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Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice

Nature Neuroscience volume 27, pages 1794–1804 (2024)Cite this article

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Abstract

Across systems, higher-order interactions between components govern emergent dynamics. Here we tested whether contextual threat memory retrieval in mice relies on higher-order interactions between dorsal CA1 hippocampal neurons requiring learning-induced dendritic spine plasticity. We compared population-level Ca2+ transients as wild-type mice (with intact learning-induced spine plasticity and memory) and amnestic mice (TgCRND8 mice with high levels of amyloid-β and deficits in learning-induced spine plasticity and memory) were tested for memory. Using machine-learning classifiers with different capacities to use input data with complex interactions, our findings indicate complex neuronal interactions in the memory representation of wild-type, but not amnestic, mice. Moreover, a peptide that partially restored learning-induced spine plasticity also restored the statistical complexity of the memory representation and memory behavior in Tg mice. These findings provide a previously missing bridge between levels of analysis in memory research, linking receptors, spines, higher-order neuronal dynamics and behavior.

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Fig. 1: High Aβ in dorsal hippocampus decreased surface GluA2-containing AMPAR levels, disrupted learning-induced spine growth and spatial memory, effects reversed by a peptide designed to disrupt GluA2 endocytosis.
Fig. 2: High Aβ in dorsal hippocampus impaired long-term contextual threat memory, an effect reversed by a peptide designed to disrupt GluR2 AMPAR endocytosis.
Fig. 3: TAT-GluA23Y restored deficit in contextual threat memory representation at level of individual neuron in Tg mice.
Fig. 4: Deficits in threat memory representation at neuronal population level in Tg mice restored by TAT-GluA23Y.
Fig. 5: TAT-GluA23Y partially restores impaired pattern completion-like process in Tg mice.

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Data availability

The datasets generated and analyzed in the current study are available at https://github.com/jf-lab/ad_network_dysfunction. Source data are provided with this paper.

Code availability

Custom code for behavioral and calcium imaging analysis available at https://github.com/jf-lab/ad_network_dysfunction.

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Acknowledgements

We thank A. DeCristofaro, D. Lin and M. Yamamoto for technical support, C. Yuan and D. Abdo for behavior scoring, and the entire Josselyn and Frankland laboratories for advice and discussion. This work was supported by grants from NIMH (R01 MH119421-01) and Brain Canada Foundation to S.A.J. and P.W.F., CIHR (FDN-159919) to S.A.J., CIHR (FDN-143227) to P.W.F., NSERC CGS-D and a National Institutes of Health grant (F31 MH120920-01) to A.R.M. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Chen Yan

    Present address: DeepMind, London, UK

  2. Valentina Mercaldo

    Present address: Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland

  3. Nathan Insel

    Present address: Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada

  4. These authors contributed equally: Chen Yan, Valentina Mercaldo, Alexander D. Jacob.

Authors and Affiliations

  1. Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada

    Chen Yan, Valentina Mercaldo, Alexander D. Jacob, Emily Kramer, Andrew Mocle, Adam I. Ramsaran, Lina Tran, Asim J. Rashid, Sungmo Park, Nathan Insel, Paul W. Frankland & Sheena A. Josselyn

  2. Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada

    Chen Yan, Emily Kramer, Lina Tran, Paul W. Frankland & Sheena A. Josselyn

  3. Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada

    Alexander D. Jacob, Adam I. Ramsaran, Paul W. Frankland & Sheena A. Josselyn

  4. Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada

    Andrew Mocle, Paul W. Frankland & Sheena A. Josselyn

  5. Dept. of Psychology, University of Montana, Missoula, MT, USA

    Nathan Insel

  6. Dept. of Neuroscience, University of Minnesota, Minneapolis, MN, USA

    A. David Redish

  7. Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada

    Paul W. Frankland

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Contributions

Conceptualization and methodology was carried out by C.Y., V.M., A.D.J., P.W.F. and S.A.J. Investigation and formal analysis was conducted by C.Y., V.M., A.D.J., E.K., A.M., A.I.R., L.T., A.J.R., S.P., N.I. and A.D.R. Writing of the original draft was carried out by C.Y., V.M., A.D.J. and S.A.J. Writing (reviewing and editing) was conducted by all authors. Funding was acquired by P.W.F. and S.A.J. Supervision was carried out by P.W.F. and S.A.J.

Corresponding author

Correspondence to Sheena A. Josselyn.

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The authors declare no competing interests.

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Nature Neuroscience thanks Takashi Kitamura and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Increasing Aβ in CA1 region of dorsal hippocampus disrupts spatial memory.

(A) (Left) Higher Aβ1-42 levels in hippocampal extract from TgCRND8 (Tg) than WT littermate control mice (one-way ANOVA, F(1,11) = 5990.70, p < .0001). n = number of mice, (Right) Representative image showing no evidence of plaques in Tg mice at this age. (B) Top-down depiction of watermaze pool (60 cm radius). During training, the platform (5 cm radius) was located in a fixed location. In probe test, spatial memory was quantified by removing platform from pool and measuring amount of time mice spent searching in target zone (20 cm radius, centered on location of platform during training; 11% of pool surface) versus average time spent in three other equivalent (in terms of size and distance from edge) zones in other quadrants of pool. (C) (Left) Tg mice showed deficits in training phase of watermaze task. Decreased latency (time to reach the platform) over the 3d of training (D1-D3) in WT mice, but not in Tg littermates (2-way ANOVA, Genotype ⨯ Day interaction, F(2,34) = 4.86, p = .014). (Right) Swim speeds not different between Tg and WT littermates (unpaired t-test, t(17) = .794, p = .438). n = number of mice. (D) Microinjection of vAPP (vAPP+GFP) in CA1 of dorsal hippocampus of WT mice induced robust transgene expression [GFP expression, DAPI (blue)]. (E) Higher Aβ1-42 in hippocampal extract from WT mice microinjected with virus expressing vAPP than WT mice microinjected with control virus (vGFP expressing GFP only, or vAPPMV expressing a form of APP that cannot produce Aβ (1-way ANOVA, between- group factor Vector, F(2,10) = 7.54, p = .010). n = number of mice. (F) Intact watermaze training performance in WT mice microinjected with vAPP (even though these mice showed profound spatial memory deficits during probe test). WT mice microinjected with vAPP or control vectors (vGFP, vAPPMV) showed similar escape latencies over 3d of training (2-way ANOVA with between-group factor Vector and within-group factor Day, no significant Vector ⨯ Day interaction, F(4,56) = 1.604, p = .186, but significant effect of Day, F(2,56) = 49.07, p < .0001 and Vector, F(2,28) = 3.99, p = .0298, mice from all groups showed similar escape latencies on the first and second training days). (Right) Swim speeds not different between groups (one-way ANOVA, between-group factor Vector, F(2,28) = 1.90, p = .168). n = number of mice. (G) TAT-GluA23Y, but not control peptides, restored spatial memory deficit produced by vAPP in WT mice. Mice microinjected with vAPP that received control peptides before training (TAT inactive, TAT-Cntrl, GluA23Y no TAT) spent less time in T zone compared to mice microinjected with GFP (TAT-Cntrl) or mice microinjected with vAPP and treated with TAT-GluA23Y peptide (2-way repeated-measures ANOVA, between- group factors Treatment and Zone, Treatment × Zone interaction, F(4,34) = 8.39, p < .0001, * indicates different from percent time in T zone in WT mice microinjected with GFP vector+TAT-Cntrl). n = number of mice. (H) TAT-GluA23Y rescued spatial memory impairments in 5×FAD mice (2-way repeated- measures ANOVA, between-group factors Treatment and Zone, Treatment × Zone interaction, F(3,49) = 7.28, p = 3.93 × 10-4). n = number of mice. (I) TAT-GluA23Y peptide did not influence levels of Aβ1-40 (2-way ANOVA, between- group factors Genotype and Treatment, main effect of Genotype only, F(1, 12) = 2814, p <.0001) or Aβ1-42 (same factors, main effect of Genotype only, F(1,13) = 319, p < .0001) in WT or Tg primary hippocampal neurons. As expected, neurons from WT mice showed low Aβ1 − 40 or Aβ1 − 42 and neurons from Tg mice showed high Aβ1 − 40 and Aβ1 − 42 levels, regardless of TAT-GluA23Y treatment. n = primary hippocampal cultures.All data presented as mean ± SEM. Details of statistical analysis are listed in the Methods. *p < .05, n.s. = not statistically different.

Extended Data Fig. 2 Low-order statistics do not explain differences in Tg + TAT-Cntrl mice from other groups.

(A) The number of neurons detected per animal does not differ significantly between groups. 2-way ANOVA, no significant main effects of Genotype or Treatment, no significant Genotype ⨯ Treatment interaction. (B) Peri-event plot of GCaMP activity of all neurons in seconds before and during freezing in the Test session (all freezing bouts aligned to Time 0, yellow dotted line) appears similar across groups. n = number of neurons. (C) Differences in number of recorded cells does not account for classifier performance across groups. All classifiers were trained on data from a randomly selected, fixed number of cells (10, 25, 100, or all cells; see Methods; Subsampling cells and frames). One-way analyses of variance on each experimental condition revealed a significant main effect of Classifier in all conditions except Tg TAT-Cntrl for all cell subsample conditions. (D) Differences in amount of freezing does not account for classifier performance across groups. All classifiers were trained on data from a randomly selected 300 or 1000 freezing frames and an equivalent number of non-freezing frames (see Methods; Subsampling cells and frames). One-way analyses of variance on each experimental condition revealed a significant main effect of Classifier in all conditions except Tg+-TAT-Cntrl for all frame subsample conditions. WT+TAT-Cntrl n = 10, WT+TAT-GluA23Y n = 6, Tg+TAT-Cntrl n = 4, Tg+TAT-GluA23Y = 7. (E) Differences in amount of freezing and number of cells together do not account for classifier performance across groups. Combining approaches from C and D, all classifiers were trained on data from a randomly selected 25 cells, comprising 1000 freezing frames and an equivalent number of non-freezing frames (see Methods; Subsampling cells and frames). One- way analyses of variance on each experimental condition revealed a significant main effect of Classifier in all conditions except Tg+TAT-Cntrl for all frame subsample conditions. WT+TAT-Cntrl n = 10, WT+ TAT-GluA23Y n = 6, Tg+TAT-Cntrl n = 4, Tg+TAT-GluA23Y = 7. (F) Test subsampling approach does not account for classifier performance across groups. To account for imbalances between classes in the test dataset, we compared a balanced accuracy approach to a balanced subsampling approach (see Methods; Balanced subsampling procedure). There were no significant differences between sampling types in any pairing. (G) Different linear classifiers have equivalent performance decoding freezing behavior. Linear discriminant analysis (LDA), logistic regression (LR) and linear support vector machine (SVM) classifiers showed no difference in balanced accuracy across groups. A 3- way ANOVA revealed no significant main effect of Classifier, and no significant interactions between Classifier and Genotype and/or Treatment. (H) Normalizing the high overall cell activity in Tg+TAT-Cntrl mice to WT+TAT-Cntrl levels does not increase accuracy of high-capacity NN classifier in decoding freezing behavior in Tg mice. (Left) Average cell activity in WT+TAT-Cntrl mice (dark gray bars), Tg+TAT-Cntrl mice (orange bars) and in Tg+TAT-Cntrl mice in which highly active cells were removed (light gray bar) such that average cell activity does not differ from that observed in WT +TAT-Cntrl mice. Boxplot presented as 1.5 × the interquartile range (whiskers), 25th and 75th percentile (box) and median (center line). (Right) NN classifier accuracy in decoding freezing behavior is higher in WT mice (dark gray bar) than Tg mice with all cells considered (orange bar). Removing cells with high activity from Tg mice such that overall cell activity does not differ between Tg and WT mice does not increase NN classifier accuracy (light gray bar). Therefore, normalizing cell activity in Tg+TAT-Cntrl does not improve the ability of the NN classifier to decode freezing behavior, suggesting that high neuronal activity alone cannot account for the relatively poor accuracy of the NN classifier in Tg mice relative to WT mice. n = number of neurons. (I) No difference in levels of pairwise cell correlation during test session across mouse groups (average pairwise correlation, no significant main effects of Genotype or Treatment, no significant Genotype ⨯ Treatment interaction.). (J) Eliminating coordinated population information impairs high-capacity classifier performance. Training classifiers on single-cell data (see Methods; Single-cell classifier analysis) reduces classification accuracy compared to classifiers trained on population data. (K) Differences in numbers of cells or amount of freezing between groups does not explain performance of single-cell classifiers. Training data was limited to a randomly selected 25 cells and 1000 frames of freezing and non-freezing data in all animals (as in E, see Methods; Subsampling cells and frames). Single-cell classifiers (see Methods; Single-cell classifier analysis) showed significantly worse performance than full classifiers when trained on this restricted dataset. † = p = 0.054. All data presented as mean ± SEM. Details of statistical analysis are listed in the Methods. *p < .05, **p < 0.01, *** p < 0.001, n.s. = not statistically different. In panels A, C-G & I-K, n = number of mice. In panels A,C,F,I-K, group sizes: WT+TAT-Cntrl n = 10, W +TAT-GluA23Y n = 7, Tg+TAT-Cntrl n = 6, Tg+TAT-GluA23Y = 7.

Extended Data Table 1 Summary data from calcium imaging mice
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Supplementary information

Supplementary Information

Detailed statistical analyses for Extended Data Fig. 2.

Reporting Summary

Source data

Source Data Fig. 1

Unprocessed gels supporting Fig. 1e.

Source Data Fig. 1

Statistical Source Data supporting Fig. 1.

Source Data Fig. 2

Statistical Source Data supporting Fig. 2.

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Yan, C., Mercaldo, V., Jacob, A.D. et al. Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice. Nat Neurosci 27, 1794–1804 (2024). https://doi.org/10.1038/s41593-024-01713-4

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