Author response:
Public Reviews:.
Reviewer #1 (Public review):
Wang, Zhou et al. investigated coordination between the prefrontal cortex (PFC) and the hippocampus (Hp), during reward delivery, by analyzing beta oscillations. Beta oscillations are associated with various cognitive functions, but their role in coordinating brain networks during learning is still not thoroughly understood. The authors focused on the changes in power, peak frequencies, and coherence of beta oscillations in two regions when rats learn a spatial task over days. Inconsistent with the authors' hypothesis, beta oscillations in those two regions during reward delivery were not coupled in spectral or temporal aspects. They were, however, able to show reverse changes in beta oscillations in PFC and Hp as the animal's performance got better. The authors were also able to show a small subset of cell populations in PFC that are modulated by both beta oscillations in PFC and sharp wave ripples in Hp. A similarly modulated cell population was not observed in Hp. These results are valuable in pointing out distinct periods during a spatial task when two regions modulate their activity independently from each other.
The authors included a detailed analysis of the data to support their conclusions. However, some clarifications would help their presentation, as well as help readers to have a clear understanding.
(1) The crucial time point of the analysis is the goal entry. However, it needs a better explanation in the methods or in figures of what a goal entry in their behavioral task means.
We appreciate Reviewer 1 pointing out this shortcoming and will clarify the description in the revised manuscript. Each goal is located at the end of the arm, and is equipped with a reward delivery unit. The unit has an infrared sensor. The rat breaks the infrared beam when it enters the goal.
(2) Regarding Figure 2, the authors have mentioned in the methods that PFC tetrodes have targeted both hemispheres. It might be trivial, but a supplementary graph or a paragraph about differences or similarities between contralateral and ipsilateral tetrodes to Hp might help readers.
We will provide the requested analysis in the full revision. We saw both hemispheres had similar properties.
(3) The authors have looked at changes in burst properties over days of training. For the coincidence of beta bursts between PFC and Hp, is there a change in the coincidence of bursts depending on the day or performance of the animal?
We will provide the requested analysis in the full revision.
(4) Regarding the changes in performance through days as well as variance of the beta burst frequency variance (Figures 3C and 4C); was there a change in the number of the beta bursts as animals learn the task, which might affect variance indirectly?
The analysis we can do here is to control for differences in the number of bursts for each category (days/performance quintile) by resampling the data to match the burst count between categories.
(5) In the behavioral task, within a session, animals needed to alternate between two wells, but the central arm (1) was in the same location. Did the authors alternate the location of well number 1 between days to different arms? It is possible that having well number 1 in the same location through days might have an effect on beta bursts, as they would get more rewards in well number 1?
The central arm remained the same across days since we needed the animals to learn the alternation task. In our experience, the animal needs a few days to learn the alternation rule when we switch the central arm location. For this experiment, we were interested in the initial learning process, and we kept the central constant. Switching the central arm location is a great suggestion for a follow up experiment where we can understand the effects of reward contingency change has on beta bursts.
(6) The animals did not increase their performance in the F maze as much as they increased it in the Y maze. It would be more helpful to see a comparison between mazes in Figure 5 in terms of beta burst timing. It seems like in Y maze, unrewarded trials have earlier beta bursts in Y maze compared to F maze. Also, is there a difference in beta burst frequencies of rewarded and unrewarded trials?
We will add this analysis in the revised manuscript.
(7) For individual cell analysis, the authors recorded from Hp and the behavioral task involved spatial learning. It would be helpful to readers if authors mention about place field properties of the cells they have recorded from. It is known that reward cells firing near reward locations have a higher rate to participate in a sharp wave ripple. Factoring in the place field propertiesd of the cells into the analysis might give a clearer picture of the lack of modulation of HP cells by beta and sharp wave ripples.
This is a great suggestion, and we will address this in the full revision.
Reviewer #2 (Public review):
We thank Reviewer 2 for their helpful comments and will address these in full in the revision. These are great suggestions to provide greater detail on the spectral and behavioral data at the goal.
(1) When presenting the power spectra for the representative example (Figure 1), it would be appropriate to display a broader frequency band-including delta, theta, and gamma (up to ~100 Hz), rather than only the beta band.
We will show more examples of power spectra with a wider frequency range. We did examine the wider spectra and noticed power in the beta frequency band was more prominent than others.
What was the rat's locomotor state (e.g., running speed) after entering the reward location, during which the LFPs were recorded?
We will add the time aligned speed profile to the spectra and raw data examples. Because goal entry is defined as the time the animals break the infrared beam at the goal (response to Reviewer 1), the rat would have come to a stop.
If the rats stopped at the goal but still consumed the reward (i.e., exhibited very low running speed), theta rhythms might still occasionally occur, and sharp-wave ripples (SWRs) could be observed during rest.
We typically find low theta power in the hippocampus after the animal reaches the goal location and as it consumes reward. Reviewer 2 is correct about occasional theta power at the goal. We have observed this but mostly before the animal leaves the goal location. We did find SWRs during goal periods. One example is shown in Fig. 7A.
Do beta bursts also occur during navigation prior to goal entry?
We did not find consistent beta bursts in PFC or CA1 on approach to goal entry. We can provide the analyses in our full revision. In our initial exploratory analysis, we found beta bursts was most prominent after goal entry, which led us to focus on post-goal entry beta for this manuscript. However, beta oscillations in the hippocampus during locomotion or exploration has been reported (Ahmed & Mehta, 2012; Berke et al., 2008; França et al., 2014; França et al., 2021; Iwasaki et al., 2021; Lansink et al., 2016; Rangel et al., 2015).
It would be beneficial to display these rhythmic activities continuously across both the navigation and goal entry phases. Additionally, given that the hippocampal theta rhythm is typically around 7-8 Hz, while a peak at approximately 15-16 Hz is visible in the power spectra in Figure 1C, the authors should clarify whether the 22 Hz beta activity represents a genuine oscillation rather than a harmonic of the theta rhythm.
To ensure we fully address this concern, we can provide further spectral analysis in our revised manuscript to show theta power in CA1 is reduced after goal entry. We were initially concerned about the possibility that the 22Hz power in CA1 may be a harmonic rather than a standalone oscillation band. If these are harmonics of theta, we should expect to find coincident theta at the time of bursts in the beta frequency. In Fig. 1B, Fig. 2A, we show examples of the raw LFP traces from CA1. Here, the detected bursts are not accompanied by visible theta frequency activity. For PFC, we do not always see persistent theta frequency oscillations like CA1. In PFC, we found beta bursts were frequent and visually identifiable when examining the LFP. We provided examples of the PFC LFP (Fig. 1B, Fig. 1-1, and Fig. 2A). In these cases, we see clear beta frequency oscillations lasting several cycles and these are not accompanied by any oscillations in the theta frequency in the LFP trace.
(2) The authors claim that beta activity is independent between CA1 and PFC, based on the low coherence between these regions. However, it is challenging to discern beta-specific coherence in CA1; instead, coherence appears elevated across a broader frequency band (Figure 2 and Figure 2-1D). An alternative explanation could be that the uncoupled beta between CA1 and PFC results from low local beta coherence within CA1 itself.
This is a legitimate concern, and we used three methods to characterize coherence and coordination between the two regions. First, we calculated coherence for tetrode pairs for times when the animal was at goals (Fig. 2B), which provides a general estimation of coherence across frequencies but lack any temporal resolution. Second, we calculated burst aligned coherence (Fig. 2-1), which provides temporal resolution relative to the burst, but the multi-taper method is constrained by the time-frequency resolution trade off. Third, we quantified the timing between the burst peaks (Fig. 2D), which will describe timing differences but the peaks for the bursts may not be symmetric. Thus, each method has its own caveats, but we drew our conclusion from the combination of results from these three analyses, which pointed to similar conclusions.
Reviewer 2 is correct in pointing out the uniformly high coherence within CA1 across the frequency range we examined. When we inspected the raw LFP across multiple tetrodes in CA1, they were similar to each other (Fig. 2A). This likely reflects the uniformity in the LFP across recording sites in CA1, which is what we saw with coherence values across the frequency range (Fig. 2B). We found CA1 coherence between tetrode pairs within CA1 across the range, were statistically higher, compared to tetrode pairs in PFC (Fig. 2B and C), thus our results are unlikely to be explained by low beta coherence within CA1 itself. The burst aligned coherence using a multi-taper method also supports this. The coherence values within CA1 at the time of CA1 bursts is ~0.8-0.9.
(3) In Figure 2-1E-F, visual inspection of the box plots reveals minimal differences between PFC-Ind and PFC-Coin/CA1-Coin conditions, despite reported statistical significance. It may be necessary to verify whether the significance arises from a large sample size.
We will include the sample sizes for each of the boxplots, these should be the same as the power comparison in Fig. 2-1 A-C. The LFP within a one second window centered around the bursts are usually very similar, and the multi-taper method will return high coherence values. The p-values from statistical comparisons between the boxes are corrected using the Benjamini-Hochberg method.
(4) In Figure 3 and Figure 4, although differences in power and frequency appear to change significantly across days, these changes are not easily discernible by visual inspection. It is worth considering whether these variations are related to increased task familiarity over days, potentially accompanied by higher running speeds.
We agree with Reviewer 2 that familiarity increases across days, and the animal is likely running faster. The analysis for Fig. 3 and 4 includes only data from periods when the animal was at the goal and was not moving. We used linear mixed effects models to quantify the relationship between power, frequency and day or behavioral quintile.
(5) The stronger spiking modulation by local beta oscillations shown in Figure 6 could also be interpreted in the context of uncoupled beta between CA1 and PFC. In this analysis, only spikes occurring during beta bursts should be included, rather than all spikes within a trial. The authors should verify the dataset used and consider including a representative example illustrating beta modulation of single-unit spiking.
We agree with Reviewer 2 that the stronger modulation to local beta is another piece of evidence indicating uncoupled beta between the two regions. We appreciate this suggestion and will add examples illustrating beta modulation for single units. We want to clarify the spikes were only from periods when the animal is at the goal location on each trial and does not include the running period between goals.
(6) As observed in Figure 7D, CA1 beta bursts continue to occur even after 2.5 seconds following goal entry, when SWRs begin to emerge. Do these oscillations alternate over time, or do they coexist with some form of cross-frequency coupling?
This is a very interesting and helpful suggestion. Although we found SWRs generally appear later than beta bursts, it is possible the two are related on a finer timescale pointing to coordination. Our cross-correlation analysis between PFC and CA1 beta bursts only showed the relationship on the timescale of seconds. We will show a higher time-resolution version of this analysis in the revision.
Reviewer #3 (Public review):
Summary:
This paper explored the role of beta rhythms in the context of spatial learning and mPFC-hippocampal dynamics. The authors characterized mPFC and hippocampal beta oscillations, examining how their coordination and their spectral profiles related to learning and prefrontal neuronal firing. Rats performed two tasks, a Y-maze and an F-maze, with the F-maze task being more cognitively demanding. Across learning, prefrontal beta oscillation power increased while beta frequency decreased. In contrast, hippocampal beta power and beta frequency decreased. This was particularly the case for the well-performed and well-learned Y-maze paradigm. The authors identified the timing of beta oscillations, revealing an interesting shift in beta burst timing relative to reward entry as learning progressed. They also discovered an interesting population of prefrontal neurons that were tuned to both prefrontal beta and hippocampal sharp-wave ripple events, revealing a spectrum of SWR-excited and SWR-inhibited neurons that were differentially phase locked to prefrontal beta rhythms.
In sum, the authors set out to examine how beta rhythms and their coordination were related to learning and goal occupancy. The authors identified a set of learning and goal-related correlates at the level of LFP and spike-LFP interactions, but did not report on spike-behavioral correlates.
Strengths:
Pairing dual recordings of medial prefrontal cortex (mPFC) and CA1 with learning of spatial memory tasks is a strength of this paper. The authors also discovered an interesting population of prefrontal neurons modulated by both beta and CA1 sharp-wave ripple (SWR) events, showing a relationship between SWR-excited and SWR-inhibited neurons and beta oscillation phase.
Weaknesses:
Moreover, there is little detail provided about sample sizes and how data sampling is being performed (e.g., rats, sessions, or trials), raising generalizability concerns.
We appreciate Reviewer 3’s thoughtful suggestions for making our claims convincing. We will include information about sample sizes and address each detailed recommendation in the revised manuscript.
The authors report on a task where rats were performing sub-optimally (F-maze), weakening claims.
Our experiment was designed to allow us to examine within the same animal, a well-performed task (Y) and a less well-performed task (F). This contrast allows us to determine differences in neural correlates. We can further dissect the relevant differences to take advantage of this experiment design.
Likewise, it is questionable as to whether mPFC and hippocampus are dually required to perform a no-delay Y-maze task at day 5, where rats are performing near 100%.
We agree with Reviewer 3 that the mPFC and hippocampus may not be required when the animal reaches stable performance on day 5 (Deceuninck & Kloosterman, 2024). The data we collected spans the full range of early learning (day 1) to proficiency (day 5). We wanted to understand the dynamics of beta across these learning stages.
Recent studies suggest mPFC and hippocampus are likely to be needed, in some capacity, for learning continuous spatial alternation tasks on a range of maze geometries. Lesions, inactivation or waking activity perturbation of hippocampus or hippocampus and mPFC on the W maze alternation task slowed learning (Jadhav et al., 2012; Kim & Frank, 2009; Maharjan et al., 2018). More recently, optogenetic silencing of mPFC after sharp wave ripples on the Y maze alternation affected performance when the center arm was switched (den Bakker et al., 2023). The Y and F mazes in our study both share the continuous alternation rule, where the animal needed to avoid visiting a previously visited location on the outbound choice relative to the center, and always return to the center location.
Further, the performance characteristics on the outbound and inbound components of our Y task is similar to the W task. We have analyzed the “inbound” and “outbound” performance of the animals on the Y maze alternation task, and they are similar to the W maze alternation task. The “inbound” or reference location component is learned quickly whereas the ”outbound”, alternation component is learned slowly. We can add this analysis to the revised manuscript.
There would be little reason to suspect strong oscillatory coupling when task performance is poor and/or independent of mPFC-HPC communication (Jones and Wilson, 2005) potentially weakening conclusions about independent beta rhythms.
Although many studies have examined the oscillatory coupling properties at the theta frequency between mPFC-HPC (Hyman et al., 2005; Jones & Wilson, 2005; Siapas et al., 2005), our understanding of beta frequency coordination between the two regions is less established, especially at goal locations. Beta frequency coordination at goal locations may or may not follow similar properties to theta frequency coupling. In this manuscript we are reporting the properties of goal-location beta frequency activity in mPFC-HPC networks. We are not aware of prior work describing these properties at this stage of a spatial navigation task, especially their coordination in time.
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