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Disentangling sensory contributions to postural control regulation through sample entropy and neural modeling: A preliminary study.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Behavioral experiment › Detrended fluctuation analysis. ↔ python/dfa.py, the whole file · a weak match · score 0.79 · Detrended Fluctuation, temporal correlations, scaling exponent, DFA, log, quantify
  2. [2] § Materials and methods › Behavioral experiment › Detrended fluctuation analysis. ↔ matlab/dfa.m, the whole file · a weak match · score 0.68 · Detrended Fluctuation, scaling exponent, DFA, log, quantify

Paper

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The authors' code

Python · 104 lines · 3.9 KB · BSD-3-Clause · 1 match

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. def dfa(data, scales, order=1, plot=True):
  6. """Perform Detrended Fluctuation Analysis on data
  8. Inputs:
  9. data: 1D numpy array of time series to be analyzed.
  10. scales: List or array of scales to calculate fluctuations
  11. order: Integer of polynomial fit (default=1 for linear)
  12. plot: Return loglog plot (default=True to return plot)
  14. Outputs:
  15. scales: The scales that were entered as input
  16. fluctuations: Variability measured at each scale with RMS
  17. alpha value: Value quantifying the relationship between the scales
  18. and fluctuations
  20. ....References:
  21. ........Damouras, S., Chang, M. D., Sejdi, E., & Chau, T. (2010). An empirical
  22. ..........examination of detrended fluctuation analysis for gait data. Gait &
  23. ..........posture, 31(3), 336-340.
  24. ........Mirzayof, D., & Ashkenazy, Y. (2010). Preservation of long range
  25. ..........temporal correlations under extreme random dilution. Physica A:
  26. ..........Statistical Mechanics and its Applications, 389(24), 5573-5580.
  27. ........Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995).
  28. ..........Quantification of scaling exponents and crossover phenomena in
  29. ..........nonstationary heartbeat time series. Chaos: An Interdisciplinary
  30. ..........Journal of Nonlinear Science, 5(1), 82-87.
  31. # =============================================================================
  32. ------ EXAMPLE ------
  34. - Generate random data
  35. data = np.random.randn(5000)
  37. - Create a vector of the scales you want to use
  38. scales = [10, 20, 40, 80, 160, 320, 640, 1280, 2560]
  40. - Set a detrending order. Use 1 for a linear detrend.
  41. order = 1
  43. - run dfa function
  44. s, f, a = dfa(data, scales, order, plot=True)
  45. # =============================================================================
  46. """
  48. # Check if data is a column vector (2D array with one column)
  49. if data.shape[0] == 1:
  50. # Reshape the data to be a column vector
  51. data = data.reshape(-1, 1)
  52. else:
  53. # Data is already a column vector
  54. data = data
  56. # =============================================================================
  57. ########################## START DFA CALCULATION ##########################
  58. # =============================================================================
  60. # Step 1: Integrate the data
  61. integrated_data = np.cumsum(data - np.mean(data))
  63. fluctuation = []
  65. for scale in scales:
  66. # Step 2: Divide data into non-overlapping window of size 'scale'
  67. chunks = len(data) // scale
  68. ms = 0.0
  70. for i in range(chunks):
  71. this_chunk = integrated_data[i*scale:(i+1)*scale]
  72. x = np.arange(len(this_chunk))
  74. # Step 3: Fit polynomial (default is linear, i.e., order=1)
  75. coeffs = np.polyfit(x, this_chunk, order)
  76. fit = np.polyval(coeffs, x)
  78. # Detrend and calculate RMS for the current window
  79. ms += np.mean((this_chunk - fit) ** 2)
  81. # Calculate average RMS for this scale
  82. fluctuation.append(np.sqrt(ms / chunks))
  84. # Perform linear regression
  85. alpha, intercept = np.polyfit(np.log(scales), np.log(fluctuation), 1)
  88. # Create a log-log plot to visualize the results
  89. if plot:
  90. plt.figure(figsize=(8, 6))
  91. plt.loglog(scales, fluctuation, marker='o', markerfacecolor = 'red', markersize=8,
  92. linestyle='-', color = 'black', linewidth=1.7, label=f'Alpha = {alpha:.3f}')
  93. plt.xlabel('Scale (log)')
  94. plt.ylabel('Fluctuation (log)')
  95. plt.legend()
  96. plt.title('Detrended Fluctuation Analysis')
  97. plt.grid(True)
  98. plt.show()
  100. # Return the scales used, fluctuation functions and the alpha value
  101. return scales, fluctuation, alpha

dfa.py at commit 5056783, under BSD-3-Clause · at the source

Overview

Authors: Silvia Zanchi1, Eleonora Montagnani1, Victoria Marchetti2, Davide Esposito1, Marta Guarischi1,3, Melissa Monti2, Cristiano Cuppini2, Monica Gori1,4
ORCID iDs: Eleonora Montagnani, Marta Guarischi, Cristiano Cuppini
  1. Italian Institute of Technology, Unit for Visually Impaired People (UVIP), Genoa, Italy
  2. Department of Electrical, Electronic, and Information Engineering Guglielmo Marconi, University of Bologna, Bologna, Italy
  3. Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS), Genoa, Italy
  4. Institute for Human & Machine Cognition (IHMC), Pensacola, Florida, United States of America
Institutions: Italian Institute of Technology (Italy); University of Bologna (Italy); University of Genoa (Italy); Florida Institute for Human and Machine Cognition (United States)
Journal: PloS one, volume 21, issue 8, article e0355402
Dates: received 14 April 2026; accepted 21 July 2026; published online 18 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0355402 · PMID 42611886 · PMCID PMC13485045 · OpenAlex W7203695426
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Complexity, Preprocessing
MeSH: Models, Neurological*, Postural Balance*, Acoustic Stimulation, Adult, Entropy, Female, Humans, Male, Proprioception, Vibration, Young Adult (* major topic)
Topic: Balance, Gait, and Falls Prevention (Physical Therapy, Sports Therapy and Rehabilitation, Health Professions), according to OpenAlex
Funding: European Research Council (948349)
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Postural control relies on the integration of visual, vestibular, proprioceptive, and auditory inputs to maintain stability. While previous studies have explored the effects of individual sensory modalities, the combined influence of multisensory disruptions on postural predictability remains unclear. We preliminary assessed postural behavior during quiet standing under manipulated sensory conditions in healthy participants. Eight adults stood barefoot on a Wii Balance Board, wearing a mixed reality headset with headphones and receiving vibrotactile stimulation to the Achilles tendons. Experimental conditions combined vibrotactile, static, or moving auditory stimuli across three visual states (Eyes Open, Eyes Closed, Blurred Vision). Center of pressure (CoP) in anterior-posterior (AP) and medio-lateral (ML) planes was collected and analyzed for signal predictability using Sample Entropy. The same experimental procedures were simulated by a biologically inspired neural mass model to investigate multisensory integration in postural control at the neural level. Behaviorally, proprioceptive perturbation via Achilles tendon vibration significantly increased Sample Entropy in both spatial planes, indicating reduced system predictability, while blurred vision decreased only in the AP plane, suggesting less flexible postural dynamics. The neural model replicated key behavioral trends and also revealed distinct central mechanisms underlying sensory interactions: while auditory inputs had minimal behavioral effects, at the simulated neural level, they increased Sample Entropy, especially under dynamic conditions. This revealed subtle modulation of predictability from auditory cues, which was not detectable at the behavioral level. These preliminary results highlight the modality-specific contributions to postural control and demonstrate the utility of Sample Entropy as measure of predictability, with our computational modeling uncovering, for the first time to our knowledge, neural integration processes related to multisensory integration and postural control predictability.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

nonlinear-analysis-core/nonanlibrary

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5056783d16b48958430ebbc1e5854628b784b0fe, 3 August 2026
Languages: MATLAB (84), JavaScript (72), Python (25)
Size: 316 files, 181 scripts
Software Heritage: not archived
Found in: the text, “Input parameter selection.”
Holds: README, license file, environment (requirements.txt), tests, documentation
Not found: CITATION.cff, continuous integration
Tools: NumPy (24 files), Statistics and Machine Learning Toolbox (9 files), Image Processing Toolbox (7 files), SciPy (5 files), Matplotlib (4 files), Signal Processing Toolbox (2 files), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
183 files
At the source: github.com/Nonlinear-Analysis-Core/NONANLibrary/tree/main/matlab

Tracing map

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  • 181 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data Availability

All relevant data for this study are publicly available from the figshare repository (https://doi.org/10.6084/m9.figshare.33120242).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 11 MeSH terms, 1 funder, 45 references.

Cite

This paper

Zanchi, S., Montagnani, E., Marchetti, V., Esposito, D., Guarischi, M., Monti, M., Cuppini, C., & Gori, M. (2026). Disentangling sensory contributions to postural control regulation through sample entropy and neural modeling: A preliminary study. PloS one, 21(8), e0355402. https://doi.org/10.1371/journal.pone.0355402

BibTeX

@article{zanchi2026disentangling,
author = {Zanchi, Silvia and Montagnani, Eleonora and Marchetti, Victoria and Esposito, Davide and Guarischi, Marta and Monti, Melissa and Cuppini, Cristiano and Gori, Monica},
title = {{Disentangling sensory contributions to postural control regulation through sample entropy and neural modeling: A preliminary study}},
journal = {PloS one},
year = {2026},
month = aug,
volume = {21},
number = {8},
pages = {e0355402},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0355402},
url = {https://doi.org/10.1371/journal.pone.0355402},
pmid = {42611886},
pmcid = {PMC13485045}
}

RIS

TY - JOUR
AU - Zanchi, Silvia
AU - Montagnani, Eleonora
AU - Marchetti, Victoria
AU - Esposito, Davide
AU - Guarischi, Marta
AU - Monti, Melissa
AU - Cuppini, Cristiano
AU - Gori, Monica
TI - Disentangling sensory contributions to postural control regulation through sample entropy and neural modeling: A preliminary study
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/08/18
VL - 21
IS - 8
SP - e0355402
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0355402
UR - https://doi.org/10.1371/journal.pone.0355402
LA - en
ER -

CSL-JSON

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