PCOMPBIOL-D-26-00334

Network analysis of surface deformation reveals trunk modularity and synchronization during gait

PLOS Computational Biology

Dear Dr. Wang,

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Kind regards,

Feng Fu

Section Editor

PLOS Computational Biology

Feng Fu

Section Editor

PLOS Computational Biology

Additional Editor Comments:

The reviewers think the work of interest to the journal and recommend some revisions, particularly regarding the sample size of the experiment. Please address them constructively in your revised manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript presents an innovative methodological framework integrating high-density surface topography (utilizing 95 reflective markers) with complex network analysis to decode the dynamic deformation of the human trunk during gait. By modeling the dorsal skin as a dynamic continuum and applying community detection algorithms, the authors identified Synchronous Deformation Regions (SDRs) which are robust under different walking speeds (0.7 m/s to 1.7 m/s). Furthermore, the study uncovered long-range synchronization between the scapular and contralateral pelvic regions at speeds higher than 1.1 m/s. Projecting multi-dimensional, non-linear musculoskeletal synergies onto a 2D topological network is a highly effective approach. This methodology has significant potential to inform the design and control strategies of spinal exoskeletons and enhance precision rehabilitation assessments. The mathematical modeling is sound. However, the small sample size of only one subject limit the generalizability of the conclusions making this work more appropriate as a feasibility or pilot study. To solidify the manuscript for publication, the robustness of certain network parameters and the limitation of experiments require further clarification. In addition, some specific comments are listed as follows.

1. The construction of the signed correlation network relies on a strict absolute threshold of to prune weak edges. Such hard thresholding can fundamentally alter the topology and the resulting modularity partition (reported as). How do the spatial boundaries of the SDRs shift if or is applied? Confirming the topological robustness against this hyperparameter will significantly strengthen the mathematical rigor of the study.

2. When discussing the Posterior Oblique Subsystem and the myofascial 'Spiral Line', please explicitly connect how the discrete muscle forces mathematically translate into the positive correlation edges observed in the mid-thoracic topological network.

3. The distinct grey cluster along the spinal midline is identified as a kinematic boundary. Please clarify if the stability of this topological region is primarily governed by the rigid-body dynamics of the underlying spinous processes rather than the viscoelastic deformation of the adjacent paraspinal skin.

4. In Figure 4, the baseline value is set to 1.00. Please ensure it is explicitly stated in the figure legend that this represents the normalized average calculated across all tested walking speeds to prevent any misinterpretation of the geometric variance.

5. Grammatical mistakes and typos still exist. In Abstract, the phrase "addressing prevalence disorders like low back pain" is not appropriate. Consider changing "prevalence disorders" to "prevalent disorders" for better clarity. In Section 3.1, line 191, there is a missing numerical value “the superior angle of the respective contralateral scapulae at speeds ≥ m/s”. In Section 3.3, the section title contains a spelling error “Comparision”.

Reviewer #2: This paper proposes the use of a large number of force markers to measure surface tension while a human subject walks on a treadmill at different speeds. Through correlation and network analyses, the authors identify local clusters across the sampling points under different gait conditions. The study provides a potentially effective approach for capturing detailed surface-tension patterns and estimating trunk deformation during walking. The manuscript is generally well written, and the figures are of high quality. I have the following comments before recommending publication:

1. Is there a specific reason for choosing such a large number of sensors (95) for this task? Would it be possible to reach similar conclusions with, for example, half that number? In addition, are all sampling channels synchronized? If not, could any delay between channels affect the analysis?

2. From my reading, it appears that there is only one participant and one trial for each walking speed. I understand that this is not an easy experiment to conduct. However, a single example may not be sufficient to justify the generality of either the method or the conclusions.

3. It seems that the full 15-second dataset was pooled for the time-series analysis. Have the authors considered using a moving window, for example 5 seconds, to examine the temporal dynamics of clustering? This could reveal whether the cluster structure changes between the beginning and the end of walking.

4. The authors also collected motion-capture and force data during walking, but I do not see much analysis of these measurements. It would be helpful to clarify how these data were used, or to include additional analysis if relevant.

Minor comments:

Line 316: Please add a space between “treadmill.” and “The”.

Line 319: “irrelevant noise5.” should be corrected to “irrelevant noise.”

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Reviewer #1: Yes

Reviewer #2: None

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Reviewer #1: No

Reviewer #2: No

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Dear Professor Wang,

We are pleased to inform you that your manuscript 'Network analysis of surface deformation reveals trunk modularity and synchronization during gait' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Feng Fu

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed my comments accordingly and I support the publication of the paper in the journal.

Reviewer #2: All of my questions have now been addressed, Thanks.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: None

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Reviewer #1: No

Reviewer #2: No