Correlated spontaneous activity sets up multi-sensory integration in the developing higher-order cortex

Reviewer #1 (Public review):

Dwulet et al. combined experimental and modeling approaches to investigate how correlated spontaneous activity in the mouse's primary visual (V1) and primary somatosensory (S1) areas drives the development of multisensory integration in area RL. Notably, they focused on early developmental stages, before sensory experience occurs. Consistent with previous experimental findings, the authors first demonstrated that spontaneous activity becomes more sparse across development in all three areas, as measured by event amplitude, event duration, and participation ratio. Using a linear mixed model analysis to compare the maturation of this spontaneous activity, they found evidence that S1 matured the fastest. The authors then presented experimental evidence suggesting that these spontaneous events were moderately correlated both spatially and temporally.

They hypothesized that activity-dependent mechanisms use these correlations to establish connectivity across these regions. To test this hypothesis, the authors modeled a feedforward network with connections from S1 to RL and from V1 to RL, where the strength of connections depended on a Hebbian term for potentiation and a heterosynaptic term for depression. By investigating different levels of V1-S1 correlations, they found that moderate levels of correlation led to the significant development of topographically organized connectivity while maintaining a mix of bimodal and unimodal cells in RL. Additionally, when simulating a network with a more mature S1, they observed that topographical maps improved not only between S1 and RL but also between V1 and RL. Finally, the authors use linear regression to suggest that the mixture of bimodal and unimodal cells in RL is optimal for encoding the maximum amount of information from both V1 and S1.

However, there are significant gaps between the experimental data and the modeling setup, which weaken the paper's conclusions. Additionally, some key details are omitted, making it difficult to fully assess their analysis and interpret some of their figures.

(1) Some of the statistical measures and techniques in Figure 1 could benefit from clearer definitions. While the thresholds for activation (peak with at least 5% dF/F0) and events (20% of recorded cells activated simultaneously) are provided, event duration and participation rate are not clearly defined. Based on this definition of event alone, it is unclear why the minimum participation rate in Figure 1F is not 20%. Additionally, the conclusion that S1 matures earlier than RL and V1 could be strengthened by including a direct comparison between S1 and RL, as the current analysis only compares these areas to V1.

(2) The wide-field experiments in Figure 2 could be expanded to support the feedforward modeling assumptions. Currently, the spatial and temporal correlations presented leave open the possibility that these spontaneous events are traveling waves propagating from V1 to RL to S1 (or vice versa). This scenario would suggest a different connectivity scheme for the model. Clarifying this point with additional data analysis, specifically including temporal correlations involving RL, could provide stronger support for the model's assumptions.

(3) The functional correlation map in Figure 2D appears contradictory to the authors' modeling assumption that inputs are correlated spatially in V1 and S1. While V1 seed points align topographically with RL, this organization breaks down when extended into S1. In contrast, and in support of the modeling assumption, Figure 2E shows clearer topography across all three regions. A discussion of this discrepancy would be helpful, as it's a key conclusion of the figure. Additionally, it is unclear when this data was collected during development. Clarifying the developmental stage and analyzing how this map changes over time could strengthen the results.

(4) The modeling of spontaneous events with fixed amplitude and duration seems inconsistent with the experimental data in Figure 1, which shows variability in these parameters. This is particularly confusing in Figure 4, where S1 maturation is modeled as a stronger topographical alignment with RL, but the experimental data defines maturation based on amplitude, duration, and event rates. Justifying these modeling choices or adapting the model to reflect experimental variability would create a better connection between the theory and data.

(5) Several important details of the mathematical model are missing or unclear, partly due to typos. The Results section mentions the general framework of the input correlation matrix (e.g., "S1 and V1 neurons were driven by a combination of events, independent and shared in each V1 and S1" and "each independent event activated a randomly chosen, contiguous set of neurons"), but the specifics are not fully explained. Additionally, the caption of Figure 5 refers to a non-linear transfer function (a sigmoid), but these details are not provided in the Methods section, which instead suggests a linear model was used. A careful review of the main text and Methods section would help ensure that all the necessary details are included and that the story is both complete and accurate.

(6) While Figure 5 supports the paper's conclusion that a mixture of unimodal and bimodal neurons in RL optimizes information encoding, the authors missed an opportunity to strengthen the connection between the model and experimental data. Specifically, they could apply this reconstruction method to the experimental data and examine how RL's ability to reconstruct V1/S1 activity changes across development. Their model predicts that this performance would improve over time, and if this trend is observed in the experimental data, it would provide strong validation that these feedforward connections are developing in line with the model's predictions.

Reviewer #2 (Public review):

The authors aim to investigate the role of spontaneous activity in shaping the development of multisensory integration in the brain, specifically focusing on the connections between primary visual and somatosensory sensory areas (V1 and S1) and a higher-order cortical area rostro-lateral to V1 (RL). They seek to understand how spontaneous activity guides the formation of aligned topographic maps and the emergence of bimodal neurons in RL.

First, the authors found that spontaneous activity in all three areas sparsifies over time, but S1 exhibits more mature patterns earlier than V1 and RL. They claimed that correlated activity among neighboring regions of these areas during development carries topographic information. These data were used to implement a computational model that employed Hebbian rules of synaptic plasticity. The model indicated that correlated spontaneous activity can generate topographic connectivity between S1/V1 and RL and bimodal neurons in RL. The model suggested that the more mature spontaneous activity in S1 can guide map alignment between V1 and RL. In addition, the model also suggested that a mixture of bimodal and unimodal neurons in RL is optimal for decoding information from V1 and S1.

While the data presented in the manuscript is promising and provides preliminary insights into the role of spontaneous activity in multisensory integration, it would be beneficial to strengthen the experimental foundation regarding the correlation between V1, S1, and RL. Incorporating more rigorous spatio-temporal analyses of spontaneous activity could enhance the robustness of these findings.

Here are some important concerns:

(1) The analysis of how spatial topography influences activity correlations in Figure 2 has several issues.
1a. While squares in V1 and S1 covered a small area of these sensory areas, the correlated territories in RL covered the entire area of RL. The topographic map in V1 continues caudally, so where is the rest of the map in RL? Something similar applies to the relationship between S1 and RL.
1b. It is essential to know how areas were drawn. High precision is required.
1c. It is not clear if correlated activity means different events in sync or large events that cover 2 or all 3 cortical areas of interest. The figure points to the second option, which contradicts the size of events at these stages, mainly in the oldest mice analyzed here.
1d. It is fundamental to know in detail and provide examples of how the detection of events was performed. For instance, could the dispersion of light from an event in V1 close to RL cause the detection of activity in RL?

(2) For the correlations among V1, S1, and RL, it is crucial to have a consistent method to delineate the borders of cortical areas. The authors mention in one sentence that areas were drawn according to a reference map. More details are needed to convince the reader that the borders are accurate, especially because their shape and position change with age.

(3) The results from the model seem to be based on the initial bias in connectivity between neighboring cells from the different areas. Then, it seems straightforward that implementing correlated activity with Hebbian and synaptic depression rules will force the strengthening of connections between spatially close cells. Despite this apparent predisposition of the model towards a defined outcome, the flaws in the experimental data used prevent a rigorous interpretation of the computational model.

(4) In the Introduction, the authors nicely and briefly explain the role of primary and higher-order sensory cortices in information processing. They also explain how spontaneous activity during development helps to build these circuits by refining connections or establishing hierarchies. They continue explaining the relevance of aligning different topographic maps to allow multisensory integration. Then they provide some examples of sites of multisensory integration. This provides a general context for the data presented in the Results section; however, and importantly, there is no specific introduction of why they are interested in RL and its interaction with V1 and S1. The authors should introduce the RL area and explain why it is an interesting site for multisensory processing.

(5) The results shown in Figure 1 corroborate published data from Golshani et al, Rochefort et al, Murakami et al. While the reproduction of data is more than welcome, the authors should specify which part of the data is completely new and acknowledge clearly the rest as corroboration of previous data. The sentence "As described in previous experiments ..." partially acknowledges this fact but is not clear enough. In addition, the transition between this part of the manuscript and the next data is not smooth. Data seems to be used to feed the model so perhaps the organization of the manuscript leaves room for improvement.

Reviewer #3 (Public review):

Summary:

The study by Dwulet et al. explores how the development of spontaneous neural activity in primary sensory cortices influences the co-alignment of multiple sensory modalities in higher-order brain areas (HOAs). To address this question, they focus on connectivity between the primary visual (V1) and somatosensory (S1) cortices and an associative cortical area (RL) in mice. The authors combine experimental (wide-field and two-photon calcium imaging) and computational approaches to show that spontaneous activity matures at a different pace across these brain regions. Their data indicate that S1 develops more rapidly than V1, which is possibly beneficial for RL's integration of visual and somatosensory inputs through correlated spontaneous activity. Using a computational model, they demonstrate that a moderate correlation between V1 and S1 activity can optimally guide the formation of bimodal neurons in RL, which are crucial for maximizing the decodability of multisensory stimuli. This finding highlights the role of correlated spontaneous activity in primary sensory cortices in establishing co-aligned topographic multimodal sensory representations in downstream circuits.

Strengths:

The manuscript is well written and it provides strong enough evidence to support the main claim of the authors. The insights on the role of correlated activity on instructing co-aligned multisensory maps in HOAs are not trivial and are an important advancement for the field.

Weaknesses:

In the opinion of this reviewer, the study has no major weaknesses. A drawback of the work is that none of the predictions of the computational modeling have been corroborated through mechanistic experimental manipulations of early brain activity.