Interdigitating Modules for Visual Processing During Locomotion and Rest in Mouse V1

Reviewer #1 (Public review):

Processing in the primary visual cortex (V1) of mice is not only based on sensory inputs but also strongly modulated by locomotion. In this study, Meier et al. ask whether neurons that are modulated by locomotion form clusters in V1. Their work is based on previous studies from their lab establishing a modularity in the organization of primary visual cortex based on M2-muscarinic-acetylcholine-receptor-positive patches and interpatches (Ji et al. 2015, D'Souza et al. 2019). In these studies, they have highlighted the clustering of specific visual pathways and inhibition. In the current study, they extend this modularity to motor inputs, confirming a clustering of locomotion modulated neurons but also show that these clusters overlap with the M2-negative interpatches of layer 1. Finally, they establish a blueprint for visual processing streams in V1, segregating projections to and from lateral visual areas (LM, AL, and RL) from projections to and from the lateral areas, including the visual area PM, the retrosplenial cortex (RSP), and the secondary motor area (MOs).

Conceptually, this study provides an important finding in the organization of locomotion-related signaling in primary visual cortex, which clearly has substantial implications for sensory processing in visual cortex. While the anatomical data are solid, the link to physiology is incomplete. In conclusion, there are numerous issues that leave the main findings in some doubt, so the authors have some work to do before I find this story convincing.

Major issues:

(1) The major results in this study rely on proper quantification of neuronal responses during resting and running. Recently, it has been reported that hemodynamic occlusion can strongly influence measurements of fluorescent changes using two-photon imaging (Yogesh et al. 2025, doi.org/10.1101/2024.10.29.620650). Since it is unclear whether there is an inherent bias in vasculature and hemodynamic occlusion in M2 patches and interpatches, a quantification of the effect of hemodynamic occlusion would be necessary. This control would ideally be done using mice with GFP expression to test if there is still a clustering of locomotion-modulated neurons that overlaps with M2-negative interpatches. Alternatively, the authors should at the very least quantify the vascularization in M2 patches and interpatches.

(2) To assess the effects, the authors use a correlation analysis for many of their findings (e.g., Figures 2b,c, 4j,k, ...). This, however, is inappropriate to assess the significance of the results. I suggest redoing all statistics with hierarchical bootstrap sampling (Saravanan et al. 2020, PMID: 33644783) or similar.

(3) The authors use two different measures to assess whether and to what extent a neuron is locomotion sensitive, the LMI and "locomotion-responsive". While the LMI is defined based on recording in the light and dark (Figure 2), the "locomotion-responsiveness" is defined only in the dark (Figure 3a,c,d). The link between the two measures should be clarified.

a) Additionally, Figure 2b shows higher average LMI for interpatches, but the locomotion-responsive fraction is similar in interpatches and patches (relative number of pairs in Figure 3c and Figure 3d). How do the authors explain this discrepancy?

b) How is the LMI calculated - based on the average or the maximum response over stimuli? One particular stimulus? If the LMI is defined for each stimulus separately, what is plotted in Figure 2b?

(4) In the last panels of Figures 4-7, the authors analyze the alignment of cell bodies with the M2 patches. While in superficial layers it might be straightforward to align the cell body locations with the M2 patches and interpatches in layer 1, this alignment does not appear to be trivial for deeper layers. The authors should provide additional material to convince the reader of the proper alignment.

(5) Related to point 4 above - Given the importance of a proper alignment of M2 patches with the in vivo imaging, the in vivo - ex vivo alignment should be more convincing than Figure 1 C-E. Measuring M2 patches in vivo (as the authors have tried to do) would have provided more solid evidence. Have the authors tried to remove the dura for their in vivo imaging to increase signal-to-noise? In any case, more examples of proper alignment are necessary.

(6) The authors state that locomotion selectively affects M2-/M2- pairs based on Figure 3c. However, to make this claim, there should be a significant difference between the correlation of stimulus-driven noise of M2-/M2- locomotion-responsive pairs and M2-/M2- locomotion-unresponsive pairs, AND no significant difference in the same analysis for M2+/M2+ pairs (i.e., testing the differences between the bars in Figure 3c and Figure 3d).

Reviewer #2 (Public review):

Summary:

Meier et al. explore the variability of locomotion-related modulations in mouse area V1. They present 4 major findings: V1 L2/3 neurons beneath M2- interpatches are more strongly locomotion-modulated than those beneath M2+ patches, while V1 L2/3 neurons are more strongly orientation tuned. They then use viral tracing to examine the relationship of M2- interpatches and M2+ patches with inputs from and outputs to HVOs, MO, RSP, and LP, and find evidence for different closed-loop subnetworks within L1; these relationships, however, are more complicated for cell bodies in L2/3. Finally, they also describe an overlap between M2- interpatches and SOM+ dendrites/axons.

Strengths:

The strength of the manuscript is the detailed anatomical quantification of closed-loop connectivity, and the description of the organizing principles of M2- interpatches and M2+ patches.

Weaknesses:

The major weakness of the manuscript is the lack of a direct connection between the functional and the anatomical data, and the somewhat puzzling effects observed in the analysis of noise correlations. The former issue might be alleviated by modelling, where the authors could explore the space of possibilities that could explain the functional data based on the anatomical connectivity. Some control analyses could be done, for the comparison of noise correlations.

Reviewer #3 (Public review):

The authors build on the large body of their previous research, which showed that the mouse primary visual cortex is organised into two types of clusters, M2+ and M2-, which exhibit distinct input patterns from thalamus and higher visual cortical areas and distinct visual tuning preferences. The current study reveals that a like-to-like projection from within-cluster neurons to the areas that provide feedback projections and, furthermore, that neurons in the M2- clusters are more strongly affected by non-visual signals about the locomotion of the animal.

The study adds fundamental insights to our understanding of the principles of cortical organisation and computation, specifically how the cortex integrates sensory and action-related signals.

While the tracing data are very convincing, data analysis should be strengthened to support the claims:

(1) The locomotion modulation index (LMI) compares the mean activity during running and not running but does not seem to account for differences between visual stimuli, so that the LMI could be influenced by the neuron's visual tuning rather than its sensitivity to locomotion, e.g. if the mouse was running more when the neuron's preferred stimulus was presented. Trials should first be averaged per stimulus, and then across stimuli. Alternatively, only the preferred stimulus could be considered.

The significance test (unpaired t-test) suffers from the same flaw. Instead an ANOVA (with stimulus parameter as factor) would resolve the problem, or testing whether fitting the data with two tuning curves (one per locomotion state) or a single curve results in a lower error (using cross-validation).

Given that there is evidence that specific visual stimuli can induce more or less running in mice, this issue is very important to account for behavioural differences across stimuli.

(2) All bars in Figure 2b show a lower LMI than the reported mean LMI of 0.19. This should be checked.

(3) Correlation tests: Pearson correlation is only meaningful when applied to continuous data. A more suitable test for discrete data like the M2 patch quantile is a rank test like Kendall's coefficient of rank correlation. This applies to data in Figure 2b,c, 4j,k, Figure 2 - Supplement 2,1a, etc.

(4) How OSI was determined should be clarified. Specifically, were R_pref and R_ortho the mean responses to the two opposite movement directions? Similarly, how was the half-width at half-maximum of orientation determined? From the fits in Figure 2a, it looks like the widths of both Gaussians can be different.

(5) The correlation measures in Figure 3 would greatly benefit from additional analyses to help interpretation of the results.

a) Correlations between neurons typically increase with increasing firing rates (e.g., de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802-6. doi:10.1038/nature06028). Could the higher correlations in M2+ pairs (Figure 3a) be explained by higher firing rates in M2+ compared to M2- neurons?

b) To determine correlations in Figure 3a, trials during locomotion and stationarity were pooled. As locomotion impacts the firing rate of the neurons, it would be helpful to separate correlations between the two states, locomotion vs stationarity, so the measures reflect something closer to "noise correlations" rather than tuning to locomotion.

c) Similarly, in Figure 3b, I wonder whether the large correlations in M2- pairs are driven by locomotion rather than functional connectivity. As suggested in b, a better test of noise correlations would be to account for locomotion, i.e., separate trials by stimulus identity and locomotion state. To prevent conditions with few trials from having greater weight in the overall noise correlations, I suggest the authors first z-score responses per condition, then determine noise correlations across all trials (as explained in Renart et al., 2010).

d) Correlations in Figure 3a,b should be tested with an ANOVA and a control for multiple tests.

(6) In plots like Figure 4j-l, it would be very informative to show individual measures (per ROI and mouse) in addition to mean +- SEM. As the counts are low (<10) it wouldn't obstruct the plot.

(7) The caption of Figure 4l says that most retrogradely labelled cells are located in L2/3. However, the plot only shows data from L2/3 and a single section of L4, so one cannot compare it to other layers. Can the authors corroborate the claim with data from other layers?

(8) Methods:
The authors should provide more details on the visual stimuli: What was the background on which gratings were presented? How long was the inter-stimulus interval? What was presented during the inter-stimulus interval? How large were gratings used to map tuning to SF, TF, and orientation?