Author Response
eLife assessment
This valuable paper presents a thoroughly detailed methodology for mesoscale-imaging of extensive areas of the cortex, either from a top or lateral perspective, in behaving mice. While the examples of scientific results to be derived with this method are in the preliminary stages, they offer promising and stimulating insights. Overall, the method and results presented are convincing and will be of interest to neuroscientists focused on cortical processing in rodents.
Authors’ Response: We thank the reviewers for the helpful and constructive comments. They have helped us plan for significant improvements to our manuscript. Our preliminary response and plans for revision are indicated below.
Public Reviews:
Reviewer #1 (Public Review):
Summary:
The authors introduce two preparations for observing large-scale cortical activity in mice during behavior. Alongside this, they present intriguing preliminary findings utilizing these methods. This paper is poised to be an invaluable resource for researchers engaged in extensive cortical recording in behaving mice.
Strengths:
-Comprehensive methodological detailing:
The paper excels in providing an exceptionally detailed description of the methods used. This meticulous documentation includes a step-by-step workflow, complemented by thorough workflow, protocols, and a list of materials in the supplementary materials.
-Minimal movement artifacts:
A notable strength of this study is the remarkably low movement artifacts. To further underscore this achievement, a more robust quantification across all subjects, coupled with benchmarking against established tools (such as those from suite2p), would be beneficial.
Authors’ Response: This is a good suggestion. Since we used suite2p for our data analysis, and have records of the fast-z correction applied by the microscope, we can supply these as quantifications of movement corrections that were applied across our sample of mice. We hope to supply this information as a supplement in the revised manuscript.
Currently, we have chosen to show that the corrected, post- suite2p registration movement artifacts are very close to zero. We will revise the manuscript with clear descriptions of methods that we have found important, such as fully tightening all mounting devices, utilizing the air table properly, implanting the cranial window with proper, even pressure across its entire extent, and mounting the mouse so that it is not too close or far from the surface of the running wheel.
Insightful preliminary data and analysis:
The preliminary data unveiled in the study reveal interesting heterogeneity in the relationships between neural activity and detailed behavioral features, particularly notable in the lateral cortex. This aspect of the findings is intriguing and suggests avenues for further exploration.
Weaknesses:
-Clarification about the extent of the method in the title and text:
The title of the paper, using the term "pan-cortical," along with certain phrases in the text, may inadvertently suggest that both the top and lateral view preparations are utilized in the same set of mice. To avoid confusion, it should be explicitly stated that the authors employ either the dorsal view (which offers limited access to the lateral ventral regions) or the lateral view (which restricts access to the opposite side of the cortex). For instance, in line 545, the phrase "lateral cortex with our dorsal and side mount preparations" should be revised to "lateral cortex with our dorsal or side mount preparations" for greater clarity.
Authors’ Response: We will revise the manuscript so that it is clear that we made use of two imaging configurations for the 2-photon mesoscope data and the benefits and limitations of these two preparations. The dorsal mount and the side mount each have their advantages and disadvantages, but together form a powerful tool for imaging much of the dorsal and lateral cortex in awake, behaving mice.
-Comparison with existing methods:
A more detailed contrast between this method and other published techniques would add value to the paper. Specifically, the lateral view appears somewhat narrower than that described in Esmaeili et al., 2021; a discussion of this comparison would be useful.
Authors’ Response: We will modify the manuscript so that a more detailed comparison with other published techniques is included. The preparation by Esmaeili et al. 2021 has some similarities, but also differences, from our preparation. Our preliminary reading is that their through-the-skull field of view is approximately the same as our through-the-skull field of view that exists between our first (headpost implantation) and second (window implantation) surgeries, although our preparation appears to include more anterior areas both near to and on the contralateral side of the midline. We will compare these preparations more accurately in the revised manuscript.
If you compare the imageable extent of our cranial window for mesoscale 2-photon imaging to that of their through-the-skull widefield preparation, which is a bit of an “apples to oranges” comparison, then you are likely correct that their field of view is larger than ours, if you are referring to our 10 mm radius-bend glass. However, use of our 9 mm radius bend glass (i.e. a tighter bend) allows us to image additional ventral auditory areas. We could show an example of this, perhaps, although we did not make as much use of this alternative window in the large FOV experiments, because the increased curvature of the glass relative to the 10 mm radius bend window prevents imaging of the entire preparation in a single 2-photon z-plane. With the 9 mm radius bend glass we mostly imaged in the multiple, small FOV configuration (see Fig. S2).
Furthermore, the number of neurons analyzed seems modest compared to recent papers (50k) - elaborating on this aspect could provide important context for the readers.
Authors’ response: With respect to the “modest” number of neurons analyzed (between 2000 and 8000 neurons per session for our dorsal and side mount preparations with medians near 4500; See Fig. S2e) we would like to point out that factors such as use of dual-plane imaging or multiple imaging planes, different mouse lines, use of different duration recording sessions (see our Fig S2c), use of different imaging speeds and resolutions (see our Fig S2d), use of different Suite2p run-time parameters, and inclusion or areas with blood vessels and different neuron cell densities, may all impact the count of total analyzed neurons. We could provide additional documentation of these issues, but we would like to point out that, in our case, we were not trying to maximize neuron count at the expense of other factors such as imaging speed and total spatial FOV extent.
-Discussion of methodological limitations:
The limitations inherent to the method, such as the potential behavioral effects of tilting the mouse's head, are not thoroughly examined. A more comprehensive discussion of these limitations would enhance the paper's balance and depth.
Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this situation (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, will be discussed more thoroughly in the revised manuscript.
One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparison across mice indicates that side and dorsal mount mice show similar behavioral variability.
It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.
Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective to 20 degrees to the right, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.
-Preliminary nature of results:
The results are at a preliminary stage; for example, the B-soid analysis is based on a single mouse, and the validation data are derived from the training data set. The discrepancy between the maps in Figures 5e and 6e might indicate that a significant portion of the map represents noise. An analysis of variability across mice and a method to assign significance to these maps would be beneficial.
Authors’ Response: In this methods paper, we have chosen to supply proof of principle examples, without a complete analysis of animal-to-animal variance. The dataset for this paper contains both neural and behavioral data for 91 sessions across 18 mice from both dorsal and side mount preparations. The complete analysis of this dataset exceeds the capacity of the present study. We will include more individual examples in the revised version, along with data showing the amount of between session and across mouse variance. We will include in the revised manuscript a comparison of the stability of B-SOiD measures across sessions, as a demonstration of what may be expected with this method.
-Analysis details:
More comprehensive details on the analysis would be beneficial for replicability and deeper understanding. For instance, the statement "Rigid and non-rigid motion correction were performed in Suite2p" could be expanded with a brief explanation of the underlying principles, such as phase correlation, to provide readers with a better grasp of the methodologies employed.
Authors’ Response: We are revising the manuscript to give more detail without reducing readability, so as to increase clarity of presentation. Since this is a methods paper, we are modifying the manuscript to include more details and clear explanations so that the reader may replicate our methods and results.
Reviewer #2 (Public Review):
Summary:
The authors present a comprehensive technical overview of the challenging acquisition of large-scale cortical activity, including surgical procedures and custom 3D-printed headbar designs to obtain neural activity from large parts of the dorsal or lateral neocortex. They then describe technical adjustments for stable head fixation, light shielding, and noise insulation in a 2-photon mesoscope and provide a workflow for multisensory mapping and alignment of the obtained large-scale neural data sets in the Allen CCF framework. Lastly, they show different analytical approaches to relate single-cell activity from various cortical areas to spontaneous activity by using visualization and clustering tools, such as Rastermap, PCA-based cell sorting, and B-SOID behavioral motif detection.
Authors’ Response: Thank you for this excellent summary of the scope of our paper.
The study contains a lot of useful technical information that should be of interest to the field. It tackles a timely problem that an increasing number of labs will be facing as recent technical advances allow the activity measurement of an increasing number of neurons across multiple areas in awake mice. Since the acquisition of cortical data with a large field of view in awake animals poses unique experimental challenges, the provided information could be very helpful to promote standard workflows for data acquisition and analysis and push the field forward.
Authors’ Response: We very much support the idea that our work here will contribute to the development of standard workflows across the field including multiple approaches to large-scale neural recordings.
Strengths:
The proposed methodology is technically sound and the authors provide convincing data to suggest that they successfully solved various problems, such as motion artifacts or high-frequency noise emissions, during 2-photon imaging. Overall, the authors achieved their goal of demonstrating a comprehensive approach for the imaging of neural data across many cortical areas and providing several examples that demonstrate the validity of their methods and recapitulate and further extend some recent findings in the field.
Weaknesses:
Most of the descriptions are quite focused on a specific acquisition system, the Thorlabs Mesoscope, and the manuscript is in part highly technical making it harder to understand the motivation and reasoning behind some of the proposed implementations. A revised version would benefit from a more general description of common problems and the thought process behind the proposed solutions to broaden the impact of the work and make it more accessible for labs that do not have access to a Thorlabs mesoscope. A better introduction of some of the specific issues would also promote the development of other solutions in labs that are just starting to use similar tools.
Authors’ Response: We will re-write the motivation behind the study to clarify the general problems that are being addressed. As the 2-photon imaging component of these experiments were performed on a Thorlabs mesoscope, the imaging details will necessarily deal specifically with this system. We will briefly compare the methods and results from our Thorlabs system to that of other systems, based on what we are able to glean from the literature on their strengths and weaknesses.
Reviewer #3 (Public Review):
Summary
In their manuscript, Vickers and McCormick have demonstrated the potential of leveraging mesoscale two-photon calcium imaging data to unravel complex behavioural motifs in mice. Particularly commendable is their dedication to providing detailed surgical preparations and corresponding design files, a contribution that will greatly benefit the broader neuroscience community as a whole. The quality of the data is high, but it is not clear whether this is available to the community, some datasets should be deposited. More importantly, the authors have acquired activity-clustered neural ensembles at an unprecedented spatial scale to further correlate with high-level behaviour motifs identified by B-SOiD. Such an advancement marks a significant contribution to the field. While the manuscript is comprehensive and the analytical strategy proposed is promising, some technical aspects warrant further clarification. Overall, the authors have presented an invaluable and innovative approach, effectively laying a solid foundation for future research in correlating large-scale neural ensembles with behaviour. The implementation of a custom sound insulator for the scanner is a great idea and should be something implemented by others.
Authors’ Response: Thank you for the kind words.
We intend to make the data set used in making our main figures available to the public, perhaps using FigShare, so that they may check the validity of the methods and analysis. We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with a second in-depth analysis paper that is currently in preparation.
This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other. This is described in the methods, but a visual representation would greatly benefit the readers looking to implement something similar.
Authors’ Response: This is an excellent suggestion. We will include a workflow diagram in the revised manuscript for the methods, data collection, and analysis.
The authors should cite sources for the claims stated in lines 449-453 and cite the claim of the mouse's hearing threshold mentioned in lines 463.
Authors’ Response: For the claim stated in lines 449-453, “The unattenuated or native high-frequency background noise generated by the resonant scanner causes stress to both mice and experimenters, and can prevent mice from achieving maximum performance in auditory mapping, spontaneous activity sessions, auditory stimulus detection, and auditory discrimination sessions/tasks,” we can provide the following references: (i) for mice: Sadananda et al, 2008 (“Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain”, Neuroscience Letters, Vol 435, Issue 1, p 17-23), and (ii) for humans: Fletcher et al, 2018 (“Effects of very high-frequency sound and ultrasound on humans. Part I: Adverse symptoms after exposure to audible very-high frequency sound”, J Acoust Soc A, 144, 2511-2520). We will include these references in the revised paper.
For line 463, “i.e. below the mouse hearing threshold at 12.5 kHz of roughly 15 dB”, we can provide the following reference: Zheng et al, 1999 (“Assessment of hearing in 80 inbred strains of mice by ABR threshold analyses”, Vol 130, Issues 1-2, p 94-107). We will also include this reference in the paper. Thank you for identifying these citation omissions.
No stats for the results shown in Figure 6e, it would be useful to know which of these neural densities for all areas show a clear statistical significance across all the behaviors.
Authors’ Response: There are two statistical comparisons that we feel may be useful to add to the single session data displayed in this figure, in order to address the point that you raise. The first would allow us to assess whether for each Rastermap group, the distribution of neuron densities across CCF areas differs from a null, uniform distribution. The second would allow us to examine differences between Rastermap groups associated with different qualitative behaviors in order to know with which patterns of neural activity they are reliably associated.
For the first comparison, we could provide a statistic similar to what we provide for Fig. S6c and f, in which for each CCF area we compare the observed mean correlation values to a null of 0, or, in this case, the population densities of each Rastermap group for each CCF area to a null value equal to the total number of CCF areas divided by the total number of recorded neurons for that group (i.e. a Rastermap group with 500 neurons evenly distributed across ~30 CCF areas would contain ~17 neurons (or ~6% density) per CCF area.) Our current figure legend states that the maximum of the scale bar look-up value (reds) for each group ranges from ~8% to 32%. So indeed, adding these significances would be informative in this case.
For the second comparison, we could compare the density of neurons for each CCF area across Rastermap groups for this session. For example, it may be the case that the density of neurons in primary and secondary visual areas belonging to Rastermap groups that predominate during the “walk” behavior is higher than in the Rastermap group that predominates during the “whisk” behavior, or that the density of neurons in the “whisk” and “twitch” Rastermap groups in primary and secondary motor areas is higher than in the Rastermap groups that are active during the “walk” and “oscillate” behaviors.
Such a comparison should in fact be robust to Rastermap group variability across sessions and mice, as long as the same qualitative behaviors recur. However, our current qualitative methods for discretization of the Rastermap groups likely limits our ability to extend such an analysis accurately across our entire dataset. We are pursuing more rigorous analysis methods in this vein for our second, results oriented paper.
While I understand that this is a methods paper, it seems like the authors are aware of the literature surrounding large neuronal recordings during mouse behavior. Indeed, in lines 178-179, the authors mention how a significant portion of the variance in neural activity can be attributed to changes in "arousal or self-directed movement even during spontaneous behavior." Why then did the authors not make an attempt at a simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc). These models are straightforward to implement, and indeed it would benefit this work if the model extracts information on par with what is known from the literature.
Authors’ Response: This is an excellent suggestion, but beyond the scope of the current methods paper. We are following up this methods paper with an in depth analysis of neural activity and corresponding behavior across the cortex during spontaneous and trained behaviors, but this analysis goes well beyond the scope of the present manuscript. Here, we prefer to present examples of the types of results that can be expected to be obtained using our methods, and how these results compare with those obtained by others in the field.
Specific strengths and weaknesses with areas to improve:
The paper should include an overall cartoon diagram that indicates how the various modules are linked together for the sampling of both behaviour and mesoscale GCAMP. This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other.
Authors’ Response: This is an excellent suggestion and will be included in the revised manuscript, so that readers can more readily follow our workflow, data collection, and analysis.
The paper contains many important results regarding correlations between behaviour and activity motifs on both the cellular and regional scales. There is a lot of data and it is difficult to draw out new concepts. It might be useful for readers to have an overall figure discussing various results and how they are linked to pupil movement and brain activity. A simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc) may help in this regard.
Authors’ Response: This is an excellent suggestion, but beyond the scope of the present methods paper. Such an analysis is a significant undertaking with such large and heterogeneous datasets, and we provide proof-of-principle data here so that the reader can understand the type of data to be expected using our methods. We hope to provide a more complete analysis of data obtained using our methodology in the near future in a second manuscript.
However, we may be amenable to including preliminary linear model fit results, as supplementary material, for the two example sessions highlighted in this paper (i.e. the one dorsal mount session in Fig. 4, and the one side mount session shown in Figs. 5 and 6).
Previously, widefield imaging methods have been employed to describe regional activity motifs that correlate with known intracortical projections. Within the authors' data it would be interesting to perhaps describe how these two different methods are interrelated -they do collect both datasets. Surprisingly, such macroscale patterns are not immediately obvious from the authors' data. Some of this may be related to the scaling of correlation patterns or other factors. Perhaps there still isn't enough data to readily see these and it is too sparse.
Authors’ Response: Unfortunately, we are unable to directly compare widefield GCaMP6s activity with mesoscope 2-photon GCaMP6s activity. During widefield data acquisition, animals were stimulated with visual, auditory, or somatosensory stimuli, while 2-photon mesoscope data collection occurred during spontaneous changes in behavioral state, without sensory stimulation. The suggested comparison is, indeed, an interesting project for the future.
In lines 71-71, the authors described some disadvantages of one-photon widefield imaging including the inability to achieve single-cell resolution. However, this is not true. In recent years, the combination of better surgical preparations, camera sensors, and genetically encoded calcium indicators has enabled the acquisition of single-cell data even using one-photon widefield imaging methods. These methods include miniscopes (Cai et al., 2016), multi-camera arrays (Hope et al., 2023), and spinning disks (Xie et al., 2023).
Cai, Denise J., et al. "A shared neural ensemble links distinct contextual memories encoded close in time." Nature 534.7605 (2016): 115-118.
Hope, James, et al. "Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton." bioRxiv (2023).
Xie, Hao, et al. "Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution." Nature Biomedical Engineering (2023): 1-14.
Authors’ Response: We will correct these statements and incorporate these, and other relevant, references. There are advantages and disadvantages to each chosen technique, such as ease of use, field of view, accuracy, speed, etc., and we will highlight a few of these without an extensive literature review.
Even the best one-photon imaging techniques typically have ~10-20 micrometer resolution in xy (we image at 5 micrometer resolution for our large FOV configuration, but the xy point-spread function for the Thorlabs mesoscope is 0.61 x 0.61 micrometers in xy with 970 nm excitation) and undefined z-resolution (4.25 micrometers for Thorlabs mesoscope). A coarser resolution increases the likelihood that activity data from neighboring cells may contaminate the fluorescence observed from imaged neurons. Reducing the FOV and using sparse expression of the indicator lessens this overlap problem.
We do appreciate these recent advances, however, particularly for use in cases where more rapid imaging is desired over a large field of view (CCD acquisition can be much faster than that of standard 2-photon galvo-galvo or even galvo-resonant scanning, as the Thorlabs mesoscope uses). This being said, there are few currently available genetically encoded Ca2+ sensors that are able to measure fluctuations faster than ~10 Hz, which is a speed achievable on the Thorlabs 2-photon mesoscope with our techniques using the “small, multiple FOV” method (Fig. S2d, e).
The authors' claim of achieving optical clarity for up to 150 days post-surgery with their modified crystal skull approach is significantly longer than the 8 weeks (approximately 56 days) reported in the original study by Kim et al. (2016). Since surgical preparations are an integral part of the manuscript, it may be helpful to provide more details to address the feasibility and reliability of the preparation in chronic studies. A series of images documenting the progression optical quality of the window would offer valuable insight.
Authors’ Response: As you suggest, we will include images and data demonstrating the average changes in the window preparation, as well as the degree of variability and a range of outcome scenarios that we observed over the prolonged time periods of our study. We will also include methodological details that we found were useful for facilitating long term use of these preparations.
