Author response:
The following is the authors’ response to the original reviews.
eLife assessment
This study provides important new insights into how multisensory information is processed in the lateral cortex of the inferior colliculus, a poorly understood part of the auditory midbrain. By developing new imaging techniques that provide the first optical access to the lateral cortex in a living animal, the authors provide convincing in vivo evidence that this region contains separate subregions that can be distinguished by their sensory inputs and neurochemical profiles, as suggested by previous anatomical and in vitro studies. Additional information and analyses are needed, however, to allow readers to fully appreciate what was done, and the comparison of multisensory interactions between awake and anesthetized mice would benefit from being explored in more detail.
Public Reviews:
Reviewer #1 (Public Review):
Summary:
In this paper, the authors provide a characterisation of auditory responses (tones, noise, and amplitude-modulated sounds) and bimodal (somatosensory-auditory) responses and interactions in the higher-order lateral cortex (LC) of the inferior colliculus (IC) and compare these characteristics with the higher order dorsal cortex (DC) of the IC - in awake and anaesthetised mice. Dan Llano's group has previously identified gaba'ergic patches (modules) in the LC distinctly receiving inputs from somatosensory structures, surrounded by matrix regions receiving inputs from the auditory cortex. They here use 2P calcium imaging combined with an implanted prism to - for the first time - get functional optical access to these subregions (modules and matrix) in the lateral cortex of IC in vivo, in order to also characterise the functional difference in these subparts of LC. They find that both DC and LC of both awake and anaesthetised mice appear to be more responsive to more complex sounds (amplitude-modulated noise) compared to pure tones and that under anesthesia the matrix of LC is more modulated by specific frequency and temporal content compared to the gabaergic modules in LC. However, while both LC and DC appear to have low-frequency preferences, this preference for low frequencies is more pronounced in DC. Furthermore, in both awake and anesthetized mice, somatosensory inputs are capable of driving responses on their own in the modules of LC, but very little (possibly not at all) in the matrix. However, bimodal interactions may be different under awake and anesthesia in LC, which warrants deeper investigation by the authors: They find, under anesthesia, more bimodal enhancement in modules of LC compared to the matrix of LC and bimodal suppression dominating the matrix of LC. In contrast, under awake conditions bimodal enhancement is almost exclusively found in the matrix of LC, and bimodal suppression dominates both matrix and modules of LC.
The paper provides new information about how subregions with different inputs and neurochemical profiles in the higher-order auditory midbrain process auditory and multisensory information, and is useful for the auditory and multisensory circuits neuroscience community.
Strengths:
The major strength of this study is undoubtedly the fact that the authors for the first time provide optical access to a subcortical region (the lateral cortex of the inferior colliculus (i.e. higher order auditory midbrain)) which we know (from previous work by the same group) have optically identifiable subdivisions with unique inputs and neurotransmitter release, and plays a central role in auditory and multisensory processing. A description of basic auditory and multisensory properties of this structure is therefore very useful for understanding auditory processing and multisensory interactions in subcortical circuits.
Weaknesses:
I have divided my comments about weaknesses and improvements into major and minor comments. All of which I believe are addressable by the reviewers to provide a more clear picture of their characterisation of the higher-order auditory midbrain.
Major comment:
(1) The differences between multisensory interactions in LC in anaesthetised and awake preparations appear to be qualitatively different, though the authors claim they are similar (see also minor comment related to figure 10H for further explanation of what I mean). However, the findings in awake and anaesthetised conditions are summarised differently, and plotting of similar findings in the awake figures and anaesthetised figures are different - and different statistics are used for the same comparisons. This makes it very difficult to assess how multisensory integration in LC is different under awake and anaesthetised conditions. I suggest that the authors plot (and test with similar statistics) the summary plots in Figure 8 (i.e. Figure 8H-K) for awake data in Figure 10, and also make similar plots to Figures 10G-H for anaesthetised data. This will help the readers understand the differences between bimodal stimulation effects on awake and anaesthetised preparations - which in its current form, looks very distinct. In general, it is unclear to me why the awake data related to Figures 9 and 10 is presented in a different way for similar comparisons. Please streamline the presentation of results for anaesthetised and awake results to aid the comparison of results in different states, and explicitly state and discuss differences under awake and anaesthetised conditions.
We thank the reviewer for the valuable suggestion. We only highlighted the similarities between the data obtained from anesthetized and awake preparations to indicate the ability to reproduce the technique in awake animals for future assessment. Identifying those similarities between the two experimental setups was based on the comparison between modules vs matrix or LC vs DC within each experimental setup (awake vs anesthetized). Therefore, the statistics were chosen differently for each setup based on the size of the subjects (n) within each experimental preparation. However, we agree with the reviewer’s comment that there are differences between the anesthetized and awake data. To examine these differences, we ran the same statistics for Figure 5 (tonotopy of LC vs. DC-anesthetic animals) and Figure 9 (tonotopy of LC vs DC-awake animals). In addition, we added a new figure after Figure 9 to separate the statistical analysis from the maps. Accordingly, Figures 4 and 5 (maps and analysis, respectively -anesthetized animals) now match Figures 9 and 10 (maps and analysis, respectively – awake animals). We also did the same thing for Figures 7 (microprism imaging of the LC - anesthetized animals), 8 (imaging of the LC from the dorsal surface - anesthetized animals) as well as Figure 11 or old Figure 10 (microprism imaging of the LC - awake animals) to address the similarities and differences of the multisensory data between awake and anesthetized animals. We edited the text accordingly in the result and discussion sections.
(2) The claim about the degree of tonotopy in LC and DC should be aided by summary statistics to understand the degree to which tonotopy is actually present. For example, the authors could demonstrate that it is not possible/or is possible to predict above chance a cell's BF based on the group of other cells in the area. This will help understand to what degree the tonotopy is topographic vs salt and pepper. Also, it would be good to know if the gaba'ergic modules have a higher propensity of particular BFs or tonotopic structure compared to matrix regions in LC, and also if general tuning properties (e.g. tuning width) are different from the matrix cells and the ones in DC.
Thank you for the reviewer’s suggestion. We have examined the tonotopy of LC and DC using two regression models (linear and quadratic polynomial) between the BFs of the cells and their location on the anatomical axis. Therefore, the tonotopy is indicated by a significant regression fit with a high R2 between the BFs the cells, and their location within each structure. For the DC, there was a significant regression fit between the BFs of the cells and their locations over the rostromedial to the caudolateral axis. Additionally, the R2 of the quadratic polynomial fit was higher than that of the linear fit, which indicates a nonlinear distribution of cells based on their BFs, which is consistent with the presence of high-low-high tuning over the DC surface. Given that the microprism cannot image the whole area of the LC, and it images a slightly different area in each animal, it was very difficult to get a consistent map for the LC as well as a solid conclusion about the LC tonotopy. However, we have examined the regression fit between the BFs of cells and their location along the main four anatomical axes of the field of view obtained from each animal (dorsal to ventral), (rostral to caudal), (dorsocaudal to ventrorostral) (dorsorostral to ventrocoudal). Unlike the DC, the LC imaged via microprism showed a lower R2 for both linear and quadratic regression mostly in the dorsoventral axis. We show the fitting curves of these regressions in Figure 4-figure supplement 1 (anesthetized data) and Figure 9-figure supplement 1 (awake data). Despite the inconsistent tonotopy of the LC imaged via microprism, the modules were found to have a higher BFs median at 10 kHz compared to matrix that had a lower BFs median at 7.1 kHz, which was consistent across the anesthetized and awake animals. We have added these results in the corresponding spot in the results section (lines 193-197 and 361-364). We have examined the tuning width using the binarized receptive field sum (RFS) method in which each neuron was given a value of 1 if it responds to a single frequency (Narrow RF), but this value increases if the neuron responds to more neighbor frequencies (wide RF). We did this calculation across all the sound levels. Both DC and LC of the anesthetized animals had higher RFS mean and median than those of awake animals given that ketamine was known to broaden the RF. However, in both preparations (anesthetized and awake), the DC had a higher RFS mean than that of the LC, which could be consistent with the finding that the DC had a relatively lower SMI than the LC. To show these new data, we made a new Figure 10-figure supplement 1, and we edited the text accordingly [lines 372-379 & 527-531].
(3) Throughout the paper more information needs to be given about the number of cells, sessions, and animals used in each panel, and what level was used as n in the statistical tests. For example, in Figure 4 I can not tell if the 4 mice shown for LC imaging are the only 4 mice imaged, and used in the Figure 4E summary or if these are just examples. In general, throughout the paper, it is currently not possible to assess how many cells, sessions, and animals the data shown comes from.
Thank you for the reviewer’s comment. We do apologize for not adding this information. We added all the information regarding the size of the statistical subjects (number of cells or number of animals used) for every test outcome. To keep the flow of the text, we added the details of the statistical tests in the legends of the figures.
(4) Throughout the paper, to better understand the summary maps and plots, it would be helpful to see example responses of the different components investigated. For example, given that module cells appear to have more auditory offset responses, it would be helpful to see what the bimodal, sound-only, and somatosensory responses look like in example cells in LC modules. This also goes for just general examples of what the responses to auditory and somatosensory inputs look like in DC vs LC. In general example plots of what the responses actually look like are needed to better understand what is being summarised.
Thank you for the reviewer’s comment and suggestion. We modified Figure 6 and the text accordingly to include all the significant examples of cells discussed throughout the work.
Reviewer #2 (Public Review):
Summary:
The study describes differences in responses to sounds and whisker deflections as well as combinations of these stimuli in different neurochemically defined subsections of the lateral and dorsal cortex of the inferior colliculus in anesthetised and awake mice.
Strengths:
The main achievement of the work lies in obtaining the data in the first place as this required establishing and refining a challenging surgical procedure to insert a prism that enabled the authors to visualise the lateral surface of the inferior colliculus. Using this approach, the authors were then able to provide the first functional comparison of neural responses inside and outside of the GABA-rich modules of the lateral cortex. The strongest and most interesting aspects of the results, in my opinion, concern the interactions of auditory and somatosensory stimulation. For instance, the authors find that a) somatosensory-responses are strongest inside the modules and b) somatosensory-auditory suppression is stronger in the matrix than in the modules. This suggests that, while somatosensory inputs preferentially target the GABA-rich modules, they do not exclusively target GABAergic neurons within the modules (given that the authors record exclusively from excitatory neurons we wouldn't expect to see somatosensory responses if they targeted exclusively GABAergic neurons), and that the GABAergic neurons of the modules (consistent with previous work) preferentially impact neurons outside the modules, i.e. via long-range connections.
Weaknesses:
While the findings are of interest to the subfield they have only rather limited implications beyond it. The writing is not as precise as it could be. Consequently, the manuscript is unclear in some places. For instance, the text is somewhat confusing as to whether there is a difference in the pattern (modules vs matrix) of somatosensory-auditory suppression between anesthetized and awake animals. Furthermore, there are aspects of the results which are potentially very interesting but have not been explored. For example, there is a remarkable degree of clustering of response properties evident in many of the maps included in the paper. Taking Figure 7 for instance, rather than a salt and pepper organization we can see auditory responsive neurons clumped together and non-responsive neurons clumped together and in the panels below we can see off-responsive neurons forming clusters (although it is not easy to make out the magenta dots against the black background). This degree of clustering seems much stronger than expected and deserves further attention.
Thank you for the reviewer’s comment. We do apologize if some areas in the manuscript were imprecisely written. For anesthetized and awake data, we have only emphasized the similarities between the two setups to show the ability to use microprism in awake animals for future assessment. To highlight the differences between anesthetized and awake animals, we have now run uniform statistics for all the data collected from both setups. Accordingly, we have edited Figures 4 and 5 (tonotopy-anesthetized) to match Figures 9 and new Figure 10 (tonotopy-awake). Also, we edited Figures 7 and 8 (multisensory- anesthetized) to match Figure 11 or old Figure 10 (multisensory- awake). We edited the text accordingly in the results section and discussed the possible differences between anesthetized and awake data in the discussion section [lines 521-553].
We agree with the reviewer’s comment that the cells were topographically clustered based on their responses. Some of these clusters include the somatosensory responsive cells, which were located mostly in the modules (Figures 7D and 8E). Also, the auditory responsive cells with offset responses were clustered mostly in the modules (Figures 7C and 8F). Accordingly, we have edited the text to emphasize this finding.
We noticed also that some responsive cells to the tested stimulations were surrounded by nonresponsive cells. By comparing the response of the cells to different stimuli we found that while Figures 7 and 11 (old Figure 10) showed only the response of the cells to auditory stimulation (unmodulated broadband noise at 80 dB) and somatosensory stimulation (whisker deflection), some nonresponsive cells to these specific stimulations were found to be responsive to pure tones of different frequencies and amplitudes. As an indicator of the cells' viability, we additionally examined the spontaneous activity of the nonresponsive cells across different data sets. We note that spontaneous activity was rare for all cells even among the responsive cells to sound or somatosensory stimulations. This finding could be related to the possibility that the 2P imaging of calcium signals may not be sensitive enough to track spontaneous activity that may originate from single spikes. However, in some data sets, we have found that the cells that did not respond to any tested stimuli showed spontaneous activity when no stimulation was given indicating the viability of those cells. We have addressed the activity of the non-responsive cells in the text along with a new Figure 11-figure supplement 1.
We changed the magenta into a green color to be suitable for the dark background. Also, we have completely changed the color palette of all of our images to be suitable for color-blind readers as suggested by reviewer 1.
Reviewer #3 (Public Review):
The lateral cortex of the inferior colliculus (LC) is a region of the auditory midbrain noted for receiving both auditory and somatosensory input. Anatomical studies have established that somatosensory input primarily impinges on "modular" regions of the LC, which are characterized by high densities of GABAergic neurons, while auditory input is more prominent in the "matrix" regions that surround the modules. However, how auditory and somatosensory stimuli shape activity, both individually and when combined, in the modular and matrix regions of the LC has remained unknown.
The major obstacle to progress has been the location of the LC on the lateral edge of the inferior colliculus where it cannot be accessed in vivo using conventional imaging approaches. The authors overcame this obstacle by developing methods to implant a microprism adjacent to the LC. By redirecting light from the lateral surface of the LC to the dorsal surface of the microprism, the microprism enabled two-photon imaging of the LC via a dorsal approach in anesthetized and awake mice. Then, by crossing GAD-67-GFP mice with Thy1-jRGECO1a mice, the authors showed that they could identify LC modules in vivo using GFP fluorescence while assessing neural responses to auditory, somatosensory, and multimodal stimuli using Ca2+ imaging. Critically, the authors also validated the accuracy of the microprism technique by directly comparing results obtained with a microprism to data collected using conventional imaging of the dorsal-most LC modules, which are directly visible on the dorsal IC surface, finding good correlations between the approaches.
Through this innovative combination of techniques, the authors found that matrix neurons were more sensitive to auditory stimuli than modular neurons, modular neurons were more sensitive to somatosensory stimuli than matrix neurons, and bimodal, auditory-somatosensory stimuli were more likely to suppress activity in matrix neurons and enhance activity in modular neurons. Interestingly, despite their higher sensitivity to somatosensory stimuli than matrix neurons, modular neurons in the anesthetized prep were far more responsive to auditory stimuli than somatosensory stimuli (albeit with a tendency to have offset responses to sounds). This suggests that modular neurons should not be thought of as primarily representing somatosensory input, but rather as being more prone to having their auditory responses modified by somatosensory input. However, this trend was reversed in the awake prep, where modular neurons became more responsive to somatosensory stimuli than auditory stimuli. Thus, to this reviewer, the most intriguing result of the present study is the dramatic extent to which neural responses in the LC changed in the awake preparation. While this is not entirely unexpected, the magnitude and stimulus specificity of the changes caused by anesthesia highlight the extent to which higher-level sensory processing is affected by anesthesia and strongly suggest that future studies of LC function should be conducted in awake animals.
Together, the results of this study expand our understanding of the functional roles of matrix and module neurons by showing that responses in LC subregions are more complicated than might have been expected based on anatomy alone. The development of the microprism technique for imaging the LC will be a boon to the field, finally enabling much-needed studies of LC function in vivo. The experiments were well-designed and well-controlled, and the limitations of two-photon imaging for tracking neural activity are acknowledged. Appropriate statistical tests were used. There are three main issues the authors should address, but otherwise, this study represents an important advance in the field.
(1) Please address whether the Thy1 mouse evenly expresses jRGECO1a in all LC neurons. It is known that these mice express jRGECO1a in subsets of neurons in the cerebral cortex, and similar biases in the LC could have biased the results here.
Thank you for the reviewer’s comment. In the work published by Dana, et al, the expression of jRGECO1a in all Thy1 mouse lines was determined by the brightness of the jRGECO1a in the soma. Given that some cells do not show a detected level of jRGECO1a fluorescence until activated, the difference in expression shown in different brain regions could be related to the level of neuronal activity at the time of sample processing and not the expression levels of the indicator itself. To the best of our knowledge, there is no antibody for jRGECO1a, which can be used for detecting the expression levels of the indicator regardless of the neuronal activity. To test the hypothesis that DC and LC have different levels of jRGECO1a, we examined the expression levels of jRGECO1a after we perfused the mice with high potassium saline to elicit a general neuronal depolarization in the whole brain. Then we immunostained against NeuN (the neuronal marker) to quantify the percentage of the neurons expressing jRGECO1a to the total number of neurons (indicated by NeuN). To have a fair comparison, we restricted our analysis to include the areas imaged only by 2P as some regions were not accessible by microprism such as the deep ventral regions of the LC. There is a similar % of cells expressing jRGECO1a in DC and LC. As expected, the neurons expressing jRGECO1a were only nonGABAergic cells. We addressed these findings in the new Figure 3-figure Supplement 1 as well as the corresponding text in the results [lines 178-184] and methods sections [lines 878-892].
(2) I suggest adding a paragraph or two to the discussion to address the large differences observed between the anesthetized and awake preparations. For example, somatosensory responses in the modules increased dramatically from 14.4% in the anesthetized prep to 63.6% in the awake prep. At the same time, auditory responses decreased from 52.1% to 22%. (Numbers for anesthetized prep include auditory responses and somatosensory + auditory responses.). In addition, the tonotopy of the DC shifted in the awake condition. These are intriguing changes that are not entirely expected from the switch to an awake prep and therefore warrant discussion.
Thank you for the reviewer’s comment. To determine if differences exist between anesthetized and awake data, we have now used the same statistics and edited Figures 4,5,7,8,9, and 10 as well as added a new Figure 11. Accordingly, we have edited the result section and added a paragraph addressing the possible differences between the two preparations in the Discussion section [lines 521-553]..
(3) For somatosensory stimuli, the authors used whisker deflection, but based on the anatomy, this is presumably not the only somatosensory stimulus that affects LC. The authors could help readers place the present results in a broader context by discussing how other somatosensory stimuli might come into play. For example, might a larger percentage of modular neurons be activated by somatosensory stimuli if more diverse stimuli were used?
We agree with the reviewer’s point. Indeed, the modules are receiving different inputs from different somatosensory sources such as somatosensory cortex and dorsal column nuclei, which could indicate that the activity of the cells in the modular areas could be evoked by different types of somatosensory stimulations, which is an open area for future studies. We have discussed this point in the revised Discussion section [lines 516-520].
Recommendations for the authors:
Reviewer #1 (Recommendations For The Authors):
Minor comments:
(1) Figure 3H: The lateral surface seems quite damaged by the prism. An example slice of the imaging area of each mouse would help the reader better understand the extent of damage the prism leaves in the area of interest.
Thank you for the reviewer’s comment. We already have included such images in Figures 4A, 7A, and 9A to present the field of view of all prism experiments. However, we need to clarify the point of tissue damage. The insertion of microprism may be associated with some tissue damage as a result of making the pocket for the microprism to be inserted, but it is not possible to get neuronal signals from a damaged field of view. Therefore, we do not believe that there is tissue damage to the parts of the LC imaged by microprism. However, there may be some areas where the microprism is not in direct contact with the LC surface. These areas are located mostly in the periphery of the field of view, and they are completely black as they are out of focus (i.e., the left side of Figure 3B). The right side of Figure 3b as well as Figure 3A have some black areas, which present the vasculatures, where there are no red signals because of the lack of jRGECO1a expression in those areas.
(2) In relation to the data shown in Figure 4E it is claimed that LC is tuned to higher frequencies (lines 195-196). However, the majority of cells appear to be tuned to frequencies below 14kHz (with a median of 7.5 kHz), which is quite low for the mouse. I assume that the authors mean frequencies that are relatively higher than the DC, but it is worth mentioning in the text that the BFs found in the LC are quite low-frequency responses for the mouse.
Thank you for the reviewer’s comment, which we agree with. We edited this part by acknowledging that around 50% of the LC cells had a low-frequency bias to 5 and 7.1 kHz. Then we mentioned that most of the LC cells are tuned to relatively higher frequencies than those of the DC [lines 215-218].
(3) Figure 5A-C: Is it the tone-responsive cells plus an additional ~22% of cells that respond to AM, or are there also cells that respond to tones that do not respond to AM. Please break down to which degree the tone and AM responsive cells are overlapping.
Thank you for the reviewer’s comment and suggestion. We broke down the responsive cells into cells responsive only to pure tone (tone selective cells or Tone-sel) or to only AM-noise (noise selective cells or Noise-sel) as well as cells responding to both sounds (nonselective cells or Non-sel). We examined the fractions of these categories of cells in both LC and DC within all responsive neurons. Accordingly, we have edited Figure 5A-C as well as the text [lines 229-243].
(4) Figure 5D. It is unclear to me how a cell is classified as SMI or TMI responsive after computing the SMI or TMI for each cell. What statistic was used to determine if the cell was responsive or not?
Thank you for the reviewer’s comment. We do apologize for the confusion caused by Figures 5D and E. These figures do not show the values of SMI or TMI, respectively. Rather, the figures show the percentage of the spectrally or temporally modulated cells, respectively. At each sound level, the cells were categorized into two main types. The spectrally modulated cells are those responsive to pure tones or unmodulated noise, so they can detect the spectral features of the sound (old Figure 5D or new Figure 5E). The temporally modulated cells are those responsive to AM-noise, so they can detect the temporal features of the sound of complex spectra like the broadband noise (old Figure 5E or new Figure 5F). To clear this confusion, we removed the words SMI and TMI from the figures, and then we renamed the x-axis label into “% of spectrally modulated cells” and “% of temporally modulated cells” for Figures 5D (new 5E) and E (new 5F), respectively.
(5) Figure 5 D, E: Is the decrease in SMI and TMI modulated cells in the modules a result of simply lower sensitivity to sounds (i.e. higher response thresholds)? If a cell responds to neither tone, AM, or noise it will have a low SMI and TMI index. If this is the case that affects the interpretation, as it is then not a decrease in sensitivity to spectral or temporal modulation, but instead a difference in overall sound sensitivity.
Thank you for the reviewer’s comment. We apologize for the confusion about Figures 5E and D, which did not show the SMI and TMI values. Rather, they show the percentage of spectrally or temporally modulated cells, respectively, as explained in our previous response. Therefore, Figure 5D shows the percentage of cells that can detect the spectral features of sound, while Figure 5E shows the percentage of cells that can detect the temporal features of sounds of complex spectra like broadband noise. Accordingly, Figures 5D and E show the sensitivity to different features of sound and not the overall sound sensitivity.
(6) Figure 7 and 8: What is the false positive rate expected of the responsive cells using the correlation cell flagging criteria? Especially given that the fraction of cells responsive to somatosensory stimulation in LC (matrix) is 0.88% and 1.3% in DC, it is important to know what the expected false positive rate is in order to be able to state that there are actually somatosensory responses there or if this is what you would expect from false positives given the inclusion test used. Please provide an estimate of the false positive rate given your inclusion test and show that the rate found is statistically significantly above that level - and show this rate with a line in Figure 7 H, I.
Thank you for the reviewer’s comment. To test the efficiency of the correlation method to determine the responsive cells, we initially ran an ROC curve comparing the automated method to a blinded human interpretation. The AUC of the ROC curve was 0.88. This high AUC value indicates that the correlation method can rank the random responsive cells than the random nonresponsive cells. At the correlation coefficient (0.4), which was the cutoff value to determine the responsive cells for somatosensory stimulation, the specificity was 87% and the sensitivity 72%, the positive predictive value was 73%, and the negative predictive value was 86%. Although the above percentages indicate the efficiency of the correlation method, we excluded all the false responsive cells from the analysis. Therefore, the fractions of cells in the graphs are the true responsive cells with no contamination of the non-responsive cells. We also modified Figures 7H and I to match the other data sets obtained from awake animals. Therefore, Figures 7H and I no longer show the average of the responsive cells. Instead, they show the % of different fractions of responsive cells within each cellular motif (modules and matrix). Accordingly, we believe that there is no need to include a rate line on the graph. We added the section describing the validation part to the methods section [lines 808-815].
(7) Figure 7: Please clarify what is meant by a cell responding to 'both responding to somatosensory and auditory stimulation'. Does it mean that the cell has responses to both auditory and somatosensory stimulation when presented individually or if it responds to both presented together? If it is the former, I don't understand how the number to both can be higher than the number of somatosensory alone (as both requires it also to respond to somatosensory alone). If it is the latter (combined auditory and somatosensory) then it seems that somatosensory inputs remove the responsiveness of most cells that were otherwise responsive to auditory alone (e.g. in the module while 42% respond to sound alone, combined stimulation would leave only 10% of cells responsive). Please clarify what exactly the authors are plotting and stating here.
Thank you for the reviewer’s comment. The responsive cells in Figure 7 are divided into three categories. Each category has a completely different group of cells. The first category is for the cells responding only to auditory stimulation (auditory-selective cells or Aud-sel). The second category is for the cells that respond only to somatosensory stimulation (somatosensory selective cells or Som-sel). The third category is for the cells that respond to both auditory and somatosensory stimulations when both stimulations are presented individually (auditory/somatosensory nonselective cells or Aud/Som-nonsel). Accordingly, the number of cells may be different across all these categories. We have clarified this part in the text [lines 299-303]. We have modified Figures 7, 8, and 11 (old Figure 10) to match the data from anesthetized and awake animals, so Figures 7H and I now show the collective % of the cells from all animals within modules vs matrix.
(8) Why are the inferential statistics used in Figure 9F (chi-square test) and Figure 5A-C (t-test) when it tests the same thing (the only difference is one is anaesthetised data and the other awake)? Indeed, all Figure 9 and 10 (awake data figures) plots use chi-square tests to test differences in percentages instead of t-tests used in earlier (anaesthetised data figures) plots to test differences in percentages between groups. Please clarify the reason for this change in statistics used for similar comparisons.
Thank you for the reviewer’s comment. Imaging the LC via microprism from awake animals confirmed the ability to run this technique with no interference to the ambulatory functions of the animals. Therefore, the main goal was to highlight the similarities between the data obtained from awake and anesthetized setups by highlighting the comparison between the LC and DC or between modules and matrix within each preparation (anesthetized vs awake). Accordingly, the statistics used to run these comparisons were chosen based on the number of the tested animals at each setup (7 anesthetized animals and 3 awake animals for prism insertion). The low number of animals used for awake data made us use the number of cells collectively from all animals instead of the number of animals, so we used the Chi-square test to examine the differences in percentages.
(9) Figure 10H: The main text describes the results shown here as similar to what was seen in anaesthetised animals. But it looks to me like the results in awake animals are qualitatively different from the multisensory interaction seen in anaesthetised animals. In anaesthetised animals the authors find that there is a higher chance of auditory responses being enhanced by somatosensory inputs when cells are in the modules compared to in the matrix. However, in awake data, this relationship is flipped, with more bimodal enhancement found in the matrix compared to the modules. Furthermore, almost all cells in the modules are suppressed by combined somatosensory input which looks like it is different from what is found in anaesthestised mice and what is described in the discussion: 'we observed that combined auditory-somatosensory stimulation generally suppressed neural responses to auditory stimuli and that this suppression was most prominent in the LC matrix'.
Thank you for the reviewer’s comment. Our statement was meant to show how the data obtained from awake and anesthetized animals were generally similar. However, we agree that the statement may not be suitable due to the possible differences between awake and anesthetized animals. To address a fair comparison between the anesthetized and awake preparations, we ran similar statistics and graphs for Figures 7, 8, and 11 (old Figure 10). Given that the areas occupied by modules and matrix are different across animals due to the irregular shape of the modules, we chose to run a chi-square test for all the data to quantify the collective % of responding cells within modules vs matrix from all tested animals for each experimental setup (anesthetized vs awake). The anesthetized and awake animals similarly showed that modules and matrix had higher fractions of auditory responsive cells. However, matrix had more cells responding to auditory stimulations than modules, while modules had more cells responding to somatosensory stimulation than matrix. In contrast, while the anesthetized animals showed higher fractions of offset auditory-responsive cells, which were mostly clustered in the modules, the offset auditory-responsive cells were very rare in awake animals (6 cells/one animal).
Based on the fractions of cells with suppressed or enhanced auditory response induced by bimodal stimulation, the data obtained from anesthetized and awake animals showed that the auditory response in the matrix was suppressed more than enhanced by bimodal stimulation. In contrast, modules had different profiles across the experimental setups and locations. For instance, the modules imaged via microprism in the anesthetized and awake animals showed suppressed more than enhanced auditory responses, but modules imaged from the dorsal surface in anesthetized animals showed enhanced more than suppressed auditory responses. Additionally, modules had less suppressed and more enhanced auditory responses compared to matrix in the anesthetized animals regardless of the location of the modules (microprism or dorsal surface). Yet, modules from awake animals had more suppressed and less enhanced auditory responses compared to matrix. We have addressed these differences in the results and discussion section.
Additional minor comments that I think the authors could use to aid their manuscript clarity:
(1) The figure colour selection - especially in Figures 7 and 8 - is really hard to tell apart. Please choose more distinct colours, and a colour scheme that is appropriate for colour blind readers.
Thank you for the reviewer’s suggestion. We have noticed that the magenta color assigned for the cells with offset responses was very difficult to distinguish from the black background. We have changed the magenta color to green to be different from the color of other cells. Using Photoshop, we chose a color scheme that is suitable for color-blind readers in all our maps.
(2) The sentence in lines 331-334 should be rephrased for clarity.
Thank you for the reviewer’s suggestion. We have rephrased the statement for clarity [lines 364-371].
Reviewer #2 (Recommendations For The Authors):
As mentioned in the public review the strong clustering evident in some of the maps (some of which may be related to module/matrix differences but certainly not all of it) seems worth scrutinizing further. Would we expect such a strong spatial segregation of auditory responsive and non-responsive neurons? Would we expect response properties (e.g. off-responsiveness) other than frequency tuning to show evidence of a topographic arrangement in the IC? In addressing this it would, of course, be important to rule out that this clustering is not down to some trivial experimental variables and truly reflects functional organization. For instance, are the patches of non-responsive neurons found in parts of the field of view with poor visibility, poor labelling, etc which may explain why it is difficult to pick up responses there? Are the neurons in non-responsive areas otherwise active (i.e. do they show spontaneous activity) or could they be 'dead'? Could the way neuropil signals are dealt with play a role here (it is weighted by 0.4 which strikes me as quite low)? In relation to this, I am also wondering to what extent the extreme overrepresentation (Figure 4) of neurons with a BF of 5kHz (some of this is, of course, down to the fact that the lower end of the frequency range was 5kHz and that the step size was 0.5 octaves), especially in the DC, is to be interpreted.
Thank you for the reviewer’s comment. Before analysis, the ROIs of all cells were set around the cell bodies using the jRGECO1a signals as a reference, so all cells (responsive and nonresponsive) were collected from areas of good visibility of jRGECO1a signals. In other words, no cells were collected from regions having poor jRGECO1a signals. In Figures 7, 8, and 11 (old Figure 10), the cells showed response either only to unmodulated broadband noise at 80 dB as an auditory stimulus or to whisker deflection with specific speed and power as a somatosensory stimulus. Given that the two stimuli above had specific parameters, the remaining non-responsive cells may respond to auditory or somatosensory stimulations with other features. For instance, some nonresponsive cells to the unmodulated broadband noise were responding to pure tones with different amplitudes and frequencies or to different AM-noise with different amplitudes and modulation frequencies. Also, these nonresponsive cells may not respond to any of our tested stimuli and may respond to other sensory stimulations. Some of the non-responsive cells showed spontaneous activity when no stimulations were presented. However, we can not rule out the possibility that some of these nonresponsive cells may not be viable. We have addressed the clustering properties in the revised version of the manuscript in the corresponding spots of the results and discussion sections. We have added a new supplementary figure (Figure 11- Figure Supplement 1) to show how the nonresponsive cells to the unmodulated noise may respond to other types of sound and to show the spontaneous activity of some non-responsive cells.
For the neuropil, previous reports used the contamination factor (r) in a range of 0.3-0.7 (we referenced these studies in the method section [line 776) based on the tissue or cells imaged, vasculatures, and the objective used for imaging. Therefore, we optimized the contamination factor (r) to be 0.4 through a preliminary analysis based on the tissue we image (LC), and the objective used (16x with NA = 0.8 and 3 mm as a working distance).
We agree that there is an overrepresentation of 5 kHz as the best tuning frequency for DC cells. The previous report (A. B. Wong & Borst, 2019) showed a large zone of the DC where cells were tuned to (2-8 kHz). Given that 5kHz was the lowest tested frequency in our experiment, we think that the low-frequency bias of the DC surface is consistent between studies. This finding also could be supported by the electrophysiology data obtained by spanning the recording electrodes through the IC tissue along the dorsoventral axis. In those experiments, the cells were tuned to lower frequencies at the dorsal surface of the IC.
We have changed the magenta-colored cells to green ones, so it will be easier to identify the cells. As required by another reviewer, we changed the color pallets of some images and cellular maps to be suitable for color-blind readers.
The manuscript would benefit from more precise language in a number of places, especially in the results section.
Line 220/221, for instance: "... a significant fraction of cells that did not respond to pure tones did respond to AM-noise" Strictly speaking, this sentence suggests that you considered here only the subset of neurons that did not respond to pure tones and then ran a test on that subset. The test that was done seems to suggest though that the authors tested whether the percentage of responsive cells was greater for pure tones or for AM noise.
Thank you for the reviewer’s comment. We do apologize for the confusion. In the revised manuscript, we categorized the cells according to their response into cells responding to pure tone only (tone-selective cells or Tone-sel), Am-noise only (noise-selective cells or Nose-sel), and to both pure tone and am-noise (nonselective cells or Non-sel). We have modified Figure 5 accordingly. We did the same thing for the data obtained from awake animals and showed that in a new figure to easily match the analysis done for the anesthetized animals.
Please refer to the figure panels in the text in consecutive order. 2B, for instance, is mentioned after 2H.
Thank you for the reviewer’s comment. Throughout the paper, we kept the consecutive order of the figure panels within each figure to be in a smooth flow with the text. Yet, figure 2 was just the only exception for a good reason. Figure 2 is a complex one that includes many panels to show a parallel comparison between LC imaged via microprism and DC through single photon images, two-photon images, validating laser lesioning, and histology. Accordingly, we navigated many panels of the figure to efficiently highlight the aspects of this comparison. We prefer to keep Figure 2 as one figure with its current format to show this parallel comparison between LC and DC.
The legend for Figure 2 could be clearer. For instance, there are two descriptions for panel D. Line 1009: "(C-E)" [i.e. C, D, E] and line 1010: "(D and F)".
Thank you for the reviewer’s comment. It should be C and E, not C-E. We have fixed the mistake [line 1224]
Line 275: What does 'with no preference' mean?
Thank you for the reviewer’s comment. We do apologize for the confusion. There are three categories of cells. Some cells respond only to auditory stimulation, while others respond to only somatosensory stimulation. However, there is another group of cells that respond nonselectively to auditory and somatosensory stimulations or Aud/Som-nonsel cells. We edited the sentence to be clearer [lines 303-304].
Line 281 (and other places): What does 'normalized against modules' mean?
Thank you for the reviewer’s comment. This normalization was done by dividing the number of responsive cells of the same response type in the matrix by that in the modules. Therefore, the value taken by modules was always 1 and the value taken by the matrix is something around 1. Accordingly, the value for matrix could be > 1 if matrix had more cells than modules. In contrast, the value of matrix would be < 1 if matrix had fewer cells than modules. In the revised version, we used this normalization method to make the revised Figures 5C and 10C to describe the cell fractions responding to pure tone only, AM-noise only, or to both stimuli in the matrix vs modules.
Sentence starting on line 288. I don't find that point to be as obvious from the figures as the sentences seem to suggest. Are we to compare magenta points (auditory off cells) from 7C with green points in 7F?
Thank you for the reviewer’s comment. We came to this conclusion based on our visual comparison of magenta points (now green in the revised version to increase the visibility) representing the auditory offset cells in Figure 7C and the green points in Figure 7F representing the cells responding to both somatosensory and auditory stimulations. In the revised manuscript, we statistically examined if the percentage of onset auditory response and offset auditory responses are different within the responsive cells to both somatosensory and auditory stimulations in the modules vs matrix. We have found that most of the cells responding to both somatosensory and auditory stimulations inside the modules had offset auditory responses, which could indicate a level of multisensory integration between somatosensory input and the offset auditory responses in these cells. We have added the statistical results to the revised manuscript to address this effect [lines 312-317]
Lines 300-302: "These data suggest that the module/matrix system permits preservation of distinct multimodal response properties in the face of massive integration of inputs in the LC". First, I'm not quite sure what that sentence means. Second, it would be more appropriate for the discussion. Third, the fact that we are more likely to find response enhancement in the modules than in the matrix is nicely consistent with the idea (supported by work from the senior author's lab and others) that excitatory somatosensory input predominantly targets neurons in the modules (which is why we see mostly response enhancement in the modules) and that this input targets GABAergic neurons which then project to and inhibit neurons both outside and inside of their module. Therefore, I would recommend that the authors replace the aforementioned sentence with one that interprets these results in light of what we know about this somatosensory-auditory circuitry.
Thank you for the reviewer’s comment. Despite the massive multimodal inputs, the LC receives from auditory vs nonauditory regions, the module/matrix system is a platform for distinct multimodal responses indicated by more somatosensory responsive cells in modules versus more auditory responsive cells in matrix, which matches the anatomical differences that were reported before. We edited the sentence in the light of the comparison between the data obtained from awake and anesthetized animals and moved it to the discussion section [lines 503-506].
The term 'LC imaged via microprism' is used dozens of times throughout the manuscript. Replacing it with a suitable acronym or initialism could improve the flow of the text and would make some of the sentences less cumbersome.
Thank you for the reviewer’s suggestion. We changed the term “LC imaged via microprism” into LC (microprism) throughout the revised manuscript.
5A-C: It is unclear what is being compared here. What are the Ns? Different animals?
Thank you for the reviewer’s comment. We do apologize for this missing information. We have added the number of subjects used in every statistical test in each corresponding figure legend.
5G: minus symbol missing on the y-axis.
Thank you for the reviewer’s comment. We gladly have fixed that.
Figure 6: Are these examples or population averages?
Thank you for the reviewer’s question. Every figure panel of the old Figure 6 represents a single trace of an example cell. However, we modified Figure 6 to include more examples of cells showing different responses complying with another reviewer’s suggestion. Each panel of the new Figure 6 represents the average response of 5 stimulations of the corresponding stimulus type. We preferred to show the average signal because it was the one used for the subsequent analysis.
How are module borders defined?
Thank you for the reviewer’s question. The modules were defined based on the intensity of the green channel that shows the expression of the GFP signals. The boundaries of modules were determined according to the distinction between high and low GFP signal boundaries of the modules. This step was done before data analysis to avoid any bias.
7JKL: How are these to be interpreted? Does panel 7K, for instance, indicate that the fraction of neurons showing 'on' responses was roughly twice as large in the matrix than in the modules and that the fraction of neurons showing 'off' responses was roughly 10 times larger in the modules than in the matrix (the mean seems to be at about 1/10).
Thank you for the reviewer’s comment. The data represented by Figures 7J-L defined the normalization of the number of cells of the same response type in the matrix against the modules. This normalization was done per animal, and then the data of the matrix were plotted against the normalization line at 1 representing the modules. The matrix will be claimed to have more cells than modules if the median of the matrix values > 1. In contrast, the matrix will be claimed to have fewer cells than the modules if the median of the matrix values < 1. Finally, if the median of matrix values = 1, this means there is no difference between matrix and modules. However, to match the data obtained from anesthetized animals (Figures 7 and 8) with those obtained from awake animals (Figure 11 or old Figure 10), we ran all data through the Chi-square test in the revised manuscript. Therefore, the format of Figures 7K-L was changed in the revised manuscript, so they became new Figures 7I-K.
10A suggests that significantly more than half the neurons shown here are not auditory responsive. Perhaps I am misinterpreting something here but isn't that in contrast to what is shown in panel 9F?
Thank you for the reviewer’s comment. The data shown in Figure 10A (or revised Figure 11A) represents the cellular response to only one stimulus (broadband noise at 80 dB with no modulation frequency), while Figure 9F (revised 10B) represents the cells responding to varieties of auditory stimulations of different combinations of frequencies and amplitudes (pure tones) as well as to AM-noise of different amplitudes and modulation frequencies. Accordingly, the old Figure 9F or revised Figure 10B shows different cell types based on their responses. For instance, some cells respond only to pure tone. Others respond only to AM-noise or to both pure tones and AM-noise. This may also support that the nonresponsive cells in Figure 10A (revised 11A) can respond to other types of sound features.
The way I understood panels 7L and 8K there were more suppressed neurons in the matrix than in the modules (line 296: "cells in the modules had a higher odds of having an enhancement response to bimodal stimulation than matrix, while cells in the matrix had a higher odds of having a suppressive response to bimodal stimulation"). Now, panel 10F indicates that in awake mice there is a greater proportion of suppressed neurons in the modules than in the matrix. I may very well have overlooked or misread something but I may not be the only reader confused by this so please clarify.
Thank you for the reviewer’s comment. We do apologize for this confusion. The ambiguity between Figures 7 and 8 (anesthetized animals) as well as Figure 10 (awake animals) comes from the fact that different statistics have been used for each preparation. In the revised version, we have fixed that by running the same statistics for all the data, and we accordingly revised Figures 7, 8, and 10 (new Figure 11). In brief, the matrix preserves a higher percentage of cells with suppressed auditory responses than those with enhanced auditory responses induced by bimodal stimulation in all conditions (anesthetized vs awake). In contrast, modules act differently across all tested conditions. While modules had more cells with enhanced auditory responses induced by bimodal interaction in anesthetized animals, they had more cells with suppressed response in awake animals indicating that modules could be sensitive to the effect of anesthesia compared to matrix. We addressed this effect in the discussion of the revised manuscript [lines 521-553].
Line 438: ...as early AS...
Thank you for the reviewer’s comment. We gladly fixed that [line 512].
Reviewer #3 (Recommendations For The Authors):
My minor recommendations for the authors are as follows:
(1) The text can be a bit difficult to follow in places. This is partly due to the convoluted nature of the results, but I suggest a careful read-through to look for opportunities to improve the prose. In particular, there is a tendency to use long sentences and long paragraphs. For example, the third paragraph of the introduction runs for almost fifty lines.
Thank you for the reviewer’s comment and suggestion. We have fixed that.
(2) This might be due to journal compression, but some of the bar graphs in the figures are difficult to read. For example, the individual data points, especially when filled with striped background colors get lost. Axes can become invisible, like the y-axis in 7L, and portions of bars, like in 7F, are not always rendered correctly. Error bars are sometimes hidden behind data points, as in 5C. Increasing line thickness and shifting individual data points away from error bars might help with this.
Thank you for the reviewer’s comment and suggestion. We made all the data points with black color and filled circles to make the data points visible. We put all the data points behind the main columns, so they don’t block the error bars. We have fixed figures 7 and 5.
(3) Throughout the manuscript, the authors use a higher SMI to indicate a preference of cells for auditory stimuli with "greater spectral... complexity" (e.g., lines 219 and 372). I find this interpretation a bit challenging since SMI compares a neuron's preference for tones over noise, and to me, tones seem like the least spectrally complex of all auditory stimuli. Perhaps some clarification of what the authors mean by this would help. For example, is the assumption that a neuron that prefers tones over noise is, either directly or indirectly, receiving input sculpted by inhibitory processes?
Thank you for the reviewer’s comment. In general, higher SMI values indicate an increase in the preference of the cells to respond to pure tones than noise with no modulation (less spectral complexity). We will clarify this statement throughout the manuscript. However, the SMI value was not mentioned in lines 219 and 372. The statement mentioned in line 219 describes the revised figure 5C (old 5B), where more cells in matrix specifically respond to AM-noise compared to modules, which indicates the preference of the matrix to respond to sounds of greater spectral and temporal complexity. The statement in 372 in the discussion section refers to the finding in revised figures 5E and F (old 5D and E). In the revised figure 5E or old 5D, the data show that matrix has more cells responding to pure tones or noise with no modulation than modules, so matrix has a lower threshold to detect the spectral features of sound (revised figure 5E or old 5D). In the revised figure 5F or old 5E, the data show that matrix has more cells responding to AM-noise than modules, which indicates that matrix functions more to process the temporal features of sound. As explained above, all findings were related to the percentage of cells responding to specific sound stimuli and not the exact SMI values. We have revised the figures accordingly by removing the terms SMI and TMI from the figures, and we have clarified that in the text.
(4) Lines 250-253: How does a decrease in SMI correspond to "an increase in pure tone responsiveness?" Doesn't a decrease suggest the opposite?
Thank you for the reviewer’s comment, which we agree with. We do apologize for that. We have fixed this statement [lines 275-277] and any related findings accordingly.
(5) Line 304: Add "imaged via microprism" or similar after "response profiles with the LC.".
Thank you for the reviewer’s suggestion. We have fixed that. However, we changed the term “LC imaged via microprism” into “LC(microprism)” for simplicity as suggested by another reviewer [line 330].
(6) Figure 5A and C: Both plots show that more neurons responded to AM-noise than tones, but it would be interesting to know how much the tone-responsive and AM-noise responsive populations overlapped. Were all tone-responsive neurons also responsive to AM-noise?
Thank you for the reviewer’s comment. We have categorized the cells based on their response to pure tone only, AM-only, and both pure tone and AM-noise when each stimulus is presented individually. We have modified Figures 5A and C, and they are now Figures 5B and D.
(7) Figure 5G: Missing negative sign before "0.5.".
Thank you for the reviewer’s suggestion. We gladly have fixed that. However, old Figure 5G became a revised Figure 5H.
(8) Figure 7 legend, Line 1102: Missing period after "(C and E)".
Thank you for the reviewer’s suggestion. We think that the period should be placed before (C and E) at the end of “respectively”. The parentheses refer to the statements after them. We gladly fixed that. [line 1394]
