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Breakdown of spatial coding and interneuron synchronization in epileptic mice

Nature Neuroscience volume 23, pages 229–238 (2020)Cite this article

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Abstract

Temporal lobe epilepsy causes severe cognitive deficits, but the circuit mechanisms remain unknown. Interneuron death and reorganization during epileptogenesis may disrupt the synchrony of hippocampal inhibition. To test this, we simultaneously recorded from the CA1 and dentate gyrus in pilocarpine-treated epileptic mice with silicon probes during head-fixed virtual navigation. We found desynchronized interneuron firing between the CA1 and dentate gyrus in epileptic mice. Since hippocampal interneurons control information processing, we tested whether CA1 spatial coding was altered in this desynchronized circuit, using a novel wire-free miniscope. We found that CA1 place cells in epileptic mice were unstable and completely remapped across a week. This spatial instability emerged around 6 weeks after status epilepticus, well after the onset of chronic seizures and interneuron death. Finally, CA1 network modeling showed that desynchronized inputs can impair the precision and stability of CA1 place cells. Together, these results demonstrate that temporally precise intrahippocampal communication is critical for spatial processing.

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Fig. 1: Desynchronization of hippocampal inhibition in epileptic mice.
Fig. 2: Disrupted spatial coding in epileptic mice.
Fig. 3: Disrupted stability of place cells across sessions.
Fig. 4: Spatial instability emerges 6 weeks after pilocarpine treatment.
Fig. 5: Desynchronization disrupts place cell coding in a CA1 network model.

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Data availability

The experimental data that support the findings of this study are available from Peyman Golshani (pgolshani@mednet.ucla.edu) or Tristan Shuman (tristan.shuman@mssm.edu) upon reasonable request.

Code availability

The software and codes related to the CA1 network model and its analysis are available from the Poirazi lab (poirazi@imbb.forth.gr) on reasonable request. The model is available on ModelDB, accession number 256311. Data analysis scripts are available on reasonable request from Peyman Golshani (pgolshani@mednet.ucla.edu) or Tristan Shuman (tristan.shuman@mssm.edu).

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Acknowledgements

We thank K. Maguire, J. Lou, A. Fariborzi, J. Daneshrad, S. Ghiaee, R. Manavi, C. Araradian, M. Song, B. Wei, C. Zhou, A. Meyer, H. Chen, J. Davis, N. Abduljawad, J. Hodson, I. Bachmutsky, L. Zilbermintz, H. Karbasforoushan, J. Friedman, T. Kotze, D. McCoy, K. Casale and E. Goldblatt (all of UCLA); and N. Berryman, G. Condori, M. Abdelmageed, C. Rosado, and B. Nunez (all of Mount Sinai) for excellent technical assistance and help with experiments. This work was supported by VA Merit Award 1 I01 BX001524–01A1, U01 NS094286, R01MH101198, R01 MH105427, U54 HD87101, R01NS099137, and NSF Neurotech Hub 1700408 to P.G.; a David Geffen School of Medicine Dean’s Fund for development of open-source miniaturized microscopes to B.S.K., A.J.S., and P.G.; a CURE Epilepsy Taking Flight Award, an American Epilepsy Society Junior Investigator Award, R03 NS111493, a Cellular Neurobiology Training Grant T32 NS710133, and an Epilepsy Foundation Postdoctoral Research Training Fellowship to T.S.; Neurobehavioral Genetics Training Grant T32 NS048004 and Neural Microcircuits Training Grant T32 NS058280 to D.A.; DP2 MH122399, a Klingenstein-Simons Fellowship, a McKnight Memory and Cognitive Disorder Award, a NARSAD Young Investigator Award, a Fay/Frank Seed Grant Program award, a One Mind Rising Star Research Award, National Research Service Award F32 MH97413, and Behavioral Neuroscience Training Grant T32 MH15795 to D.J.C.; DP1 MH104069 to B.S.K.; a McKnight Technological Innovations in Neuroscience Award to S.C.M.; and Dr. Miriam and Sheldon G. Adelson Medical Research Foundation funding to A.J.S. S.C., I.P. and P.P. were supported by the European Research Council Starting Grant dEMORY (GA 311435) and the Fondation Sante.

Author information

Author notes

  1. These authors contributed equally: Tristan Shuman, Daniel Aharoni and Denise J. Cai.

Authors and Affiliations

  1. Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tristan Shuman, Denise J. Cai, Christopher R. Lee, Lucia Page-Harley, Lauren M. Vetere, Yu Feng, Lingxuan Chen & Zachary T. Pennington

  2. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Tristan Shuman, Daniel Aharoni, Christopher R. Lee, Chen Yi Yang, Irene Mollinedo-Gajate, Jiannis Taxidis, Sergio E. Flores, Kevin Cheng, Milad Javaherian, Christina C. Kaba, Naina Rao & Peyman Golshani

  3. Integrative Center for Learning and Memory, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel Aharoni, Alcino J. Silva & Peyman Golshani

  4. Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Denise J. Cai, Mimi La-Vu, Konstantin I. Bakhurin, Sotiris C. Masmanidis & Alcino J. Silva

  5. Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece

    Spyridon Chavlis, Ioanna Pandi & Panayiota Poirazi

  6. School of Medicine, University of Crete, Heraklion, Greece

    Ioanna Pandi

  7. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

    Matthew Shtrahman

  8. Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Baljit S. Khakh & Peyman Golshani

  9. Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA

    Alcino J. Silva & Peyman Golshani

  10. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA

    Alcino J. Silva

  11. West LA Veterans Affairs Medical Center, Los Angeles, CA, USA

    Peyman Golshani

  12. Intellectual and Developmental Disabilities Research Center, University of California, Los Angeles, Los Angeles, CA, USA

    Peyman Golshani

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  1. Tristan Shuman
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Contributions

T.S. and P.G. designed the calcium imaging and electrophysiology experiments. T.S., D.A., D.J.C., B.S.K., A.J.S., and P.G. developed the wire-free miniscope. D.A. designed and built the wire-free Miniscope. T.S., D.A., D.J.C., C.R.L. tested the wire-free miniscope. T.S., D.J.C., C.R.L., L.P.-H., L.M.V., Y.F., C.Y., I.M.-G., and M.L.-V. performed calcium imaging experiments. T.S., D.A., L.P.-H., and Z.T.P. analyzed calcium imaging experiments. T.S., M.J., C.C.K., M.S., and P.G. designed the virtual reality apparatus. T.S., S.E.F., K.C., M.J., C.C.K., and N.R. performed electrophysiology training and recording. T.S., K.I.B., S.C.M., P.G. built the in vivo electrophysiology system. T.S. and J.T. analyzed in vivo electrophysiology data. T.S., K.C., and C.C.K. performed spike sorting of single units. T.S., D.A., D.J.C., S.C., I.P., P.P., and P.G. designed modeling experiments. T.S., S.C., I.P., and P.P. performed and analyzed modeling experiments. T.S., D.J.C., and L.C. performed slice electrophysiology. T.S., L.P.-H., L.M.V., and Y.F. performed immunohistochemistry. T.S., D.A., D.J.C., S.C., P.P., and P.G. wrote the manuscript and all authors edited the manuscript.

Corresponding authors

Correspondence to Tristan Shuman, Panayiota Poirazi or Peyman Golshani.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Spatial memory deficits in epileptic mice.

a. Morris water maze task. Mice (at least 6 weeks after pilocarpine or control treatment) were trained to find a hidden platform for 6 days at random start locations. The next day, mice were given a probe trial to assess learning. On the probe trial, control mice spent more time in the target quadrant than epileptic mice, while epileptic mice spent more time in the opposite quadrant (n = 6 Control, n = 5 Epileptic, 2-way ANOVA FGroupXQuadrant(3,36) = 14.6, P < 0.001, Training Quadrant Post-hoc: P < 0.001, Opposite Quadrant Post-hoc: P = 0.02). b. Mice were trained to find a visible platform with a flag extending above the water. There were no differences between the control and epileptic mice on this task (n = 5 Control, n = 8 Epileptic, 2-way ANOVA FGroup(1,11) = 0.424, P = 0.53). c. Mice were trained on a cued, delayed alternation T-maze task for 5 days. On each trial, animals were cued to one direction (Cue Phase), returned to the start position for a 15 s delay (Delay Phase), and then released for the Test Phase. If the animal went to the opposite side it received a water reward. Epileptic mice performed worse than control mice (n = 5 Control, n = 6 Epileptic, 2-way ANOVA FGroup(1,45) = 44.98, P < 0.001). d–f. Activity during virtual reality recordings. No differences were found in trials per minute (n = 5 per group, Unpaired t-test, P = 0.16), percent time running (n = 5 animals per group, Unpaired t-test, P = 0.14), or running speed (n = 5 animals per group, Mann-Whitney test, P = 0.94). Error bars represent 1 S.E.M. *P < 0.05. g-i. Activity during calcium imaging on the linear track. No differences were found in the number of trials per minute (n = 5 animals per group, Mann-Whitney test, P = 0.15) or percent time running (n = 5 animals per group, Unpaired t-test, P = 0.64). Epileptic mice ran faster than control mice on the linear track (n = 5 animals per group, Unpaired t-test, P = 0.03). Error bars represent 1 S.E.M. *P < 0.05, ***P < 0.001.

Extended Data Fig. 2 Local field potential power across frequencies.

a–f. LFP power in beta, slow gamma, mid-gamma, fast gamma, ripple and fast ripple frequencies throughout CA1 and DG. No differences were found in beta (FGroupXRegion(6,48) = 1.23, P = 0.31, FGroup(1,8) = 0.47, P = 0.51), fast gamma (FGroupXRegion(6,48) = 1.88, P = 0.10, FGroup(1,8) = 2.03, P = 0.19), ripple (FGroupXRegion(6,48) = 0.93, P = 0.48, FGroup(1,8) = 0.67, P = 0.44), or fast ripple (FGroupXRegion(6,48) = 1.38, P = 0.24, FGroup(1,8) = 0.24, P = 0.64) power. Epileptic mice had reduced slow gamma and mid-gamma power in the hilus of DG (Slow Gamma: FGroupXRegion(6,48) = 6.76, P < 0.001, FGroup(1,8) = 7.31, P = 0.03, Hilus: P < 0.001; Mid-gamma: FGroupXRegion(6,48) = 4.49, P = 0.001, FGroup(1,8) = 3.48, P = 0.09, Hilus: P = 0.01). n = 5 animals per group for all graphs. Error bars represent 1 S.E.M. **P < 0.01, ***P < 0.001.

Extended Data Fig. 3 Theta phase locking changes are not caused by decreased power or specific to reference location.

a. Example clustering of single units from a single channel set in one animal using principal components, peak amplitude, and trough amplitude. b. Units were characterized as putative interneurons based on complex spike index (CSI), mean autocorrelogram (Mean AC), and mean firing rate. N = 5 Control, N = 5 Epileptic animals. c. Example phase locking in a CA1 interneuron. Top, mean waveforms from each recorded channel within the pyramidal layer. Middle, spike raster of 300 theta cycles during running, with the proportion of spikes occurring during each phase of theta. Bottom, Rose plot of firing phase relative to theta. This cell has a modulation index (r-value) of 0.41 and preferred firing phase (mu) of 189°. d–g. Theta cycles were subsampled to match power between control and epileptic mice. This did not change the results as CA1 interneurons in epileptic mice were less phase locked than in control mice (d; n = 71 Control cells, n = 34 Epileptic cells, Unpaired t-test, P < 0.001), while their preferred phase was not different (e; Kuiper circular test, P > 0.05). There were no differences in the modulation of DG interneurons (f; n = 34 Control cells, n = 22 Epileptic cells, Unpaired t-test, P = 0.80) but there were differences in the preferred phase of DG interneurons (g; Kuiper circular test, P < 0.001). h, i. Phase locking of DG interneurons to theta in the lacunosum moleculare. No difference was found in modulation (h; n = 34 Control cells, n = 41 Epileptic cells, Unpaired t-test, P = 0.25) but the distributions of preferred phases differed between the groups (i; Kuiper circular test, P < 0.001). j-k. Phase locking of DG interneurons to the theta in hilus of DG. No difference was found in modulation (j; n = 34 Control cells, n = 41 Epileptic cells, Unpaired t-test, P = 0.64) but the distributions of preferred phases differed between the groups (k; Kuiper circular test, P < 0.001). N = 5 animals per group for all panels. Error bars represent 1 S.E.M. ***P < 0.001.

Extended Data Fig. 4 Altered gamma synchronization in epileptic mice.

a. Gamma power throughout CA1 and DG. Epileptic mice had reduced gamma power in the hilus of DG (2-way RM ANOVA, FGroupXRegion(6,48) = 5.26, P < 0.001, Hilus: P < 0.001). b. Gamma coherence between each channel pair along the recording probe in control (left) and epileptic (middle) mice. Right, p-value matrix for each pair of recording sites (unpaired t-tests, uncorrected for multiple comparisons). c. Phase locking to CA1 gamma for each interneuron in CA1 of control and epileptic mice. Each dot represents one interneuron and the data is double plotted for visualization. Closed circles were significantly phase locked (Raleigh test, P < 0.05) and open circles were not. d. Phase locking to CA1 gamma for each interneuron in DG of control and epileptic mice. e. Mean phase modulation (r) of CA1 interneurons to CA1 gamma. Interneurons in epileptic mice had increased phase modulation (n = 69 Control cells, n = 57 Epileptic cells, Mann-Whitney test, P < 0.001). f. Mean phase modulation (r) of DG interneurons to CA1 gamma. Interneurons in epileptic mice had no change in phase modulation (n = 34 Control cells, n = 41 Epileptic cells, Mann-Whitney test, P = 0.42). g. Rose plot of preferred firing phases of significantly phase locked CA1 interneurons. There were no differences between interneurons in control and epileptic mice (Kuiper circular test, P > 0.05). h. Rose plot of preferred firing phases of significantly phase locked DG interneurons. The distribution of preferred phases in control and epileptic mice were different (Kuiper circular test, P < 0.001). i. Aggregate inhibition of all interneurons in CA1 and DG relative to CA1 gamma phase (2-way RM ANOVA, CA1: FGroupXPhase(17,136) = 1.22, P = 0.25; DG: FGroupXPhase(17,136) = 2.196, P = 0.007). j. Pearson’s correlation between CA1 and DG aggregate inhibition relative to gamma. No difference was found between epileptic and control animals (Unpaired t-test, P = 0.19). All data in this figure came from 5 animals per group. Error bars represent 1 S.E.M. *P < 0.05, ***P < 0.001, ns: not significant.

Extended Data Fig. 5 Wire-free Miniscope design and behavior.

a. Schematic of wire-free Miniscope printed circuit board (PCB). b. Picture of top and bottom of wire-free sensor board. This camera sensor board attaches to the Miniscope body, which contains all optic components. c. The wire-free Miniscope can be used to probe naturalistic behaviors including social interaction. We tested social behavior with the wire-free Miniscope compared to no Miniscope or a wired version. d. During a social interaction test mice with either no Miniscope or the wire-free Miniscope chose a social cup over an empty cup, while mice with a wired Miniscope showed no significant preference (n = 3 per group, Unpaired t-tests, No Scope: P = 0.03, Wire-Free: P < 0.001, Wired: P = 0.10). Error bars represent 1 S.E.M. *P < 0.05, ***P < 0.001.

Extended Data Fig. 6 Stability of spatial representations.

a. Population Vector Correlation (PVC) of all cells in Control (left) and Epileptic (right) mice across all sessions recorded. b. Mean PVC as a function of offset distance (from the diagonal) across animals. Epileptic mice had higher PVC (that is, less distinct firing patterns) across distances of 30–150 cm (2-way RM ANOVA, FGroup(1,8) = 10.39, P = 0.01, post hoc P < 0.05 for each bin from 30–150 cm). c. Population Vector Correlation (PVC) of place cells in Control (left) and Epileptic (right) mice across all sessions recorded. d. Mean PVC as a function of offset distance (from the diagonal) across animals. Epileptic mice had higher PVC (that is, less distinct firing patterns) across distances of 32–150 cm (2-way RM ANOVA, FGroup(1,8) = 10.19, P = 0.01, post hoc P < 0.05 for each bin from 32–150 cm). N = 5 animals per group for all panels. Shading represents 1 S.E.M. *P < 0.05. e, f. Example cross registration in a Control (e) and Epileptic (f) mouse. Top, Aligned mean frame from each session (~ 550 um x 550 um). Bottom, overlaid cells from each session with white X indicating matched cells. g. Spatial correlation and centroid distance were calculated for all cell pairs. Dotted lines indicate thresholds used as matching criteria. All matched cells had spatial correlation ≥ 0.6 and centroid distance ≤ 4 pixels (~ 7 um). h. We assessed within-session stability of spatial representations in two ways. We first examined stability of the first half of trials against the second half of trials. We then examined stability of odd trials versus even trials. In both cases, we used the Fisher z-transformed correlation of the spatial firing rates between trials. We report stability as the average of the two different stability measures. i. We found a slight but significant difference between the two stability measures as the stability of odd/even trials was higher than in the first/second half of trials (2-way RM ANOVA FMeasurement(2,16) = 13.59, P < 0.001; post hoc tests: ***P < 0.001). j. The two stability measures were highly correlated in both control (Pearson’s correlation: All Cells: r = 0.83; Place Cells: r = 0.75) and epileptic (Pearson’s correlation: All Cells: r = 0.73; Place Cells: r = 0.72) neurons. N = 5 animals per group for all panels.

Extended Data Fig. 7 Example processing of one training session neuron for Bayesian decoding.

a. For each frame, temporal neural activity is calculated and classified into rightward trials, leftward trials, or non-trial times. b. Temporal neural activity is binarized and non-trial times are removed. c. The per trial binarized neural activity rate is calculated for rightward and leftward trials. d. The spatial probability function is constructed for each cell. A Gaussian distribution is first generated for each spatial bin using the mean and standard deviation of the binarized neural activity rate. The overall distribution is normalized across binarized neural activity rate rows and this data is entered into the Bayesian decoder for each cell. See Supplementary Video 3 for example decoding across all cells.

Extended Data Fig. 8 CA1 network model reveals that high levels of interneuron disruption can reduce information content, but not stability.

a. We tested how increasing amounts of desynchronization altered information content, stability, and place cell percent in our CA1 network model. We found significant reductions in all metrics with desynchronization of 15–35 ms (1-way ANOVA, Information Content: F(7,72) = 77.84, P < 0.001; Stability: F(7,72) = 70.02, P < 0.001; Place cell percent: F(7,72) = 99.44, P < 0.001; post hoc tests: *P < 0.05, **P < 0.01, ***P < 0.001). b. We next tested how reducing somatostatin-expressing neurons altered spatial coding in our model. We found that reducing SOM by 50–100% reduced information content (1-way ANOVA, F(4,45) = 38.01, P < 0.001, post hoc tests: ***P < 0.001) but not stability (1-way ANOVA, F(4,45) = 0.564, P = 0.69) or place cell percent (1-way ANOVA, F(4,45) = 2.369, P = 0.067). c. We next tested how reducing parvalbumin-expressing neurons altered spatial coding in our model. We found that reducing PV by 50–100% reduced information content (1-way ANOVA, F(4,45) = 134, P < 0.001, post hoc tests: **P < 0.01, ***P < 0.001) but also increased stability at 75–100% PV reduction (1-way ANOVA, F(4,45) = 18.26, P < 0.001, post hoc tests: ***P < 0.001). We also found that place cell percent was lower at 75–100% PV reduction (1-way ANOVA, F(4,45) = 41.78, P < 0.001, post hoc tests: ***P < 0.001).

Extended Data Fig. 9 Cell death and gliosis in epileptic mice.

a, b. Example immunohistochemistry staining for parvalbumin (PV), somatostatin (SOM), NeuN, and glial fibrillary acidic protein (GFAP) in dentate gyrus (a) and CA1 (b) of control and epileptic mice. For epileptic mice, tissue was collected at least 19 weeks after pilocarpine. c. PV staining was reduced in epileptic mice (2-way RM ANOVA, FGroup(1,14) = 7.086, P = 0.02, post hoc for CA1, CA3, DG: P > 0.05). N = 9 Control, N = 7 Epileptic. d. SOM staining was reduced in the DG of epileptic mice (2-way RM ANOVA, FGroup x Region(2,16) = 12.12, P < 0.001, post hoc for DG: P < 0.001; CA1, CA3: P > 0.05). N = 5 Control, N = 5 Epileptic. e. NeuN staining was reduced in CA3 of epileptic mice (2-way RM ANOVA, FGroup(1,16) = 5.581, P = 0.03, post hoc for CA3: P = 0.002; CA1, DG Hilus: P > 0.05; DG Blade: Unpaired t-test, t = 0.302, P = 0.77). N = 9 Control, N = 9 Epileptic. f. Because GFAP expression can be altered in the soma or processes we analyzed both the cell counts and percent of activated pixels within each image. We did not detect a difference in the number of GFAP + neurons (2-way RM ANOVA, FGroup(1,8) = 4.60, P = 0.06), however we did find increased percent of activated pixels in CA1, CA3, and DG (2-way RM ANOVA, FGroup(1,8) = 14.15, P = 0.005, post hoc for all regions: P < 0.05). N = 5 Control, N = 5 Epileptic. g–i. In entorhinal cortex and area V1 we found no differences between groups in PV (2-way RM ANOVA, FGroup(1,7) = 0.371, P = 0.56, N = 5 Control, N = 4 Epileptic), SOM (2-way RM ANOVA, FGroup(1,8) = 0.815, P = 0.39, N = 5 Control, N = 5 Epileptic), or NeuN (2-way RM ANOVA, FGroup(1,8) = 0.288, P = 0.61, N = 5 Control, N = 4 Epileptic). j. We did find an increased number of GFAP positive neurons in EC and V1 (2-way RM ANOVA, FGroup(1,7) = 432.1, P < 0.001; post hoc: EC, P < 0.001; V1, P < 0.01, N = 5 Control, N = 4 Epileptic), and increased percent of activated pixels for GFAP in EC (2-way RM ANOVA, FGroup(1,7) = 31.1, P < 0.001; post hoc: EC, P < 0.001; V1, P = 0.31, N = 5 Control, N = 4 Epileptic). EC, entorhinal cortex; DG, dentate gyrus; Error bars denote 1 S.E.M. All sample sizes are by animal. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 10 Chemogenetic inhibition of PV + or SOM + interneurons does not alter spatial coding in CA1.

a. To selectively inhibit PV + and SOM + interneurons, we used PV-Cre and SOM-Cre mice and injected a cre-dependent virus expressing hM4Di (AAV5-Syn-DIO-hM4Di-mCherry) or control virus (AAV5-Syn-DIO-mCherry). We also injected virus to express GCaMP6f in all neurons (AAV1-Syn-GCaMP6f) to allow for calcium imaging. We then trained mice to run on a linear track and delivered CNO (5 mg/kg) or Vehicle (VEH) 45 min prior to imaging on the track. b. To confirm that CNO was effective in reducing firing in vitro we used whole-cell recordings of hippocampal mCherry + interneurons in acute brain slices and applied ACSF or ACSF with CNO during stimulation. Interneurons expressing hM4Di had reduced spiking to stimulation (middle, right) while control virus did not reduce spiking. c. We found no differences in information content between VEH and CNO in any of the groups examined (Paired t-tests – PV: t(4) = 1.769, P = 0.15; SOM: t(3) = 0.717, P = 0.53). d. We found no differences in stability between VEH and CNO in any of the groups examined (Paired t-tests – PV: t(4) = 2.047, P = 0.11; SOM: t(3) = 0.672, P = 0.55). e. We found no differences in place cell percent between VEH and CNO in any of the groups examined (Paired t-tests – PV: t(4) = 2.29, P = 0.08; SOM: t(3) = 0.169, P = 0.88). f. We found increased Activity with CNO compared to VEH in the PV-hM4Di mice, but not SOM mice (Paired t-tests – PV: t(4) = 2.897, P = 0.04; SOM: t(3) = 0.96, P = 0.41). N = 2 PV-Cre mCherry animals, N = 4 SOM-Cre hM4Di animals, N = 5 PV-Cre hM4Di animals. Error bars denote 1 S.E.M. *P < 0.05.

Supplementary information

Supplementary Information

Supplementary Table 1.

Reporting Summary

Supplementary Video 1

Mouse Running in Virtual Linear Track. Example mouse running through virtual linear track for water rewards during recording. Animals are head-fixed atop a Styrofoam ball and a virtual linear track is projected around them. As the animal runs on the ball the environment moves forward until it reach the end and the animal receives a water reward. Simultaneously, in vivo electrophysiology with silicon probes measures local field potentials and interneuron firing in the hippocampus.

Supplementary Video 2

Example Calcium Imaging on a Linear Track. Example Control and Epileptic mice running on the linear track during calcium imaging. The top row shows the behavior of the mouse. The middle row is the raw video synchronized with the behavior. Bottom row is the neuronal activity extracted using CNMF-E. Place cells are colored according to preferred position along the track (see top row) and non-place cells are gray.

Supplementary Video 3

Decoding Examples. Example decoding performed across two session separated by 30 minutes. Top row shows the animal’s position (white) with the decoded position in red (leftward) or green (rightward). Bottom row shows the extracted neurons used in the decoder and the distribution of decoder position error.

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Shuman, T., Aharoni, D., Cai, D.J. et al. Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nat Neurosci 23, 229–238 (2020). https://doi.org/10.1038/s41593-019-0559-0

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