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  1. eLife

    Response to Reviewer #3 (Public Review):

    [...] The task-phase specific manipulation of the MS cholinergic neurons is a good and appropriate approach. The effect on learning in the Y-maze task after goal location specific stimulation is both clear and convincing. The lack of a behavioral effect with navigation-only stimulation may be due to ACh levels already being high during this task phase (as the authors suggest). It would have been nice if the authors had also used inhibition to address the importance of timing of ACh neuromodulation.

    We agree with the reviewer that inhibiting MS cholinergic neurons during navigation or at the goal location would provide compelling evidence for the importance of adequate ACh concentration in learning. The experiment would be technically challenging because of the spatial extent of the MS. Thus, it would be difficult to ensure sufficient optogenetic silencing throughout the MS without using a laser power that might in itself induce non-specific and potentially damaging effects on the tissue underneath the optic fiber. We have included a sentence in the Discussion section on the possible experiment suggested by the Reviewer here (page 20):

    “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.”

    The authors used prolonged excitatory optogenetic stimulation that lasted anywhere from several seconds (e.g. at goal without reward or running towards goal) to over 30 seconds (e.g. at goal with reward). There are several potential issues with this stimulation protocol:

    — From Figure 1B, it appears that the light-induced increase of mean spike frequency is sustained for quite some time after the light is turned off. The sustained activity will make the manipulation in the behavioral task less temporally specific (and thus less task-phase specific). To assess the possible impact of the sustained activation on the findings in the paper, it should be quantified (i.e. duration of sustained activity, dependence on duration of prior light stimulation) - ideally in awake animals (i.e. under the same conditions as the behavioral experiments). Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

    The reviewer is correct to point out that the data in Figure 1B shows that MS neurons sustain their increased firing beyond the duration of the optogenetic stimulation. These neurons could be cholinergic neurons and/or non-cholinergic MS neurons. Ma and collaborators (2020) monitored the firing in identified cholinergic neurons during and after a period of light pulses. They found an almost perfect spike locking to 50-s long stimulation at 1 to 10 Hz during sleep, and spiking stopped immediately at the end of the stimulation (their Figure 4C). It is possible, therefore, that the sustained firing in our experiments was due to firing, not in cholinergic, but in other types of neurons. Moreover, monitoring the firing rate would not accurately describe the duration of cholinergic receptor stimulation, and, ideally, the experiment should be complemented with monitoring acetylcholine levels during behavior, e.g. using a genetically encoded ACh indicator. In any case, we would argue that the sustained MS neuron activity after the optogenetic stimulation would not materially change our conclusions about the task-phase specific effects of cholinergic stimulation on learning. First, the effects of the optogenetic stimulation were short-lived, as we observed SWRs reappear almost immediately after the stimulation was terminated in sleeping animals (Figure 5A, B), and we have no reason to believe that the time course would be slower during behavior. Second, a potential spilling over of the cholinergic effect from the ‘exploration’ phase into the ‘goal’ phase would only diminish the differences we see between the groups. Nevertheless, we can not exclude the possibility that spilling over of the cholinergic activation to the time after the trial ended could contribute to the learning impairment. However, it appears unlikely to us that this would be a decisive period for memory formation as it is when mice were taken off the maze and their optic fiber detached.

    We now include a brief discussion of this point in the Discussion section (page 19):

    “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. This observation supports the idea that cholinergic modulation is timely controlled, but further experiments, for instance using acetylcholine sensors in the hippocampus (Jing et al., 2020), will be necessary to confirm this hypothesis.”

    — Prolonged light stimulation could lead to non-specific side-effects. Importantly, the authors controlled for this by performing the same light-stimulation protocol in animals that did not express ChR2. Although non-specific effects of light stimulation were found for theta power, the effects on learning and SWR rate at the goal location could not be explained by non-specific light effects. These data add confidence to the main findings. Still, the number of control animals is low (n=2) and increasing the sample size would make these control experiments more robust. This potential caveat should be mentioned.

    We acknowledge that the number of control animals used for electrophysiological recordings during the learning was low. We present two results for the effects of MS cholinergic stimulation in ChAT-ChR2 mice during learning that benefit from comparison with the control animals:

    1. theta power increased in the ChAT-ChR2 mice but the increase was not different which in the ChAT-GFP mice and could be explained by non-specific laser effects.

    2. SWR incidence at goal location decreased in ChAT-Chr2 mice but was unaffected in ChATGFP mice (mouse group – laser interaction: F(1,42) = 4.5, p = 0.04).

    Increasing the number of control animals would only have limited benefit for strengthening (2) but could have helped detecting smaller-size effect of the stimulation on theta power in (1).

    We have revised the Discussion section to discuss this caveat (page 23):

    “It is also possible that the small sample of control animals (n = 2) has prevented us from detecting a subtle theta power change.”

    — Because the time that animals spent at the goal location is much longer than the travel time to the goal location, the behavioral difference between the "navigation" and "goal" groups could be due to the duration of optical stimulation. The authors point out that the "throughout" group has overall the longest stimulation duration, but an "intermediate" behavioral performance, which would suggest that stimulation duration is not the determining factor.

    Unfortunately, the statistical analysis that the authors performed is inconclusive (i.e. the throughout group is not different from either "navigation" or "goal" groups). However, if duration is an important factor, the hypothesis would be that days-to-criterion for "throughout" condition is larger than "goal" condition (i.e. H0: throughout<=goal and H1: throughout>goal). Authors could test this directly (rather than H0: throughout=goal and H1: throughout≠goal). Bayes Factor analysis could help to assess the confidence in H0 rather than concluding that there is a lack of evidence due to low sampling.

    As suggested by the reviewer, we compared these hypotheses using Bayes Factor analysis. We found inconclusive evidence for the number of days-to-criterion to be lower in the ‘throughout’ than in the ‘goal’ group (BF10 = 1.6) or to be higher in the ‘throughout’ than in the ‘no stimulation‘ group (BF10 = 2.0).

    The updated results on page 8-9 say:

    “The ‘throughout’ group received the longest stimulation (42 ± 1 s) but presented an intermediate learning curve. Using Bayes Factor 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).”

    The Methods section was also amended to describe the Bayes Factor analysis (page 33):

    “To distinguish between the absence of effects and inconclusive results, we calculated Bayes Factors (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.”

    Even so, the authors' argument could be weakened if long-term stimulation has reduced efficacy (as suggested by the authors on page 18). To exclude this possibility, changes in the long-term stimulation efficacy should be quantified, e.g. by quantifying the stability of light-induced firing of ACh neurons with the same stimulation protocol as used in awake animals, and/or by checking whether the stimulation-induced reduction of SWR rate gets smaller across trials within a day. Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

    The reviewer raises the possibility of two potential changes in the efficacy of the optogenetic stimulation: (1) prolonged optogenetic stimulation can have an effect that diminishes with the time of stimulation within a trial; (2) the efficacy of the stimulation can drop on repeated trials. To assess (1) the reviewer suggests quantifying the stability of the light-induced firing of cholinergic neurons. This experiment was performed by Ma and collaborators (2020) who showed a perfect stability of MS cholinergic neurons over a 50-s-long stimulation at 10 Hz, confirming that cholinergic neurons can be activated reliably over a long period of time. However, this would not accurately determine the change that the cholinergic stimulation exerts on the hippocampus and the other targets, for example, because the efficacy could drop at the ACh release sites or cholinergic receptors could desensitize. To assess (2), the Reviewer suggests comparing the reduction of SWR rates across trials on the same day. However, other factors could explain such change, for example, the baseline SWR rate decreasing over the trials. Because our experiments were not designed for such comparisons, we do not think they could provide a definite answer. For example, if we compared the SWR incidence in the first and the last stimulated trial in the day, the number of samples would be 2.5 lower than used for the analysis of the stimulation effects on the SWR incidence.

    We now discuss how diminishing efficacy could limit the effect sizes we observed (page 24):

    “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 stimulation, suggesting that any decrease in the stimulation efficacy would be biologically minor.”

    The main novelty of the study is that specific stimulation of cholinergic neurons in the medial septum when animals reach the goal location results in a learning deficit. The reduction of SWRs upon cholinergic stimulation was shown before, but the authors now show that this reduction coincides with and may provide an explanation of the delayed learning. However, the link between the effect of the stimulation on SWRs and the behavioral deficit is indirect and not extremely convincing. This caveat should be discussed and conclusions tempered accordingly. Specific points related to this that should be discussed are described below.

    We agree that we have no direct evidence that the learning deficit and reduction in SWR incidence are causally related. We have now reviewed the manuscript to tone down any claim that could be misconstrued in this way. Specifically, we explicitly state that the SWR analysis was done in a different set of animals to those used to show the effects of the cholinergic activation on learning performance.

    — First, the analysis of SWR rate is performed in a separate set of experiments as in which the behavioral effect is assessed. This makes it difficult to more directly relate the change of SWR rate to the learning deficit.

    We now explicitly state in the summary of the findings that the hippocampal recordings were performed in a different set of experiments than the learning (page 18):

    “SWRs at 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.”

    — Second, the reduction of SWR rate is not absolute and SWRs are still present at lower rate. The data in Figure 4E indicate that for some animals the average SWR rate with stimulation is higher than for other animals without stimulation.

    The reviewer is correct to point out that SWRs were still present during optogenetic stimulation at the goal location. The between-animal differences in SWR incidence could be caused by slight differences in the placement of the recording electrodes, in the level of noise or because of biological differences between animals. Similarly, the efficacy of the optogenetic stimulation could differ between the animals depending on the precise placement of the optic fiber. Therefore, we do expect variation in the observed effect between the animals. Because the variation of the effects measured in the same animal is smaller than between the animals, our approach to test the same mice for the control and stimulation trials led to higher statistical power and reduction of the number of experimental animals required (3Rs).

    In the Discussion section, we now highlight the fact that the effect of optogenetic stimulation on reduction in SWR incidence in the behaving animals was lower than in the sleeping 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.”

    — Third, the Y-maze task used by the authors tests the acquisition of spatial reference memory and bears similarities to the inbound phase of the continuous spatial alternation task in 3-arm mazes. In Jadhav et al. (2012), the inbound phase was not sensitive to selective SWR disruption. These prior data would be an argument against a causative role of the reduction of SWR rate in the observed behavioral deficit.

    In our opinion, the Y-maze task and the W-maze task differ in ways that do not warrant this comparison. In the W-maze task, the inbound navigation requires a choice of the central arm and can be solved with a hippocampus-independent rule learning strategy. In fact, although hippocampal lesioned animals show slower learning on this task, this has been attributed to perseverative behavior rather than a spatial learning deficit (Kim & Frank, PLoS ONE 2009). In contrast, the Y-maze task would be hard to solve with a rule learning strategy, and would rather require an allocentric navigation strategy, which requires an intact hippocampus.

    — Fourth, while the authors briefly discuss other possible causes (e.g. effects on plasticity), they do not appear to consider non-hippocampal contributions or possible interference with reward-related dopamine signaling.

    We agree that non-hippocampal contributions of cholinergic stimulation are possible and have updated the Discussion accordingly (page 21):

    “However, additional effects of MS cholinergic stimulation on synaptic plasticity (Brzosko et al., 2019) or synaptic inhibition can not be ruled out at this stage (Hasselmo and Sarter, 2011; Haam and Yakel, 2017), and an extrahippocampal target or interference with reward-related signaling is also possible.”

  2. eLife

    Reviewer #3 (Public Review):

    In this manuscript, the authors studied how cholinergic neurons in the medial septum contribute to the acquisition of spatial memory. The question that is addressed is that of the requirement for the appropriate timing of cholinergic neurotransmission in memory formation. The main finding is that in mice optogenetic stimulation of cholinergic neurons in the medial septum slowed acquisition of a spatial memory task when the stimulation was applied at the goal location, but not during navigation toward the goal location. Stimulation at the goal location also reduced the rate of hippocampal sharp-wave ripples (SWRs), which the authors point to as a possible explanation of the observed learning deficit.

    The task-phase specific manipulation of the MS cholinergic neurons is a good and appropriate approach. The effect on learning in the Y-maze task after goal location specific stimulation is both clear and convincing. The lack of a behavioral effect with navigation-only stimulation may be due to ACh levels already being high during this task phase (as the authors suggest). It would have been nice if the authors had also used inhibition to address the importance of timing of ACh neuromodulation.

    The authors used prolonged excitatory optogenetic stimulation that lasted anywhere from several seconds (e.g. at goal without reward or running towards goal) to over 30 seconds (e.g. at goal with reward). There are several potential issues with this stimulation protocol:

    — From Figure 1B, it appears that the light-induced increase of mean spike frequency is sustained for quite some time after the light is turned off. The sustained activity will make the manipulation in the behavioral task less temporally specific (and thus less task-phase specific). To assess the possible impact of the sustained activation on the findings in the paper, it should be quantified (i.e. duration of sustained activity, dependence on duration of prior light stimulation) - ideally in awake animals (i.e. under the same conditions as the behavioral experiments). Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

    — Prolonged light stimulation could lead to non-specific side-effects. Importantly, the authors controlled for this by performing the same light-stimulation protocol in animals that did not express ChR2. Although non-specific effects of light stimulation were found for theta power, the effects on learning and SWR rate at the goal location could not be explained by non-specific light effects. These data add confidence to the main findings. Still, the number of control animals is low (n=2) and increasing the sample size would make these control experiments more robust. This potential caveat should be mentioned.

    — Because the time that animals spent at the goal location is much longer than the travel time to the goal location, the behavioral difference between the "navigation" and "goal" groups could be due to the duration of optical stimulation. The authors point out that the "throughout" group has overall the longest stimulation duration, but an "intermediate" behavioral performance, which would suggest that stimulation duration is not the determining factor.

    Unfortunately, the statistical analysis that the authors performed is inconclusive (i.e. the throughout group is not different from either "navigation" or "goal" groups). However, if duration is an important factor, the hypothesis would be that days-to-criterion for "throughout" condition is larger than "goal" condition (i.e. H0: throughout<=goal and H1: throughout>goal). Authors could test this directly (rather than H0: throughout=goal and H1: throughout≠goal). Bayes Factor analysis could help to assess the confidence in H0 rather than concluding that there is a lack of evidence due to low sampling.

    Even so, the authors' argument could be weakened if long-term stimulation has reduced efficacy (as suggested by the authors on page 18). To exclude this possibility, changes in the long-term stimulation efficacy should be quantified, e.g. by quantifying the stability of light-induced firing of ACh neurons with the same stimulation protocol as used in awake animals, and/or by checking whether the stimulation-induced reduction of SWR rate gets smaller across trials within a day. Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

    The main novelty of the study is that specific stimulation of cholinergic neurons in the medial septum when animals reach the goal location results in a learning deficit. The reduction of SWRs upon cholinergic stimulation was shown before, but the authors now show that this reduction coincides with and may provide an explanation of the delayed learning. However, the link between the effect of the stimulation on SWRs and the behavioral deficit is indirect and not extremely convincing. This caveat should be discussed and conclusions tempered accordingly. Specific points related to this that should be discussed are described below.

    — First, the analysis of SWR rate is performed in a separate set of experiments as in which the behavioral effect is assessed. This makes it difficult to more directly relate the change of SWR rate to the learning deficit.

    — Second, the reduction of SWR rate is not absolute and SWRs are still present at lower rate. The data in Figure 4E indicate that for some animals the average SWR rate with stimulation is higher than for other animals without stimulation.

    — Third, the Y-maze task used by the authors tests the acquisition of spatial reference memory and bears similarities to the inbound phase of the continuous spatial alternation task in 3-arm mazes. In Jadhav et al. (2012), the inbound phase was not sensitive to selective SWR disruption. These prior data would be an argument against a causative role of the reduction of SWR rate in the observed behavioral deficit.

    — Fourth, while the authors briefly discuss other possible causes (e.g. effects on plasticity), they do not appear to consider non-hippocampal contributions or possible interference with reward-related dopamine signaling.

  3. eLife

    Reviewer #2 (Public Review):

    Hay et al. investigated the effect of optogenetic activation of MS cholinergic inputs on hippocampal spatial memory formation, which extended our current knowledge of the relationship between MS cholinergic neurons and hippocampal ripple oscillations.

    The authors showed that optogenetic stimulation at the goal location during Y maze task impaired the formation of hippocampal dependent spatial memory. They also found that opto-stimulation at the goal location reduced the incidence of ripple oscillations, while having no effect on the power and frequency of theta and slow gamma oscillations.

    Interestingly, the authors reported different results compared to previously published work by applying the analytical methods developed by Donoghue et al. (Donoghue et al., Nat Neurosci, 2020). They showed that optogenetic activation of MS cholinergic neurons during sleep not only reduced the incidence of hippocampal ripple oscillations, but also increased the power of both theta and slow gamma oscillations, which is contradict to both decreased or no change of theta and gamma power by previous reports (Vandecasteele et al., 2014, Ma et al., 2020). These results are valuable to the community of hippocampal oscillation studies.

  4. eLife

    Reviewer #1 (Public Review):

    In this study, the authors set out to address the interesting question of how activating septal cholinergic neurons affects learning and memory of reward locations. The work provides compelling evidence showing that activation of septal cholinergic cells at reward zones suppresses sharp wave-ripples and impairs memory performance in freely behaving animals. The data are properly controlled and analyzed, and the results support the conclusions. The results shed new light on the functional significance of cholinergic projections in reward learning. Future follow-up studies designed to selectively activate cholinergic projections specifically at times when sharp wave-ripples occur will be interesting to determine the importance of cholinergic sharp wave-ripple suppression for these effects.

  5. eLife

    Evaluation 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 memory performance in freely behaving animals. The work extends our knowledge of the effect of medial septum cholinergic inputs on hippocampal dependent spatial memory formation.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

  6. Version 2 published on bioRxiv
  7. Version 1 published on bioRxiv