Author response:

Reviewer #1 (Public review):

Summary:

The manuscript by Yang et al. investigates the relationship between multi-unit activity in the locus coeruleus, putatively noradrenergic locus coeruleus, hippocampus (HP), sharp-wave ripples (SWR), and spindles using multi-site electrophysiology in freely behaving male rats. The study focuses on SWR during quiet wake and non-REM sleep, and their relation to cortical states (identified using EEG recordings in frontal areas) and LC units.

The manuscript highlights differential modulation of LC units as a function of HP-cortical communication during wake and sleep. They establish that ripples and LC units are inversely correlated to levels of arousal: wake, i.e., higher arousal correlates with higher LC unit activity and lower ripple rates. The authors show that LC neuron activity is strongly inhibited just before SWR is detected during wake. During non-REM sleep, they distinguish "isolated" ripples from SWR coupled to spindles and show that inhibition of LC neuron activity is absent before spindle-coupled ripples but not before isolated ripples, suggesting a mechanism where noradrenaline (NA) tone is modulated by HP-cortical coupling. This result has interesting implications for the roles of noradrenaline in the modulation of sleep-dependent memory consolidation, as ripple-spindle coupling is a mechanism favoring consolidation. The authors further show that NA neuronal activity is downregulated before spindles.

Strengths:

In continuity with previous work from the laboratory, this work expands our understanding of the activity of neuromodulatory systems in relation to vigilance states and brain oscillations, an area of research that is timely and impactful. The manuscript presents strong results suggesting that NA tone varies differentially depending on the coupling of HP SWR with cortical spindles. The authors place their findings back in the context of identified roles of HP ripples and coupling to cortical oscillations for memory formation in a very interesting discussion. The distinction of LC neuron activity between awake, ripple-spindle coupled events and isolated ripples is an exciting result, and its relation to arousal and memory opens fascinating lines of research.

Weaknesses:

I regretted that the paper fell short of trying to push this line of idea a bit further, for example, by contrasting in the same rats the LC unit-HP ripple coupling during exploration of a highly familiar context (as seemingly was the case in their study) versus a novel context, which would increase arousal and trigger memory-related mechanisms. Any kind of manipulation of arousal levels and investigation of the impact on awake vs non-REM sleep LC-HP ripple coordination would considerably strengthen the scope of the study.

We agree that conducting specific behavioral tests before electrophysiological recordings, as well as manipulating arousal during the recording session, would strengthen the study. These experiments are planned for future work, and we will acknowledge this point in the discussion.

The main result shows that LC units are not modulated during non-REM sleep around spindle-coupled ripples (named spRipples, 17.2% of detected ripples); they also show that LC units are modulated around ripple-coupled spindles (ripSpindles, proportion of detected spindles not specified, please add). These results seem in contradiction; this point should be addressed by the authors.

We found that LC suppression was generally weak around both types of coupled events (spRipples and ripSpindles). Specifically, session-averaged spRipple-associated LC suppression reached a significance level (exceeding 95% CI) in 4 (n = 3 rats) out of 20 sessions (Line 177). The significant ripSpindle-associated LC suppression was observed in 3 (n = 2 animals) out of 20 sessions (Line 213). When comparing the modulation index (MI) around spRipples and ripSpindles, we found a significant correlation (Pearson r = 0.72, p = 0.0003). As shown in Author response image 1 below, the three sessions (blue square, MI < 95%CI) with significant ripSpindle-associated LC suppression coincide with those sessions showing LC modulation around spRipples. Although, the detection of coupled events was performed independently, some overlap can not be excluded. We will be happy to provide this additional information in the results section.

Author response image 1.

Results are displayed per recording session, with 20 sessions total recorded from 7 rats (2 to 8 sessions per rat), which implies that one of the rats accounts for 40% of the dataset. Authors should provide controls and/or data displayed as average per rat to ensure that results are now skewed by the weight of that single rat in the results.

Since high-quality recordings from the LC in behaving rats are challenging and rare, we used all valid sessions for this study. In Author response image 2 below, we plotted the average MIs for each animal (A) and each session (B). The dashed lines indicate the mean ± 2 standard deviations across all sessions. The rat ID and number of sessions is indicated in parentheses in A. All animal-averaged MIs fall within this range, indicating that the MI distribution is not driven by a single animal (rat 1101, 8 sessions). The MIs of eight sessions from rat1101 are shown in grey-filled triangles (B). Comparison of the MI distribution for these eight sessions versus the remaining 12 sessions from six other animals revealed no significant difference (Kolmogorov-Smirnov test, p = 0.969). We will be happy to provide this additional information in the Results section.

Author response image 2.

In its current form, the manuscript presents a lack of methodological detail that needs to be addressed, as it clouds the understanding of the analysis and conclusions. For example, the method to account for the influence of cortical state on LC MUA is unclear, both for the exact methods (shuffling of the ripple or spindle onset times) and how this minimizes the influence of cortical states; this should be better described. If the authors wish to analyze unit modulation as a function of cortical state, could they also identify/sort based on cortical states and then look at unit modulation around ripple onset? For the first part of the paper, was an analysis performed on quiet wake, non-REM sleep, or both?

As shown in Figure 3A and described in the main text (Lines 113–116), LC firing rate was negatively correlated with cortical arousal as quantified by Synchronisation Index (SI), whereas ripple rate was positively correlated with arousal. When computing LC activity (0.05 sec bins) aligned to the ripple onset over a longer time window ([–12, 12] sec), we observed a slow decrease in the LC firing rate beginning as early as 10 s before the ripple onset. In Author response image 3 below, a blue trace shows this slower temporal dynamic in a representative session. In addition to LC activity modulation at this relatively slow temporal scale, we also observed a much sharper drop in the LC firing rate ~ 2 s before the ripple onset. Considering two temporal scales, we hypothesized that slow modulation of LC activity might be related to fluctuations of the global brain state. Specifically, a higher SI (more synchronized cortical population activity) corresponded to a lower arousal state and reduced LC tonic firing; this brain state was associated with a higher ripple activity. Thus, slow LC modulation was likely driven by cortical state transitions. To correct for the influence of the global brain state on the LC/ripple temporal dynamics, we generated surrogate events by jittering the times of detected ripples (Lines 415–421). First, we confirmed that the cortical state did not differ around ripples and surrogate events (Figure 3C), while triggering the hippocampal LFP on the surrogate events lacked the ripple-specific frequency component (Figure 3B,). Thus, LC activity around surrogate evens captured its cortical state dependent dynamics (see orange trace in Author response image 3 below). Finally, to characterize state-independent ripple-related LC activity, we subtracted the state-related LC activity (orange trace in Author response image 3 below) from the ripple-triggered LC activity (blue trace). This yielded a corrected estimate of ripple-associated LC activity that was largely free from the confounding influence of cortical state transitions.

Author response image 3.

In the results subsection “LC-NE neuron spiking is suppressed around hippocampal ripples”, we reported LC modulation without accounting for the cortical state. The state-dependent effects were instead examined in the subsequent subsection, “Peri-ripple LC modulation depends on the cortical–hippocampal interaction,” where we characterized LC activity around ripples across different cortical states (quite awake and NREM sleep). We will provide more methodological details and a rationale for each analysis, as requested.

Reviewer #2 (Public review):

Summary:

In this study, the authors studied the synchrony between ripple events in the Hippocampus, cortical spindles, and Locus Coeruleus spiking. The results in this study, together with the established literature on the relationship of hippocampal ripples with widespread thalamic and cortical waves, guided the authors to propose a role for Locus Coeruleus spiking patterns in memory consolidation. The findings provided here, i.e., correlations between LC spiking activity and Hippocampal ripples, could provide a basis for future studies probing the directional flow or the necessity of these correlations in the memory consolidation process. Hence, the paper provides enough scientific advances to highlight the elusive yet important role of Norepinephrine circuitry in the memory processes.

Strengths:

The authors were able to demonstrate correlations of Locus Coeruleus spikes with hippocampal ripples as well as with cortical spindles. A specific strength of the paper is in the demonstration that the spindles that activate with the ripples are comparatively different in their correlations with Locus Coeruleus than those that do not.

Weaknesses:

The claims regarding the roles of these specific interactions were mostly derived from the literature that these processes individually contribute to the memory process, without any evidence of these specific interactions being necessary for memory processes. There are also issues with the description of methods, validation of shuffling procedures, and unclear presentation and the interpretation of the findings, which are described in the points that follow. I believe addressing these weaknesses might improve and add to the strength of the findings.

We believe that our responses to the Reviewer 1 and planned revisions as described above will adequately address the issues raised by the Reviewer 2.

Reviewer #3 (Public review):

Summary:

This manuscript examines how locus coeruleus (LC) activity relates to hippocampal ripple events across behavioral states in freely moving rats. Using multi-site electrophysiological recordings, the authors report that LC activity is suppressed prior to ripple events, with the magnitude of suppression depending on the ripple subtype. Suppression is stronger during wakefulness than during NREM sleep and is least pronounced for ripples coupled to spindles.

Strengths:

The study is technically competent and addresses an important question regarding how LC activity interacts with hippocampal and thalamocortical network events across vigilance states.

Weaknesses:

The results are interesting, but entirely observational. Also, the study in its current form would benefit from optimization of figure labeling and presentation, and more detailed result descriptions to make the findings fully interpretable. Also, it would be beneficial if the authors could formulate the narrative and central hypothesis more clearly to ease the line of reasoning across sections.

We will do our best to optimize presentation, revise the main text and figure labelling. When appropriate, we will add specific hypotheses and a rationale for specific analyses.

Comments:

(1) Stronger evidence that recorded units represent noradrenergic LC neurons would reinforce the conclusions. While direct validation may not be possible, showing absolute firing rates (Hz) across quiet wake, active wake, NREM, and REM, and comparing them to published LC values, would help.

We will provide the requested data in the revised manuscript.

(2) The analyses rely almost exclusively on z-scored LC firing and short baselines (~4-6 s), which limits biological interpretation. The authors should include absolute firing rates alongside normalized values for peri-ripple and peri-spindle analyses and extend pre-event windows to at least 20-30 s to assess tonic firing evolution. This would clarify whether differences across ripple subtypes arise from ceiling or floor effects in LC activity; if ripples require LC silence, the relative drop will appear larger during high-firing wake states. This limitation should be discussed and, if possible, results should be shown based on unnormalized firing rates.

We can provide absolute firing rates alongside normalized values for peri-ripple and peri-spindle analyses for isolated single LC units. However, we are reluctant to average absolute firing rates for multiunit activity, as it is unknown how many neurons contributed to each MUA recording. We can add the plots with extended pre-event windows ([–12, 12] sec). Please see our response to the Reviewer 1 about the two temporal scales of LC modulation.

(3) Because spindles often occur in clusters, the timing of ripple occurrence within these clusters could influence LC suppression. Indicate whether this structure was considered or discuss how it might affect interpretation (e.g., first vs. subsequent ripples within a spindle cluster).

We did not consider spindle clusters and classified the event as ripple coupled spindle if the ripple occurred between the spindle on- and offset. We will clarify this point in the Method section.

(4) While the observational approach is appropriate here, causal tests (e.g., optogenetic or chemogenetic manipulation of LC around ripple events and in memory tasks) would considerably strengthen the mechanistic conclusions. At a minimum, a discussion of how such approaches could address current open questions would improve the manuscript.

We agree that conducting causal tests would strengthen the study. We will acknowledge in the discussion that our results shall inspire future studies addressing many open questions.

(5) Please show how "Synchronization Index" (SI) differs quantitatively across behavioral states (wake, NREM, REM) and discuss whether it could serve as a state classifier. This would strengthen interpretations of the correlations between SI, ripple occurrence, and LC activity.

We will add the plot showing the average SI values across behavioral states. Although SI could potentially serve as a classifier, we have chosen not to discuss this in detail to maintain focus in the discussion.

(6) The current use of SI to denote a delta/gamma power ratio is unconventional, as "SI" typically refers to phase-locking metrics. Consider adopting a more standard term, such as delta/gamma power ratio. Similarly, it would be easier to follow if you use common terminology (AUC) to describe the drop in LC-MUA rather than using "MI" and "sub-MI".

The ranges of delta and gamma bands might vary across studies; therefore, we prefer using SI, as defined here and in our previous publications (Yang, 2019; Novitskaya, 2012). We calculated the modulation index (MI) as the area under the curve of the peri-event time histogram within the 1 second preceding ripple onset. To avoid potential confusion with the AUC calculated over the entire signal window, we opted to use MI.

(7) The logic in Figure 3 is difficult to follow. The brain state (delta/gamma ratio) appears unchanged relative to surrogate events (3C), while LC activity that is supposedly negatively correlated to delta/gamma changes markedly (3D-E). Could this discrepancy reflect the low temporal resolution (4-s windows) used to calculate delta/gamma when the changes occur on a shorter time scale?

Figure 3D and 3E show the 'state-corrected' ripple-related LC activity. Specifically, the cortical state related LC modulation was subtracted from the non-corrected ripple-associated LC activity. Please, see our detailed response to the Reviewer 1. We will revise the results and Figure 3 legend to clarify this point.

(8) There are apparent inconsistencies between Figures 4B and 4C-D. In B, it seems that the difference between the 10th and 90th percentile is mostly in higher frequencies, but in C and D, the only significant difference is in the delta band.

We will re-do this analysis and clarify this inconsistency.

(9) Because standard sleep scoring is based on EEG and EMG signals, please include an example of sleep scoring alongside the data used for state classification. It would also be relevant to include the delta/gamma power ratio in such an example plot.

We removed ‘standard’ and will add a supplementary Figure illustrating sleep scoring.

(10) Can variability in modulation index (subMI) across ripple subsets reflect differences in recording quality? Please report and compare mean LC firing rates across subsets to confirm this is not a confounding factor.

We will plot this result averaged per rat.

(11) Figure 6B: If the brown trace represents LC-MUA activity around random time points, why would there be a coinciding negative peak as relative to real sleep spindles? Or is it the subtracted trace?

We will clarify this point in the figure legend.

(12) On page 8, lines 207-209, the authors write "Importantly, neither the LC-MUA rate nor SIs differed during a 2-sec time window preceding either group of spindles". It is unclear which data they refer to, but the statement seems to contradict Figure 6E as well as the following sentence: "Across sessions, MI values exceeded 95% CI in 17/20 datasets for isoSpindles and only 3/20 for ripSpindles". This should be clarified.

We will clarify the description of this result.

(13) The results in Figures 5C and 6F do not align. It seems surprising that ripple-coupled spindles show a considerably higher LC modulation than spindle-coupled ripples, as these events should overlap. Could the discrepancy be due to Z-score normalization as mentioned above? Please include a discussion of this to help the interpretation of the results.

We will clarify this point in the revised manuscript. Please, also see our response to the Reviewer 1.

(14) The text implies that 8 recordings came from one rat and two each from six others. This should be confirmed, and it should be explained how the recordings were balanced and analyzed across animals.

Since high-quality recordings from LC in behaving animals are challenging and rare, we used all valid sessions. We will also present the main results averaged per rat, as also requested by the Reviewer 1.

Figures and data

Multi-site extracellular recordings in freely behaving rats.

LC-NE neuron spiking is suppressed around hippocampal ripples.

The relationship between LC and ripple activity at different temporal scales.

Differential LC modulation across ripple subsets.

State-dependent modulation of LC activity across ripple subtypes.

LC modulation around sleep spindles.