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Impaired spatial learning and suppression of sharp wave ripples by cholinergic activation at the goal location

  1. Przemyslaw Jarzebowski
  2. Clara S Tang
  3. Ole Paulsen
  4. Y Audrey Hay  Is a corresponding author
  1. Department of Physiology, Development and Neuroscience, Physiological Laboratory, United Kingdom
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Cite this article as: eLife 2021;10:e65998 doi: 10.7554/eLife.65998

Abstract

The hippocampus plays a central role in long-term memory formation, and different hippocampal network states are thought to have different functions in this process. These network states are controlled by neuromodulatory inputs, including the cholinergic input from the medial septum. Here, we used optogenetic stimulation of septal cholinergic neurons to understand how cholinergic activity affects different stages of spatial memory formation in a reward-based navigation task in mice. We found that optogenetic stimulation of septal cholinergic neurons (1) impaired memory formation when activated at goal location but not during navigation, (2) reduced sharp wave ripple (SWR) incidence at goal location, and (3) reduced SWR incidence and enhanced theta-gamma oscillations during sleep. These results underscore the importance of appropriate timing of cholinergic input in long-term memory formation, which might help explain the limited success of cholinesterase inhibitor drugs in treating memory impairment in Alzheimer’s disease.

Introduction

The role of the neuromodulator acetylcholine (ACh) in learning and memory is debated. On one hand, degeneration of cholinergic neurons and a low tone of ACh correlate with memory impairment in humans and rodents (Bartus, 2000; Berger-Sweeney et al., 2001; Hasselmo and Sarter, 2011) and drugs blocking ACh degradation ameliorate memory impairment in Alzheimer’s disease (AD) (Ehret and Chamberlin, 2015). Moreover, extensive lesions of the medial septum (MS), where the cholinergic neurons projecting to the hippocampus are located, produce learning deficits (Hepler et al., 1985). On the other hand, selective lesions of MS cholinergic neurons, which account for ~5% of neurons in the MS, have relatively little impact on learning and memory (for review, see Hasselmo and Sarter, 2011; Solari and Hangya, 2018). A reason for these apparently conflicting results may lie in the fact that memory formation is a dynamic process during which ACh levels vary (Fadda et al., 2000), a property lesioning or pharmacological studies cannot directly address.

In an influential model, Buzsáki, 1989 suggested that long-term memory forms in two stages: first, neuronal activity produces a labile trace, then a delayed potentiation of the same synapses forms a long-lasting memory trace. He suggested that the two stages are associated with different network states of the hippocampus. In rodents, phase-amplitude-coupled theta (5–12 Hz)-gamma (30–100 Hz) oscillations (Csicsvari et al., 2003) occur during exploratory behaviors, while large-amplitude sharp waves combined with high-frequency ripples (sharp wave ripples [SWRs]) (O’Keefe and Nadel, 1978; Csicsvari et al., 2000) occur during immobility and slow-wave sleep. These two network states are mutually exclusive (O’Keefe and Nadel, 1978; Buzsáki, 1986; Csicsvari et al., 2000), and it was suggested that memory encoding is associated with theta/gamma oscillations while memory consolidation relies on SWR activity (O'Neill et al., 2010; Colgin, 2013).

The local release of ACh controls hippocampal network states. In hippocampal CA3, cholinergic activation ex vivo induces a slow gamma rhythm primarily by activating M1 muscarinic receptors (Fisahn et al., 1998; Betterton et al., 2017), while in the CA1, cholinergic activation in vivo promotes theta/gamma oscillations and suppresses ripple oscillations through the activation of M2/M4 muscarinic receptors (Vandecasteele et al., 2014; Zhou et al., 2019; Ma et al., 2020). This suggests that regulation of cholinergic tone allows the switching between online attentive processing (theta/gamma oscillations) and offline memory consolidation (SWRs) as described in the two-stage model of memory trace formation (Buzsáki, 1989). Evidence from microdialysis and electrophysiology experiments shows a high cholinergic tone during exploration, promoting theta activity, and a lower cholinergic tone during subsequent rest, permitting SWRs (Fadda et al., 2000; Giovannini et al., 2001; Fadel, 2011). Disruption of cholinergic activity at different stages of learning and memory impairs performance in memory tasks (for review, see Hasselmo and Sarter, 2011; Solari and Hangya, 2018). However, the differential effects of ACh in distinct phases of memory formation are not well understood.

To clarify the function of the MS cholinergic system during hippocampus-dependent learning and memory, we investigated the behavioral phase-specific effects of optogenetic cholinergic stimulation in the appetitive Y-maze long-term memory task, a simple reward-based spatial memory task with two distinct behavioral phases: one of navigation toward a reward and another after arriving in the goal area (Bannerman et al., 2012). We show that stimulation of cholinergic neurons does not affect learning when applied during navigation toward a reward but impairs learning when applied at the goal location. Our simultaneous recordings of hippocampal local field potential (LFP) indicate that impaired memory was related to disruption of awake SWRs, supporting the two-stage model of memory trace formation (Buzsáki, 1989). We also show that activation of MS cholinergic neurons promotes a switch from ripple activity to enhanced theta/gamma oscillations in the hippocampus of naturally sleeping mice.

Results

Functional expression of ChR2 in cholinergic neurons

We aimed to investigate the effects of cholinergic modulation on hippocampal oscillations and performance in a spatial navigation task. To this end, we optogenetically controlled the activity of cholinergic neurons using ChAT-Ai32 crossbred mice that expressed enhanced YFP-tagged channelrhodopsin-2 (ChR2-eYFP) under the control of the choline-acetyl transferase (ChAT) promoter. We first confirmed the expression of ChR2 in MS cholinergic neurons by performing double immunostaining for ChAT and YFP (Figure 1A). In sections sampled from two mice, 98 out of 150 ChAT+ cells counted were YFP+ (YFP+/ChAT+=65%). In independently sampled sections, 111 out of 111 YFP+ cells counted were ChAT+ (ChAT+/YFP+=100%).

Choline acetyl transferase (ChAT)-Ai32 mice express enhanced YFP-tagged channelrhodopsin-2 (ChR2-eYFP) selectively in cholinergic cells.

(Ai) Overlay of DAPI, ChAT, and eYFP-positive immunostaining in a coronal section of the medial septum (MS) in a ChAT-Ai32 mouse. Scale bar 500 μm. VDB, ventral diagonal band. (Aii-v) Higher magnification of the MS (rectangle in Ai), triple immunostaining of DAPI (blue, ii), ChAT (red, iii), and eYFP (green, iv), showing their colocalization (overlay, v). Scale bar 50 μm. (B) Sample trace of multi-unit recording from the MS in a ChAT-Ai32 mouse. Top: the stimulation protocol (blue) beginning at 15 s. Inset shows a section of the 50-ms-long square stimulation pulses at 10 Hz. Middle: an example recording trace; inset shows an example unit recorded. Bottom: mean spike frequency (n = 6). *p=0.03, two-tailed paired Wilcoxon signed-rank test. Gray lines represent mean ± SEM.

We probed the functional expression of ChR2 in MS neurons by recording multi-unit activity in the MS of urethane-anesthetized mice; 473 nm light was delivered through an optic fiber implanted just above the MS while multi-unit activity was recorded from a co-assembled electrode the tip of which protruded ~200 µm further than the optic fiber tip. Local light delivery (50 ms pulses at 10 Hz) resulted in an increase in multi-unit activity (baseline spike frequency: 7.9 ± 2.8 Hz vs. spike frequency during light delivery: 22.7 ± 7.3 Hz, two-tailed Wilcoxon matched pair signed-rank test: p=0.03; n = 6 recordings from two mice; Figure 1B), confirming our ability to increase neuronal activity in the MS using optogenetics.

Activation of septal cholinergic neurons in the goal zone slows place learning

We investigated the effect of cholinergic activation during different phases of the appetitively motivated Y-maze task, a hippocampus-dependent task commonly used to study long-term spatial memory (Bannerman et al., 2012; Shipton et al., 2014). Mice had to learn to find a food reward on an elevated three-arm maze that remained at a fixed location in relation to visual cues in the room, while the mice pseudo-randomly started from one of the other two arms. Because short-term memory errors caused by re-entry during a single trial have previously been shown to interfere with the acquisition of this spatial long-term memory task (Schmitt et al., 2003), mice were only allowed to make a single choice of arm in each trial (Figure 2A). Previous studies have reported sharp wave ripples at the reward location of different spatial navigation tasks (O'Neill et al., 2006; Dupret et al., 2010). Based on these studies, we defined a navigation phase corresponding to the arms of the maze except for the distal ends (20 cm from the edge), which we considered as goal zones. Cholinergic activation was achieved by light stimulation (473 nm, 25 mW, 50-ms-long pulses at 10 Hz) delivered via an optic fiber implanted in the MS of ChAT-Ai32 mice. ChAT-Ai32 mice were split into four groups to test four experimental conditions: (i) no stimulation (n = 13), (ii) optogenetic stimulation during navigation – from the start of the trial until they reached the goal zone (n = 9), (iii) optogenetic stimulation throughout the maze (n = 9), and (iv) optogenetic stimulation in the goal zone only – from the entry of the goal zone until the mice were removed from the maze either after they had eaten the food or they had reached the empty food well (n = 15; Figure 2B).

Figure 2 with 2 supplements see all
Cholinergic stimulation in the goal zone slows learning of the appetitive Y-maze task.

(A) Mice were trained on an elevated three-arm maze to find a food reward (red dot) in an arm that remained at a fixed location relative to visual cues in the room. Mice were allowed to consume the reward if they chose the correct arm but were removed from the maze if they chose the incorrect arm. (B) Mice were pseudo-randomly split into four groups to test four optogenetic stimulation conditions. Blue indicates stimulation for the four conditions: (i) no stimulation, (ii) stimulation only until the goal zone was reached (gray line), (iii) stimulation throughout the maze, and (iv) stimulation only in goal zone. (C) Choline acetyl transferase (ChAT)-Ai32 mice received blocks of 10 trials each day for 7 consecutive days and the number of entries to the rewarded arm was recorded. The performance of all four groups of mice improved with time but at different rates. (D) As in (C) but for wild-type (WT) mice. (E) The number of days required for each group of the ChAT-Ai32 mice to reach the learning criterion of ≥80%. Horizontal bars indicate the median within each group. The p-values for differences between groups were calculated using post hoc Dunn tests. (F) As in (E) but for WT mice.

Each mouse received 10 trials per day for 6–10 consecutive days, and we set a learning criterion of ≥80% rewarded trials in a day. Mice from all four groups of ChAT-Ai32 mice learned the task (Figure 2C, Figure 2—figure supplement 1) but comparison between the groups revealed differences in the number of days taken to reach this criterion (one-way ANOVA on ranks χ2(3)=14, p=0.003, BF10 = 20; Figure 2E). Post hoc tests indicated that the ChAT-Ai32 ‘goal’ group was delayed at learning the task compared to the ChAT-Ai32 ‘no stimulation’ group (4.5 ± 0.3 vs. 2.9 ± 0.3 days, Dunn test with Holm-Bonferroni correction for multiple comparisons: p=0.002, BF10 = 45). Similarly, the ChAT-Ai32 ‘goal’ group was delayed compared to the ChAT-Ai32 ‘navigation’ group (4.5 ± 0.3 vs. 3.1 ± 0.4 days, p=0.05, BF10 = 4.4). Whilst the stimulation for the ‘goal’ group lasted longer (34 ± 1 vs. 8 ± 1 s), the duration alone cannot explain the different effects of the optogenetic stimulation. The ‘throughout’ group received the longest stimulation (42 ± 1 s) but presented an intermediate learning curve. Using Bayes Factor (BF) analysis, we found inconclusive evidence for the ‘throughout’ group to learn more slowly than the ‘no stimulation’ group (post hoc test for difference in means: p=0.28, test for higher mean days-to-criterion in the ‘throughout’ group: BF10 = 1.6) and learn faster than the ‘goal’ group (post hoc test for difference in means: p=0.54, test for lower mean days-to-criterion in the ‘throughout’ group: BF10 = 2.0). Therefore, the spatial location in the maze where the optogenetic stimulation took place was most likely the factor that decided the behavioral outcome. However, the MS neurons sustained an increased level of firing after the optogenetic stimulation ceased (Figure 1B). Therefore, we cannot exclude the possibility that this sustained activity contributes to the learning deficit in the ‘goal’ group.

To control for possible aversive or other non-specific effects of the illumination, we performed an additional experiment with MS-implanted wild-type (WT) mice split into two groups: no stimulation (n = 7) and light delivery in the goal zone (n = 9; Figure 2D). We did not observe any learning difference between the ‘goal’ and ‘no stimulation’ groups of this control WT mice cohort (goal: 3.0 ± 0.37 days; no stimulation: 3.4 ± 0.37 days; one-way ANOVA F(1, 14) = 0.34, p=0.57; Figure 2F).

We confirmed that the memory of the rewarded arm was retained by retesting the mice on the Y-maze task 1 week after the end of the acquisition period for each group of the ChAT-Ai32 mice (no stimulation: 100 ± 0%; navigation: 99 ± 1%; throughout: 100 ± 0%; goal: 94 ± 3%). After behavioral testing, implant placement and the level of eYFP expression were verified by immunohistochemistry, confirming that there were no significant differences in implant placement between the behavioral groups (Figure 2—figure supplement 2).

Our results show that cholinergic activation in the goal zone for as short as 50 s (95% percentile of stimulation duration) slows learning of the appetitive Y-maze task. In contrast, optogenetic stimulation during navigation or throughout the maze had no significant effect on task acquisition.

Activation of MS cholinergic neurons in the goal zone does not significantly affect theta-gamma power but reduces the incidence of SWRs

To understand why stimulating MS cholinergic neurons in the goal zone impairs task acquisition, we performed LFP recordings from the hippocampus during task performance. We recorded CA1 field potentials during the Y-maze task in five ChAT-ChR2 and two control ChAT-GFP mice implanted with recording electrodes and an optic fiber. We used staggered wire electrodes to record the field potentials and subtracted the signal in one electrode from that in the other. This subtraction procedure cancels out synchronous changes on both electrodes, like those caused by movement artifacts, and enhances locally generated phase-reversed signals, such as theta, gamma, and ripple events. Optogenetic stimulation was applied on alternating trials when the mouse reached the goal zone, comparing the CA1 activity between the stimulated and non-stimulated trials (111 non-stimulated and 109 stimulated at the goal location rewarded trials and 56 non-stimulated and 36 stimulated at the goal location unrewarded trials). To evaluate the effects of laser (on vs. off), mouse group (ChAT-ChR2 vs. ChAT-GFP), and their interaction, while accounting for correlations between the trials for the same mouse, we used a linear mixed-effects model (see 'Materials and methods'). The cholinergic activation did not overtly affect the behavior once the mice were at the goal location: we did not detect any effect of the laser on the time the mice spent at the goal location (linear mixed-effects model, mouse group–laser interaction: F(1, 78)=0.01, p=0.94, laser effect: F(1, 78)=0.1, p=0.73).

The theta power (5–12 Hz) peaked in the central section of the maze where the mice ran the fastest (Figure 3A,B) and was reduced at the goal location in rewarded trials (Figure 3B, right panel). Power spectral density (PSD) from electrophysiological recordings measures the summation of periodic activity and aperiodic activity. The intensity of the aperiodic component of the PSD has a pink noise distribution (1/f) (Donoghue et al., 2020). Therefore, to quantify the power of theta and gamma oscillations, we measured relative peaks above the estimated aperiodic component (Donoghue et al., 2020, see 'Materials and methods', Figure 3C,D).

Figure 3 with 2 supplements see all
Cholinergic stimulation during the Y-maze task did not change theta-gamma oscillations.

(A) Schematic showing the maze zones. (B) Spectrogram of the local field potential (LFP) recorded in a single non-stimulated trial. The values were z-scored to show relative changes in frequency. Note transient increases in high-frequency power throughout the recording and high theta power at Center. Right: mean z-score value at Center and Goal as a function of frequency. (C) Left: power spectral density (PSD) of the LFP recorded from a representative animal on non-stimulated and stimulated-at-Goal rewarded trials. The dashed lines show the fitted aperiodic component. Right: difference in PSD between day-averaged trials with stimulation off and on. Ribbons extend ±1 SEM of log power. Gray background marks the frequency range of theta and slow gamma bands. (D) PSD parameters that were assessed for the stimulation effect: relative theta power (E), spectral peak frequency in the theta band (F), slow gamma power (G), and the aperiodic component (H). The aperiodic component was fitted for two frequency ranges, 3–15 and 15–150 Hz, and compared using the area under curve (AUC). (E–H) Values plotted for individual trials. Lines connect means for individual animals. p-values were calculated with linear mixed-effects models for the interaction of mouse group–laser effects.

Both theta and slow gamma (25–80 Hz) oscillations were present at the goal location in the rewarded non-stimulated trials (theta peak present in 98 ± 2% of trials; slow gamma peak present in 86 ± 7% trials, Figure 3C and Figure 3—figure supplement 1). Light did not affect the relative theta power differently in the ChAT-GFP and ChAT-ChR2 mice (linear mixed-effects model, mouse group–laser interaction: F(1, 5)=0.01, p=0.94, Figure 3E). However, theta power increased by 16 ± 3% in both mouse groups indiscriminately (laser effect: F(1, 5)=6.9, p=0.05), suggesting another, non-specific effect of the laser on theta power. Spectral peak frequency in the theta band was not significantly affected by the stimulation (linear mixed-effects model, mouse group–laser interaction: F(1, 5)=1.3, p=0.31, laser effect: F(1, 5)=3.5, p=0.12, Figure 3F).

To independently confirm that the stimulation did not differentially affect relative theta power in ChAT-GFP and ChAT-ChR2 mice, we looked at the difference in the PSD between day-averaged trials with the stimulation off and on (Figure 3C right panel, Figure 3—figure supplement 1). Differences for a given frequency can be caused by a change in oscillatory power, change in the aperiodic component, or by a shift of the spectral peak frequency or change of the peak’s width (bandwidth). To minimize the impact of the peak frequency shift and change in bandwidth, we compared maximum changes within frequency bands that were wider than the bandwidth of the theta peak and shift in theta peak frequency. In the ChAT-ChR2 mice, the difference between the negative power change in the theta band and in the surrounding bands was not significantly different than in the ChAT-GFP mice (Figure 3—figure supplement 2). Hence, our results indicate that the stimulation did not affect the theta power significantly differently between the mouse groups.

Similarly, quantification of the relative slow gamma power indicated no effect of the stimulation (linear mixed-effects model, mouse group–laser interaction: F(1, 6)=1.7, p=0.24, laser effect: F(1, 6)=0.1, p=0.77, Figure 3G). This result was independently confirmed by looking at the difference in the PSD between day-averaged trials with the stimulation off and on (Figure 3—figure supplement 2).

The only effect of the laser that affected PSD of the ChAT-GFP and ChAT-ChR2 mice differently was a reduced aperiodic component of the PSD in the 3–15 Hz range (linear mixed-effects model, mouse group–laser interaction: F(1, 135)=29, p=10−7, Figure 3H).

Previous studies have shown that activation of septal cholinergic neurons suppresses CA1 SWRs (Vandecasteele et al., 2014; Ma et al., 2020), and we investigated whether impaired place learning was associated with changes in SWRs. To identify the SWRs, we detected ripple events in the LFP and excluded any candidate ripples that co-occurred with electromyography (EMG)-detected muscle activity. Only ripples with spectral peak frequency ≥140 Hz were identified as SWRs (Sullivan et al., 2011; Figure 4A, Figure 4—figure supplements 1 and 2).

Figure 4 with 2 supplements see all
Cholinergic stimulation during the Y-maze task reduced incidence of sharp wave ripples (SWRs).

(A) Example SWRs recorded at the Goal location: local field potential (LFP) traces (top), the same traces after 100–250 Hz bandpass filtering (middle), and simultaneously recorded electromyography (EMG) (bottom). (B) Time of SWRs recorded from a representative mouse over multiple trials the same day. Time measured relative to the trial start and arrival at Goal. Stimulated and non-stimulated trials are grouped for clarity. (C) Percentage of trials with SWRs at Goal compared between unrewarded and rewarded non-stimulated trials. One line per animal shown. p-value calculated with paired t-test. (D) SWR incidence as a function of trial day. Data shown for choline acetyl transferase (ChAT)-GFP and ChAT-ChR2 mice together, values plotted for individual non-stimulated rewarded trials. Dashed vertical line separates early and late trials. p-value calculated with linear mixed-effects model for the effects of early vs. late trials. (E) Effect of cholinergic stimulation on SWR incidence at Goal location in rewarded trials. Data shown for ChAT-GFP and ChAT-ChR2 mice, values plotted for individual trials. Lines connect means per animal. p-values calculated with linear mixed-effects model for the interaction of mouse group–laser effects; groups were compared with post hoc test on least-square means.

The SWRs occurred at the start and the goal locations (Figure 4B). Mice learned over 6 days and on day 5 reached 80 ± 10% rewarded trials. We detected SWRs in significantly more rewarded than unrewarded trials (82 ± 7% of rewarded non-stimulated trials vs. 32 ± 13% of unrewarded non-stimulated trials, paired t-test on percentages per animal: p=0.02, n = 7 animals, Figure 4C). The difference could be due to the shorter immobility when the mice visited the non-rewarded arms: on unrewarded trials, mice spent 6.5 ± 0.5 s in the goal zone before leaving compared to 34.0 ± 1.0 s on rewarded trials. Because we detected few SWRs in the unrewarded trials, we restricted the further analysis to the rewarded trials.

We first assessed whether SWR incidence changed during learning by quantifying the incidence of SWRs in the non-stimulated rewarded trials during early and late learning (Figure 4D). We did not observe any significant difference between early (before day 5) and late learning (linear mixed-effects model, effect of early vs. late learning: F(1, 110)=0.3, p=0.58, Figure 4D). However, optogenetic stimulation had a significantly different effect in the ChAT-GFP and the ChAT-ChR2 mice (log-linear mixed-effects model, mouse group–laser interaction, F(1, 42) = 4.5, p=0.04, Figure 4E), whose SWR incidence at the goal location was reduced by 52 ± 7% from 0.06 ± 0.01 to 0.03 ± 0.01 Hz (post hoc test: t(44) = 4.2, p=0.001, Figure 4E). Spectral peak frequency of SWRs was not affected by the stimulation (frequency: 168 ± 2 Hz; linear mixed-effects model for non-stimulated trials, mouse group–laser interaction: F(1, 3.6)=0.02, p=0.88, Figure 4—figure supplement 2A), nor was the SWR duration (duration: 37 ± 1 ms; log-linear mixed-effects model for non-stimulated trials, mouse group–laser interaction: F(1, 148)=0.1, p=0.76, Figure 4—figure supplement 2B).

Overall, these results show that optogenetic stimulation of MS cholinergic neurons reduced ripple incidence in the CA1 in rewarded trials but did not cause a detectable change in theta-gamma power. Hence, this result suggests that the reduced SWR incidence is a mechanism relevant for the memory impairment induced by cholinergic stimulation in this task.

MS cholinergic neuron stimulation reduces SWRs and increases theta and slow gamma activity in sleeping animals

Because cholinergic input has been implicated in theta activity in the hippocampus (Buzsáki, 2002), we were surprised that we could not detect any effect on theta-gamma oscillations by cholinergic stimulation at the goal location. However, there are at least two distinct forms of theta oscillations in the hippocampus, only one of which is dependent on cholinergic receptors and can be observed during sleep (Kramis et al., 1975). Also, the reduction of SWR incidence of 52 ± 7% at the goal location was smaller than the 92% median suppression reported during free behavior (Vandecasteele et al., 2014), which could be due to a smaller effect of ACh at the reward location or an already high level occluding the effect of the optogenetic stimulation. We therefore compared the effects of cholinergic stimulation during task performance with the effects of cholinergic stimulation during sleep when cholinergic tone is at the lowest.

We recorded LFP signal while the mice slept in a cage, to which they had been familiarized over the 2 previous days and alternated periods without optogenetic stimulation (60–120 s) and periods with optogenetic stimulation (30 s). We compared the signal in the 30-s-long epochs preceding the stimulation with the 30-s-long epochs during the stimulation without a distinction between SWS and REM sleep. Only epochs during which the mouse was asleep for their full duration were used for the analysis (n = 369 epochs from 10 animals, IQR of 5–16 epochs in succession without interrupted sleep).

Optogenetic stimulation reduced the SWR incidence throughout the stimulation in ChAT-ChR2 mice but not in ChAT-GFP mice (Figure 5A–C). SWR incidence in ChAT-ChR2 mice was reduced from 0.21 ± 0.01 to 0.03 ± 0.01 Hz (85 ± 3% reduction, linear mixed-effects model, mouse group–laser interaction: F(1, 22)=47, p=10−6, n = 369 epochs from 10 animals; post hoc test for laser effect in ChAT-ChR2: t(86) = 9.7, p=10−14, Figure 5C). The stimulation did not change the spectral peak frequency of the SWRs of 168 ± 1 Hz (mouse group–laser interaction: F(1, 262)=0.51, p=0.48, Figure 5—figure supplement 1A), nor ripple duration of 38 ± 0.3 ms (log-linear mixed-effects model, mouse group–laser interaction: F(1, 28)=0.9, p=0.35, Figure 5—figure supplement 1B). Hence, our results confirm that optogenetic activation of MS cholinergic neurons almost completely suppresses SWRs during sleep in the CA1.

Figure 5 with 1 supplement see all
Activation of medial septum cholinergic neurons reduced incidence of sharp wave ripples (SWRs) during sleep.

(A) Local field potential (LFP) from the CA1 of a sleeping mouse recorded before, during, and after optogenetic stimulation with 100–250 Hz bandpass filtered trace for ripple detection shown below. The detected SWRs are marked with arrows. The inset (lower left) shows two example SWRs at greater time resolution. (B) Histogram of SWR incidence before, during, and after 30 s of stimulation with 50-ms-long pulses at 10 Hz (n = 103 epochs from eight ChAT-ChR2 mice). (C) Comparison of SWR incidence during the stimulated and non-stimulated epochs for ChAT-GFP and ChAT-ChR2 mice. Lines connect mean incidence in individual mice. p-values were calculated with linear mixed-effects model for the interaction of mouse group–laser effects; groups were compared with post hoc test on least-square means.

We next investigated the effect of optogenetic stimulation of MS cholinergic neurons on theta-gamma activity in the sleeping mouse (Figure 6A). We determined the PSD for frequencies ranging between 1 and 200 Hz in control condition and during stimulation. We observed a reduction of the PSD across the full frequency range upon light stimulation (Figure 6B–D, Figure 6—figure supplement 1), as reported previously in freely behaving mice (Vandecasteele et al., 2014).

Figure 6 with 2 supplements see all
Cholinergic stimulation increased theta and slow gamma activity in sleeping mice.

(A) Local field potential (LFP) recording from the CA1 of a sleeping mouse recorded without (left) and with (right) optogenetic stimulation. (B) Power spectral density (PSD) of the LFP recorded from a single animal during the epochs without and with optogenetic stimulation. Ribbons extend ±1 SEM of PSD. Gray background marks the frequency range of theta and slow gamma bands. The dashed lines show the fitted aperiodic component. (C) Difference in PSD between subsequent epochs with stimulation off and on. Data shown for the animal in (B). Ribbons extend ±1 SEM of log power. (D) Optogenetic stimulation in the choline acetyl transferase (ChAT)-ChR2 mice reduced the power of the aperiodic component in 15–150 Hz frequency range. (E) Optogenetic stimulation in the ChAT-ChR2 mice, but not in the ChAT-GFP mice, increased relative theta power, (F) decreased spectral peak frequency in the theta band, and (G) increased relative slow gamma power. Values were calculated on individual epochs, lines connect means for individual animals. p-values were calculated with linear mixed-effects model for the interaction of mouse group–laser effects; groups were compared with post hoc test on least square means.

Broadband power of the aperiodic component decreased with light stimulation in the ChAT-ChR2 but not in the ChAT-GFP mice (linear mixed-effects model on area under curve [AUC] of estimated aperiodic component on PSD log-log plot, significant mouse group–laser interaction: F(1, 4.6)=22, p=0.006, n = 369 epochs from 10 mice; AUC significantly decreased from −5.96 ± 0.04 to −6.23 ± 0.04, post hoc test for laser effect in ChAT-ChR2: t(7.1) = 10.1, p=10−4, Figure 6D). Even though theta power was low during sleep outside of REM sleep, 99 ± 0.1% of the control and 100 ± 0% of the stimulated epochs had a relative theta peak. Optogenetic stimulation had a significantly different effect in the ChAT-GFP and the ChAT-ChR2 animals on the relative theta power (log-linear mixed-effects model, mouse group–laser interaction: F(1, 4.8)=7.3, p=0.04, n = 368 epochs with theta peak from 10 animals, Figure 6E). In the ChAT-ChR2 mice, the power increased by 51 ± 9% (post hoc test: t(9) = 4.8, p=0.01) and the spectral peak frequency in the theta band decreased from 7.7 ± 0.2 to 7.2 ± 0.1 Hz (log-linear mixed-effects model, mouse group–laser interaction: F(1, 4.8)=7.3, p=0.04, Figure 6F, post hoc test: t(30) = 4.5, p=0.001). To independently confirm that the stimulation increased relative theta power, we looked at the difference in the PSD between subsequent epochs with the stimulation off and on (Figure 6C, Figure 6—figure supplement 1). In the ChAT-ChR2 mice, the negative change in the theta band was significantly smaller than in the 12–15 Hz band (linear mixed-effects model: F(1, 10)=21, p=0.001, Figure 6—figure supplement 2A). We conclude that the stimulation reduced the power in the theta frequency band significantly less than in higher frequency bands.

The stimulation also increased by 30 ± 4% oscillations in the slow gamma band (25–45 Hz) (log-linear mixed-effects model: mouse group–laser interaction: F(1, 232)=26, p=10−6, n = 338 epochs with slow gamma peak from 10 animals, post hoc test for laser effect in ChAT-ChR2: t(295) = −8.2, p=10−14, Figure 6G), while the spectral peak frequency in the slow gamma of 38 ± 1 Hz did not significantly change (linear mixed-effects model, mouse group–laser interaction: F(1,10) = 0.4, p=0.57). We independently confirmed the increase in relative slow gamma power by looking at the PSD change between subsequent epochs with the stimulation off and on. In the ChAT-ChR2 mice, the negative change of power in the slow gamma band was significantly smaller than in the 12–15 Hz band (mouse group effect in the linear mixed-effects model: compared to the 12–15 Hz band: F(1, 8)=35, p=10−4; compared to the 90–110 Hz band: F(1, 10)=3.7, p=0.08, Figure 6—figure supplement 2B).

Our results demonstrate that in sleeping mice, optogenetic stimulation of MS cholinergic neurons promotes theta-gamma oscillations in the CA1, an effect that was not seen in awake mice. This suggests a difference between the effect of cholinergic stimulation in the sleeping and awake behaving animal.

Discussion

Using optogenetics, we investigated the effects of stimulating MS cholinergic neurons on learning and hippocampal LFPs when delivered at different phases of an appetitively motivated spatial memory task. We found that: (1) MS cholinergic activation at the goal location, but not during navigation, impairs spatial memory formation; (2) MS cholinergic stimulation at the reward location reduces SWR incidence; and (3) cholinergic stimulation reduces SWR incidence and promotes theta-gamma rhythm in the sleeping mouse. These results show that timely control of cholinergic modulation is important for spatial learning on a time scale of seconds.

Our results indicate that cholinergic stimulation almost completely suppresses SWRs in sleeping animals and suppresses SWRs by about one half in awake, behaving animals. SWRs at the rewarded locations are thought to be crucial for learning (Dupret et al., 2010). Their suppression at the goal location in the experiments with the same stimulation protocol as that used in mice during learning suggests a possible explanation for the learning deficit induced by inappropriately timed cholinergic activity. Moreover, the effect of cholinergic stimulation on theta-gamma oscillations, which was prominent during sleep, was not observed when we applied the same stimulation at the goal location during learning, suggesting that learning was impaired through a mechanism independent of theta-gamma oscillations.

Importance of timely regulation of cholinergic tone for memory formation

We found that temporally controlled optogenetic stimulation of MS cholinergic neurons could affect learning of the appetitive Y-maze task. Stimulation of cholinergic neurons during navigation did not affect the performance, while, strikingly, cholinergic stimulation in the goal zone significantly impaired task acquisition (Figure 2). The stimulation duration differed between the groups: it was longest in the ‘throughout’ group, followed by ‘goal’ and by ‘navigation’ group. The only significant impairment of task acquisition was seen in the ‘goal’ group, indicating that it was cholinergic activation at the goal location that interfered with memory (Figure 2C,E). It may appear surprising that we did not also see a significant impairment with cholinergic stimulation throughout the task. However, the task performance in the ‘throughout’ group was not significantly different from the ‘goal’ group. We cannot exclude the possibility that prolonged optogenetic stimulation becomes less effective over time, either because the MS neurons become less activated or because vesicular ACh might be depleted with prolonged stimulation.

The lack of behavioral effect of the stimulation during the navigation phase, when the cholinergic tone is naturally high (Fadda et al., 2000; Giovannini et al., 2001; Fadel, 2011), may suggest that release of ACh in the hippocampus is already optimal or maximal, or that ACh receptors are saturated. MS cholinergic neurons are slow spiking neurons with a maximal rate of ~10 Hz during active exploration (Ma et al., 2020), the stimulation frequency used here. Thus, it is plausible that ACh receptor activation in the hippocampus had already reached a plateau, which our stimulation protocol would not increase further. The lack of behavioral effect of the stimulation during the navigation phase suggests that the effect of optogenetic stimulation was short-lived and restricted to the stimulation period, albeit with a short period of sustained activity following the stimulation (Figure 1B). This observation supports the idea that cholinergic modulation is timely controlled, but further experiments, for instance using ACh sensors in the hippocampus (Jing et al., 2020), will be necessary to confirm this hypothesis.

The impairment of memory formation by cholinergic stimulation in the goal zone, where the cholinergic tone is naturally lower (Fadda et al., 2000; Giovannini et al., 2001; Fadel, 2011), suggests that any potential beneficial effect of increased excitability or synaptic plasticity is outweighed by a requirement of reduced cholinergic activity. An interesting complementary experiment would be to silence cholinergic inputs during navigation or at the goal location to further explore the role of cholinergic tone during memory formation. There is evidence to suggest that CA1 SWRs, which occur during low cholinergic activity, play a crucial role in memory formation: disruption of SWRs in the first 15–60 min following training impairs learning of spatial navigation tasks (Girardeau et al., 2009; Ego-Stengel and Wilson, 2010), while their disruption or prolongation during the continuous alternation task impairs or improves learning respectively (Jadhav et al., 2012; Fernández-Ruiz et al., 2019). In exploring animals, SWRs occur during transient immobility periods, including periods at goal locations (O'Neill et al., 2006; Dupret et al., 2010; Roux et al., 2017). These SWRs stabilize spatial representations of the CA1 place cells supporting navigation toward the newly learned goals (Roux et al., 2017) and are predictive of performance in a spatial memory task (O'Neill et al., 2006; Dupret et al., 2010). During these SWRs, sequences of neuronal activation are replayed in both forward and reverse order (Foster and Wilson, 2006; Csicsvari et al., 2007; Diba and Buzsáki, 2007; Karlsson and Frank, 2009; Ambrose et al., 2016). We found that MS cholinergic activation for the brief time the mice spent in the reward zone, shorter than 50 s (95% percentile of stimulation duration), is sufficient to significantly impair memory formation in the Y-maze task (Figure 2). Therefore, we speculate that disruption of the normally occurring replay events in the reward zone is sufficient to impair long-term memory formation (Figure 5). However, selective disruption of SWRs at the reward zone did not affect rats’ performance in the inbound phase of the W-maze task (Jadhav et al., 2012), which is comparable to the Y-maze task. In both of these tasks, animals could use either an allocentric place strategy or an egocentric rule-based strategy, or a combination thereof, and the relative importance of each could lead to differences in their reliance on SWRs. Alternatively, additional effects of MS cholinergic activation on intracellular signaling cascades and synaptic plasticity (Brzosko et al., 2019), synaptic inhibition (Hasselmo and Sarter, 2011; Haam and Yakel, 2017), or interference with extra-hippocampal reward-related signaling cannot be ruled out at this stage.

Because learning can be affected by the interruption of SWRs during post-learning sleep (Girardeau et al., 2009), and because our cholinergic activation during sleep achieves a similar effect on the SWRs (Figure 3; Ma et al., 2020), it would be of interest to see if the cholinergic activation during post-learning sleep would also impair spatial learning. This would show whether low cholinergic states are important also for memory consolidation during sleep and provide further evidence for a role of SWRs in memory.

Cholinergic influence on hippocampal network activity

Hippocampal network activity varies with cholinergic tone and MS cholinergic neuron activity. MS cholinergic neurons discharge at a maximal rate when the animal is running (Ma et al., 2020), which corresponds to the highest theta power intensity in the CA1 and highest cholinergic tone measured in the pyramidal cell layer of CA1 (Fadda et al., 2000; Fadel, 2011). Conversely, cholinergic tone and MS cholinergic neuron discharge are at their lowest during slow-wave sleep and wake immobility, which are associated with the highest ripple incidence (Fadda et al., 2000; Ma et al., 2020). In accordance with these observations, we found that stimulation of MS cholinergic neurons reduces SWR incidence in both awake behaving animals and naturally sleeping animals, consistent with previous reports (Figures 4 and 5; Vandecasteele et al., 2014; Zhou et al., 2019; Ma et al., 2020).

We observed that stimulation of MS cholinergic neurons of sleeping mice causes an apparent decrease of the PSD across the entire frequency spectrum (Figure 6). A similar effect was reported previously for anesthetized and freely behaving animals (Vandecasteele et al., 2014). Signal decomposition into aperiodic and periodic components (Donoghue et al., 2020) showed that the optogenetic stimulation enhanced the periodic components with peaks in the theta and slow gamma bands and decreased the aperiodic component of the signal (1/f background). Our observation of the enhanced theta-gamma activity might appear at odds with previous reports that such manipulation does not change theta-gamma power during sleep (Ma et al., 2020) and quiet wakefulness (Zhou et al., 2019). The combined effect of the cholinergic stimulation on the periodic and aperiodic signal sums to near-zero values, which could explain the different conclusions, showing the advantage of PSD decomposition (Donoghue et al., 2020) when assessing the power of periodic signals.

Pharmacological evidence in vivo indicates that there are two distinct mechanisms of theta oscillations in the hippocampus, an atropine-sensitive and an atropine-resistant component (Petsche et al., 1962; Buzsáki, 2002; Colgin, 2013). The atropine-sensitive component is mediated by the combination of cholinergic and GABAergic neurons in the MS (Buzsáki, 2002; Manseau et al., 2008) and is slower than the atropine-insensitive theta rhythm, which is generated primarily by the entorhinal cortex (Buzsáki, 2002; Colgin, 2013). Moreover, atropine-sensitive theta was best detected in the anesthetized animal, while atropine-insensitive theta was shown to predominate in the running animal (Kramis et al., 1975; Newman et al., 2013). Consistent with this division, MS cholinergic stimulation in sleeping mice, in addition to increasing theta power, shifted the spectral peak in the theta band to a lower frequency (Figure 6F). Both effects were limited to the ChAT-ChR2 animals. The stimulation frequency of 10 Hz provided faster activation than the kinetics of metabotropic muscarinic receptors. Therefore, we did not expect to observe indirect effects on the network activity mirroring the stimulation frequency. Indeed, the spectral peak frequency in the theta band was lower than the stimulation frequency, and PSD did not show a spectral peak at 10 Hz (Figure 6F, Figure 6—figure supplement 1). In behaving mice, the MS cholinergic stimulation at the goal location did not have a significantly different effect on theta power and spectral peak in ChAT-GFP and ChAT-ChR2 mice (Figure 3E,F) and we observed in both groups of animals an increase of theta power. The lack of effect on theta-gamma rhythm during the memory task could be explained by the prominence of an atropine-resistant entorhinal-driven theta that would override any atropine-sensitive theta. It is also possible that the small sample of control animals (n = 2) has prevented us from detecting a subtle theta power change. Alternatively, a diminishing efficacy with the prolonged optogenetic stimulation could have prevented us from detecting a change in the theta-gamma oscillations. However, we did observe a reduction of SWR incidence at the goal location for the entire duration of the stimulation, suggesting that any decrease in the stimulation efficacy would be biologically minor.

Possible implications for neurodegenerative disorders

Loss of cholinergic neurons in the basal forebrain is one of the hallmarks of AD (Whitehouse et al., 1982; Bowen et al., 1982), which is also associated with a reduction of ACh transporter expression in most cortical and subcortical areas (Davies and Maloney, 1976). These observations have led to the cholinergic hypothesis of AD, which suggests that loss of cholinergic inputs plays a role in the cognitive impairment of AD patients. However, the association between the loss of basal forebrain cholinergic neurons and AD is not completely clear (Mesulam, 2004), and a growing body of anatomical and functional studies suggests that MS cholinergic neuronal loss occurs in both healthy aging and AD brain (Schliebs and Arendt, 2011; Hampel et al., 2018). Drugs compensating for the decline of cholinergic tone are seen as a rational treatment of aging-related memory loss and AD. However, so far, drugs targeting the cholinergic systems have shown limited beneficial effects on cognitive deficits of aging and AD, but the reasons why are not entirely clear (Farlow et al., 2010; Ehret and Chamberlin, 2015). Our results shed some light on one possible reason why cholinergic drugs have largely failed to improve the cognitive impairments in AD. Cholinesterase inhibitors prolong cholinergic activity by ~100 times and increase the basal cholinergic tone in the absence of spontaneous activity (Hay et al., 2016). In rodents, the cholinergic tone is high during exploration, which maintains the ‘online’ hippocampal state dominated by theta and gamma oscillations (Buzsáki, 1989; Fadda et al., 2000; Giovannini et al., 2001). Cholinesterase inhibitors may maintain a high cholinergic tone, preventing the network from transitioning into an SWR-dominant state. This could impair memory formation as our results suggest that artificially increasing ACh release for as short a time as 50 s during a low cholinergic state is sufficient to impair task acquisition (Figure 2). Moreover, enhancing cholinergic activity during the ‘online’ state did not bring beneficial effects to memory formation. Thus, our results suggest that suboptimal timing of cholinergic activity impairs long-term memory formation and supports the idea that appropriate timing of cholinergic modulation is crucial in learning and memory (Micheau and Marighetto, 2011).

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)ChAT-CreThe Jackson LaboratoryCat. #: 006410; RRID:MGI:3689420Dr Bradford Lowell
Genetic reagent (Mus musculus)Ai32The Jackson LaboratoryCat. #: 012569; RRID:MGI:104735Hongkui Zeng, Allen Institute for Brain Science
AntibodyAnti-GFP (chicken polyclonal)AbcamCat. #: AB13970; RRID:AB_300798IHC (1:1000)
AntibodyAnti-ChAT (goat polyclonal)MilliporeCat. #: AB144; RRID:AB_90650IHC (1:500)
AntibodyAnti-chicken Alexafluor488Life TechnologiesCat. #: A11039; RRID:AB_142924IHC (1:400)
AntibodyAnti-goat Alexafluor594AbcamCat. #: AB150132; RRID:AB_2810222IHC (1:1000)
Software, algorithmRR Project for Statistical ComputingRRID:SCR_001905
Software, algorithmMATLABMATLABRRID:SCR_001622
Software, algorithmIGOR ProIGOR ProRRID:SCR_000325

Animals

A total of 16 adult male WT (C57Bl/6), 51 adult male ChAT-Ai32 mice, and 5 ChAT-Cre mice were used in this study. ChAT-Ai32 mice were bred from ChAT-Cre mice that express Cre-recombinase under the control of the ChAT promoter (ChAT-Cre, Jackson Labs strain #006410; RRID:MGI:3689420) and mice of the Cre-reporter Ai32 line (Jackson Labs strain #012569; RRID:MGI:104735), which carries a Cre-dependent, enhanced YFP-tagged channelrhodopsin-2 (ChR2-eYFP)-containing expression cassette (Madisen et al., 2012). All animal experiments were performed under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB) under personal and project licenses held by the authors.

In vivo electrophysiology in anesthetized mice

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Mice were anesthetized with intraperitoneal injection of 1.2 g kg−1 urethane and their head was secured in a stereotaxic frame. Body temperature was maintained at 35 ± 1°C with a heating pad. The head was shaved, and the skin opened; Bregma and Lambda were aligned horizontally, and craniotomies were made above the MS and CA3. Simultaneous optical activation in the MS (AP +1 mm, ML 0 mm, DV −3.6 mm, coordinates from Bregma) with a stripped optic fiber (200 µm, 0.22 NA; Doric Lenses) and electrical recordings in the MS or in the hippocampus (ML +2.4 mm, AP −2.46 mm, DV −2.5 mm) using an extracellular parylene-C insulated tungsten microelectrode (127 µm diameter, 1 MΩ; A-M Systems) were performed.

Surgery

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Surgeries were carried out following minimal standard for aseptic surgery. Meloxicam (2 mg kg−1 intraperitoneal) was administered as analgesic 30 min prior to surgery initiation. Mice were anesthetized with isoflurane (5% induction, 1–2% maintenance, Abbott Ltd, Maidenhead, UK) mixed with oxygen as carrier gas (flow rate 1.0–2.0 L min−1) and placed in a stereotaxic frame (David Kopf Instruments, Tujunga, CA). The skull was exposed after skin incision and Bregma and Lambda were aligned horizontally. A hole was drilled above the MS at coordinates AP +1 mm and ML 0 mm, and an optic fiber (200 µm, 0.22 NA; Doric Lenses) was lowered toward the MS (DV −3.6 mm) at low speed (1 mm min−1). Once positioned just above the MS, the optic fiber was secured to the skull using dental cement (Prestige Dental).

Five ChAT-Cre mice underwent viral transduction of MS cholinergic neurons upon injection of viral particles. Two mice were injected with 0.5 µL of AAV5/9-EF1a-dio-EGFP-WPRE and three with 0.5 µL of AAV5/9-EF1a-dio-ChR2(H134R)-EYFP-WPRE (titers ranging 1.2–13.1012 vg mL−1; UNC Vector Core, Chapel Hill, NC), which were delivered through a metal cannula fixed to a 5 µL Hamilton syringe.

To perform recordings in freely moving animals, we implanted 10 mice with paired wire LFP electrodes, each consisting of two twisted 75 μm Teflon-coated silver wires (AGT0510, World Precision Instruments, Hitchin, UK) with tips spaced 150–300 µm and with one tip in the pyramidal cell layer. Mice were implanted bilaterally in CA1 (AP −1.7, ML ±1.2, DV 1 and 1.35, DV being taken from the surface of the brain). Ground and reference silver wires were connected to a stainless microscrew implanted over the cerebellum: AP −5.3, ML ±1.5. To record the electromyogram activity, a 75 μm Teflon-coated silver wire was implanted in the neck muscle. All wires were connected to a 32 pins Omnetics connector (Genalog, Cranbrook, UK). The exposed brain was covered with a protective dura gel (Cambridge NeuroTech, Cambridge, UK) to prevent damage upon cementing of the electrodes. LFP electrodes were individually glued to the skull using UV-cured glue (Tetric EvoFlow) and the implant was secured to the skull using dental cement (Super-Bond C and B; Prestige Dental, Bradford, UK). At the end of the implantation, 300–500 µL saline was injected subcutaneously for hydration and animals were placed in a post-surgery chamber at 34°C until full recovery from anesthesia. The mice were allowed to recover for 5 days before habituation started and during these 5 days were daily monitored and given oral Meloxicam as analgesic.

Appetitive Y-maze task

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Long-term spatial memory was assessed using the appetitive Y-maze task, as described in full by Shipton et al., 2014. Mice had to learn to find a food reward (condensed milk) on a three-arm maze that remained at a fixed location in relation to visual cues in the room. The three-arm maze, elevated 82 cm from the floor, consisted of gray-painted 50 × 13 cm arms bordered by 1 cm high white plastic walls, which extended from a central triangular platform. Plastic food wells (1.5 cm high) were positioned 5 cm from the distal end of the arms. Mice were kept on a restricted feeding schedule, allowing them to maintain at least 85% of their free food body weight. Before testing, the mice were habituated to the food reward and the maze in a different room to where behavioral testing would occur. During testing, mice were only allowed to make one arm-choice each trial and were only allowed to consume the reward if the correct arm was chosen, otherwise mice were removed from the maze and the trial was ended. Target arm assignments were counterbalanced such that at least one mouse of each experimental group was designated to each arm. Each mouse received 10 trials per day for 6–10 consecutive days, five starts from the left of the target arm and five starts from the right in a pseudo-random order with no more than three consecutive starts from the left or right. The interval between the within-day trials averaged 10 min. The maze was rotated either clockwise or anticlockwise after each trial to discourage the use of intra-maze cues to help solve the task. Optogenetic stimulation started either from the beginning of the trial (navigation and throughout cohorts) or when the mouse reached the goal zone (goal cohort). Light stimulation ceased when the mouse reached the goal zone for the navigation cohort. Light stimulation was performed using a blue laser at 473 nm (Ciel, Laser Quantum, Cheshire, UK), powered at 25 ± 1 mW with 50-ms-long pulses at 10 Hz. Stimulation was controlled using custom-made procedures in Igor Pro (WaveMetrics, Lake Oswego, OR; RRID:SCR_000325).

Optogenetic stimulation and electrophysiological recordings

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Data were acquired from five ChAT-Ai32 male mice, three ChAT-Cre mice expressing ChR2 (both referred to as ChAT-ChR2) and two ChAT-Cre mice expressing GFP in the MS (ChAT-GFP). The mice were implanted with LFP electrodes for electrophysiological recordings and optic fiber for optogenetic stimulation. These mice were recorded during sleep and while performing the appetitive Y-maze task.

For recordings during sleep, after connecting the electrodes to the Whisper acquisition system (Neural Circuits, LLC, Ashburn, VA) and optic fiber to the laser, the animals were placed in a cage (different to their home cage), to which the animal was habituated over a period of 2 days. The floor of the cage was covered with standard bedding. The recordings started after the mice visibly stopped moving and consisted of 30-s-long laser stimulation at 473 nm, power 25 ± 1 mW using 50-ms-long pulses at 10 Hz alternating with 60–120 s interval without the stimulation. An overhead webcam camera tracked the movement and position of the animal. The videos were manually reviewed together with the recorded EMG signal to exclude trials that were interrupted by the mice moving.

For Y-maze task, the mice underwent the same habituation and learning protocol as described above in the appetitive Y-maze task section. During learning, mice were connected to the laser and to the Whisper acquisition system and placed at the starting arm of the maze. The laser was activated in the goal zone on alternating trials to allow within-subject comparison. Data from these five mice were not used in the behavioral analysis as the stimulation protocol (50% of the trials) was different from that used in behavior only (stimulation performed for all trials).

The position of the animal was tracked with an overhead webcam and automatically extracted using custom procedures in MATLAB, 2019. All recordings were performed using the Whisper acquisition system sampling at 25 kHz, laser stimulation was triggered using custom-made procedures in Igor Pro and synchronized with the electrophysiological and webcam recordings.

Electrophysiology data analysis

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Data analysis was performed in MATLAB, 2019; RRID:SCR_001622 and R version 3.4.4 (R Development Core Team, 2018; RRID:SCR_001905). To reduce contamination by volume conducted signal, we used staggered wire electrodes targeting the CA1 with one electrode in the pyramidal cell layer, and the differential signal was used to enhance signal differences between the hippocampal layers. To remove noise artifacts caused by wire movement and muscle contractions, changes in the consecutive samples of the EMG signal were detected. If the change exceeded a threshold set to two standard deviations, a 500-ms-long window of the signal centered on the noise timestamp was removed. We conducted the analysis on one of the bilaterally implanted CA1 LFP electrodes, selected based on the quality of signals for both theta oscillations and ripples.

For ripple detection, we adapted the method from Vandecasteele et al., 2014. The signal was down-sampled to 1.25 kHz and 100–250 Hz bandpass filtered with Type II Chebyshev phase-preserving filter (filter order = 4, stopband attenuation = 20 dB). Next, the filtered signal was squared, mean-subtracted, and smoothed by applying a moving average with 10-ms-long window. Ripples were detected when the squared signal crossed two standard deviations for 20–300 ms duration and its peak crossed seven standard deviations. Spectral peak frequency of a ripple was estimated as the frequency with maximum value in the PSD estimated with multitaper method on the signal from the ripple start to end. Ripple incidence was calculated as the number of detected ripples divided by the duration of the recording.

PSD was estimated using Welch’s method (MATLAB built-in pwelch function with 0.5 s window and 0.25 s overlap) for frequencies spanning the range from 1 to 200 Hz. To visualize instantaneous changes in PSD during Y-maze trials, spectrogram was created with continuous wavelet transform using Morlet wavelets (MATLAB built-in cwt function with default parameters). Throughout the study, we defined theta band as 5–12 Hz and slow gamma as 25–45 Hz. The slow gamma frequency upper bound was chosen to exclude any line noise contamination at 50 Hz.

To estimate relative theta and slow gamma power, we used the FOOOF tool (Donoghue et al., 2020https://github.com/fooof-tools/fooof). It models the estimated PSD as the sum of an aperiodic component and Gaussian peaks in narrowband frequencies. The aperiodic component was fitted on the PSD log-log plot with a straight line, which corresponds to a pink noise-like (1/f) background. To minimize the model error – the difference between the actual and the modeled PSD – the aperiodic component was estimated in two frequency ranges separately (3–15 and 15–150 Hz). Relative theta and slow gamma peaks and their spectral peak frequencies were taken from the Gaussian peaks fitted above the aperiodic component.

Statistical analysis

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Statistical analysis was performed in R version 3.4.4 (R Development Core Team, 2018). Data are reported as mean ± SEM unless otherwise stated. For significance testing, the normality of the data was assessed by Shapiro-Wilk test and by inspection of the quantile-quantile plot. If the normality criterion was satisfied (p>0.05), a parametric test (one-way ANOVA or t-test) was used, otherwise a non-parametric test (one-way ANOVA on ranks or Wilcoxon test) was used, as described in the Results section. Following a significant one-way ANOVA on ranks, differences between groups were tested using a Dunn post hoc test with Holm-Bonferroni correction. To distinguish between the absence of effects and inconclusive results, we calculated BFs for the behavioral results (Keysers et al., 2020). Bayesian ANOVA was conducted using JASP software with default priors. BFs were calculated as the ratio between the likelihood of the data given the model with the effect of mouse group vs. the intercept only model. The post hoc pairwise comparisons were conducted using Bayes t-test in JASP with Cauchy priors without correcting for multiple comparisons.

Only mice with SWRs at the goal location were included in the analysis of the optogenetic stimulation effects during the Y-maze task (n = 5 ChAT-ChR2 and n = 2 ChAT-GFP mice). The statistical analysis of the effects in sleeping mice was performed using all CA1-implanted mice (n = 8 ChAT-ChR2 and n = 2 ChAT-GFP mice).

The effects of the optogenetic stimulation were assessed with linear mixed-effects models. This method allows for correlated samples from trials repeated in the same mouse and allows for an unbalanced number of samples between mice. Laser stimulation (L ∈ {0 for inactive, one for active}) and effect of the mice group (G ∈ {0 for ChAT-GFP, one for ChAT-ChR2}) were fixed effects in the models; the random variable representing the animal (a) was treated as a random effect in the intercept and slope estimation. The quantity Y in the trial i for animal a was modeled as:

Yi=β0+R0a+(β1+R1a)L+β2G+ϵi,

where

  • β012, are linear regression coefficients for the fixed effects.

  • R0a,R1a are random effects: normally distributed animal-specific corrections for linear regression coefficients with zero mean and maximum-likelihood standard deviation estimated by the model.

  • εi is a random error with a normal distribution with zero mean and maximum-likelihood standard deviation estimated by the model.

The residual errors were checked for the assumptions of the linear models: mean of zero, no correlation with the predicted values and homoscedasticity. To satisfy these assumptions, in some models, a log-linear model of variable Y was created instead, otherwise as described above. The significant interactions were reported and the post hoc tests were performed on differences in least-square means of the paired groups. The tests used Satterthwaite estimation of degrees of freedom and adjusted p-values using Holm-Bonferroni correction.

The linear mixed-effects models were built in R with package ‘lme4’ and p-values for the fixed effects were obtained using Satterthwaite estimation of degrees of freedom implemented in the ‘lmerTest’ R package. Least square means were calculated and tested with ‘lsmeansLT’ function from the same package.

Histological processing

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Animals were terminally anesthetized by intra-peritoneal injection of pentobarbital (533 mg kg−1) and then transcardially perfused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde. Brains were removed and post-fixed for 24–48 hr, then rinsed and subsequently cryoprotected overnight in 30% (w/v) sucrose dissolved in PBS. Coronal sections of 30–40 μm thickness of the MS and hippocampus were cut using a microtome (Spencer Lens Co., Buffalo, NY).

To verify the expression of ChR2 fused with the eYFP tag or ArchT fused with the eGFP tag and visualize the location of cholinergic neurons, sections were immunostained for eYFP/eGFP and ChAT. After rinsing in PBS, sections were incubated for 1 hr in a blocking solution comprising PBS with 0.3% (w/v) Triton X-100% and 5% (w/v) donkey serum (Abcam) containing 1% (w/v) bovine serum (Sigma). Sections were then incubated for ≥15 hr at 4°C with chicken anti-GFP (1:1000, Abcam AB13970; RRID:AB_300798) and goat anti-ChAT (1:500, Millipore AB144; RRID:AB_90650) antibodies. The sections were then washed, followed by 2 hr of incubation in blocking solution containing anti-chicken Alexafluor488 (1:400; Life Technologies A11039; RRID:AB_142924) and anti-goat Alexafluor594 (1:1000, Abcam AB150132; RRID:AB_2810222) at room temperature. Finally, the sections were rinsed and mounted in Fluoroshield with DAPI (Sigma).

To identify the placement of the electrodes (aided by Dil application on electrodes prior their insertion in the brain) and optic fiber tracks for each mouse, sections containing evidence of the implants were selected and mounted in Fluoroshield (Sigma).

Sections were examined with a Leica Microsystems SP8 confocal microscope using the 10× and 20× magnification objectives. The eYFP+/GFP+ and ChAT+ cells were quantified manually using the ImageJ software. The location at which the implant appeared the deepest was determined and used to plot the implant location on the corresponding section in a mouse brain atlas (Franklin and Paxinos, 2007).

Data and code availability

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Code used for the analysis and to generate the figures can be accessed on the authors’ GitHub site: https://github.com/przemyslawj/ach-effect-on-hpc (Jarzebowski et al., 2021; copy archived at swh:1:rev:3d4f5f8cecf7e6cc1b4bee7713bc582d5797674b).

Data availability

Code used for the analysis and to generate the figures can be accessed on the authors' GitHub site: https://github.com/przemyslawj/ach-effect-on-hpc (copy archived at https://archive.softwareheritage.org/swh:1:rev:3d4f5f8cecf7e6cc1b4bee7713bc582d5797674b/). Raw data are available on Zenodo.

The following data sets were generated
    1. Jarzebowski P
    2. Tang CS
    3. Paulsen O
    4. Hay YA
    (2021) Zenodo
    ID 4708331#.YIKw7qlKhpQ. Impaired spatial learning and suppression of sharp wave ripples by cholinergic activation at the goal location.

References

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    1. Franklin KBJ
    2. Paxinos G
    (2007)
    The Mouse Brain in Stereotaxic Coordinates. Third Edn Academic Press
    New York, NY: Elsevier Inc.
  2. Software
    1. MATLAB
    (2019) MATLAB, Release 2019b
    The MathWorks, Inc.
  3. Book
    1. O’Keefe J
    2. Nadel L
    (1978)
    The Hippocampus as a Cognitive
    Oxford University Press.
  4. Software
    1. R Development Core Team
    (2018) R: A Language and Environment for Statistical Computing
    R Foundation for Statistical Computing, Vienna, Austria.

Decision letter

  1. Laura L Colgin
    Senior and Reviewing Editor; University of Texas at Austin, United States
  2. Jiamin Xu
    Reviewer
  3. Fabian Kloosterman
    Reviewer; KU Leuven, Belgium

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

This paper is of interest for those interested in the roles of cholinergic projections from the medial septum and sharp wave-ripples on reward learning. The work provides compelling evidence showing that activation of septal cholinergic cells at reward zones suppresses sharp wave-ripples and impairs behavioral performance in freely behaving animals. The work extends our knowledge of the effect of medial septum cholinergic projections on spatial memory.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Cholinergic suppression of sharp wave-ripples impairs hippocampus-dependent spatial memory" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Stefan Remy (Reviewer #2); Jiamin Xu (Reviewer #3).

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

Reviewers were enthusiastic about the question and the system that was studied. However, major concerns were raised about the paper in its current form. Reviewers were not convinced that the conclusions of the paper are robustly supported by the results. Specifically, reviewers raised concerns about the low sample size and other methodological aspects of the study. Reviewers felt that, for the results to fully support the conclusions, additional experimentation and additional control experiments would be required. Concerns were also raised about the novelty of some of the results. Some of the results provide insights that are similar to those reported in previous studies (e.g., Vandecasteele et al., 2014 and Ma et al., 2020), specifically regarding cholinergic suppression of hippocampal ripple oscillations and the involvement of muscarinic receptors in this effect.

We would be willing to consider a majorly revised version of this study in the future, should you choose to go that route.

Reviewer #1:

This paper attempts to address the interesting question of how activating septal cholinergic neurons affects learning and memory. Unfortunately, however, the conclusions are not strongly supported by the results.

1. Proper controls for the ontogenetic experiments are missing. For example, a light only control is only included for the goal vs. no stimulation comparison in the first part of the paper. No control for light artifacts is included in the last part of the paper in which theta and gamma are examined. 10 Hz stimulation is. used, yet there is no discussion of effects of light artifacts on spectral measurements within this range.

2. A major problem is that the duration of the light stimulation protocol differs across groups. The authors did not consider the possibility that effects were due to the circuitry being perturbed differently by different durations of stimulation (longer cholinergic stimulation vs. shorter cholinergic stimulation).

3. It is difficult to interpret the data from the "stimulation throughout" group. The authors did not statistically compare "goal stimulation" and "stimulation throughout" groups. If the interpretation is correct that activating MS cholinergic neurons around the goal impairs learning, then one would expect "stimulation throughout" to also impair learning since it also includes stimulation at the goal.

4. The authors do not provide convincing causal evidence that suppression of sharp waves around the goal causes impaired learning, although they seem to imply this throughout the paper.

5. Some of the main results in the paper have been reported previously. The SWR suppression due to cholinergic stimulation is not novel, as the authors acknowledge. Vandecasteele et al. (2014) showed this effect in behaving and urethane anesthetized animals, and Ma et al. (2020) showed it in sleep. Ma et al. also reported muscarinic receptor involvement in SWR suppression.

6. Are these methods able to reliably detect sharp wave ripples? Some muscle artifacts can look like sharp wave ripples. In the absence of depth profiles from a linear probe or detection of populations bursts from single units, it is difficult to evaluate the extent to which the authors' sharp wave-ripple detection methods are reliable.

Reviewer #2:

In this manuscript Jarzebowski et al. investigate the dynamical aspect of cholinergic modulation. The authors report phase-specific effect of optogenetic cholinergic modulation in the appetitive Y-maze long-term memory task, as well as the switches of the CA3 and CA1 activity from ripple activity to theta/gamma oscillations. This work builds on the previously published studies (Vandecasteele et al., 2014; Ma et al., 2020) that have already reported reduction in ripple incidence upon MS cholinergic stimulation. The novelty of this study lies in reporting the differences on learning dependent on the time (and location) of the simulation and apparent differences on the CA1 and CA3 ripples incidence. However, the conclusions in some cases are not strongly enough supported by the data. In my view several points need clarification and in particular the statistical underpowering resulting from very low number of animals in the results presented in Figure 5 speak against publication of the manuscript in the current form.

1. The results showing decrease in learning following the MS cholinergic stimulation (2C-E) in the goal zones are convincing. However, it is known that SWRs may differ substantially in length, frequency and amplitude dependent on the behavioural state of the animal (ex. Joo and Frank, Nat Neuro Review 2019), which may have specific relevance for memory. Thus, by stimulating both the rewarded and the non-rewarded goal zones the effects of the stimulation on SWR may have differential effects on SWRs in rewarded and non-rewarded locations. The authors should compare SWRs and the effects of stimulation in rewarded and non-rewarded goal zones. A question is whether effects on SWRs in the non-rewarded goal zone are similar or substantially different, in which case the interpretation of how stimulation may have mechanistically contributed to memory formation could be fundamentally different.

2. The authors show the reduction of ripples incidence in CA1 and CA3 upon MS cholinergic stimulation in both in sleeping and anesthetized animals. However, they also claim that "…MS cholinergic stimulation enhanced a scopolamine-sensitive theta oscillation in both anesthetized and sleeping mice (supplementary Figure 3)". To substantiate this claim by data, they need to block muscarinic receptors in sleeping animals to assure that it holds in both conditions. I did not find these data in the manuscript. Scopolamine sensitivity is a classical criterion for differentiation of types of theta oscillations so that this information would be highly relevant for the interpretation of the results with respect to effects of stimulation on theta oscillations. If the authors did not perform these experiments, they should not extend the interpretation to the sleep condition.

3. Figure 4: Using the method presented in Haller et al., 2018, the authors show an increase in theta power and gamma power in both CA1 and CA3, following MS cholinergic stimulation. While these results are interesting, it would be much more convincing if the authors showed the grouped data of all animals, including the error bars in the Figure 4B, instead of only the data obtained from an individual animal. More importantly the statistical analysis depends on a linear fit of the background spectrum intensity, which from inspecting the data seems moderately imprecise. Since the differences reported are rather small, the authors should use a 1/f polynomial fit (as also used in Haller et al., 2018) of the background spectrum intensity which would confirm the robustness of the described differences.

4. Related to Figure 4: The authors state: "The increase in the power was associated with significantly lower frequency of the theta peak in the CA1, but not in the CA3." It would be helpful for the reader to also show these results in the Figure.

5. The authors are reporting: "Slow gamma (25-45 Hz) increased in CA1 by 54 {plus minus} 6 % and in CA3 by 8 {plus minus} 8 % ". It has been previously shown that slow gamma is driven by CA3 and propagates to CA1 (Colgin et al., 2014, 2016, Schomburg et al., 2014). To my understanding, this would imply that the low gamma activity should reach CA1 with a smaller amplitude than measured in CA3. As the opposite is observed here, does that imply that the local CA1 network activity lead to amplification of the gamma power in CA1? This should be discussed.

6. In the Figure 5, the authors report decreases in CA1, but not in CA3 ripples incidence in the goal zone following the MS cholinergic stimulation. I have several difficulties with the interpretation of the results presented in this Figure. First, the authors state that only successful trials were analysed. As mentioned above already, given that the stimulation was performed also during the unsuccessful trials, the results obtained in the unrewarded goal zones should be presented. Furthermore, it is stated: "…n=3 mice included in the analysis with the ripple incidence at goal {greater than or equal to} 0.03". I think that "{less than or equal to} 0.03" is meant here. In any case, looking carefully at the Figure 5D, a decrease of the ripple incidence at goal can be observed in 2 of the 3 animals while the increase observed in one single animal accounts for no difference which underlies the conclusion that the effects are specific for CA1. This experiment is strongly underpowered and the conclusions are not convincingly supported by the data.

7. Furthermore, no difference in theta and gamma power in CA1, but increase in slow gamma in CA3 (5E-5G) are reported. Looking carefully at 5I, a trend in increase could be also observed in CA1. Also here, the number of animals must be increased.

8. The title of the paper is: "Cholinergic suppression of sharp wave-ripples impairs hippocampus dependent spatial memory" The authors are also showing the effect of MS cholinergic stimulation on the SWRs during sleep, but the link between this effect and spatial memory is not explored. It is known that supressing SWRs specifically during the post-learning sleep impairs spatial memory (Girardeau et al., Nature Neuroscience 2009). Thus, it would be interesting to see whether the MS cholinergic stimulation and SWRs suppression during sleep impairs spatial memory. This should be at least discussed.

Reviewer #3:

The study focused on the effect of MS cholinergic activity on hippocampal oscillations and extended our knowledge of cholinergic function in hippocampus-dependent spatial learning. Impairment of hippocampal ripple oscillations during awake immobility leads to significant performance deficit in the Y-maze task, highlighting the importance of precise timing of cholinergic input in memory formation.

1. The conclusion points 1/2/3 in the abstract maybe more coherent if rearranged to 3/1/2. But this might lead to structural re-organization of the article and the figures (suggested figure order: Figure 1-3-4-2-5). The logic behind this order is:

a. Optogenetic stimulation of MS cholinergic neurons impair hippocampal ripples during SWS (Figure 3/4).

b. What about ripples during awake immobility?

c. Optogenetic stimulation during goal period of the Y-maze impairs learning (Figure 2).

d. Such impairment was due to reduced CA1 ripple incidence (Figure 5).

2. In the introduction: "activation of MS cholinergic neurons switches the CA3 and CA1 network states from ripple activity to theta/gamma oscillations". The phrasing might be questionable

3. Page 18, first paragraph, the description seems a little bit redundant.

4. Figure 3A, it seems that theta oscillations emerge with the optogenetic stimulation, was the stimulation strictly delivered during SWS or was this particular theta represents REM states? It would be better if the authors also show the LFP and ripple trace after the light stimulation.

5. Page 22, last sentence of the first paragraph, "…while the peak frequency did not significantly change from 39 {plus minus} 1 Hz in the CA1 and 38 {plus minus} 2 Hz in the CA3…", is a little bit confusing, needs further explanation.

6. Page 22, second paragraph, relative theta and gamma power change was reported in CA3, how about CA1?

7. In previous reports (Vandecasteele, 2014 and Ma, 2020), MS cholinergic stimulation can completely inhibit hippocampal ripple oscillations. But in this study, there are still a lot of ripples not suppressed. Please explain the difference.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Impaired spatial learning and suppression of sharp wave ripples by cholinergic activation at the goal location" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Laura Colgin as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Jiamin Xu (Reviewer #2); Fabian Kloosterman (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

Some conclusions that are strongly stated in the current version will need supporting data in the future or will need to be toned down with caveats discussed. Please refer to the individual reviews below for specific details.

Reviewer #1 (Recommendations for the authors):

The authors satisfactorily addressed my prior major concerns. I have no major concerns remaining. I only have a few concerns remaining that can be easily addressed by the authors without affecting the major conclusions of the paper.

Page 5: "In hippocampal CA3, cholinergic activation induces a slow gamma rhythm primarily by activating M1 muscarinic receptors": It should be noted that these are in vitro studies. As this sentence is written currently, the comparisons between CA3 and CA1 are potentially misleading.

Page 10: "We did not detect an effect of the laser on the duration the mice spent at the goal location (linear mixed-effects model, mouse group – laser interaction: F(1, 78) = 0.01, p = 0.94, laser effect: F(1, 78) = 0.1, p = 0.73)." I am confused by this passage. If optogenetic stimulation at the reward location affects memory, as the authors claim, one would expect a differential effect of laser stimulation on the ChR2 mice compared to the GFP-only control mice (and a significant effect of the laser in the ChR2 group). Yet, a non-significant interaction effect is reported.

Page 13: "Because we detected few SWRs in the unrewarded trials, we restricted the statistical analysis of the effects of optogenetic stimulation to rewarded trials. The SWR incidence in the non-stimulated trials was not significantly different between early (before day 5) and late learning (linear mixed-effects model, effect of early vs late learning: F(1, 110) = 0.3, p = 0.58, Figure 4D).": The authors state that they restricted analysis of effects of optogenetic stimulation to rewarded trials but then analyze SWR incidence in the very next sentence and paragraph. Is there a typo here?

Reviewer #2 (Recommendations for the authors):

This submitted work is clearly better structured and well-controlled version of the previously submitted manuscript. The authors addressed most of the comments. I only have two concerns.

1. The optogenetic stimulation induced ripple inhibition effect at the goal location and during sleep seems a little bit inconsistent (Figure 4E and Figure 5C). The authors stated in the text the ripple inhibition statistics under the two conditions: ripple incidence was reduced from 0.11 {plus minus} 0.01 Hz to 0.05 {plus minus} 0.01 Hz when stimulated at the goal location, and from 0.21 {plus minus} 0.01 Hz to 0.03 {plus minus} 0.01 Hz during sleep. Also, from the authors' response to the review comments (Reviewer #3, major comment 7), "We report that SWR incidence in 4 ChAT-ChR2 decreased by 100% and for another 4 reduced by a median of 88 {plus minus} 2%.". I assume that the authors were referring to the situation during sleep (based on the statistics and the number of animals used in each condition), which means that the ripple inhibition effect would be somewhere around 50% during stimulation at the goal location. Please elaborate on this.

2. Although the analyses of the effect of optogenetic stimulation on hippocampal theta and gamma oscillations during sleep reveal some very interesting results, it seems that the relevance of these analyses with the key claim of the submitted work (as indicated by the title) is a little bit farfetched.

Reviewer #3 (Recommendations for the authors):

1. Figure 3B: the spectrogram shows high relative (z-scored) power for the high frequencies in start and center, but not goal. I would have expected to see at least some high power in the ripple band in the goal. Could the authors clarify how exactly the z-scoring was performed? If the spectrogram is an average across multiple trials, then this will tend to obscure transient, non-time locked oscillations like SWRs.

2. What is the effect of optical stimulation of cholinergic neurons on theta/gamma/SWRs in the "throughout" and "navigation" conditions? Are these effects consistent with the hypothesis that the learning deficit is caused by a reduction of SWRs at the goal location? Could additional insights be obtained into possible changes induced by stimulation (e.g. theta oscillations during navigation) that do *not* correlate with the learning deficit?

3. Page 19: the authors cite Jadhav et al. 2012 when stating "disruption of SWRs in the first 15 to 60 minutes following training impairs learning of spatial navigation tasks". However, Jadhav et al. disrupted SWRs during the training and not following the training.

4. Page 20: Both reverse and forward replay are observed during brief pauses or reward consumption in the awake state when animals explore a maze or learn a task. So, it is likely that in the reward zone in the Y-maze task one will observe both forward and reverse replay. While it is fine to speculate that disruption of reverse replay mediates the behavioral deficit, it cannot be based on the assumption that replay at the goal location is only of the reverse kind.

5. What is the time in between individual trials?

6. To characterize the learning in the Y-maze, the authors determine the day at which criterion is reached. This metric is rather coarse. Instead, the authors could fit a learning curve (e.g. sigmoid function) to the trial responses and estimate the learning rate for each animal. Furthermore, it would be informative to show individual learning curves for all animals, in addition to the average learning curves that are shown now.

7. To assess the effect of stimulation at the goal on hippocampal activity, the authors look at average SWR rate and average theta/gamma power. However, when the animals are in the goal region, they likely show a mixture of behavioral states that is associated with periods of theta and non-theta (incl. SWRs). Is more (or less) time spent in theta state during stimulation? Could it be that time spent in non-theta states is lower, but SWR rate within this state has not changed? Judging from the example in figure 4B, it may be the case that with stimulation the first SWR after arriving at the goal is delayed compared to no stimulation condition – is this consistent across all subjects?

https://doi.org/10.7554/eLife.65998.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Reviewer #1:

This paper attempts to address the interesting question of how activating septal cholinergic neurons affects learning and memory. Unfortunately, however, the conclusions are not strongly supported by the results.

1. Proper controls for the ontogenetic experiments are missing. For example, a light only control is only included for the goal vs. no stimulation comparison in the first part of the paper. No control for light artifacts is included in the last part of the paper in which theta and gamma are examined. 10 Hz stimulation is. used, yet there is no discussion of effects of light artifacts on spectral measurements within this range.

As pointed out by the Reviewer, the manuscript’s initial version did not control for potential artifacts of the optogenetic stimulation on theta-gamma activity or SWR events. To address this concern, we performed additional recordings during natural sleep and the Y-maze task from two ChAT-Cre mice injected with a GFP-expressing AAV in the MS. The results have been added to Figures 3-6 and the associated supplementary figures. To compare the effects of stimulation between the ChAT-GFP and ChAT-ChR2 mice, we have extended our linear mixed-effects model to include the effects of the stimulation, the mouse group, and their interaction. We observed that the effect of optogenetic stimulation on SWR incidence was different between the two mouse groups in the natural sleep and the memory task (significant interaction between the stimulation condition and the mouse group effects). The effect on the theta-gamma activity was different between the mouse groups in the natural sleep but not the memory task. We used Dunn post hoc test to estimate p-values for the effect of the stimulation within the groups. We amended the Methods and the Results sections for Figures 3-6 accordingly, and we rule out the potential problem that the stimulation frequency is within the theta frequency range in the Discussion section. We do not report all the changes here as the modifications have been extensive.

2. A major problem is that the duration of the light stimulation protocol differs across groups. The authors did not consider the possibility that effects were due to the circuitry being perturbed differently by different durations of stimulation (longer cholinergic stimulation vs. shorter cholinergic stimulation).

Indeed, the duration of the light stimulation differed between the mouse groups with the ‘navigation’ group receiving the shortest stimulation (8 ± 1 s), followed by ‘goal’ (34 ± 1 s) and by the ‘throughout’ stimulation (42 ± 1 s). The experimental design does not allow us to rule out a role of duration fully; however, if the duration was the decisive variable then we would expect light throughout the maze to induce more impairment than light only at goal location, which is the opposite of what we observed. This argument supports our claim that stimulation at different stages of the task has a differential effect. These results are now better described (page 8), and we added a paragraph to the Discussion about this potential problem (page 17-18).

Page 8: “Whilst the stimulation for the ‘goal’ group lasted longer than the ‘navigation’ group (34 ± 1 s vs 8 ± 1 s), the duration alone cannot explain the different effects of the optogenetic stimulation. The ‘throughout’ group received the longest stimulation (42 ± 1 s) but presented an intermediate learning curve: it was not significantly different from either the ‘no stimulation’ group (p = 0.28) or the ‘goal’ group (p = 0.54). Therefore, the spatial location in the maze where the optogenetic stimulation took place was most likely the factor that decided the behavioral outcome.”

and page 17-18: “The stimulation duration differed between the groups: it was longest in the ‘throughout’ group, followed by ‘goal’ and by ‘navigation’ group. The only significant impairment of task acquisition was seen in the ‘goal’ group, indicating that it was cholinergic activation at the goal location that interfered with memory (Figure 2C,E). It may appear surprising that we did not also see a significant impairment with cholinergic stimulation throughout the task. However, the task performance in the ‘throughout’ group was not significantly different from the ‘goal’ group. Nevertheless, we cannot exclude the possibility that prolonged optogenetic stimulation becomes less effective over time, either because the MS neurons become less activated or because vesicular ACh might be depleted with prolonged stimulation.”

3. It is difficult to interpret the data from the "stimulation throughout" group. The authors did not statistically compare "goal stimulation" and "stimulation throughout" groups. If the interpretation is correct that activating MS cholinergic neurons around the goal impairs learning, then one would expect "stimulation throughout" to also impair learning since it also includes stimulation at the goal.

We have now added the comparison between the ‘goal stimulation’ and ‘stimulation throughout’ groups to the main text, and we observed no statistical difference between the groups. This statistical result is consistent with the Reviewer’s interpretation that stimulation ‘throughout’ also impairs the learning of the task, and, indeed, although not statistically significant, we observed a trend towards delayed learning with stimulation throughout. One possible explanation for the apparently reduced effect of the stimulation in the “throughout” group could be that prolonged optogenetic stimulation becomes less effective over time, resulting in a reduced amount of ACh released when the mouse reaches the goal area.

We have now added the statistical comparison in the main text, which reads (page 8):

“The ‘throughout’ group received the longest stimulation (42 ± 1 s) but presented an intermediate learning curve: it was not significantly different from either the ‘no stimulation’ group (p = 0.28) or the ‘goal’ group (p = 0.54).”

We also discuss possible reasons why the ‘throughout’ group fails to show a significant difference from the ‘no stimulation’ group (page 18):

“It may appear surprising that we did not also see a significant impairment with cholinergic stimulation throughout the task. However, the task performance in the ‘throughout’ group was not significantly different from the ‘goal’ group. Nevertheless, we cannot exclude the possibility that prolonged optogenetic stimulation becomes less effective over time, either because the MS neurons become less activated or because vesicular ACh might be depleted with prolonged stimulation.”

4. The authors do not provide convincing causal evidence that suppression of sharp waves around the goal causes impaired learning, although they seem to imply this throughout the paper.

We agree with the Reviewer that our study provides correlational rather than causal evidence. We updated the manuscript, including the title, to remove any implication of a causal effect. Based on previous research on the SWRs role in memory and because SWR incidence reduction was the most prominent effect induced by ACh neuron stimulation on the CA1 LFP in the behaving mouse, we believe the most parsimonious explanation for the effect of ACh on spatial learning is through the reduced incidence of SWRs. However, and as discussed in the original manuscript, we now also discuss that ACh promotes synaptic depression (Brzosko et al., 2019) and synaptic inhibition (Hasselmo and Sarter, 2011; Haam and Yakel, 2017) and we cannot rule out here that ACh could act not only through changes in SWRs but also on plasticity or inhibition.

We changed the title of the article to reflect the findings better:

“Impaired spatial learning and suppression of sharp wave ripples by cholinergic activation at the goal location”

and changed the text on page 14, which now reads:

“Hence this result suggests that the reduced SWR incidence is a mechanism relevant for the memory impairment induced by cholinergic stimulation in this task.”

5. Some of the main results in the paper have been reported previously. The SWR suppression due to cholinergic stimulation is not novel, as the authors acknowledge. Vandecasteele et al. (2014) showed this effect in behaving and urethane anesthetized animals, and Ma et al. (2020) showed it in sleep. Ma et al. also reported muscarinic receptor involvement in SWR suppression.

It is correct that some of our results have been reported previously, which we acknowledge throughout the manuscript. Because the focus of our paper is on the learning impairment induced by cholinergic stimulation and the observation that this impairment correlates with a decrease of SWR incidence at goal location, we have now removed from the manuscript the replication of results for urethane anesthetized animals and removed the description of results on the role of muscarinic receptors. However, we kept results for naturally sleeping mice for two main reasons.

First, the results obtained in naturally sleeping animals are a useful comparison to the results obtained in the memory task, especially since a change in theta-gamma activity observed in the sleeping animals with cholinergic stimulation might also have been expected in the memory task.

Second, we believe that the use of an alternative analysis technique, one that distinguishes between periodic and aperiodic components of the PSD (originally referred to as Haller et al. bioRxiv 2018, now Donoghue et al., Nat Neurosci 2020), will be of significant interest to the readership since it may help resolve some apparent discrepancies in the field. Vandecasteele et al. (2014) report that MS stimulation ‘increased theta power in anesthetized mice but it decreased or had no effect on theta power in behaving mice’ and Ma et al. (2020) report in sleeping mice that ‘During the stimulation period, there was no obvious evidence of hippocampal theta oscillations … But we observed a significant change on hippocampal theta power as it decreased from 0.59 ± 0.17 to 0.41 ± 0.12’ (our italics). Distinguishing between periodic and aperiodic components of the PSD could help explain these non-intuitive results, which are only alluded to in Vandecasteele et al. and Ma et al. We observed a reduction of the aperiodic component of the PSD but an increase in the periodic component in the theta band by cholinergic stimulation in naturally sleeping animals (Figure 6). Thus, a reduction in the aperiodic component may cancel out the increase in the periodic component. Arguably, it is the periodic component that better reflects theta oscillatory activity. A similar argument could be made for the slow gamma oscillation.

We believe it is important to report these results, that they will be of interest, and that they deserve to be published.

6. Are these methods able to reliably detect sharp wave ripples? Some muscle artifacts can look like sharp wave ripples. In the absence of depth profiles from a linear probe or detection of populations bursts from single units, it is difficult to evaluate the extent to which the authors' sharp wave-ripple detection methods are reliable.

To record sharp wave ripples we used the signal from paired wire electrodes staggered in the dorso-ventral direction. The electrode placement allowed us to record ripple activity with phase-reversal, but we observed the sharp-waves only on some of the paired electrodes. As pointed out by the Reviewer, electrical noise or muscle movement can cause ripple-like profile in the signal. Therefore, we applied two methods that aim to limit the events we might otherwise incorrectly classify:

1. We coupled the LFP and the EMG recordings. Whenever we detected a high amplitude change in the EMG signal, we excluded any ripple-like events from the surrounding time window.

2. We subtracted the signal between paired wire electrodes whose tips were spaced 150-300 μm in the dorso-ventral direction. This procedure canceled out synchronous changes on both electrodes like those caused by electrical noise or muscle artifacts, and it strengthened the same frequency, the out-of-phase signal on both electrodes, e.g. due to ripple phase reversal.

Both of these methods ensure the detected events are phenomena in the CA1 local field potential.

The short bursts of fast oscillations could be sharp-wave-associated ripples or, as differentiated by some studies, fast gamma ripples (Sullivan et al. 2011). These two were described as having different physiological mechanisms and different spectral peak frequency (Sullivan et al. 2011). The spectral peak frequency for the detected ripples showed bimodality (Figure 5—figure supplement 1A) as previously described in Sullivan et al. 2011, and we changed our methods to only classify as SWRs the events with spectral peak frequency ≥140 Hz. The stimulation significantly decreased ripple incidence in sleep and the Y-maze task when tested for all ripples and tested for ripples with ≥140 spectral peak frequency.

Finally, we would like to comment that because the SWR detection methods rely on a set threshold for ripple detection, the distinction between ripple and non-ripple events is always somewhat arbitrary (Buzsaki et al. 2015).

We added a Figure 3—figure supplement 1 with examples demonstrating the signal from the paired electrodes, its processing, and the detected ripples. Figure 3—figure supplement 2A and Figure 5—supplement 1A show peak power frequency of all detected ripple events.

On page 10 we added the following about the LFP signal:

“We used staggered wire electrodes to record the field potentials and subtracted the signal in one electrode from that in the other. This subtraction procedure cancels out synchronous changes on both electrodes, like those caused by movement artifacts, and enhances locally generated phase-reversed signals, such as theta, gamma and ripple events.”

On pages 12-13 we added the following about the SWR detection:

“To identify the SWRs, we detected ripple events in the LFP and excluded any candidate ripples that co-occurred with electromyography (EMG)-detected muscle activity. Only ripples with spectral peak frequency ≥140 Hz were identified as SWRs (Sullivan et al. 2011, Figure 4A, Figure 4—figure supplement 1 and 2).”

Reviewer #2:

In this manuscript Jarzebowski et al. investigate the dynamical aspect of cholinergic modulation. The authors report phase-specific effect of optogenetic cholinergic modulation in the appetitive Y-maze long-term memory task, as well as the switches of the CA3 and CA1 activity from ripple activity to theta/gamma oscillations. This work builds on the previously published studies (Vandecasteele et al., 2014; Ma et al., 2020) that have already reported reduction in ripple incidence upon MS cholinergic stimulation. The novelty of this study lies in reporting the differences on learning dependent on the time (and location) of the simulation and apparent differences on the CA1 and CA3 ripples incidence. However, the conclusions in some cases are not strongly enough supported by the data. In my view several points need clarification and in particular the statistical underpowering resulting from very low number of animals in the results presented in Figure 5 speak against publication of the manuscript in the current form.

1. The results showing decrease in learning following the MS cholinergic stimulation (2C-E) in the goal zones are convincing. However, it is known that SWRs may differ substantially in length, frequency and amplitude dependent on the behavioural state of the animal (ex. Joo and Frank, Nat Neuro Review 2019), which may have specific relevance for memory. Thus, by stimulating both the rewarded and the non-rewarded goal zones the effects of the stimulation on SWR may have differential effects on SWRs in rewarded and non-rewarded locations. The authors should compare SWRs and the effects of stimulation in rewarded and non-rewarded goal zones. A question is whether effects on SWRs in the non-rewarded goal zone are similar or substantially different, in which case the interpretation of how stimulation may have mechanistically contributed to memory formation could be fundamentally different.

We delivered light-stimulation in the goal area, in either the rewarded or the non-rewarded arm. The rationale for stimulating in both arms was to apply a similar duration of stimulation to all mice, irrespective of the progression of learning. We agree with the reviewer that SWRs occurring after rewarded and unrewarded trials likely differ. To give an overview of SWRs in the unrewarded goal zone, we now quantified and included data showing that a significantly smaller fraction of unrewarded than rewarded trials had one or more ripples in the goal zone (Figure 4D). Ripples in the unrewarded trials were likely less frequent than in the rewarded arm because mice spent less time in the arm (6.5 ± 0.5 s compared to 34.0 ± 1.0 s on rewarded trials), they were immobile for shorter periods, and because the reward promotes SWR activity (Singer and Frank 2009). We only recorded 21 ripples in the unrewarded non-stimulated trials that we could compare with the 171 ripples in the non-stimulated rewarded trials, but we do not think this could provide a conclusive comparison.

However, we do not attempt to disambiguate the role of rewarded and unrewarded trials in learning of the task. It is reasonable to assume that visits to the rewarded arm contributed to the memory of the rewarded location. By showing that SWRs were reduced by optogenetic stimulation at that stage of the task, we support the claim that reducing SWRs at that stage was relevant for the correlated memory impairment.

The Result section has been amended accordingly and now reads (page 12-13):

“We detected SWRs in significantly more rewarded than unrewarded trials (82 ± 7% of rewarded non-stimulated trials vs 32 ± 13% of unrewarded non-stimulated trials, paired t-test on percentages per animal: p = 0.02, n = 7 animals, Figure 4C). The difference could be due to the shorter immobility when the mice visited the non-rewarded arms: on unrewarded trials, mice spent 6.5 ± 0.5 s in the goal zone before leaving compared to 34.0 ± 1.0 s on rewarded trials. Because we detected few SWRs in the unrewarded trials, we restricted the statistical analysis of the effects of optogenetic stimulation to rewarded trials.”

“Spectral peak frequency of SWRs was not affected by the stimulation (Frequency: 168 ± 2 Hz; linear mixed-effects model for non-stimulated trials, mouse group – laser interaction: F(1, 3.6) = 0.02, p = 0.88, Figure 4 —figure supplement 2A), nor was the SWR duration (Duration: 37 ± 1 ms; log-linear mixed-effects model for non-stimulated trials, mouse group – laser interaction: F(1, 148) = 0.1, p = 0.76, Figure 4—figure supplement 2B).”

2. The authors show the reduction of ripples incidence in CA1 and CA3 upon MS cholinergic stimulation in both in sleeping and anesthetized animals. However, they also claim that "…MS cholinergic stimulation enhanced a scopolamine-sensitive theta oscillation in both anesthetized and sleeping mice (supplementary Figure 3)". To substantiate this claim by data, they need to block muscarinic receptors in sleeping animals to assure that it holds in both conditions. I did not find these data in the manuscript. Scopolamine sensitivity is a classical criterion for differentiation of types of theta oscillations so that this information would be highly relevant for the interpretation of the results with respect to effects of stimulation on theta oscillations. If the authors did not perform these experiments, they should not extend the interpretation to the sleep condition.

The Reviewer is correct – we are sorry for the inadvertent extrapolation from anesthetized to sleeping mice. In this majorly revised version of the manuscript, we have removed results showing the involvement of muscarinic receptors, which have been described extensively by others (Vandecasteele et al., 2014; Ma et al., 2020). We would argue that scopolamine injection to a non-anesthetized mouse, which could cause arrhythmia and have serious neurological effects, is probably not important enough to our study to be ethically justified.

3. Figure 4: Using the method presented in Haller et al., 2018, the authors show an increase in theta power and gamma power in both CA1 and CA3, following MS cholinergic stimulation. While these results are interesting, it would be much more convincing if the authors showed the grouped data of all animals, including the error bars in the Figure 4B, instead of only the data obtained from an individual animal. More importantly the statistical analysis depends on a linear fit of the background spectrum intensity, which from inspecting the data seems moderately imprecise. Since the differences reported are rather small, the authors should use a 1/f polynomial fit (as also used in Haller et al., 2018) of the background spectrum intensity which would confirm the robustness of the described differences.

We would argue that averaging spectra from different animals can blur variability in the PSD as the oscillatory frequency bands differ between individual mice (Haller et al. 2018, now out in Nat. Neuroscience as Donoghue et al., 2020). Therefore, we present PSDs from each animal individually, a representative one in Figure 3C and Figure 6B, and the others in the extended Figure 3—figure supplement 1 and Figure 6—figure supplement 1. To summarize the results, in the previous version of the manuscript, Figure 6B showed the mean PSD per animal and grey ribbons extending ±1 SEM. In some cases, the ribbons were narrower than the width of the mean line, so we updated the figures to only include the ribbons (Figure 4B, Figure 4—figure supplement 1, Figure 6B, Figure 6—figure supplement 1).

The Reviewer is correct to point out that fitting the background spectrum is imprecise. Following the Reviewer’s suggestions, we compared the fitting of linear and polynomial models for the background spectrum. The polynomial fit in 76 % of cases failed to detect relative theta peak in control trials. This is because the fitted polynomial background bent around the theta peak without any gaussian peaks added on top of the background. In comparison, 99 % of the same trials had a relative theta peak detected when fit with the linear background spectrum. Please Author response image 1 demonstrating the fits (shown with the dashed line) calculated on an animal-averaged PSD.

Author response image 1

To independently confirm the increase in relative theta and slow gamma power in sleeping mice, we looked at the change in PSD between subsequent epochs with the stimulation off and on. For both theta and slow gamma, the negative power change was smaller than in the surrounding frequency bands. These significance-tested comparisons are presented in Figure 6C and Figure 6—figure supplement 2. The changes in the main text read (page 16):“To independently confirm that the stimulation increased relative theta power, we looked at the difference in the PSD between subsequent epochs with the stimulation off and on (Figure 6D, Figure 6—figure supplement 1). In the ChAT-ChR2 mice, the negative change in the theta band was significantly lower than in the 12–15 Hz band (mixed-effects model: F(1, 10) = 21, p = 0.001, Figure 6—figure supplement 2A).”

“We independently confirmed the increase in relative slow gamma power by looking at the PSD change between subsequent epochs with the stimulation off and on. In the ChAT-ChR2 mice, the negative change of power in the slow gamma band was significantly lower than in the 12–15 Hz band (mouse group effect in the linear mixed-effects model: compared to the 12–15 Hz band: F(1, 8) = 35, p = 10-4; compared to the 90–110 Hz band: F(1, 10) = 3.7, p = 0.08, Figure 6—figure supplement 2B).”

4. Related to Figure 4: The authors state: "The increase in the power was associated with significantly lower frequency of the theta peak in the CA1, but not in the CA3." It would be helpful for the reader to also show these results in the Figure.

As suggested, we included these results for CA1 in Figure 6F. The text on page 16 reads:

“…spectral peak frequency in the theta band decreased from 7.7 ± 0.2 Hz to 7.2 ± 0.1 Hz (log-linear mixed-effects model, mouse group – laser interaction: F(1, 4.8) = 7.3, p = 0.04, Figure 6F, post hoc test: t(30) = 4.5, p = 0.001).”

5. The authors are reporting: "Slow gamma (25-45 Hz) increased in CA1 by 54 {plus minus} 6 % and in CA3 by 8 {plus minus} 8 % ". It has been previously shown that slow gamma is driven by CA3 and propagates to CA1 (Colgin et al., 2014, 2016, Schomburg et al., 2014). To my understanding, this would imply that the low gamma activity should reach CA1 with a smaller amplitude than measured in CA3. As the opposite is observed here, does that imply that the local CA1 network activity lead to amplification of the gamma power in CA1? This should be discussed.

We now removed the CA3 data from the manuscript, so this question is not relevant to the current version. We did not draw any conclusions about the relative differences in slow gamma oscillatory power between the CA1 and CA3 because there is no simple relation between the LFP and network activity. Just for interest, it is entirely possible that inhibitory currents are responsible for the major component of the slow gamma LFP in the CA3, while slow gamma is mediated primarily by excitatory currents in the CA1.

6. In the Figure 5, the authors report decreases in CA1, but not in CA3 ripples incidence in the goal zone following the MS cholinergic stimulation. I have several difficulties with the interpretation of the results presented in this Figure. First, the authors state that only successful trials were analysed. As mentioned above already, given that the stimulation was performed also during the unsuccessful trials, the results obtained in the unrewarded goal zones should be presented. Furthermore, it is stated: "…n=3 mice included in the analysis with the ripple incidence at goal {greater than or equal to} 0.03". I think that "{less than or equal to} 0.03" is meant here. In any case, looking carefully at the Figure 5D, a decrease of the ripple incidence at goal can be observed in 2 of the 3 animals while the increase observed in one single animal accounts for no difference which underlies the conclusion that the effects are specific for CA1. This experiment is strongly underpowered and the conclusions are not convincingly supported by the data.

In response to this concern, we conducted Y-maze experiments with two additional ChAT-ChR2 and two ChAT-GFP animals. The statistical comparison with the linear-mixed effects model was performed on samples from all trials. This accounts for 111 non-stimulated and 109 stimulated at goal rewarded trials. We changed the presentation in Figure 4E and other figures to display value from each trial used by the statistical test. We believe the increased number of trials, the comparison with the effect in the control mice, and the changed presentation strongly support the conclusion.

Regarding the inclusion of unrewarded trials, please see our response to the Reviewer’s point 1. Data for the CA3 is not included in the revised version of the manuscript.

7. Furthermore, no difference in theta and gamma power in CA1, but increase in slow gamma in CA3 (5E-5G) are reported. Looking carefully at 5I, a trend in increase could be also observed in CA1. Also here, the number of animals must be increased.

The increased sample size did not reject the null hypothesis of no effect of the stimulation on theta-gamma. We would like to emphasize that the statistical model was built using all of 111 non-stimulated and 109 stimulated goal trials recorded from 5 ChAT-ChR2 and 2 ChAT-GFP mice.

Data for the CA3 is not included in the revised version of the manuscript.

8. The title of the paper is: "Cholinergic suppression of sharp wave-ripples impairs hippocampus dependent spatial memory" The authors are also showing the effect of MS cholinergic stimulation on the SWRs during sleep, but the link between this effect and spatial memory is not explored. It is known that supressing SWRs specifically during the post-learning sleep impairs spatial memory (Girardeau et al., Nature Neuroscience 2009). Thus, it would be interesting to see whether the MS cholinergic stimulation and SWRs suppression during sleep impairs spatial memory. This should be at least discussed.

We agree with the Reviewer that it would be of interest to see how this impairment relates to suppressed SWRs during post-learning sleep but we think this question is out of the scope for the present paper. We added the following paragraph about the relation to post-learning SWRs in the Discussion (page 20):

“Because learning can be affected by the interruption of SWRs during post-learning sleep (Girardeau et al., 2004), and because our cholinergic activation during sleep achieves a similar effect on the SWRs (Figure 3; Ma et al., 2020), it would be of interest to see if the cholinergic activation during post-learning sleep would also impair spatial learning. This would show whether low-cholinergic states are important also for memory consolidation during sleep and provide further evidence for a possible role of SWRs in memory.”

Reviewer #3:

The study focused on the effect of MS cholinergic activity on hippocampal oscillations and extended our knowledge of cholinergic function in hippocampus-dependent spatial learning. Impairment of hippocampal ripple oscillations during awake immobility leads to significant performance deficit in the Y-maze task, highlighting the importance of precise timing of cholinergic input in memory formation.

1. The conclusion points 1/2/3 in the abstract maybe more coherent if rearranged to 3/1/2. But this might lead to structural re-organization of the article and the figures (suggested figure order: Figure 1-3-4-2-5). The logic behind this order is:

a. Optogenetic stimulation of MS cholinergic neurons impair hippocampal ripples during SWS (Figure 3/4).

b. What about ripples during awake immobility?

c. Optogenetic stimulation during goal period of the Y-maze impairs learning (Figure 2).

d. Such impairment was due to reduced CA1 ripple incidence (Figure 5).

We thank the Reviewer for this suggestion. We have now reorganized the manuscript and order of figures to mirror the conclusion points in the abstract, which is slightly different to the Reviewer’s preference. We prefer to present up front our main finding, which is that the timing of cholinergic neuron activation is crucial for learning and memory (Figure 2). Then we proceed with the recordings and optogenetics during the memory task and end with the sleep recordings, motivated by our failure to detect a significant effect of cholinergic stimulation on theta-gamma activity. Please also see response to Reviewer 1, point 5. Unless the Reviewer considers the order of the figures to be critical for the article to be acceptable, we would rather keep the current organization.

2. In the introduction: "activation of MS cholinergic neurons switches the CA3 and CA1 network states from ripple activity to theta/gamma oscillations". The phrasing might be questionable

We assume that the Reviewer questions the clear distinction we make between the neural states: ripple activity state and theta/gamma state. We have rephrased the sentence, which now reads (page 6):

“We also show that activation of MS cholinergic neurons promotes a switch from ripple activity to enhanced theta/gamma oscillations in the hippocampus of naturally sleeping mice.”

3. Page 18, first paragraph, the description seems a little bit redundant.

We agree with the Reviewer that this paragraph reiterates the already described findings. To avoid redundancy, we have now clearly separated this Result section into five paragraphs: (1) description of the task and of the mouse cohorts; (2) description of the main results; (3) influence of stimulation duration (as recommended by Reviewer 1); (4) control of potential aversive influence of light at the goal location; (5) conclusion of the section.

4. Figure 3A, it seems that theta oscillations emerge with the optogenetic stimulation, was the stimulation strictly delivered during SWS or was this particular theta represents REM states? It would be better if the authors also show the LFP and ripple trace after the light stimulation.

We delivered the stimulation with no distinction between SWS and REM sleep. We added this information to the manuscript and included the requested trace after stimulation in Figure 6A. It now reads (page 14):

“We compared the signal in the 30 s-long epochs preceding the stimulation with the 30-s-long epochs during the stimulation without a distinction between SWS and REM sleep.”

5. Page 22, last sentence of the first paragraph, "…while the peak frequency did not significantly change from 39 {plus minus} 1 Hz in the CA1 and 38 {plus minus} 2 Hz in the CA3…", is a little bit confusing, needs further explanation.

By peak frequency, we mean frequency with spectral peak power. We updated the references to spectral peak frequency throughout the manuscript.

6. Page 22, second paragraph, relative theta and gamma power change was reported in CA3, how about CA1?

Because these results have been published by two groups already (Vandecasteele et al., 2015; Ma et al., 2020), we have now removed the anesthetized data from the manuscript and focus on natural sleep and behaving mice.

7. In previous reports (Vandecasteele, 2014 and Ma, 2020), MS cholinergic stimulation can completely inhibit hippocampal ripple oscillations. But in this study, there are still a lot of ripples not suppressed. Please explain the difference.

We still observed some SWRs during MS cholinergic stimulation, but in similar proportions to those published elsewhere. We report that SWR incidence in 4 ChAT-ChR2 decreased by 100% and for another 4 reduced by a median of 88 ± 2%. These are comparable to both of the other reports:

1. In freely behaving animals, Vandecasteele et al. (2014) report a median 92% reduction in SWRs. The relevant result quoted from Vandecasteele et al:

“Ripple occurrence was significantly suppressed or abolished during MS stimulation (1–12 Hz, sine stimulation or pulse trains, 1–60 s) in mice recorded either during urethane anesthesia (n = 6 mice, median suppression −90%, P = 0.0312) or during free behavior (n = 8, median suppression −92%, P < 0.01).”

2. Ma et al. (2020) report 95 ± 5% reduction in SWRs (reported as ripple events inhibition index).

[Editors’ note: what follows is the authors’ response to the second round of review.]

Essential Revisions:

Some conclusions that are strongly stated in the current version will need supporting data in the future or will need to be toned down with caveats discussed. Please refer to the individual reviews below for specific details.

We agree. We have toned down our conclusions accordingly and included a discussion of caveats as described in our responses to the individual Reviewers.

Reviewer #1 (Recommendations for the authors):

The authors satisfactorily addressed my prior major concerns. I have no major concerns remaining. I only have a few concerns remaining that can be easily addressed by the authors without affecting the major conclusions of the paper.

Page 5: "In hippocampal CA3, cholinergic activation induces a slow gamma rhythm primarily by activating M1 muscarinic receptors": It should be noted that these are in vitro studies. As this sentence is written currently, the comparisons between CA3 and CA1 are potentially misleading.

We thank the Reviewer for pointing out this potentially misleading sentence. We now explicitly state that the CA3 results were obtained ex vivo. The sentence now reads (page 5):

“In hippocampal CA3, cholinergic activation ex vivo induces a slow gamma rhythm primarily by activating M1 muscarinic receptors (Fisahn et al., 1998; Betterton et al., 2017), while in the CA1, cholinergic activation in vivo promotes theta/gamma oscillations…”

Page 10: "We did not detect an effect of the laser on the duration the mice spent at the goal location (linear mixed-effects model, mouse group – laser interaction: F(1, 78) = 0.01, p = 0.94, laser effect: F(1, 78) = 0.1, p = 0.73)." I am confused by this passage. If optogenetic stimulation at the reward location affects memory, as the authors claim, one would expect a differential effect of laser stimulation on the ChR2 mice compared to the GFP-only control mice (and a significant effect of the laser in the ChR2 group). Yet, a non-significant interaction effect is reported.

The comparison looks at the time the mice spent at the goal location, which is not a measure of performance in the task but assesses the behavior during learning. Thus, in this passage, we compared the duration the GFP-only and ChR2-expressing mice spent at the goal locations, and not the performance of the task. The cholinergic stimulation did not result in overt behavioral changes, for example, in changes that could indicate the stimulation itself was aversive. We apologize for the confusion and have now rephrased the sentence to make the comparison more explicit.

We have amended the sentence as follows (page 10):

“The cholinergic activation did not overtly affect the behavior once the mice were at the goal location: we did not detect any effect of the laser stimulation on the time the mice spent at the goal location (linear mixed-effects model, mouse group – laser interaction: F(1, 78) = 0.01, p = 0.94, laser effect: F(1, 78) = 0.1, p = 0.73).”

Page 13: "Because we detected few SWRs in the unrewarded trials, we restricted the statistical analysis of the effects of optogenetic stimulation to rewarded trials. The SWR incidence in the non-stimulated trials was not significantly different between early (before day 5) and late learning (linear mixed-effects model, effect of early vs late learning: F(1, 110) = 0.3, p = 0.58, Figure 4D).": The authors state that they restricted analysis of effects of optogenetic stimulation to rewarded trials but then analyze SWR incidence in the very next sentence and paragraph. Is there a typo here?

We have now clarified that the statistical comparison relates to SWR incidence during early and late learning in non-stimulated rewarded trials (page 13):

“Because we detected few SWRs in the unrewarded trials, we restricted the further analysis to the rewarded trials. We first assessed whether SWR incidence changed during learning by quantifying the incidence of SWRs in the non-stimulated rewarded trials during early and late learning (Figure 4D). We did not observe any significant difference between early (before day 5) and late learning (linear mixed-effects model, effect of early vs late learning: F(1, 110) = 0.3, p = 0.58, Figure 4D). “

Reviewer #2 (Recommendations for the authors):

This submitted work is clearly better structured and well-controlled version of the previously submitted manuscript. The authors addressed most of the comments. I only have two concerns.

1. The optogenetic stimulation induced ripple inhibition effect at the goal location and during sleep seems a little bit inconsistent (Figure 4E and Figure 5C). The authors stated in the text the ripple inhibition statistics under the two conditions: ripple incidence was reduced from 0.11 {plus minus} 0.01 Hz to 0.05 {plus minus} 0.01 Hz when stimulated at the goal location, and from 0.21 {plus minus} 0.01 Hz to 0.03 {plus minus} 0.01 Hz during sleep. Also, from the authors' response to the review comments (Reviewer #3, major comment 7), "We report that SWR incidence in 4 ChAT-ChR2 decreased by 100% and for another 4 reduced by a median of 88 {plus minus} 2%.". I assume that the authors were referring to the situation during sleep (based on the statistics and the number of animals used in each condition), which means that the ripple inhibition effect would be somewhere around 50% during stimulation at the goal location. Please elaborate on this.

The Reviewer is correct. We apologize for the ambiguity in our previous response to Reviewer 3. As requested, in the Results section, we now present the effect sizes as percentage change along with the incidence of SWRs in control and during optogenetic stimulation. In the revised Discussion section, we elaborate on the lower efficacy of optogenetic stimulation on reduction of SWR incidence in the awake behaving animal compared to during sleep.

In addition, when we recalculated the mean statistics, we found a mistake in the reported mean SWR incidence at the goal location. In the optogenetically stimulated trials, the incidence decreased by 52 ± 7% from 0.06 ± 0.01 Hz to 0.03 ± 0.01 Hz.

We have amended the main text to include the percentages and have updated the statistics. The Results section now reads:

Page 13 reporting the reduction at goal location:

“…optogenetic stimulation had a significantly different effect in the ChAT-GFP and the ChAT-ChR2 mice (log-linear mixed-effects model, mouse group – laser interaction, F(1,42) = 4.5, p = 0.04, Figure 4E). In the ChAT-ChR2 mice, optogenetic stimulation reduced the SWR incidence at the goal location by 52 ± 7% from 0.06 ± 0.01 Hz to 0.03 ± 0.01 Hz (post hoc test: t(44) = 4.2, p = 0.001, Figure 4E).”

Page 14 reporting the mean ± SEM reduction at goal location: “Also, the reduction of SWR incidence of 52 ± 7% at the goal location was smaller than the 92% median suppression reported during free behavior (Vandecasteele et al., 2014), which could be due to a smaller effect of ACh at the reward location or an already high level occluding the effect of the optogenetic stimulation.”

Page 15 reporting the mean ± SEM reduction in sleeping animals: “Optogenetic stimulation reduced the SWR incidence throughout the stimulation in ChAT-ChR2 mice but not in ChAT-GFP mice (Figure 5A–C). SWR incidence in ChAT-ChR2 mice was reduced from 0.21 ± 0.01 Hz to 0.03 ± 0.01 Hz (85 ± 3% reduction), linear mixed-effects model, mouse group – laser interaction: F(1, 22) = 47, p = 10-6, n = 369 epochs from 10 animals…”

Page 18: “Our results indicate that cholinergic stimulation almost completely suppresses SWRs in sleeping animals and suppresses SWRs by about one half in awake, behaving animals.”

2. Although the analyses of the effect of optogenetic stimulation on hippocampal theta and gamma oscillations during sleep reveal some very interesting results, it seems that the relevance of these analyses with the key claim of the submitted work (as indicated by the title) is a little bit farfetched.

We agree with the Reviewer that the sleep recordings are not directly linked to the main results of the study. However, we think that these results are valuable for the main story and help us understand the effect of stimulating the cholinergic neurons during the Y-maze task for two main reasons.

First, the incomplete suppression of SWRs during behavior as well as the weak effect on theta and gamma oscillations could be attributed to a deficient stimulation paradigm. However, our results during sleep matches previously published results and gave us an appropriate opportunity to use the method developed by Donoghue and collaborators. Our results during slow-wave sleep, when the cholinergic tone is known to be low, highlights the powerful effect of cholinergic stimulation. In comparison, in the behaving mouse, when the cholinergic tone is higher, the stimulation has a more modest effect on the oscillations, but still results in behavioral impairment. Without recordings in the sleeping animals we would not have been able to draw such conclusions.

Second, we found it surprising that the cholinergic activation had no detectable impact on the theta-gamma oscillations at the goal location. This shows the prominent effect on theta-gamma in the sleeping animals is not seen in awake behaving animals and therefore is not likely to explain the learning impairment seen when cholinergic neurons were activated at the goal location.

We now highlight the second point explicitly in the discussion on page 18:

“Moreover, the effect of cholinergic stimulation on theta-gamma oscillations, which was prominent during sleep, was not observed when we applied the same stimulation at the goal location during learning, suggesting that learning was impaired through a mechanism independent of theta-gamma oscillations.”

Reviewer #3 (Recommendations for the authors):

1. Figure 3B: the spectrogram shows high relative (z-scored) power for the high frequencies in start and center, but not goal. I would have expected to see at least some high power in the ripple band in the goal. Could the authors clarify how exactly the z-scoring was performed? If the spectrogram is an average across multiple trials, then this will tend to obscure transient, non-time locked oscillations like SWRs.

The Reviewer is correct to point out that averaging across multiple trials obscured transient SWRs from the spectrogram. To demonstrate these, we replaced the previous day-averaged spectrogram with a single trial spectrogram example in Figure 3B. The example shows transient power increases in the ripple frequencies at the goal location. The high-frequencies power also increased during running at Center for 60–75 Hz and harmonics of this frequency band (120–150 Hz and 180–225 Hz).

2. What is the effect of optical stimulation of cholinergic neurons on theta/gamma/SWRs in the "throughout" and "navigation" conditions? Are these effects consistent with the hypothesis that the learning deficit is caused by a reduction of SWRs at the goal location? Could additional insights be obtained into possible changes induced by stimulation (e.g. theta oscillations during navigation) that do *not* correlate with the learning deficit?

Although we agree with the Reviewer that these comparisons would be informative, unfortunately, we did not perform recordings in the ‘navigation’ and ‘throughout’ groups.

3. Page 19: the authors cite Jadhav et al. 2012 when stating "disruption of SWRs in the first 15 to 60 minutes following training impairs learning of spatial navigation tasks". However, Jadhav et al. disrupted SWRs during the training and not following the training.

The Reviewer is correct. This was a mistake, for which we apologize. We have now removed the citation from that sentence.

4. Page 20: Both reverse and forward replay are observed during brief pauses or reward consumption in the awake state when animals explore a maze or learn a task. So, it is likely that in the reward zone in the Y-maze task one will observe both forward and reverse replay. While it is fine to speculate that disruption of reverse replay mediates the behavioral deficit, it cannot be based on the assumption that replay at the goal location is only of the reverse kind.

We corrected the Discussion to reflect that replay events in both directions could occur at the goal locations (page 20):

“During these SWRs, sequences of neuronal activation are replayed in both forward and reverse order (Foster and Wilson, 2006; Csicsvari et al., 2007; Diba and Buzsáki, 2007; Karlsson and Frank, 2009; Ambrose et al., 2016).”

We removed the speculation about the direction of the ripple events (page 20):

“Therefore, we speculate that disruption of the normally occurring replay events in the reward zone is sufficient to impair long-term memory formation (Figure 5).”

5. What is the time in between individual trials?

We added this information in the Methods section: “The interval between the within-day trials averaged 10 minutes.“

6. To characterize the learning in the Y-maze, the authors determine the day at which criterion is reached. This metric is rather coarse. Instead, the authors could fit a learning curve (e.g. sigmoid function) to the trial responses and estimate the learning rate for each animal. Furthermore, it would be informative to show individual learning curves for all animals, in addition to the average learning curves that are shown now.

We agree with the Reviewer that the days-to-criterion metric is coarse. However, we do not think we can convincingly fit sigmoids to the individual learning trajectories in this task for two reasons:

1. Mice learnt the task fast, for example, 10 mice reached the learning criterion in one or two days, which leaves too few data points for adequate curve fitting.

2. Day-to-day progression was variable and the learning often was not gradual because the mice received only 10 tests a day. For example, some mice with performance of 70-90% on the first learning day had low performance on the second day.

Following the Reviewer’s suggestion, we now show individual learning curves in Figure 2—figure supplement 1.

7. To assess the effect of stimulation at the goal on hippocampal activity, the authors look at average SWR rate and average theta/gamma power. However, when the animals are in the goal region, they likely show a mixture of behavioral states that is associated with periods of theta and non-theta (incl. SWRs). Is more (or less) time spent in theta state during stimulation? Could it be that time spent in non-theta states is lower, but SWR rate within this state has not changed? Judging from the example in figure 4B, it may be the case that with stimulation the first SWR after arriving at the goal is delayed compared to no stimulation condition – is this consistent across all subjects?

The Reviewer is correct to point out that both periods with theta and non-theta are present at the goal location and he poses an interesting question. Unfortunately, in our view, the distinction between theta and non-theta states is not as unambiguous as previous literature might appear to imply. Whilst theta activity can be clearly identified during locomotor activity during navigation on the arms of the maze, we do not think our recordings would allow us to unequivocally distinguish between the time spent in theta vs non-theta states at the reward location. Therefore, we would prefer reporting only what we can unambiguously measure, namely the overall power in different frequency bands and the SWR incidence.

https://doi.org/10.7554/eLife.65998.sa2

Article and author information

Author details

  1. Przemyslaw Jarzebowski

    Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Contributed equally with
    Clara S Tang
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6333-222X
  2. Clara S Tang

    Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology
    Contributed equally with
    Przemyslaw Jarzebowski
    Competing interests
    No competing interests declared
  3. Ole Paulsen

    Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2258-5455
  4. Y Audrey Hay

    Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
    Contribution
    Conceptualization, Supervision, Investigation, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    ah831@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7765-5222

Funding

Biotechnology and Biological Sciences Research Council (BB/N019008/1)

  • Ole Paulsen

Biotechnology and Biological Sciences Research Council (BB/P019560/1)

  • Ole Paulsen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Drs Julija Krupic and Mohamady El-Gaby for introducing us to recordings in freely moving animals. The authors gratefully acknowledge the Cambridge Advanced Imaging Centre for their support and assistance in this work.

Ethics

Animal experimentation: All animal experiments were performed under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB) under personal and project licenses held by the authors.

Senior and Reviewing Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Reviewers

  1. Jiamin Xu
  2. Fabian Kloosterman, KU Leuven, Belgium

Publication history

  1. Received: December 21, 2020
  2. Accepted: April 5, 2021
  3. Accepted Manuscript published: April 6, 2021 (version 1)
  4. Version of Record published: April 23, 2021 (version 2)

Copyright

© 2021, Jarzebowski et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Biochemistry and Chemical Biology
    2. Neuroscience
    Lloyd Davis et al.
    Tools and Resources

    Synthetic strategies for optically controlling gene expression may enable the precise spatiotemporal control of genes in any combination of cells that cannot be targeted with specific promoters. We develop an improved genetic code expansion system in C. elegans and use it to create a photo-activatable Cre recombinase. We laser-activate Cre in single neurons within a bilaterally symmetric pair to selectively switch on expression of a loxP controlled optogenetic channel in the targeted neuron. We use the system to dissect, in freely moving animals, the individual contributions of the mechanosensory neurons PLML/PLMR to the C. elegans touch response circuit, revealing distinct and synergistic roles for these neurons. We thus demonstrate how genetic code expansion and optical targeting can be combined to break the symmetry of neuron pairs and dissect behavioural outputs of individual neurons that cannot be genetically targeted.

    1. Neuroscience
    Charlotte Vanacker et al.
    Research Article Updated

    GnRH neurons are the final central neural output regulating fertility. Kisspeptin neurons in the hypothalamic arcuate nucleus (KNDy neurons) are considered the main regulator of GnRH output. GnRH and KNDy neurons are surrounded by astrocytes, which can modulate neuronal activity and communicate over distances. Prostaglandin E2 (PGE2), synthesized primarily by astrocytes, increases GnRH neuron activity and downstream pituitary release of luteinizing hormone (LH). We hypothesized that glial fibrillary acidic protein (GFAP)-expressing astrocytes play a role in regulating GnRH and/or KNDy neuron activity and LH release. We used adeno-associated viruses to target designer receptors exclusively activated by designer drugs (DREADDs) to GFAP-expressing cells to activate Gq- or Gi-mediated signaling. Activating Gq signaling in the preoptic area, near GnRH neurons, but not in the arcuate, increases LH release in vivo and GnRH firing in vitro via a mechanism in part dependent upon PGE2. These data suggest that astrocytes can activate GnRH/LH release in a manner independent of KNDy neurons.