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Authors of the review

Name: Sarah Faber, ORCID: https://orcid.org/0000-0002-0950-354X

• Bio: Sarah Faber is a postdoctoral scholar in computational neuroscience working on whole-brain models of aging and neurodegeneration using music.

Name: Masoumeh Golmohamadian, ORCID: https://orcid.org/0000-0002-3913-7448

• Bio: Masoumeh Golmohamadian is a postdoctoral researcher in computational neuroscience working on parameter optimization for whole-brain models, with a background in mathematics and perceptual decision-making.

Name: Justin Wang, ORCID: https://orcid.org/0009-0002-7475-4289

• Bio: Justin Wang is PhD student in computational neuroscience with a background in computer science.

Name: Leanne Rokos, ORCID: https://orcid.org/0009-0006-6640-400X

• Bio: Leanne Rokos is a Research Technician with a PhD in Medical Science and a background in computational modeling of early childhood brain networks

Name: Cathlin Jiaqi Han, ORCID: https://orcid.org/0009-0003-2450-6807

• Bio: Cathlin Jiaqi Han is a PhD student whose work investigates Alzheimer’s disease brain network dynamics using imaging and brain network modelling approaches.

Summary of the preprint 

In this manuscript, the authors test whether inter brain (neural) synchrony (IBS) and/or inter-brain coupling (IBC) can be observed in physically separated dyads in a touch-based perceptual task. The task was based on the perceptual crossing experiment (PCE): a participant pilots an avatar around a virtual space and encounters objects with haptic feedback delivered via a sensor. Participants could interact with the other participant’s avatar, the other participant’s avatar’s shadow, or a fixed object. Using a k-means clustering-based approach to identify regularly-occurring states in the brain and behaviour data, and linking them between dyad partners using dynamic time warping, they identify IBS between partners.

1. Big-picture comments 

1. Good/Excellent things 

This study investigates an important question in social neuroscience by examining whether inter-brain synchronization can emerge during minimal social interaction without visual or verbal communication. The Perceptual Crossing paradigm provides a well-controlled framework for studying reciprocal interaction while minimizing shared sensory input. A major strength of the paper is the identification of behavioral clusters and inter-brain network clusters, which provides a useful framework for understanding different patterns of reciprocal interaction and inter-brain synchronization. The manuscript is well organized, and the progression from behavioral analyses to inter-brain synchronization analyses is clear and easy to follow. Additionally, the k-means approach is well-explained and attractive in its clarity.

1. Things to be improved 

The identification of behavioral clusters and inter-brain network clusters is an interesting aspect of the study; however, the temporal relationships between these clusters are not explored. Examining transitions between clusters, the amount of time spent in each cluster, and how these patterns evolve throughout the task could provide additional insight into the dynamics of reciprocal interaction. It would also be helpful to clarify how the observed inter-brain network results differ from baseline or resting-state synchronization. In addition, more subject-level analyses could be informative. Collecting more data from individual dyads may help identify different interaction strategies and determine whether these strategies are associated with different behavioral clusters or inter-brain network clusters.

The over-representation of states in the analysis is common to state-space work, but if the authors wish to explore this more fully, they could consider focusing on the ROIs from group 3 and conduct network analyses (community detection, betweenness-centrality, etc) to see if there are sub-groups withing group 3 explaining its over-representation.

The k-means method, a big strength of the paper, used 20 replicates, which seems low. More detail would be helpful in how this number was chosen. As well, it would be interesting to see the probability matrix results surrogate-shuffled. With three brain states, there might not be extremely compelling results in surrogate dyads, but it would be interesting to see. The rationale behind chi square could also use more detail – why was a chi square chosen instead of something like a PLS?

1. Small picture comments 

1. Good/Excellent things

The figures in both the main manuscript and the supplementary material are clear and help the reader easily follow the progression from the behavioral analyses to the inter-brain network analyses. Supplementary figures 1 and 4 were particularly clear and would be helpful in the body of the main text.

1. Things to be improved 

The rationale for selecting corrected circular correlation (CCorr) as the measure of inter-brain synchronization could be discussed in greater detail. Different measures may capture different aspects of neural coupling and may lead to different network structures. Therefore, it would be helpful to explain why CCorr was preferred over other commonly used measures, such as PLV, PLI, or wPLI, for constructing brain networks from EEG data.

It would also be helpful to demonstrate that the findings remain consistent across different thresholding approaches. Because threshold selection can substantially influence the resulting connectivity patterns, demonstrating the robustness of the results across multiple thresholds would strengthen confidence in the reported findings.

The authors could consider incorporating additional behavioral variables that describe the overall spatial configuration of the avatars, shadows, and static objects. For example, measures that capture the shape of the scene as a whole, rather than only pairwise relationships, may provide additional insight into reciprocal interaction.

The text switches from IBC to IBS between the introduction and discussion: more consistency is needed.

In Line 25, the following conclusion isn’t immediately clear: “Therefore, in this paper, we explore IBC in a touch-based interaction experiment, the Perceptual Crossing Experiment (PCE)”. It was hard for to follow the reasoning before PCE was explained.

Text is missing a space in Line 38: “virtual(but not physically isolated)”. Additionally, a definition what DTW represents, on a conceptual level, would help. A sentence noting that mutual bidirectional vibration occurs when passing each others’ avatars but not when passing shadows (unidirectional vibration only) would help the reader better understand the protocol without needing to turn to the dataset paper.

For rationale in the background and discussion sections, the authors state that clearer links between tactile aspects and life are under explored, but a brief statement on why we want to explore it would help to ground the study. In the limitations section, it would also be interesting to consider whether the all-or-nothing vibration in the task limits the ecological validity. While this work focused on existing data, we understand the authors could not modify the task, however, we were curious to know whether building up the vibration as participants approached a target would make the virtual space more lifelike.

Figure 6 appears to contain panels C and D that are not clearly described in the figure caption. Expanding the caption and providing a more detailed explanation of all figure panels would improve clarity and interpretation.

Closing remarks

Overall, a clear and compelling paper presenting a novel analysis paradigm on a novel task. The addition of extra methodological detail would assist the reader in confidently replicating the analysis, and we look forward to reading the published version.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.