Author response: Synchronous activity patterns in the dentate gyrus during immobility
2021-02-27

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract The hippocampal dentate gyrus is an important relay conveying sensory information from the entorhinal cortex to the hippocampus proper. During exploration, the dentate gyrus has been proposed to act as a pattern separator. However, the dentate gyrus also shows structured activity during immobility and sleep. The properties of these activity patterns at cellular resolution, and their role in hippocampal-dependent memory processes have remained unclear. Using dual-color in vivo two-photon Ca2+ imaging, we show that in immobile mice dentate granule cells generate sparse, synchronized activity patterns associated with entorhinal cortex activation. These population events are structured and modified by changes in the environment; and they incorporate place- and speed cells. Importantly, they are more similar than expected by chance to population patterns evoked during self-motion. Using optogenetic inhibition, we show that granule cell activity is not only required during exploration, but also during immobility in order to form dentate gyrus-dependent spatial memories. Introduction The dentate gyrus receives polymodal sensory information from the entorhinal cortex, and relays it into the hippocampal network. The most prevalent view of the dentate gyrus input-output transformation in this circuit is that it acts as a pattern separator. This capability requires the animal to generate dissimilar neuronal representations from overlapping input states that represent similar but not identical environments (Cayco-Gajic and Silver, 2019). Such an operation, termed pattern separation, has been ascribed to the hippocampal dentate gyrus in species ranging from rodents to humans (Berron et al., 2016; Leutgeb et al., 2007; Sakon and Suzuki, 2019). In the dentate gyrus, polysensory inputs are mapped onto a large number of granule cells which exhibit extremely sparse firing patterns, resulting in a high probability of non-overlapping output patterns (Danielson et al., 2016; GoodSmith et al., 2017; Hainmueller and Bartos, 2018; Pilz et al., 2016; Senzai and Buzsáki, 2017; van Dijk and Fenton, 2018). This concept has been influential in understanding dentate gyrus function when processing multimodal, current, or ‘online’ sensory information during mobility and exploration. However, the dentate gyrus is far from silent during immobility. It displays prominent electrographic activity patterns such as dentate spikes and sharp waves, which occur primarily during immobility or sleep (Bragin et al., 1995; Meier et al., 2020; Penttonen et al., 1997). In downstream hippocampal regions such as CA1, neuronal activity during immobility incorporates the replay of behaviorally relevant sequences during sharp wave ripples, a process important in memory consolidation (Davidson et al., 2009; Diba and Buzsáki, 2007; Dupret et al., 2010; Foster and Wilson, 2006; Girardeau et al., 2009; Malvache et al., 2016; Skaggs and McNaughton, 1996; Wilson and McNaughton, 1994). In the dentate gyrus, little detail is known about how granule cells are active during immobility at the population level, and it is unknown whether activity during immobility reiterates behaviorally relevant information. Moreover, the role of dentate gyrus activity during immobility in memory formation is unclear. Here, we have used dual-color two-photon in-vivo Ca2+ imaging to show that in immobile mice, the dentate gyrus exhibits frequent, sparse, and synchronous population events that at the population level are similar to activity patterns during locomotion. Moreover, we have tested the idea that dentate gyrus activity during immobility is relevant for dentate-gyrus dependent spatial memory. Results Sparse, structured dentate network events in immobile animals We imaged the activity of large populations of hippocampal dentate granule cells (GCs) using a Thy1-GCaMP6s mouse line (GP4.12Dkim/J, Dana et al., 2014). In addition, we monitored the bulk activity of the major input system into the dentate gyrus, the medial perforant path (MPP). To this end, we expressed the red-shifted Ca2+ indicator jRGECO1a (Dana et al., 2016) in the medial entorhinal cortex using viral gene transfer (see Materials and methods section, Figure 1A, Figure 1—figure supplement 1A). To allow efficient excitation of both genetically encoded Ca2+ indicators, we established excitation with two pulsed laser sources at 940 and 1070 nm (see Figure 1—figure supplement 1B–F). The mice were placed under a two-photon microscope and ran on different variants of a linear track, equipped with different types of cues (see Figure 1—figure supplement 1G–J, Figure 1—video 1). Figure 1 with 5 supplements see all Download asset Open asset Synchronous dentate granule cell activations, ‘network events’, occur preferentially during immobility. (A) Expression of GCaMP6s in granule cells (Thy1-GCaMP mouse line, GP4.12Dkim/J). jRGECO was expressed in medial entorhinal cortex neurons using rAAV mediated gene transfer, and is visible in the middle molecular layer (MML) corresponding to the medial perforant path (MPP). Upper panel: Post hoc analysis in 70 μm fixed slice. Nuclei stained with DAPI (blue). Lower panel: Imaging plane for simultaneous recording of MPP bulk and individual GC activity. Scale bar 100 μm. (B) Data of representative recording session. Bulk fluorescence signal of MPP fibers (red), extracted fluorescence signals from a subset of individual GCs (black), mouse position on linear track (blue) and diameter of mouse pupil (black). (C) Participation of granule cells in synchronous network events. Representative field of view with highlighted simultaneously active GCs. Cells active during an individual network event are depicted in the same color. A subset of neurons is active in multiple network events, recognizable as white color. (D) Raster plot of network events. Dashed lines mark network events, corresponding to simultaneous activity of > four cells. Participating cells are highlighted according to color scheme from panel C. Running speed is depicted (blue) to distinguish running and resting periods. (E) Fluorescence transients of participating granule cells from NEs in panel D. Color scheme corresponds to panel C and D. Each column shows all respective transients of the respective synchronous ensemble. Shown is a time window of ±1 s around each NE. Vertical scale bars correspond to 100% ΔF/F. (F) Mean number of network events per twenty-minute recording session during running and resting. Network events occurred mainly during immobility (repeated measures ANOVA, F(1,8)=71.80, p=2×10−7, n = 9 animals, three sessions). Gray bars depict shuffled data for each condition (n = 9 animals). (G) Average number of identified network events plotted against different thresholds for the size of network events in terms of numbers of synchronously active cells (green line, shaded area depicts SEM, n = 3 mice, three sessions). Shuffled data null-distribution is created by randomly shuffling event times for every individual cell (gray line, shaded area depicts SEM). (H) Frequencies of network events calculated from equal time intervals for locomotion and immobility (Repeated measures ANOVA, F(1,8) = 117.28, p=2×10−6). (I) Cumulative probability distribution of network event occurrence during the entire twenty-minute session for individual sessions (gray lines) and the pooled sessions (Green line, n = 9 mice, three sessions). (J) Pooled cumulative probability distribution of network event occurrence during resting periods. All resting periods of one session that were longer than 5 s were normalized to their length (n = 9 mice, three sessions). As previously described, the firing of GCs was generally sparse (Danielson et al., 2016; Hainmueller and Bartos, 2018; Neunuebel and Knierim, 2012; Pilz et al., 2016), both when animals were immobile and when they were running on a textured belt without additional cues (mean event frequency 1.38 ± 0.19 events/min and 0.97 ± 0.2 events/min, respectively, n = 1415 granule cells in nine mice, Figure 1B, Figure 1—figure supplement 2C–E). Despite the sparse activity of granule cells, we observed synchronized activity patterns (Figure 1—video 2). To rigorously define such events, we used an algorithm that detects synchronized network events within a 200 ms time window, corresponding to 1 ± 1 frame at our sampling rate (see Materials and methods). Such synchronous network events could readily be observed in the dentate gyrus in all mice (Figure 1C,D, network events depicted in different colors, see examples for ΔF/F traces of participating cells in Figure 1E). Network events were sparse, incorporating on average only 5.7 ± 0.09% of the total active GC population. Shuffling analysis (see Materials and methods) confirmed that network events do not arise by chance (Figure 1F, gray bars correspond to shuffled data, n = 9 mice, three sessions/mouse). This was robust over three different types of shuffling analysis (Figure 1G, Figure 1—figure supplement 3 and Materials and methods for the description of the shuffling methods). Notably, network events occurred mainly during immobility periods and were much less prevalent during running (Figure 1D, quantification in Figure 1G). Accordingly, network event frequencies were significantly higher during immobility (repeated measures ANOVA, F(1,8)=117, p=2×10−6, n = 9 mice, three sessions, Figure 1H). During immobility periods (defined as running speeds < 4 cm/s), the vast majority of (99.6%) network events occurred when mice were completely immobile. Network events were on average evenly distributed during the 20 min imaging session (Figure 1I), as well as during individual periods of immobility (Figure 1J). Dentate network events are correlated with MPP activation We then analyzed the activity in the MPP input fiber tract expressing jRGECO, and probed the relation of this activity with GC activity patterns. As expected during exploratory states, the bulk MPP activity was increased during locomotion, consistent with increased sensory input (Figure 2A,B, red channel, Figure 2C for average value). During immobility in particular, larger fluctuations of bulk MPP activity were observed. This phenomenon was reflected in a larger variance of the bulk MPP signal during immobility (Figure 2D). Cross-correlation revealed that during immobility, the increases in bulk MPP activity were associated with peaks in average GC activity levels (Figure 2E). Both signals were significantly correlated in most sessions for periods of immobility (8/9 sessions, n = 3 mice, three sessions per mouse, Granger causality test p<0.05). This correlation was clearly visible during network events, because aligning GC activity and MPP activity to the timepoint of network events revealed a strong coactivation of GC and MPP during network events (Figure 2F, n = 3 mice). During running, MPP signals did not correlate with average GC activity, which is not unexpected given the asynchronous activation of GCs during running. 8/9 sessions, Granger causality test p>0.05. Figure 2 Download asset Open asset Granule cell and MPP activity during locomotion and immobility. (A) Mean MPP activity (red) and the sum of all granule cell activities (green) for a representative section of a recording session in an individual mouse. Dashed lines mark transition between resting and running periods (see blue line indicating running speed). (B) Average MPP fluorescence and running speeds, both aligned to running onsets (dashed line). Shaded areas indicate standard error (n = 4 mice, one mouse had only jRGECO expression in MPP, but no granule cell signal, three sessions per mouse). (C) Mean fluorescence averaged during resting (dark red) and running (light red). Asterisk indicates 5% significance threshold (repeated measures ANOVA, F(1,3) = 7,86, p=0.032, n = 4 mice, three sessions). (D) Variance of MMP bulk signal during resting (dark red) and running (light red, n = 4). Asterisk indicates results of repeated measures ANOVA, F(1,3) = 7,07, p=0.037, n = 4 mice, three sessions. (E) Cross-correlation of MPP bulk signal and summed GC signal during resting. Shaded gray area indicates standard error. (F) Average MPP activity (red) and probability of granule cell activity across all mice, three baseline sessions each, both aligned to the time point of network events (n = 3 mice, 2008 network events, shaded red area depicts SEM). (G) Amplitudes of deconvolved events in MPP bulk data during locomotion (light red) und resting (dark red). (H) Frequencies of all deconvolved MPP bulk events during locomotion and immobility (repeated measures ANOVA, F(1,2) = 255, p=3×10−6, n = 3 mice, three sessions). (I) Frequencies of deconvolved events with amplitudes above two standard deviations (repeated measures ANOVA, F(1,2) = 27, p=2×10−3, n = 3 mice, three sessions). (J) Delay of NEs to the closest identified MPP event. To explore in more detail how individual fluctuations in MPP bulk activity are associated with GC activity, we used a deconvolution algorithm to identify synchronous activity of MPP axons visible in the bulk MPP transients (see Materials and methods). We then quantified amplitude and frequency of these transients during locomotion and immobility. First, we found that bulk MPP events detected during immobility are on average larger than those detected during running (Figure 2G, Kruskal-Wallis test, n = 3 mice, three sessions per mouse, 8469 and 19,106 events during running and resting, respectively, p=2×10−44), in agreement with the larger variance of the MPP signal during these periods. When we examined the frequencies of all detected MPP bulk events during locomotion and immobility, we found that there were significantly more events during running (repeated measures ANOVA, F(1,2)=255, p=3×10−6, n = 3 mice, three sessions, Figure 2H). However, large events, defined as bulk MPP events with amplitudes above two standard deviations of the mean, were significantly more frequent during resting states (repeated measures ANOVA, F(1,2)=27, p=2×10−3, n = 3 mice, three sessions, Figure 2I). Again, this is consistent with the larger variance of the MPP signal during immobility, and likely reflects synchronized activation of MPP fibers. In line with the correlation of MPP and GC signals, there was a short temporal delay between individual bulk MPP transients and network events (Figure 2J). Dentate network events are correlated with pupil constriction Pupil diameter is an indicator of neuronal state and arousal (Reimer et al., 2014; Reimer et al., 2016), and can be used to track changes in neuronal states during quiet wakefulness (Reimer et al., 2014). Of note, pupil changes have been shown to closely track the rate of occurrence of hippocampal synchronous activity, namely sharp waves in the hippocampal CA1 region (McGinley et al., 2015). We therefore asked if dentate network events are also associated with specific changes in pupil diameter (Figure 3A for example measurement of pupil diameter over multiple resting and locomotor states). As previously described (Reimer et al., 2014; Reimer et al., 2016), we found pupil constriction during immobility with dilation at locomotion onsets (Figure 3B,C, n = 6 mice, three sessions). Intriguingly, the average pupil diameters during network events were significantly more constricted compared to the average pupil diameters during entire periods of immobility (Figure 3D, repeated measures ANOVA for all three groups F(2,28)=17.17, p=1×10−5, n = 6, data from three sessions each, Bonferroni post-tests: pupil diameters during locomotion vs. immobility p=0.0068, locomotion vs. network events p=0.0016, immobility vs. network events p=0.0017). Figure 3 Download asset Open asset Pupil dynamics during network events. (A) Representative example of pupil size measurements during different locomotor states. Green dots indicate timepoints of network events. (B, C) Average pupil diameters (gray lines) aligned to locomotion onsets (B) or offsets (C, blue lines) reveals pupil dilation at locomotion onsets, and constriction during locomotion offset. Shaded areas indicate standard error. GC activity stays on baseline value during change of behavioral state (green). (D) Average pupil diameters during locomotion, during resting periods, and during network events. Asterisks indicate significant Bonferroni post-test at 5% level. (E) Average rate of pupil diameter change during locomotion, during resting periods, and during network events. Asterisks indicate significant Bonferroni post-test at 5% level. (F) Averaging pupil diameters aligned to NE times (green bars) reveals pupil constriction during NEs. Shaded areas indicate standard error. (n = 6 mice, three sessions). (n = 6 mice, three sessions). When looking at pupillary dynamics by assessing the rate of diameter change, locomotor episodes were on average associated with pupil dilation, while network events were specifically associated with pupil constriction (Figure 3E, repeated measures ANOVA F(2,28)=34.18, p=3×10−8, n = 6 mice, data from three sessions each, Bonferroni post-tests: pupil diameters during locomotion vs. immobility p=0.0016, locomotion vs. network events p=2.53×10−5, immobility vs. network events p=0.00053). The latter finding was clearly illustrated by averaging pupil diameters aligned to NE times (Figure 3F). Together, this suggests that network events are associated with specific pupillary dynamics reflecting substates of arousal and neuronal synchronization during immobility. Network events are more orthogonal than expected by chance, but repetitively recruit GC sub-ensembles We then further characterized the participation of dentate granule cells in network events. We first asked to what extent individual network events recruit orthogonal cell populations. Indeed, while individual GCs can partake in multiple network events (see Figure 1C,D, Figure 1—video 2), we also observed network events that seemed completely distinct to others. To quantify how similar network events are to one another, we computed population vectors for each network event. We then computed the cosine similarity as a measure of similarity between vectors representing individual network events (see Materials and methods). With this measure, network event pairs recruiting the same set of neurons have a cosine similarity of 1, and completely orthogonal patterns exhibit a cosine similarity of 0. This analysis revealed that 38% of network event pairs were completely orthogonal to one another (Figure 4A, the fraction of completely orthogonal patterns corresponds to the bar with a cosine similarity of zero). Because in sparse activity patterns, orthogonality can and will arise by chance, we additionally performed a shuffling analysis to ascertain if sparse activity per se can account for the observed occurrence of orthogonal patterns. We found significantly more orthogonality than expected by chance (38 ± 4% vs. 29 ± 4% in real vs. shuffled data, respectively, see Figure 4A inset, n = 9 mice, three sessions, comparison to shuffled data: Wilcoxon test, p=0.0039). This is consistent with the capability to represent separate sets of information within network events. Figure 4 with 1 supplement see all Download asset Open asset Network events are orthogonal, but repetitively recruit GC sub-ensembles. (A) Similarity between network events. Similarity of population vectors computed for individual network events. Comparisons were carried out between all possible pairwise combinations of vectors and quantified using cosine similarity. Inset: Mean number of orthogonal NEs for baseline sessions (Black bar) compared to shuffled data (gray bar) with SEM. Gray lines depict individual sessions. (B) Graphical representation of the correlation matrix using Pearson’s r for all cell combinations, with values for r being color coded. Data from one representative session in an individual mouse. (C) Identification of clusters of correlated cells using agglomerative hierarchical clustering. Clusters were combined using a standardized Euclidean distance metric and a weighted average linkage method, until the mean of the cluster internal r-value reached a significance threshold. The 5% significance threshold was defined by creating a null-distribution of r values from randomized data sets, and is indicated for this particular experiment with a vertical line. Right panel in C depicts the reordered correlation matrix showing clusters of highly correlated cells. Only clusters whose mean intra-correlation exceeded the threshold were included in further analysis (significant clusters indicated with gray frames). (D) Raster plot showing the reactivation of clusters identified in panel C during multiple episodes of running and immobility. Individual dots indicate participation of individual cells. Clusters are color-coded according to the agglomerative tree. Network events are indicated by vertical dashed lines. Running episodes are indicated at the lower border with the running speed (blue). Even though orthogonal network events were observed, we also observed a repeated activation of granule cells in multiple network events (see i.e. Figure 1—video 2). To examine if specific sub-ensembles of granule cells are repeatedly recruited in network events, we performed a pairwise Pearson’s correlation of the activity of all cell pairs during all network events of a recording session (correlation coefficients depicted in the correlation matrix in Figure 4B). We then re-arranged the cells by hierarchical clustering. Clusters were combined using a standardized Euclidean distance metric and a weighted average linkage method (Figure 4C, more examples in Figure 4—figure supplement 1A–H). This visualization reveals the existence of subgroups of cells that are strongly correlated within network event-related activity (Figure 4C), as previously demonstrated for activity during immobility in the CA1 region (Malvache et al., 2016). To more rigorously define what we considered a cluster showing correlated activity, we used a comparison to a null distribution by Such have been shown to to define how clusters are in data et al., We combined clusters until the mean of the cluster internal r-value reached a significance which was defined by creating a null-distribution of from shuffled with a vertical line in Figure clusters were defined as a mean correlation within the cluster above chance level. Using this the average cluster size was ± cells per cluster (n = 9 mice, three sessions). The of GC cluster activation during an entire session clearly when cell activity during network events over an entire by their participation in clusters shown in Figure Participation of place- and granule cells in network events To if network events specific spatial or we identified GCs with or activity. We first identified the of GCs that significant of n = 1415 active cells imaged in nine mice, Figure for representative of three The of GCs were distributed over the linear track (Figure the fraction of cells was calculated as a fraction of only those GCs active during running, the fraction of significantly GCs was we identified a fraction of GCs of of n = 9 a significant correlation of activity with running speed (Figure This is in to a (Danielson et al., 2016), but consistent with data in mice et al., We also examined from sessions using two linear track environments with sensory cues placed on the textured First, additional sensory cues were placed randomly on the belt these the fraction of cells observed within the GC population increased of of as did the of speed cells of of n = active GCs imaged in nine mice, Figure Figure 5 with 1 supplement see all Download asset Open asset of dentate gyrus and speed cells in the dentate (A) Representative of two significantly granule cells and one without significant is depicted as each of the a transition the linear track without additional cues events are shown as The red line the The corresponding of normalized fluorescence is shown in the In lower the for from shuffled data (see Materials and methods) are shown (gray the for the individual cell is indicated by the red line. (B) field of cells showing significant (C) Representative examples of three significantly neurons running speed depicted in (D) mean fluorescence signal of a representative example Gray area indicates standard error. (E) Mean fluorescence signals of all significantly cells. fluorescence is color and running speed is normalized to every individual mouse running (F) of and speed cells and normalized to all active cells and only running active cells Only a number of cells carried encoded both (dark we tested if there is a further in cells with a used linear track into each with different spatial cues (see Materials and Figure 1—figure supplement This was not the In these mice (n = we of which were cells. As a fraction of those GCs active during running, we found cells. In all cells both speed and We then examined if or speed cells are in network events, and if this participation is when the we examined the between the baseline linear track without additional cues and the We the condition for further and because it spatial cues for a strong spatial without between on the linear We found that in the dentate gyrus network events were observed during immobility (Figure supplement 1D, of GC activity in Figure supplement and were to MPP activity (Figure supplement the did not significantly the network event frequency (Figure supplements 1D, ± respectively, n = 9 mice, ANOVA, baseline vs. vs. However, the average size of individual network events, as the number of participating was significantly larger in the condition compared to the baseline condition (Figure Kruskal-Wallis test, n = 9 mice, and network events in baseline and respectively, with individual GCs more to network events in the condition (Figure Kruskal-Wallis test, orthogonal were observed in the but this was not significantly different to the baseline condition Kruskal-Wallis test, Figure 6 Download asset Open asset sensory cues is associated with of network events and increased of cells. (A) Network events more granule cells in Cumulative probability of network event size of cells per network for baseline and condition (dark and gray (B) Cumulative probability of participation in multiple network events per cell for baseline and condition (dark and gray (C) of and speed cells that in network events number of cells (D) of and speed cells to correlated cell clusters Figure 4). of the total number of clusters cells speed cells (light or both (dark Gray indicates clusters speed cells. (E) Mean number of and speed cells per in baseline and n = 9 We then examined if the participation of and speed cells in network events is in the compared to the baseline As cells are more