROI was identified as a place cell (fraction of sessions a place cell; WT: 0.254 ± 0.004, n=3162 cells; Df(16)A±: 0.214 ± 0.004, n=3322 cells; Mann-Whitney U, U=4.55x106, p<0.0001), averaged within mice (inset; independent sample T-test, t=1.517, p=0.164). c. Transient sensitivity, defined as the fraction of laps in which a transient occurred in the place field (WT: 0.5786 ± 0.00218, n=12524 place cell*sessions; Df(16)A±: 0.5445 ± 0.0027, n=7664 place cell*sessions; Mann-Whitney U, U=4.4x107, p<0.0001), averaged within mice (inset, independent samples T-test, t=0.0142, p=0.989). d. Transient specificity, defined as the fraction of transients that occurred in the place field (WT: 0.795 ± 0.0161, n=12571 place cell*sessions; Df(16)A±: 0.872 ± 0.0018, n=7683 place cell*sessions; Mann-Whitney U, U=3.59x107, p<0.0001), averaged within mice (inset; Welch’s T-test, t=2.427, p=0.0544). e. Place field width (WT: 32.09 ± 0.125, n=14833 place fields; Df(16)A±: 29.25 ± 0.136, n=8529 place fields; Mann-Whitney U, U=5.5x107 p<0.0001), averaged within mice (inset, Welch’s T-test, t=1.990, p=0.0911). f. Single-cell sparsity (WT: 0.2325 ± 0.001, n=12571 ROI*sessions; Df(16)A+/: 0.186 ± 0.001, n=8683 ROI*sessions; Mann-Whitney U, U=3.35x107, p<0.0001), averaged within mice (inset, Welch’s T-test, t=2.064, p=0.0852). ***p<0.001.

Supplementary Figure 5 Place field correlation.

Place field correlation showed an overall similar effect as centroid shift (see Fig. 3d-f). a. Compared to WT mice, Df(16)A± mice show a significant overall decrease in place field correlation (WT: 0.232 ± 0.010, n=74 sessions; Df(16)A±: 0.130 ± 0.010, n=59 sessions; shuffle: 0.0238 ± 0.005, n=133; WT vs. shuffle: Welch’s T-test; t=18.87; p<0.0001; Df(16)A± vs. shuffle: Welch’s T-test, t=9.89, p<0.0001; WT vs. Df(16)A±, independent samples T-test, t=7.143, p<0.0001; inset aggregated by mouse: WT vs. Df(16)A±: independent samples T-test, t=2.584, p=0.0295). b. Place fields were more stable from session-to-session than day-to-day and the Df(16)A± mice were less stable across elapsed time (two-way ANOVA for time elapsed and genotype, main effect of genotype: F(1,152)=5.710, p=0.0181; main effect of elapsed time: F(1,152)=55.329, p<0.0001; elapsed time*genotype interaction: F(1,152)=8.074, p=0.00511; S-S, WT vs. Df(16)A±f: t=0.507, p=0.613; D-D, WT vs. Df(16)A±: t=4.455; p<0.0001). c. Task performance correlates with the session-mean place field correlation for WT mice (Spearman’s correlation coefficient=0.335, p=0.004) and trends similarly for Df(16)A± mice (Pearson’s correlation coefficient=0.224, p=0.088).

Supplementary Figure 6 Task performance, place cell recurrence, and spatial tuning stability by Condition.

Task performance and population stability by genotype follows similar trajectories across conditions; that is to say, performance and stability are similar in Condition I, slightly impaired in the Df(16)A± mice during Condition II and most different during Condition III (three-way ANOVA, genotype*metric*condition interaction: F(4,549)=0.484, p=0.747; condition*genotype interaction: F(2,549)=11.982, p<0.0001; metric*genotype interaction: F(2,549)=0.771, p=0.463; metric*condition interaction: F(4,549)=1.503, p=0.200; Condition I, all metrics, WT vs. Df(16)A±: independent samples T-test: t=-1.194, p=0.234; Condition II, all metrics, WT vs. Df(16)A±: independent samples T-test: t=2.67, p=0.0081; Condition III, all metrics, WT vs. Df(16)A±: Welch’s T-test: t=5.586, p<0.0001). Same a. Fraction of licks in the reward zone by Condition. Same data in Fig. 3g with values transformed to z-scores (across all conditions and both genotypes). b. Recurrence probability by Condition. Same data in Fig. 3h with values transformed to z-scores (across all conditions and both genotypes). c. Mean centroid shift by Condition. Same data in Fig. 3i with values transformed to -1 * z-scores (across all conditions and both genotypes; multiplying by -1 makes positive values represent relatively increased stability). Bonferroni-corrected post hoc tests comparing genotype per condition; *p<0.05, **p<0.01, ***p<0.0001.

Supplementary Figure 7 Impaired task performance during CA1 inactivation.

During initial learning of a reward location, local inactivation of CA1 lead to significantly reduced task performance (Days 1-3, muscimol to saline vs. saline to muscimol: Mann-Whitney U, U=126.5, p<0.0001). In addition, mice which received saline infusion during the first three initial learning days performed significantly worse on the fourth day when they were infused with muscimol (saline to muscimol, Days 1-3: 0.221 ± 0.053, n=36 sessions; Day 4: 0.084 ± 0.034, n=12 sessions; Mann-Whitney U, U=111, p=0.0235) and now performed at a similar level to mice which were initially infused with muscimol (Day 4, saline to muscimol vs. muscimol to saline: independent samples T-test, t=0.633, p=0.535).

Supplementary Figure 8 No place cells on a cue-free belt; spatial tuning near the fabric-transition is not more stable.

a. The 6 most spatially-tuned cells (lowest circular variance) on a burlap belt, plotted as in Fig. 2c. b. Place cell fraction on a ‘cue-rich’ and ‘cue-free’ belt during RF (cue-rich: 0.275 ± 0.017, n=56 sessions; cue-free: 0.057 ± 0.018, n=3 sessions; independent samples T-test, t=3.006, p=0.004). c. Transient circular variance on a ‘cue-rich’ and ‘cue-free belt during RF (cue-rich: 0.427 ± 0.003, n=7828 cell*sessions; cue-free: 0.746 ± 0.008, n=375 cell*sessions; Mann-Whitney U, U=4.99x105, p<0.0001). d. Centroid shift of cells from the last day of Condition I to the first session of Condition II separated by tuning preference relative to fabric transitions—the only features that remain constant between the two contexts. WT tuning is generally more stable (WT vs. Df(16)A±, independent samples T-test: t=-4.96, p<0.0001; see Fig. 4a), but neither genotype shows increased stability near the fabric transitions (two-way ANOVA, main effect of binned distance: F(2,24)=0.024, p=0.977). **p<0.01, ***p<0.001

Supplementary Figure 9 Sharp wave-ripples (SWR) are altered in Df(16)A+/– mice.

a. Mean SWR wavelet power for WT (left) and Df(16)A± (right) mice. b. Difference (Df(16)A± - WT) of mean SWR wavelet power in a. c. Rate of SWRs during stationary bouts (mean ± SD; WT: 0.106 ± 0.103, n=45 stationary intervals; Df(16)A±: 0.178 ± 0.120, n=61 stationary intervals; Wilcoxon rank-sum test, h=3777.5, p=0.00096). d. Mean wavelet power (mean ± SD; WT: 3.622 ± 1.133, n=145 sharp wave-ripples; Df(16)A±: 4.060 ± 0.838, n=357 sharp wave-ripples; Wilcoxon rank-sum test, h=98423, p<0.0001). e. Frequency with maximum power (mean ± SD; WT: 152.190 ± 17.174, n=145 sharp wave-ripples; Df(16)A±: 163.391 ± 29.485, n=357 sharp wave-ripples; Wilcoxon rank-sum test, h=94798, p=0.00066). f-h. Same as (c-e) for several SWR detection thresholds (significance by Wilcoxon rank-sum test as marked). *p<0.05, **p<0.001, ***p<0.0001. SWR-related place cell reactivation (for example, as in Wilson and McNaughton, Science, 1994; Lee and Wilson, Neuron, 2002; Foster and Wilson, Nature 2006) was not directly assessed.

Supplementary Figure 10 Possible enrichment mechanisms.

Comparison of three theoretical methods by which place cells could enrich a goal location. a. Place fields could be generally stable, but place cells near the reward are more likely to reoccur as place cells from session-to-session. (top) Recurrence probability as a function of distance from reward. (middle) Mean place field centroid shift as a function of distance from reward. (bottom) Mean place field shift variance as a function of distance from reward. b. Place cells could reoccur at equal probability along the belt, but place fields shift towards the reward location such that fields before the reward shift forward and fields after the reward shift backwards. Plots as in a. Place fields shifting towards the reward also leads to enrichment in our model. c. Place fields might not shift uniformly towards the reward, but if fields are generally stable, the ones near the reward could shift less than ones farther away. Plots as in a. d-f. Using the parameters from a-c, our enrichment model suggests all three hypothetical models could lead to enrichments: (d) increased place cell recurrence at reward position, (e) place fields shifting towards the reward location, or (f) place fields near the reward shifting less than ones away from the reward.

Supplementary Figure 11 Latent spatial tuning revealed across multiple sessions.

a. Scatter plots of original position versus position shift after 2 elapsed sessions for WT and Df(16)A± mice. Cells that were not a place cell in the middle session are marked in magenta and cyan for WT and Df(16)A± data, respectively. Even cells that were not a place cell in the intervening session still cluster around 0, suggesting that they retain some latent place preference that is either not expressed or not detectable. Vertical dashed line denotes reward location. Horizontal dashed line marks fields that do not shift at all. Green diagonal dashed line marks fields that remap directly to the reward location. b. Mean place field shift variance across all positions for cells paired by 1 session elapsed, 2 sessions elapsed, 2 sessions elapsed for cells that were not a place cell in the intervening session, and 2 iterations of the model. Two-session elapsed place cells are equally stable whether or not the cell was a place cell in the middle session, which is also the same as two iterations of the model.

Supplementary Figure 12 Modeled enrichment for all three Conditions.

Our model of place cell enrichment only produces goal location enrichment with parameters fit from WT mice during Condition III. a,b. Enrichment by iteration with parameters fit from each of the three Conditions of the task for (a) WT and (b) Df(16)A± mice (as in Fig. 7b, 8e). c,d. Final distribution of place fields after 8 iterations for each set of parameters fit from (c) WT and (d) Df(16)A± mice (as in Fig. 7c, 8f).

Supplementary Figure 13 Model enrichment when swapping individual parameters between WT and Df(16)A+/− values.

Mean enrichment by iteration (left, 90% confidence interval shading determined from 100 simulations) and histogram of distributions of place fields after the final iteration (right, data pooled across all simulations, vertical dashed line denotes reward location) for each parameter individually swapped between WT and flat models. a. No swap. b. Swap place cell recurrence probability (Precur). c. Swap session-to-session place field shift variance. d. Swap session-to-session place field shift offset. Swapping shift offset has the largest effect, as the flat model with only the WT shift offset parameters leads to enrichment, while the WT model with the flat shift offset parameters does not.

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Zaremba, J., Diamantopoulou, A., Danielson, N. et al. Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion. Nat Neurosci 20, 1612–1623 (2017). https://doi.org/10.1038/nn.4634

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