Neurons in the medial prefrontal cortex are involved in spatial tuning and signaling upcoming choice independently from hippocampal sharp-wave ripples

Reviewer #1 (Public review):

Summary:

The authors used high-density probe recordings in the medial prefrontal cortex (PFC) and hippocampus during a rodent spatial memory task to examine functional sub-populations of PFC neurons that are modulated vs. unmodulated by hippocampal sharp-wave ripples (SWRs), an important physiological biomarker that is thought to have role in mediating information transfer across hippocampal-cortical networks for memory processes. SWRs are associated with reactivation of representations of previous experiences, and associated reactivation in hippocampal and cortical regions have been proposed to have a role in memory formation, retrieval, planning, and memory-guided behavior. This study focuses of awake SWRs that are prevalent during immobility periods during pauses in behavior. Previous studies have reported strong modulation of a subset of prefrontal neurons during hippocampal SWRs, with some studies reporting prefrontal reactivation during SWRs that have a role in spatial memory processes. The study seeks to extend these findings by examining activity of SWR-modulated vs. unmodulated neurons across PFC sub-regions, and whether there is a functional distinction between these two kinds of neuronal populations with respect to representing spatial information and supporting memory-guided decision making.

Strengths:

The major strength of the study is the use of Neuropixels 1.0 probes to monitor activity throughput the dorsal-ventral extent of the rodent medial prefrontal cortex, permitting an investigation of functional distinction in neuronal populations across PFC sub-regions. They are able to show that SWR-unmodulated neurons, in addition to having stronger spatial tuning than SWR-modulated neurons as previously reported, also show stronger directional selectivity, and theta-cycle skipping properties.

Weaknesses:

(1) The title and abstract have been updated to reflect the updated interpretation that prefrontal neurons are involved in spatial tuning and signaling upcoming choice independently from hippocampal SWRs, implying the negative that these functions do not happen during SWRs. The evidence presented, however, is lacking and the analyses has key limitations that preclude such a conclusion. First, the fact that prefrontal neurons decode past and future choices independently of the hippocampus, not just hippocampal SWRs, is well-established (for e.g., Baeg et al., 2003, 10.1016/s0896-6273(03)00597-x). Second, the statement that prefrontal neurons are involved in spatial tuning independently from SWRs is inconsistent, since spatial tuning is assessed during exploratory behaviors that are not associated with SWRs. Apart from showing that non-local decoding occurs in prefrontal cortex outside SWR time periods, which is already established, the conclusion needs evidence this does not occur during SWR time periods, which is not presented.

(2) The results show that SWR-modulated prefrontal neurons are more linked to hippocampal non-local representations, whereas SWR-unmodulated neurons encode upcoming choice independently of SWRs. This is logical, and implies that SWR-modulated prefrontal neurons are involved in non-local decoding during hippocampal non-local representations. This hints at potentially multiple mechanisms, one involving independent prefrontal non-local decoding, and another involving prefrontal and hippocampal non-local decoding.

(3) The analyses have key limitations. The Methods section notes that decoding was performed in 50ms bins, periods with running speed less than 15cm/s were excluded, then decoded probabilities summed for each maze segment, followed by grouping probabilities together for local and non-local decoding. This implies that decoding segments can span entire maze segments or long time periods - this needs to be clarified and quantified. When examining time-locking of decoding segments to hippocampal SWRs, only non-local segments that occurred within 2 secs of SWRs were used. This raises several concerns. First, prefrontal modulation by hippocampal SWRs lasts primarily <500ms, so a 2sec temporal proximity will lead to non-SWR modulation periods being included in the analyses. In addition, even for decoding segments that may be in close temporal proximity, these can be very long, based on the analyses description. This can lead to spurious results. Second, if only running speeds >15cm/s were included, immobility periods are necessarily being excluded, which is when SWRs occur. So, this analysis cannot be used to investigate decoding during SWRs; rather, a direct approach of extracting prefrontal activity during SWRs and then decoding this activity is required.

Reviewer #2 (Public review):

Summary:

This work by den Bakker and Kloosterman contributes to the vast body of research exploring the dynamics governing the communication between the hippocampus (HPC) and the medial prefrontal cortex (mPFC) during spatial learning and navigation. Previous research showed that population activity of mPFC neurons is replayed during HPC sharp-wave ripple events (SWRs), which may therefore correspond to privileged windows for the transfer of learned navigation information from the HPC, where initial learning occurs, to the mPFC, which is thought to store this information long term. Indeed, it was also previously shown that the activity of mPFC neurons contains task-related information that can inform about the location of an animal in a maze, which can predict the animals' navigational choices. Here, the authors aim to show that the mPFC neurons that are modulated by HPC activity (SWRs and theta rhythms) are distinct from those "encoding" spatial information. This result could suggest that the integration of spatial information originating from the HPC within the mPFC may require the cooperation of separate sets of neurons.

This observation may be useful to further extend our understanding of the dynamics regulating the exchange of information between the HPC and mPFC during learning. However, my understanding is that this finding is mainly based upon a negative result, which cannot be statistically proven by the failure to reject the null hypothesis. Moreover, in my reading, the rest of the paper mainly replicates phenomena that have already been described, with the original reports not correctly cited. My opinion is that the novel elements should be precisely identified and discussed, while the current phrasing in the manuscript, in most cases, leads readers to think that these results are new. Detailed comments are provided below.

Major concerns:

ORIGINAL COMMENT: (1) The main claim of the manuscript is that the neurons involved in predicting upcoming choices are not the neurons modulated by the HPC. This is based upon the evidence provided in Figure 5, which is a negative result that the authors employ to claim that predictive non-local representations in the mPFC are not linked to hippocampal SWRs and theta phase. However, it is important to remember that in a statistical test, the failure to reject the null hypothesis does not prove that the null hypothesis is true. Since this claim is so central in this work, the authors should use appropriate statistics to demonstrate that the null hypothesis is true. This can be accomplished by showing that there is no effect above some size that is so small that it would make the effect meaningless (see https://doi.org/10.1177/070674370304801108).

AUTHOR RESPONSE: We would like to highlight a few important points here. (1) We indeed do not intend to claim that the SWR-modulated neurons are not at all involved in predicting upcoming choice, just that the SWR-unmodulated neurons may play a larger role. We have rephrased the title and abstract to make this clearer.

REVIEWER COMMENT: The title has been rephrased but still conveys the same substantive claim. The abstract sentence also does not clearly state what was found. Using "independently" in the new title continues to imply that SWR modulation and prediction of upcoming choices are separate phenomena. By contrast, in your response here in the rebuttall you state only that "SWR-unmodulated neurons may play a larger role," which is a much more tempered claim than what the manuscript currently argues. Why is this clarification not adopted in the article? Moreover, the main text continues to use the same arguments as before; beyond the cosmetic changes of title and abstract, the claim itself has not materially changed.

AUTHOR RESPONSE: (2) The hypothesis that we put forward is based not only on a negative effect, but on the findings that: the SWR-unmodulated neurons show higher spatial tuning (Fig 3b), more directional selectivity (Fig 3d), more frequent encoding of the upcoming choice at the choice point (new analysis, added in Fig 4d), and higher spike rates during the representations of the upcoming choice (Fig 5b). This is further highlighted by the fact that the representations of upcoming choice in the PFC are not time locked to SWRs (whereas the hippocampal representations of upcoming choice are; see Fig 5a and Fig 6a), and not time-locked to hippocampal theta phase (whereas the hippocampal representations are; see Fig 5c and Fig 6c). Finally, the representations of upcoming and alternative choices in the PFC do not show a large overlap in time with the representations in the hippocampus (see updated Fig 4e were we added a statistical test to show the likelihood of the overlap of decoded timepoints). All these results together lead us to hypothesize that SWR-modulation is not the driving factor behind non-local decoding in the PFC.

REVIEWER COMMENT: I do not see how these precisions address my remark. The main claim in the title used to be "Neurons in the medial prefrontal cortex that are not modulated by hippocampal sharp-wave ripples are involved in spatial tuning and signaling upcoming choice." It is now "Neurons in the medial prefrontal cortex are involved in spatial tuning and signaling upcoming choice independently from hippocampal sharp-wave ripples." The substance has not changed. This specific claim is supported solely by Figure 5.

The other analyses cited describe functional characteristics of SWR-unmodulated neurons but, unless linked by explicit new analyses, do not substantiate independence/orthogonality between SWR modulation and non-local decoding in PFC. If there is an analysis that makes this link explicit, it should be clearly presented; as it stands, I cannot find an explanation in the manuscript for why "all these results together" justify the conclusion that "All these results together lead us to hypothesize that SWR-modulation is not the driving factor behind non-local decoding in the PFC". Also: is the main result of this work a "hypothesis"? If so, this should be clearly differentiated from a conclusion supported by results and analyses.

AUTHOR RESPONSE: (3) Based on the reviewers suggestion, we have added a statistical test to compare the phase-locking based of the non-local decoding to hippocampal SWRs and theta phase to shuffled posterior probabilities. Instead of looking at all SWRs in a -2 to 2 second window, we have now only selected the closest SWR in time within that window, and did the statistical comparison in the bin of 0-20 ms from SWR onset. With this new analysis we are looking more directly at the time-locking of the decoded segments to SWR onset (see updated Fig 5a and 6a).

REVIEWER COMMENT: I appreciate the added analysis focusing on the closest SWR and a 0-20 ms bin. My understanding is that you consider the revised analyses in Figures 5a and 6a sufficient to show that predictive non-local representations in mPFC are not linked to hippocampal SWRs and theta phase.

First, the manuscript should explicitly explain the rationale for this analysis and why it is sufficient to support the claim. From the main text it is not possible to understand what was done; the Methods are hard to follow, and the figure legends are not clearly described (e.g. the shuffle is not even defined there).

Specific points I could not reconcile:

i) The gray histograms in the revised Figures 5a and 6a now show a peak at zero lag, whereas in the previous version they were flat, although they are said to plot the same data. What changed?

ii) Why choose a 20 ms bin? A single narrow bin invites false negatives. Please justify this choice.

iii) Comparing to a shuffle is a useful control, but when the p-value is non-significant we only learn that no difference was detected under that shuffle-not that there is no difference or that the processes are independent.

ORIGINAL COMMENT: (2) The main claim of the work is also based on Figure 3, where the authors show that SWRs-unmodulated mPFC neurons have higher spatial tuning, and higher directional selectivity scores, and a higher percentage of these neurons show theta skipping. This is used to support the claim that SWRs-unmodulated cells encode spatial information. However, it must be noted that in this kind of task, it is not possible to disentangle space and specific task variables involving separate cognitive processes from processing spatial information such as decision-making, attention, motor control, etc., which always happen at specific locations of the maze. Therefore, the results shown in Figure 3 may relate to other specific processes rather than encoding of space and it cannot be unequivocally claimed that mPFC neurons "encode spatial information". This limitation is presented by Mashoori et al (2018), an article that appears to be a major inspiration for this work. Can the authors provide a control analysis/experiment that supports their claim? Otherwise, this claim should be tempered. Also, the authors say that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space. How do they reconcile it with their results?

AUTHOR RESPONSE: The reviewer is right to assert caution when talking about claims such as spatial tuning where other factors may also be involved. Although we agree that there may be some other factors influencing what we are seeing as spatial tuning, it is very important to note that the behavioral task is executed on a symmetrical 4-armed maze, where two of the arms are always used for the start of the trajectory, and the other two arms (North and South) function as the goal (reward) arms. Therefore, if the PFC is encoding cognitive processes such as task phases related to decision-making and reward, we would not be able to differentiate between the two start arms and the two goal arms, as these represent the same task phases. Note also that the North and South arm are illuminated in a pseudo-random order between trials and during cue-based rule learning this is a direct indication of where the reward will be found. Even in this phase of the task, the PFC encodes where the animal will turn on a trial-to-trial basis (meaning the North and South arm are still differentiated correctly on each trial even though the illumination and associated reward are changing).

REVIEWER COMMENT: I appreciate that the departure location was pseudorandomized. However, this control does not rule out that PFC activity reflects motor preparation (left vs right turns) and associated perceptual decision-making/attentional processes that are inherently tied to a specific action. As such, it cannot by itself support the claim that PFC neurons "encode spatial information." Moreover, the authors acknowledge here that "other factors may also be involved," yet this caveat is not reflected in the manuscript. Why?

AUTHOR RESPONSE: Secondly, importantly, the reviewer mentions that we claimed that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space, but this is incorrect. Jadhav et al. (2016) showed that SWR-unmodulated neurons had lower spatial coverage, meaning that they are more spatially selective (congruent with our results). We have rephrased this in the text to be clearer.

REVIEWER COMMENT: Thanks for clarifying this.

ORIGINAL COMMENT: (3) My reading is that the rest of the paper mainly consists of replications or incremental observations of already known phenomena with some not necessarily surprising new observations:
a) Figure 2 shows that a subset of mPFC neurons is modulated by HPC SWRs and theta (already known), that vmPFC neurons are more strongly modulated by SWRs (not surprising given anatomy), and that theta phase preference is different between vmPFC and dmPFC (not surprising given the fact that theta is a travelling wave).

AUTHOR RESPONSE: The finding that vmPFC neurons are more strongly modulated by SWRs than dmPFC indeed matches what we know from anatomy, but that does not make it a trivial finding. A lot remains unknown about the mPFC subregions and their interactions with the hippocampus, and not every finding will be directly linked to the anatomy. Therefore, in our view this is a significant finding which has not been studied before due to the technical complexity of large-scale recordings along the dorsal-ventral axis of the mPFC.

REVIEWER COMMENT: This finding is indeed non-trivial; however, it seems completely irrelevant to the paper's main claim unless the Authors can argue otherwise.

AUTHOR RESPONSE: Similarly, theta being a traveling wave (which in itself is still under debate), does not mean we should assume that the dorsal and ventral mPFC should follow this signature and be modulated by different phases of the theta cycle. Again, in our view this is not at all trivial, but an important finding which brings us closer to understanding the intricate interactions between the hippocampus and PFC in spatial learning and decision-making.

REVIEWER COMMENT: Yes, but in what way does this support the manuscript's primary claim? This is unclear to me.

ORIGINAL COMMENT: b) Figure 4 shows that non-local representations in mPFC are predictive of the animal's choice. This is mostly an increment to the work of Mashoori et al (2018). My understanding is that in addition to what had already been shown by Mashoori et al here it is shown how the upcoming choice can be predicted. The author may want to emphasize this novel aspect.

AUTHOR RESPONSE: In our view our manuscript focuses on a completely different aspect of learning and memory than the paper the reviewer is referring to (Mashoori et al. 2018). Importantly, the Mashoori et al. paper looked at choice evaluation at reward sites and shows that disappointing reinforcements are associated with reactivations in the ACC of the unselected target. This points to the role of the ACC in error detection and evaluation. Although this is an interesting result, it is in essence unrelated to what we are focusing on here, which is decision making and prediction of upcoming choices. The fact that the turning direction of the animal can be predicted on a trial-to-trial basis, and even precedes the behavioral change over the course of learning, sheds light on the role of the PFC in these important predictive cognitive processes (as opposed to post-choice reflective processes).

REVIEWER COMMENT: Indeed, as I said, the new element here is that the upcoming choice can be predicted. This appears only incremental and could belong to another story; as the manuscript is currently written, it does not support the article's main claim. I would like to specify that, regarding this and the other points above, my inability to see how these minor results support the Authors' claim may reflect my misunderstanding; nevertheless, this suggests that the manuscript should be extensively rewritten and reorganized to make the Authors' meaning clear.

ORIGINAL COMMENT: c) Figure 6 shows that prospective activity in the HPC is linked to SWRs and theta oscillations. This has been described in various forms since at least the works of Johnson and Redish in 2007, Pastalkova et al 2008, and Dragoi and Tonegawa (2011 and 2013), as well as in earlier literature on splitter cells. These foundational papers on this topic are not even cited in the current manuscript.

AUTHOR RESPONSE: We have added these citations to the introduction (line 37).

REVIEWER COMMENT: This is an example of how the Authors fail to acknowledge the underlying problem with how the manuscript is written; the issue has not been addressed except with a cosmetic change like the one described above. The Results section contains a series of findings that are well-known phenomena described previously (see below). Prior results should be acknowledged at the beginning of each relevant paragraph, followed by an explicit statement of what is new, so that readers can distinguish replication from novelty. Here, I pointed specifically to the results of Figure 6, and the Authors deemed it sufficient simply to add the citations I indicated to an existing sentence in the Introduction, while keeping the Results description unchanged. As written, this reads as if these phenomena are being described for the first time. This is incorrect. It is hard to avoid the impression that the Authors did not take this concern seriously; the same issue appears elsewhere in the manuscript, and I fail to see how the Authors "have improved clarity of the text throughout to highlight the novelty of our results better."

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

The authors used high-density probe recordings in the medial prefrontal cortex (PFC) and hippocampus during a rodent spatial memory task to examine functional sub-populations of PFC neurons that are modulated vs. unmodulated by hippocampal sharp-wave ripples (SWRs), an important physiological biomarker that is thought to have a role in mediating information transfer across hippocampal-cortical networks for memory processes. SWRs are associated with the reactivation of representations of previous experiences, and associated reactivation in hippocampal and cortical regions has been proposed to have a role in memory formation, retrieval, planning, and memory-guided behavior. This study focuses on awake SWRs that are prevalent during immobility periods during pauses in behavior. Previous studies have reported strong modulation of a subset of prefrontal neurons during hippocampal SWRs, with some studies reporting prefrontal reactivation during SWRs that have a role in spatial memory processes. The study seeks to extend these findings by examining the activity of SWR-modulated vs. unmodulated neurons across PFC sub-regions, and whether there is a functional distinction between these two kinds of neuronal populations with respect to representing spatial information and supporting memory-guided decision-making.

Strengths:

The major strength of the study is the use of Neuropixels 1.0 probes to monitor activity throughout the dorsal-ventral extent of the rodent medial prefrontal cortex, permitting an investigation of functional distinction in neuronal populations across PFC sub-regions. They are able to show that SWR-unmodulated neurons, in addition to having stronger spatial tuning than SWR-modulated neurons as previously reported, also show stronger directional selectivity and theta-cycle skipping properties.

Weaknesses:

(1) While the study is able to extend previous findings that SWR-modulated PFC neurons have significantly lower spatial tuning that SWR-unmodulated neurons, the evidence presented does not support the main conclusion of the paper that only the unmodulated neurons are involved in spatial tuning and signaling upcoming choice, implying that SWR-modulated neurons are not involved in predicting upcoming choice, as stated in the abstract. This conclusion makes a categorical distinction between two neuronal populations, that SWR-modulated neurons are involved and SWR-unmodulated are not involved in predicting upcoming choice, which requires evidence that clearly shows this absolute distinction. However, in the analyses showing non-local population decoding in PFC for predicting upcoming choice, the results show that SWR-unmodulated neurons have higher firing rates than SWR-modulated neurons, which is not a categorical distinction. Higher firing rates do not imply that only SWR-unmodulated neurons are contributing to the non-local decoding. They may contribute more than SWR-modulated neurons, but there are no follow-up analyses to assess the contribution of the two sub-populations to non-local decoding.

We agree with the reviewer that this is indeed not a categorical distinction, and do not wish to claim that the SWR-modulated neurons have absolutely no role in non-local decoding and signaling upcoming choice. We have adjusted this in the title, abstract and text to clarify this for the reader. Furthermore, we have performed additional analyses to elucidate the role of SWR-modulated neurons in non-local decoding by creating separate decoding models for SWR-modulated and unmodulated PFC neurons respectively. These analyses show that the SWR-unmodulated neurons are indeed encoding representations of the upcoming choice more often than the alternative choice, whereas the SWR-modulated neurons do not reliably differentiate the upcoming and alternative choices in non-local decoding at the choice point (see new Fig 4d).

(2) Further, the results show that during non-local representations of the hippocampus of the upcoming options, SWR-excited PFC neurons were more active during hippocampal representations of the upcoming choice, and SWR-inhibited PFC neurons were less active during hippocampal representations of the alternative choice. This clearly suggests that SWR-modulated neurons are involved in signaling upcoming choice, at least during hippocampal non-local representations, which contradicts the main conclusion of the paper.

This does not contradict the main conclusion of the paper, but in fact strengthens the hypothesis we are putting forward: that the SWR-modulated neurons are more linked to the hippocampal non-local representations, whereas the SWR-unmodulated neurons seem to have their own encoding of upcoming choice which is not linked to the signatures in the hippocampus (almost no time overlap with hippocampal representations, no phase locking to hippocampal theta, no time locking to hippocampal SWRs, no increased firing during hippocampal representations of upcoming choice).

(3) Similarly, one of the analyses shows that PFC nonlocal representations show no preference for hippocampal SWRs or hippocampal theta phase. However, the examples shown for non-local representations clearly show that these decodes occur prior to the start of the trajectory, or during running on the central zone or start arm. The time period of immobility prior to the start arm running will have a higher prevalence of SWRs and that during running will have a higher prevalence of theta oscillations and theta sequences, so non-local decoded representations have to sub-divided according to these known local-field potential phenomena for this analysis, which is not followed.

These analyses are in fact separated based on proximity to SWRs (only segments that occurred within 2 seconds of SWR onset were included, see Methods) and theta periods respectively (selected based on a running speed of more than 5 cm/s and the absence of SWRs in the hippocampus, see Methods). We have clarified this in the main text.

(4) The primary phenomenon that the manuscript relies on is the modulation of PFC neurons by hippocampal SWRs, so it is necessary to perform the PFC population decoding analyses during SWRs (or examine non-local decoding that occurs specifically during SWRs), as reported in previous studies of PFC reactivation during SWRs, to see if there is any distinction between modulated and unmodulated neurons in this reactivation. Even in the case of independent PFC reactivation as reported by one study, this PFC reactivation was still reported to occur during hippocampal SWRs, therefore decoding during SWRs has to be examined. Similarly, the phenomenon of theta cycle skipping is related to theta sequence representations, so decoding during PFC and hippocampal theta sequences has to be examined before coming to any conclusions.

The histograms shown in Figure 5a (see updated Fig 5a where we look at the closest SWR in time and compare the occurrence with shuffled data) show that there is no increased prevalence of decoding upcoming and alternative choices in the PFC during hippocampal SWRs. The lack of overlap of non-local decoding between the hippocampus and PFC further shows that these non-local representations occur at different timepoints in the PFC and hippocampus (see updated Fig 4e where we added a statistical test to show the likeliness of the overlap between the decoded segments in the PFC and hippocampus). Based on the reviewer's suggestion, we have additionally decoded the information in the PFC during hippocampal SWRs exclusively, and found that the direction on the maze could not be predicted based on the decoding of SWR time points in the PFC. See figure below. Similarly, we can see from the histograms in Figure 5c that there is no phase locking to the hippocampal theta phase for non-local representations in the PFC, and in contrast there is phase locking of the hippocampal encoding of upcoming choice to the rising phase of the theta cycle (Fig 6c), further highlighting the separation between these two regions in the non-local decoding.

Reviewer #2 (Public review):

Summary:

This work by den Bakker and Kloosterman contributes to the vast body of research exploring the dynamics governing the communication between the hippocampus (HPC) and the medial prefrontal cortex (mPFC) during spatial learning and navigation. Previous research showed that population activity of mPFC neurons is replayed during HPC sharp-wave ripple events (SWRs), which may therefore correspond to privileged windows for the transfer of learned navigation information from the HPC, where initial learning occurs, to the mPFC, which is thought to store this information long term. Indeed, it was also previously shown that the activity of mPFC neurons contains task-related information that can inform about the location of an animal in a maze, which can predict the animals' navigational choices. Here, the authors aim to show that the mPFC neurons that are modulated by HPC activity (SWRs and theta rhythms) are distinct from those "encoding" spatial information. This result could suggest that the integration of spatial information originating from the HPC within the mPFC may require the cooperation of separate sets of neurons.

This observation may be useful to further extend our understanding of the dynamics regulating the exchange of information between the HPC and mPFC during learning. However, my understanding is that this finding is mainly based upon a negative result, which cannot be statistically proven by the failure to reject the null hypothesis. Moreover, in my reading, the rest of the paper mainly replicates phenomena that have already been described, with the original reports not correctly cited. My opinion is that the novel elements should be precisely identified and discussed, while the current phrasing in the manuscript, in most cases, leads readers to think that these results are new. Detailed comments are provided below.

Major concerns:

(1) The main claim of the manuscript is that the neurons involved in predicting upcoming choices are not the neurons modulated by the HPC. This is based upon the evidence provided in Figure 5, which is a negative result that the authors employ to claim that predictive non-local representations in the mPFC are not linked to hippocampal SWRs and theta phase. However, it is important to remember that in a statistical test, the failure to reject the null hypothesis does not prove that the null hypothesis is true. Since this claim is so central in this work, the authors should use appropriate statistics to demonstrate that the null hypothesis is true. This can be accomplished by showing that there is no effect above some size that is so small that it would make the effect meaningless (see https://doi.org/10.1177/070674370304801108).

We would like to highlight a few important points here. (1) We indeed do not intend to claim that the SWR-modulated neurons are not at all involved in predicting upcoming choice, just that the SWR-unmodulated neurons may play a larger role. We have rephrased the title and abstract to make this clearer. (2) The hypothesis that we put forward is based not only on a negative effect, but on the findings that: the SWR-unmodulated neurons show higher spatial tuning (Fig 3b), more directional selectivity (Fig 3d), more frequent encoding of the upcoming choice at the choice point (new analysis, added in Fig 4d), and higher spike rates during the representations of the upcoming choice (Fig 5b). This is further highlighted by the fact that the representations of upcoming choice in the PFC are not time locked to SWRs (whereas the hippocampal representations of upcoming choice are; see Fig 5a and Fig 6a), and not time-locked to hippocampal theta phase (whereas the hippocampal representations are; see Fig 5c and Fig 6c). Finally, the representations of upcoming and alternative choices in the PFC do not show a large overlap in time with the representations in the hippocampus (see updated Fig 4e were we added a statistical test to show the likelihood of the overlap of decoded timepoints). All these results together lead us to hypothesize that SWR-modulation is not the driving factor behind non-local decoding in the PFC. (3) Based on the reviewers suggestion, we have added a statistical test to compare the phase-locking based of the non-local decoding to hippocampal SWRs and theta phase to shuffled posterior probabilities. Instead of looking at all SWRs in a -2 to 2 second window, we have now only selected the closest SWR in time within that window, and did the statistical comparison in the bin of 0-20 ms from SWR onset. With this new analysis we are looking more directly at the time-locking of the decoded segments to SWR onset (see updated Fig 5a and 6a).

(2) The main claim of the work is also based on Figure 3, where the authors show that SWRs-unmodulated mPFC neurons have higher spatial tuning, and higher directional selectivity scores, and a higher percentage of these neurons show theta skipping. This is used to support the claim that SWRs-unmodulated cells encode spatial information. However, it must be noted that in this kind of task, it is not possible to disentangle space and specific task variables involving separate cognitive processes from processing spatial information such as decision-making, attention, motor control, etc., which always happen at specific locations of the maze. Therefore, the results shown in Figure 3 may relate to other specific processes rather than encoding of space and it cannot be unequivocally claimed that mPFC neurons "encode spatial information". This limitation is presented by Mashoori et al (2018), an article that appears to be a major inspiration for this work. Can the authors provide a control analysis/experiment that supports their claim? Otherwise, this claim should be tempered. Also, the authors say that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space. How do they reconcile it with their results?

The reviewer is right to assert caution when talking about claims such as spatial tuning where other factors may also be involved. Although we agree that there may be some other factors influencing what we are seeing as spatial tuning, it is very important to note that the behavioral task is executed on a symmetrical 4-armed maze, where two of the arms are always used for the start of the trajectory, and the other two arms (North and South) function as the goal (reward) arms. Therefore, if the PFC is encoding cognitive processes such as task phases related to decision-making and reward, we would not be able to differentiate between the two start arms and the two goal arms, as these represent the same task phases. Note also that the North and South arm are illuminated in a pseudo-random order between trials and during cue-based rule learning this is a direct indication of where the reward will be found. Even in this phase of the task, the PFC encodes where the animal will turn on a trial-to-trial basis (meaning the North and South arm are still differentiated correctly on each trial even though the illumination and associated reward are changing).

Secondly, importantly, the reviewer mentions that we claimed that Jadhav et al. (2016) showed that mPFC neurons unmodulated by SWRs are less tuned to space, but this is incorrect. Jadhav et al. (2016) showed that SWR-unmodulated neurons had lower spatial coverage, meaning that they are more spatially selective (congruent with our results). We have rephrased this in the text to be clearer.

(3) My reading is that the rest of the paper mainly consists of replications or incremental observations of already known phenomena with some not necessarily surprising new observations:

(a) Figure 2 shows that a subset of mPFC neurons is modulated by HPC SWRs and theta (already known), that vmPFC neurons are more strongly modulated by SWRs (not surprising given anatomy), and that theta phase preference is different between vmPFC and dmPFC (not surprising given the fact that theta is a travelling wave).

The finding that vmPFC neurons are more strongly modulated by SWRs than dmPFC indeed matches what we know from anatomy, but that does not make it a trivial finding. A lot remains unknown about the mPFC subregions and their interactions with the hippocampus, and not every finding will be directly linked to the anatomy. Therefore, in our view this is a significant finding which has not been studied before due to the technical complexity of large-scale recordings along the dorsal-ventral axis of the mPFC.

Similarly, theta being a traveling wave (which in itself is still under debate), does not mean we should assume that the dorsal and ventral mPFC should follow this signature and be modulated by different phases of the theta cycle. Again, in our view this is not at all trivial, but an important finding which brings us closer to understanding the intricate interactions between the hippocampus and PFC in spatial learning and decision-making.

(b) Figure 4 shows that non-local representations in mPFC are predictive of the animal's choice. This is mostly an increment to the work of Mashoori et al (2018). My understanding is that in addition to what had already been shown by Mashoori et al here it is shown how the upcoming choice can be predicted. The author may want to emphasize this novel aspect.

In our view our manuscript focuses on a completely different aspect of learning and memory than the paper the reviewer is referring to (Mashoori et al. 2018). Importantly, the Mashoori et al. paper looked at choice evaluation at reward sites and shows that disappointing reinforcements are associated with reactivations in the ACC of the unselected target. This points to the role of the ACC in error detection and evaluation. Although this is an interesting result, it is in essence unrelated to what we are focusing on here, which is decision making and prediction of upcoming choices. The fact that the turning direction of the animal can be predicted on a trial-to-trial basis, and even precedes the behavioral change over the course of learning, sheds light on the role of the PFC in these important predictive cognitive processes (as opposed to post-choice reflective processes).

(c) Figure 6 shows that prospective activity in the HPC is linked to SWRs and theta oscillations. This has been described in various forms since at least the works of Johnson and Redish in 2007, Pastalkova et al 2008, and Dragoi and Tonegawa (2011 and 2013), as well as in earlier literature on splitter cells. These foundational papers on this topic are not even cited in the current manuscript.

We have added these citations to the introduction (line 37).

Although some previous work is cited, the current narrative of the results section may lead the reader to think that these results are new, which I think is unfair. Previous evidence of the same phenomena should be cited all along the results and what is new and/or different from previous results should be clearly stated and discussed. Pure replications of previous works may actually just be supplementary figures. It is not fair that the titles of paragraphs and main figures correspond to notions that are well established in the literature (e.g., Figure 2, 2nd paragraph of results, etc.).

We have changed the title of paragraph 2 and Figure 2 to highlight more clearly the novel result (the difference between the dorsal and ventral mPFC), and have improved clarity of the text throughout to highlight the novelty of our results better.

(d) My opinion is that, overall, the paper gives the impression of being somewhat rushed and lacking attention to detail. Many figure panels are difficult to understand due to incomplete legends and visualizations with tiny, indistinguishable details. Moreover, some previous works are not correctly cited. I tried to make a list of everything I spotted below.

We have addressed all the comments in the Recommendations for Authors.

Reviewer #1 (Recommendations for the authors):

(1) Expanding on the points above, one of the strengths of the study is expanding the previous result that SWR-unmodulated neurons are more spatially selective (Jadhav et al., 2016), across prefrontal sub-regions, and showing that these neurons are more directionally selective and show more theta cycle skipping. Theta cycle skipping is related to theta sequence representations and previous studies have established PFC theta sequences in parallel to hippocampal theta sequences (Tang et al., 2021; Hasz and Redish, 2020; Wang et al., 2024), and the theta cycle skipping result suggests that SWR-unmodulated neurons should show stronger participation than SWR-modulated neurons in PFC theta sequences that decode to upcoming or alternative location, which can be tested in this high-density PFC physiology data. This is still unlikely to make a categorical distinction that only SWR-unmodulated neurons participate in theta sequence decoding, but will be useful to examine.

We thank the reviewer for their suggestion and have now included results based on separate decoding models that only use SWR-modulated or SWR-unmodulated mPFC neurons. From this analysis we see that indeed SWR-unmodulated neurons are not the only group contributing to theta sequence decoding, but they do distinguish more strongly between the upcoming and alternative arms at the choice point (see new Fig 4d).

(2) Non-local decoding in 50ms windows on a theta timescale is a valid analysis, but ignoring potential variability in the internal state during running vs. immobility, and as indicated by LFPs by the presence of SWRs or theta oscillations, is incorrect especially when conclusions are being made about decoding during SWRs and theta oscillation phase, and in light of previous evidence that these are distinct states during behavior. There are multiple papers on PFC theta sequences (Tang et al., 2021; Hasz and Redish, 2020; Wang et al., 2024), and on PFC reactivation during SWRs (Shin et al., 2019; Kaefer et al., 2020; Jarovi et al., 2023), and this dataset of high-density prefrontal recordings using Neuropixels 1.0 provides an opportunity to investigate these phenomena in detail. Here, it should be noted that although Kaefer et al. reported independent prefrontal reactivation from hippocampal reactivation, these PFC reactivation events still occurred during hippocampal SWRs in their data, and were linked to memory performance.

From our data we see that the time segments that represent upcoming or alternative choice in the prefrontal cortex are in fact not time-locked to hippocampal SWRs (updated Fig 5a where we look only at the closest SWR in time and compare this to shuffled data). In addition, these segments do not overlap much with the decoded segments in the hippocampus (see updated Fig 4e where we added a shuffling procedure to assess the likelihood of the overlap with hippocampal decoded segments). Importantly, we are not ignoring the variability during running and immobility, as theta segments were selected based on a running speed of more than 5 cm/s and the absence of SWRs in the hippocampus (see Methods), ensuring that the theta and SWR analyses were done on the two different behavioral states respectively. We have clarified this in the main text.

(3) The majority of rodent studies make the distinction between ACC, PrL, and IL, although as the authors noted, there are arguments that rodent mPFC is a continuum (Howland et al., 2022), or even that rodent mPFC is a unitary cingulate cortical region (van Heukelum et al., 2020). The authors choose to present the results as dorsal (ACC + dorsal PrL) vs. ventral mPFC (ventral PrL + IL), however, in my opinion, it will be more useful to the field to see results separately for ACC, PrL, and IL, given the vast literature on connectivity and functional differences in these regions.

We appreciate the reviewer’s suggestion. Initially, we did perform all analyses separately for the ACC, PLC and ILC subregions. However, we observed that the differences between subregions (strength of SWR-modulation and the phase locking to theta) varied uniformly along the dorsal-ventral axis, i.e., the PLC showed a profile of SWR-modulation and theta phase locking that fell in between that of the ACC and the ILC. This is also highlighted in paragraph 3 of the introduction (lines 52-56). For that reason, and for the sake of reducing the number of variables, increasing statistical power, and improving readability, we focused on the dorsal-ventral distinction instead, as this is where the main differences were seen.

(4) I suggest that the authors refrain from making categorical distinctions as in their title and abstract, such as "neurons that are involved in predicting upcoming choice are not the neurons that are modulated by hippocampal sharp-wave ripples" when the evidence presented can only support gradation of participation of the two neuronal sub-populations, not an absolute distinction. The division of SWR-modulated and SWR-unmodulated neurons itself is determined by the statistic chosen to divide the neurons into one or two sub-classes and will vary with the statistical threshold employed. Further, previous studies have suggested that SWR-excited and SWR-inhibited neurons comprise distinct functional sub-populations based on their activity properties (Jadhav et al., 2016; Tang et al., 2017), but it is not clear to what degree is SWR-modulated neurons a distinct and singular functional sub-population. In the absence of connectivity information and cross-correlation measures within and across sub-populations, it is prudent to be conservative about this interpretation of SWR-unmodulated neurons.

We agree with the reviewer that the distinction is not categorical and have changed the wording in the title and abstract. We also do not intend to claim that the SWR-modulated neurons are a distinct and singular functional sub-population, and for that reason the firing rates from the SWR-excited and SWR-inhibited groups are reported separately throughout the paper.

Reviewer #2 (Recommendations for the authors):

Minor detailed remarks:

(1) The authors should provide a statistical test, perhaps against shuffled data, for Figures 5a,c and 6a,c.

We thank the reviewer for their suggestion and have added statistical tests in Figures 5a, 5c, 6a and 6c.

(2) The behavioral task is explained only in the legend of Figure 1c, and the explanation is quite vague. In this type of article format, readers need to have a clear understanding of the task without having to refer to the methods section. A clear understanding of the task is crucial for interpreting all subsequent analyses. In my opinion, the word 'trial' in the figure is misleading, as these are sessions composed of many trials.

We have added a more thorough description of the behavioral task, both in the main text and the Figure legend.

(3) Figure 1d, legend of markers missing.

We have added a legend for the markers.

(4) When there are multiple bars and a single p-value is presented, it is unclear which group comparisons the p-value pertains to. For instance, Figures 2c-f and 3b, d, f (right parts), and 5b...

For all p-values we have added lines to the figures that indicate the groups that were compared and have added descriptions of the statistical test to the figure legends to indicate what each p-value represents.

(5) In Figure 3c, the legend does not explain what the colored lines represent, and the lines themselves are very small and almost indistinguishable.

We have changed the colored lines to quadrants on the maze to clarify what each direction represents.

(6) Figure 4a is too small, and the elements are so tiny that it is impossible to distinguish them and their respective colors. The term 'segment' has not been unequivocally explained in the text. All the different elements of the panel should be explicitly explained in the legend to make it easily understandable. What do the pictograms of the maze on the left represent? What does the dashed vertical line indicate?

We have added the definition of a segment in the text (lines 283-286) and have improved the clarity and readability of Figure 4a.

(7) In Figure 5, what do the red dots on the right part relate to? The legend should explicitly explain what is shown in the left and right parts, respectively. What comparisons do the p-values relate to?

We have adjusted the legend to explain the left and right parts of the figure and we have added the statistical test that was used to get to the p-value (in addition to the text which already explained this).

(8) Panels b of Figures 5 and 6 should have the same y-axis scale for comparison. The position of the p-values should also be consistent. With the current arrangement in Figure 6, it is unclear what the p-values relate to.

We have adjusted the y-scale to be the same for Figures 5 and 6, and we have added a description of the statistical test to the legend.

(9) Multiple studies have previously shown that mPFC activity contains spatial information (e.g., refs 24-27). It is important that, throughout the paper, the authors frame their results in relation to previous findings, highlighting what is novel in this work.

We thank the reviewer for this valuable suggestion. In the revised manuscript, we have indicated more clearly which results replicate previous findings and highlighted novel results.

(10) Please note that Peyrache et al. (2009) do not show trajectory replay, nor do they decode location. I am not familiar with all the cited literature, but this makes me think that the authors may want to double-check their citations to ensure they assign the correct claims to each past work.

We have adjusted the reference to the work to exclude the word ‘trajectory’ and doublechecked our other citations.

(11) The authors perform theta-skipping analysis, first described by Kay et al., but do not cite the original paper until the discussion.

Thank you pointing out this oversight. We have now included this citation earlier in the paper (line 231).

(12) Additionally, some parts of the text are difficult to grasp, and there are English vocabulary and syntax errors. I am happy to provide comments on the next version of the text, but please include page and line numbers in the PDF. The authors may also consider using AI to correct English mistakes and improve the fluency and readability of their text.

We have carefully gone through the text to correct any errors. We have now also included page and line numbers and we will be happy to address any specific issues the reviewer may spot in the revised manuscript.