State-dependent coupling of hippocampal oscillations

Article activity feed

1. 

   Version published to 10.7554/elife.80263 on eLife

   Jul 18, 2023
2. 

   eLife

   Jun 10, 2023

   Author Response

   Reviewer #2 (Public Review):

   1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these …

   Author Response

   Reviewer #2 (Public Review):

   1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

   We thank the reviewer for allowing us to clarify this subject. We completely agree with concerns raised in the comments. To avoid any confusion, we have replaced throughout the manuscript the word ‘spindle’ with ‘beta’.

   1. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

   We are grateful to the reviewer for suggesting alternative analysis. We have used this direction, to create surrogate datasets obtained by time shifting each band and obtained their respective UMAP projections (see modified Figure 2D). Additionally, as suggested, for duration-matched controls, we have selected consecutive windows, rather than random points (Figure 2 – figure supplement 1C). UMAP projections were obtained for each duration-matched control and occupancy was computed. The text in the method section has been modified to indicate the analysis. As expected, the results were identical.

   1. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

   We thank the reviewer for allowing us to clarify this aspect of the manuscript. We have now edited the main text to include considerations on the biological relevance of the findings of Figure 4, 5 and 6.

   Additions to figure 4: In particular, non-REM states in sleep2 tended to concentrate in a region of increased power in the delta and beta bands, which could be the results of increased interactions with cortical activity modulated in the same range. It is also likely that such effect was induced by the exposure to relevant behavioral experience. In fact, changes in density of individual oscillations after learning have been reported using traditional analytical methods and are thought to support memory consolidation (Bakker et al., 2015; Eschenko et al., 2008, 2006). Nevertheless, while traditional methods provide information about individual components, the novel approach used here provides additional information about the combinatorial shift in the dynamics of network oscillations after learning or exploration. Thus, it provides the basis for identifying how coordinated activity among different oscillations supports memory consolidation processes, as those occurring during non-REM sleep after exploration, which cannot be elucidated using traditional analytical methods.

   Additions to figure 5: Gamma segregation and delta decoupling offer a picture of hippocampal REM sleep as being more akin to awake locomotion (with the major difference of a stronger medium gamma presence) while also suggesting a substantial independence from cortical slow oscillations. On the other hand, the across-scale coherence of non-REM sleep is consistent with this sleep stage being dominated by brain-wide collective fluctuations engaging oscillations at every range. Distinct cross frequency coupling among various individual pairs of oscillations such as theta-gamma, delta-gamma etc., have been already reported (Bandarabadi et al., 2019; Clemens et al., 2009; Hammer et al., 2021; Scheffzük et al., 2011). However, computing cross frequency coupling on the state space provides the additional information on how multiple oscillations, obtained from distinct CA1 hippocampal layers (stratum pyramidale, stratum radiatum and stratum lacunosum moleculare), are coupled with each other during distinct states of sleep and wakefulness. Furthermore, projecting the correlation matrices on 2D plane, provides a compact tool that allows to visualize the cross-frequency interactions among various hippocampal oscillations. Altogether, this approach reveals the complex nature of coupling dynamics occurring in hippocampus during distinct behavioral states

   Additions to Figure 6: We found that transitions occurring from REM-to-REM sleep and non-REM-to-non-REM sleep (intra-state transitions) are more vulnerable to plasticity after exploration as compared to inter-state transitions (such as non-REM to REM, REM-to-intermediate etc.) (Fig 6E, F). These changes in intra-state transitions were observed to be beyond randomness (Fig S9 E, F) indicating a specificity in plastic changes in state transitions after exploration. In particular, while the average REM period duration is unaltered after exploration (Fig 4G), REM temporal structure is reorganized. In fact, increased probability of REM to REM transitions indicates a significant prolongation of REM bout duration. Similarly, the increase in non-REM to non-REM transition probability reflects an increased duration of non-REM bouts. Therefore, environment exploration was accompanied by an increased separation between REM and non-REM periods, possibly as a response to increased computational demands. More in general, the network state space allows to characterize the state transitions in hippocampus and how they are affected by novel experience or learning. By observing the state transition patterns, this analytical framework allows to detect and identify state-specific changes in the hippocampal oscillatory dynamics, beyond the possibilities offered by more traditional univariate and bivariate methods. We next investigated how fast the network flows on the state space and assessed whether the speed is uniform, or it exhibits specific region-dependent characteristics.

   Reviewer #3 (Public Review):

   1. My primary concern is to provide clear evidence that this approach will provide key insights of high physiological significance, especially for readers who may think the traditional approaches are advantageous (for example due to their simplicity). I think the authors' findings of distinct sleep state signatures or altered organization of the NLG3-KO mouse could serve this purpose. However, right now the physiological significance of these results is unclear. For example, do these sleep state signatures predict later behavior performance, or is altered organization related to other functional impairments in the disease model? Do neurons with distinct sleep state signatures form distinct ensembles and code for related information?

   We are thankful to the reviewer for raising a very interesting line of questioning regarding sleep signatures and distinct ensemble. In this study, we show that sleep state signatures can predict how individual cells may participate in information processing during open field exploration. However, further analysis exploring the recruitment of neuronal ensembles are in preparation for another manuscript and is beyond the scope of this article.

   We have further modified the description of the results (as also suggested by other reviewers) to highlight the key advantages of this approach over traditional methods.

   Regarding functional impairment: as described in the manuscript, the altered organization in animal model of autism could possibly due to alterations in cellular and synaptic mechanisms as those described in previous reports (Modi et al 2019, Foldy et al 2013)

   1. For cells with different mean firing rates during exploration: is that because they are putative fast-spiking interneurons and pyramidal cells? From the reported mean firing rates, I think some of these cells are interneurons. Since mean firing rates are well known to vary with cell type, this should be addressed. For example, the sleep state signatures may be distinct for different putative pyramidal cells and interneurons. This would be somewhat expected considering prior work that has shown different cell types have different oscillatory coupling characteristics. I think it would be more interesting to determine if pyramidal cells had distinct sleep state signatures and, if so, whether pyramidal cells from the same sleep state signature have similar properties like they code for similar things or commonly fire together in an ensemble ms the number of cells in Fig. 8 may be limited for this analysis. The authors could use the hc-11 data in addition, which was also tested in this work.

   We thank the reviewer for suggesting this additional analysis to better describe the data. To this end, we have added an additional Figure in supplementary data (analysis of hc11 dataset: Figure Figure 8 – figure supplement 3), to demonstrate that interneurons and pyramidal cells have distinct sleep signatures. These findings are in agreement with dataset presented in Figure 8D, E.

   As shown in the manuscript, the spatial firing (sparsity) has large variability for cells having similar network signatures (Fig 8E). Thus, additional parameters beside oscillations may be involved in cells encoding. Different network state spaces are required to be explored in future studies to further understand this phenomenon in detail.

   We agree that investigating neuronal ensembles and state space are an interesting direction to follow. In another study (in preparation) which are investigating in detail the recruitment of neuronal ensemble by oscillatory state space. Thus, those findings are beyond the scope of this introductory article.

   1. Example traces are needed to show how LFPs change over the state-space. Example traces should be included for key parts of the state-space in Figures 2 and 3.

   We thank the reviewer for this key insight on data representation. Example traces of how LFP varies on the state space have been added (see Figure 4 – figure supplement 1).

   1. What is the primary rationale for 200ms time bins? Is this time scale sufficient to capture the slow dynamics of delta rhythm (1-5Hz) with a maximum of 1s duration?

   Time scale of binning depends on the scale of investigation. We also replicated the results with different time bins (such as 50 ms and 1 seconds) and the results are identical. For delta rhythms, with 200 ms time bins, the dynamics will be captured across multiple bins. Additionally, the binned power time series are also smoothed before obtaining projections.

   1. Since oscillatory frequency and power are highly associated with running speed, how does speed vary over the state space. Is the relationship between speed and state-space similar to the results of previous studies for theta (Slawinska and Kasicki, Brain Res 1998; Maurer et al, Hippocampus 2005) and gamma oscillations (Ahmed and Mehta J. Neurosci 2012; Kemere et al PLOS ONE 2013), or does it provide novel insights?

   We thank the reviewer for highlighting this crucial link between oscillation and locomotion. While various articles have focused on individual oscillations, the combinatorial effects of multiple oscillations from multiple brain areas in regulating the speed of the animal during exploration is definitely worth exploring with this novel approach. These set of results will be introduced in another study, currently in preparation.

   1. The separation of 9 states (Fig. 6ABC) seems arbitrary, where state 1 (bin 1) is never visited. I suggest plotting the density distribution of the data in Fig. 2A or Fig. 6A to better determine how many states are there within the state space. For example, five peaks in such a density plot might suggest five states. Alternately, clustering methods could be useful to determine how the number of states.

   We thank the reviewer for this this useful suggestion. We agree that additional clustering methods can be used to identify non-canonical sleep states. These are currently being explored in our lab and will be part of future studies. As for this dataset, the density plots are available in figure 4E, which determines how many states are in each part of the state space.

   1. The results in Fig. 4G are very interesting and suggest more variation of sub-states during non REM periods in sleep1 than in sleep2. What might explain this difference? Was it associated with more frequent ripple events occurring in sleep2?

   The reviewer is right in looking for the source of the decreased of state variability in sleep2. Considering the distribution of relative frequency power in the state space, the higher concentration in sleep 2 corresponds to higher content in the slower delta and spindle frequency bands, rather than the higher frequencies of SWRs. This result can be interpreted in the light of enhanced cortical activity (which is known to heavily recruit those bands) and possibly of enhanced cortical-hippocampal communication following relevant behavioral experience. In fact, it is also necessary to mention that with our recording setup we cannot rule out the effects of volume conductance completely, and thus we cannot exclude that the increase in the delta and spindle bands in the hippocampus were a spurious effect of purely cortical frequency modulations.

   1. The state transition results in Fig. 6 are confusing because they include two fundamentally different timescales: fast transitions between oscillatory states and slow dynamics of sleep states. I recommend clarifying the description in the results and the figure caption. Furthermore, how can an animal transition between the same sleep state (Fig. 6EF)? Would they both be in a single sleep state?

   The transitions capture the fast oscillatory scales (as they are investigated over a timeframe of 1 second). The sleep stages (REM, non-REM etc.) are used as labels from which the states originate on the state space. This allows us to characterize fast oscillatory dynamics in various sleep stages.

   Regarding same state transition: An increase in same state transition probability corresponds to increase in prolongation of that particular state, thereby altering the temporal structure of a given sleep state.

   Read the original source
3. 

   eLife

   Jun 5, 2023

   eLife assessment

   Traditional approaches for the analysis of brain rhythms typically rely on measuring spectro-temporal properties of individual oscillations or the interactions between two different oscillations. This manuscript presents a novel multivariate approach that uses a state space model to simultaneously analyze the dynamics and interactions of multiple hippocampal oscillations. Such an approach represents a step forward in the field that highlights the need of taking into account the complexity of network interactions rather than trying to understand each component of the system in isolation.

   Read the original source
4. 

   eLife

   Jun 5, 2023

   Reviewer #1 (Public Review):

   In this manuscript, Modi et al present a novel method to analyze brain oscillations. Traditional approaches are typically based on analyzing spectral features on individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). The authors take a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations. This is a better way to study dynamics interactions in a complex system than the more traditional 'reductionist' approach and, so far, few methods exist that allow such multivariate analysis of neural oscillations. The method is well demonstrated in the paper, including several application cases. Several aspects of the results need to be better characterized, a clear discussion of the caveats and …

   Reviewer #1 (Public Review):

   In this manuscript, Modi et al present a novel method to analyze brain oscillations. Traditional approaches are typically based on analyzing spectral features on individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). The authors take a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations. This is a better way to study dynamics interactions in a complex system than the more traditional 'reductionist' approach and, so far, few methods exist that allow such multivariate analysis of neural oscillations. The method is well demonstrated in the paper, including several application cases. Several aspects of the results need to be better characterized, a clear discussion of the caveats and limitations of the method is lacking and the advantages over existing methods need to be outlined more clearly. Provided these issues are corrected I believe this would be an important contribution to the field that may have multiple applications.

   Read the original source
5. 

   eLife

   Jun 5, 2023

   Reviewer #2 (Public Review):

   Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

   1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not …

   Reviewer #2 (Public Review):

   Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

   1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

   2. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

   3. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

   Read the original source
6. 

   eLife

   Jun 5, 2023

   Reviewer #3 (Public Review):

   Modi et al. developed a novel data-driven computational framework to investigate interactions between multiple brain oscillations and validated this approach in hippocampal CA1 utilizing well-studied changes in oscillations across CA1 layers. This approach provides a new way to investigate complex interactions between diverse neural oscillations during different behaviors. In contrast to standard approaches that classify LFP recordings into a few different oscillatory states which simplify patterns in the LFP, this approach maps a complex state space. The essential idea behind the method is novel and interesting with the potential to expand to other studies of other brain regions or interactions between regions. The authors provide a comprehensive analysis showing how this state space relates to traditional …

   Reviewer #3 (Public Review):

   Modi et al. developed a novel data-driven computational framework to investigate interactions between multiple brain oscillations and validated this approach in hippocampal CA1 utilizing well-studied changes in oscillations across CA1 layers. This approach provides a new way to investigate complex interactions between diverse neural oscillations during different behaviors. In contrast to standard approaches that classify LFP recordings into a few different oscillatory states which simplify patterns in the LFP, this approach maps a complex state space. The essential idea behind the method is novel and interesting with the potential to expand to other studies of other brain regions or interactions between regions. The authors provide a comprehensive analysis showing how this state space relates to traditional oscillatory states (like delta, theta, and gamma). Among the reported results, it is sometimes unclear what is a validation of their approach versus a novel scientific finding (in the context of the larger literature) and the significance of the finding. Although the overall results seem convincing, the paper is a lacking a demonstration that shows why this approach is of high physiological significance. Furthermore, more evidence showing the specific advantages of using this method in LFP data from a single CA1 layer would make this approach more readily adoptable for the community.

   Major concerns:
   1. My primary concern is to provide clear evidence that this approach will provide key insights of high physiological significance, especially for readers who may think the traditional approaches are advantageous (for example due to their simplicity). I think the authors' findings of distinct sleep state signatures or altered organization of the NLG3-KO mouse could serve this purpose. However, right now the physiological significance of these results is unclear. For example, do these sleep state signatures predict later behavior performance, or is altered organization related to other functional impairments in the disease model? Do neurons with distinct sleep state signatures form distinct ensembles and code for related information?
   2. For cells with different mean firing rates during exploration: is that because they are putative fast-spiking interneurons and pyramidal cells? From the reported mean firing rates, I think some of these cells are interneurons. Since mean firing rates are well known to vary with cell type, this should be addressed. For example, the sleep state signatures may be distinct for different putative pyramidal cells and interneurons. This would be somewhat expected considering prior work that has shown different cell types have different oscillatory coupling characteristics. I think it would be more interesting to determine if pyramidal cells had distinct sleep state signatures and, if so, whether pyramidal cells from the same sleep state signature have similar properties like they code for similar things or commonly fire together in an ensemble. It seems the number of cells in Fig. 8 may be limited for this analysis. The authors could use the hc-11 data in addition, which was also tested in this work.
   3. Example traces are needed to show how LFPs change over the state-space. Example traces should be included for key parts of the state-space in Figures 2 and 3.
   4. What is the primary rationale for 200ms time bins? Is this time scale sufficient to capture the slow dynamics of delta rhythm (1-5Hz) with a maximum of 1s duration?
   5. Since oscillatory frequency and power are highly associated with running speed, how does speed vary over the state space. Is the relationship between speed and state-space similar to the results of previous studies for theta (Slawinska and Kasicki, Brain Res 1998; Maurer et al, Hippocampus 2005) and gamma oscillations (Ahmed and Mehta J. Neurosci 2012; Kemere et al PLOS ONE 2013), or does it provide novel insights?
   6. The separation of 9 states (Fig. 6ABC) seems arbitrary, where state 1 (bin 1) is never visited. I suggest plotting the density distribution of the data in Fig. 2A or Fig. 6A to better determine how many states are there within the state space. For example, five peaks in such a density plot might suggest five states. Alternately, clustering methods could be useful to determine how the number of states.
   7. The results in Fig. 4G are very interesting and suggest more variation of sub-states during nonREM periods in sleep1 than in sleep2. What might explain this difference? Was it associated with more frequent ripple events occurring in sleep2?
   8. The state transition results in Fig. 6 are confusing because they include two fundamentally different timescales: fast transitions between oscillatory states and slow dynamics of sleep states. I recommend clarifying the description in the results and the figure caption. Furthermore, how can an animal transition between the same sleep state (Fig. 6EF)? Would they both be in a single sleep state?

   Read the original source
7. 

   Version published to 10.1101/2022.05.03.490415v1 on bioRxiv

   May 4, 2022