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Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.

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
  1. [1] § Methods › Endpoints › Secondary endpoints. ↔ check_scripts/check_combined_data_bologna.py, lines 105–123 · score 0.65 · rectus femoris, gluteus medius, EMG
  2. [2] § Methods › Data collection procedures › Synchronization procedure of multimodal recordings. ↔ sync_scripts/qualisys2lsl.py, lines 11–30 · score 0.51 · Lab Streaming Layer, LSL

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 127 lines · 4.2 KB · no license · 1 match

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import pyxdf
  5. file_path = r"C:\Users\juliu\Desktop\kiel\stepup_setup_jw\data\Test_bologna_25_03_25\4_WALKING_14\sub-P001_ses-S001_task-Default_run-001_eeg_old6.xdf" # Replace with your XDF file path
  8. streams, fileheader = pyxdf.load_xdf(file_path, handle_clock_resets=False)
  9. print("File loaded successfully.")
  11. if streams is not None:
  12. print(f"Number of streams: {len(streams)}")
  13. for i, stream in enumerate(streams):
  14. print(f"\nStream {i+1}:")
  15. print(f"Name: {stream['info']['name'][0]}")
  16. print(f"Type: {stream['info']['type'][0]}")
  17. print(f"Channel count: {stream['info']['channel_count'][0]}")
  18. print(f"Sample rate: {stream['info']['nominal_srate'][0]}")
  19. print(f"Data points: {len(stream['time_series'])}")
  21. # find EMG stream
  22. emg_stream = [s for s in streams if s['info']['type'][0] == 'EMG'][0]
  23. # find Mocap stream
  24. mocap_stream = [s for s in streams if s['info']['type'][0] == 'MoCap'][0]
  25. # find EEG stream
  26. eeg_stream = [s for s in streams if s['info']['type'][0] == 'EEG'][0]
  28. # preparethe data
  29. emg_times = emg_stream['time_stamps'] - emg_stream['time_stamps'][0]
  30. emg_raw = emg_stream['time_series']
  32. mocap_times = mocap_stream['time_stamps'] - mocap_stream['time_stamps'][0]
  33. mocap_raw = mocap_stream['time_series']
  35. eeg_times = eeg_stream['time_stamps'] - eeg_stream['time_stamps'][0]
  36. eeg_raw = eeg_stream['time_series']
  38. # print unique marker ids, and number of occurences per makrer id
  39. marker_ids = mocap_raw[:,3]
  40. unique_marker_ids = set(marker_ids)
  41. for marker_id in unique_marker_ids:
  42. print(f"Marker {int(marker_id)}: {np.sum(marker_ids == marker_id)}")
  44. # remove all markers which have less than 100 frames
  45. for marker_id in unique_marker_ids:
  46. if np.sum(marker_ids == marker_id) < 150:
  47. idx = np.where(marker_ids == marker_id)
  48. mocap_raw = np.delete(mocap_raw, idx, axis=0)
  49. marker_ids = np.delete(marker_ids, idx)
  51. # Count occurrences of each marker ID
  52. marker_ids = mocap_raw[:, 3]
  53. unique_marker_ids, counts = np.unique(marker_ids, return_counts=True)
  55. # Get the 5 most common markers
  56. most_common_markers = sorted(zip(unique_marker_ids, counts), key=lambda x: x[1], reverse=True)[:5]
  58. # Store the data and indices for the 5 most common markers in a dictionary
  59. marker_data_dict = {}
  60. for marker_id, count in most_common_markers:
  61. indices = np.where(marker_ids == marker_id)
  62. marker_data = mocap_raw[indices]
  63. marker_data_dict[int(marker_id)] = {
  64. "data": marker_data,
  65. "indices": indices
  66. }
  68. # Print the dictionary for verification
  69. for marker_id, info in marker_data_dict.items():
  70. print(f"Marker {marker_id}:")
  71. print(f" Count: {len(info['data'])}")
  72. print(f" Indices: {info['indices']}")
  73. print(f" A total of {len(unique_marker_ids)} markers is found, where .")
  75. # make 3 subplots
  76. fig, axs = plt.subplots(3, 1, sharex=True)
  77. # plot 1 emg
  78. axs[0].plot(emg_times, emg_raw[:,:9])
  79. axs[0].set_ylabel('EMG')
  80. axs[0].set_ylim([-400, 400])
  81. #axs[0].legend(['RfEmgR', 'BfEmgR', 'RfEmgL', 'BfEmgL'])
  83. # plot 2 mocap
  84. for marker_id, info in marker_data_dict.items():
  85. marker_times = mocap_times[info['indices']]
  86. marker_positions = info['data'][:, 2] # 3rd column (Z position)
  87. axs[1].plot(marker_times, marker_positions, label=f'Marker {marker_id}')
  88. axs[1].legend()
  89. axs[1].set_xlabel('Time (s)')
  90. axs[1].set_ylabel('Z position (?)')
  91. #axs[1].legend(['Marker 34', 'Marker 294', 'Marker 296'])
  94. # plot 3 eeg
  95. axs[2].plot(eeg_times, eeg_stream['time_series'][:,0])
  96. axs[2].set_ylabel('EEG')
  97. axs[2].set_xlabel('Time (s)')
  99. # set xlim 20-30s for all subplots
  100. for ax in axs:
  101. ax.set_xlim([20, 30])
  105. # plot each emg channel in a different subplot with labels
  106. emg_labels = [
  107. "Rectus femoris RIGHT",
  108. "Rectus femoris LEFT",
  109. "Biceps femoris RIGHT",
  110. "Biceps femoris LEFT",
  111. "Gastrocnemius medialis RIGHT",
  112. "Gastrocnemius medialis LEFT",
  113. "Gluteus medius RIGHT",
  114. "Gluteus medius LEFT"
  115. ]
  117. fig, axs = plt.subplots(8, 1, sharex=True)
  118. for i in range(8):
  119. axs[i].plot(emg_times, emg_raw[:, i])
  120. axs[i].set_title(emg_labels[i])
  121. mean = np.mean(emg_raw[:, i])
  122. std = np.std(emg_raw[:, i]) * 3
  123. axs[i].set_ylim([mean - std, mean + std])
  125. # set xlim 20-30s for all subplots
  126. for ax in axs:
  127. ax.set_xlim([20, 30])

check_combined_data_bologna.py at commit 3ee7165, no license · at the source

Overview

Authors: Anina Moira van Leeuwen1, Julius Welzel2, Ilaria D’Ascanio3, Charlotte Lang4, Vaishali Vinod2, Pia Görrisen2, Johanna Geritz2, Clint Hansen2, Eran Gazit5, Shahar Siman Tov5, Revital Prusak5, Ilaria Casadei6, Angela Contri6, Francesca Tampellini6, Leonardo Pellicciari6, Giovanna Lopane6, Giovanna Calandra Buonaura6, Luca Palmerini3, Noman Zahid7, Mehwish Mehwish7
and 13 other authorsMayna Ratanapongleka7, Husna Razee7, Frederic von Wegner7,8, Bernadette C. M. van Wijk1,9, Sjoerd M. Bruijn1, Deepak K. Ravi4, Yoshiro Okubo7,8, Navrag B. Singh4,10, Matthew Brodie7, Fabio La Porta6, Jeffrey M. Hausdorff5,11,12, Walter Maetzler2, Jaap H. van Dieën1
ORCID iDs: Anina Moira van Leeuwen, Ilaria D’Ascanio, Vaishali Vinod, Angela Contri, Bernadette C. M. van Wijk, Sjoerd M. Bruijn, Navrag B. Singh, Jaap H. van Dieën
  1. Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
  2. Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
  3. Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
  4. Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
  5. Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel
  6. IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
  7. University of New South Wales, Sydney, Australia
  8. Neuroscience Research Australia, Sydney, Australia
  9. Department of Neurology, Amsterdam University Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, the Netherlands
  10. Future Health Technologies Programme, Singapore-ETH Centre, Singapore
  11. Department of Physical Therapy, Gray Faculty of Medical & Health Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
  12. Rush Alzheimer’s Disease Center, Rush University Medical Center and Department of Orthopaedic Surgery, Rush Medical College, Chicago, USA
Institutions: Amsterdam Movement Sciences (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Christian-Albrechts-Universität zu Kiel (Germany); University Hospital Schleswig-Holstein (Germany); University of Bologna (Italy); ETH Zurich (Switzerland); Institute for Biomechanics (Switzerland); Tel Aviv Sourasky Medical Center (Israel); Istituto delle Scienze Neurologiche di Bologna (Italy); UNSW Sydney (Australia); Neuroscience Research Australia (Australia); Amsterdam Neuroscience (Netherlands); Amsterdam University Medical Centers (Netherlands); University of Amsterdam (Netherlands); Singapore-ETH Centre (Singapore); Rush University Medical Center (United States); Tel Aviv University (Israel)
Journal: PloS one, volume 21, issue 6, article e0348957
Dates: received 13 May 2026; accepted 14 May 2026; published online 25 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0348957 · PMID 42348515 · PMCID PMC13298929 · OpenAlex W7165950261
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), Parkinson's (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Source localization, Physiology & signal measures
MeSH: Exercise Therapy*, Parkinson Disease*, Aged, Biomechanical Phenomena, Electroencephalography, Electromyography, Exercise Test, Female, Gait, Humans, Male, Middle Aged, Randomized Controlled Trials as Topic (* major topic)
Journal subjects: Biology and Life Sciences, Physiology, Biological Locomotion, Walking, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Electrophysiology, Neurophysiology, Neuroscience, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Anatomy, Body Limbs, Legs, Feet, Gait Analysis, Muscle Electrophysiology, Electromyography, Medical Conditions, Neurodegenerative Diseases, Movement Disorders, Parkinson Disease, Neurology, Biomechanics, Physical Sciences, Physics, Classical Mechanics, Kinematics
Topic: Balance, Gait, and Falls Prevention (Physical Therapy, Sports Therapy and Rehabilitation, Health Professions), according to OpenAlex
Funding: eu joint programme – neurodegenerative disease research (JPND2022-128); ZonMw (‭10510062210002‬); Italian Ministry of Health (ERP-2022-23682564 - ERP-2022-JPND 22-StepuP); DLR Projektträger (01ED2308); Department of Health | National Health and Medical Research Council (NHMRC) (2022885); Israel Ministry of Health (3-0000-18894); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (10ND14_213457)
Citations: cited by 1 paper (Europe PMC); 90 references in the paper

Abstract

Objective: Parkinson’s disease can impair gait and stability, leading to reduced independence and increased fall risk. While speed dependent treadmill training (SDTT) is clinically effective, the specific biomechanical and neurophysiological mechanisms driving these improvements remain unclear. The “StepuP” multicenter randomized controlled trial aims to elucidate these mechanisms and determine whether training enriched with virtual reality or mechanical perturbations (SDTT+) enhances gait efficacy and transfer to daily life.

Methods: We will recruit 126 individuals with Parkinson’s disease across four clinical sites and 21 healthy older adults as a reference group. Participants will be randomized to receive either standard SDTT or SDTT+ for 12 sessions. To capture the trajectory of recovery and retention, assessments will occur at three distinct timepoints: baseline, post-intervention, and a 12-week follow-up, each assessment including synchronized 64-channel electroencephalography (EEG), electromyography (EMG), and 3D kinematics. This multimodal setup allows for the quantification of cortical beta-band activity, corticomuscular coherence, and stability-related foot placement control. Furthermore, we will assess participant’s satisfaction, usability, and engagement through questionnaires and interviews to understand individual adherence and barriers to training.

Significance: The primary clinical endpoint is comfortable overground walking speed. We hypothesize that gait improvements are mediated by improved stability-related foot placement and cortical sensorimotor integration. By correlating lab-based mechanistic changes with real-world mobility patterns and participant experiences, this study seeks to identify specific pathophysiological mechanisms engaged during the treadmill training. These insights will help distinguish responders from non-responders, facilitating the development of personalized, acceptable, and effective rehabilitation strategies.

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.

juliuswelzel/stepup_setup

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3ee7165660bfae8c22db820f57892498919fb53c, 26 June 2025
Languages: Python (43), C++ (1)
Size: 161 files, 44 scripts
Software Heritage: not archived
Found in: the text, “Synchronization procedure of multimodal recordin”
Holds: README, environment (pyproject.toml, sync_scripts/LSLDelsysGUI/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (15 files), Matplotlib (11 files), MNE-Python (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
45 files
At the source: github.com/JuliusWelzel/StepuP_setup/tree/main/sync_scripts

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 44 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);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

Deidentified research data will be made publicly available when the study is completed and published. Data will be published open access for further use of the dataset beyond the goals of the StepuP project on OpenNeuro and the Michael J. Fox Foundation (MJFF) database.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 33 authors, 13 MeSH terms, 7 funders, 87 references.

Cite

This paper

van Leeuwen, A. M., Welzel, J., D’Ascanio, I., Lang, C., Vinod, V., Görrisen, P., Geritz, J., Hansen, C., Gazit, E., Tov, S. S., Prusak, R., Casadei, I., Contri, A., Tampellini, F., Pellicciari, L., Lopane, G., Buonaura, G. C., Palmerini, L., Zahid, N., . . . van Dieën, J. H. (2026). Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention. PloS one, 21(6), e0348957. https://doi.org/10.1371/journal.pone.0348957

BibTeX

@article{vanleeuwen2026steps,
author = {van Leeuwen, Anina Moira and Welzel, Julius and D’Ascanio, Ilaria and Lang, Charlotte and Vinod, Vaishali and Görrisen, Pia and Geritz, Johanna and Hansen, Clint and Gazit, Eran and Tov, Shahar Siman and Prusak, Revital and Casadei, Ilaria and Contri, Angela and Tampellini, Francesca and Pellicciari, Leonardo and Lopane, Giovanna and Buonaura, Giovanna Calandra and Palmerini, Luca and Zahid, Noman and Mehwish, Mehwish and Ratanapongleka, Mayna and Razee, Husna and von Wegner, Frederic and van Wijk, Bernadette C. M. and Bruijn, Sjoerd M. and Ravi, Deepak K. and Okubo, Yoshiro and Singh, Navrag B. and Brodie, Matthew and La Porta, Fabio and Hausdorff, Jeffrey M. and Maetzler, Walter and van Dieën, Jaap H.},
title = {{Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention}},
journal = {PloS one},
year = {2026},
month = jun,
volume = {21},
number = {6},
pages = {e0348957},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0348957},
url = {https://doi.org/10.1371/journal.pone.0348957},
pmid = {42348515},
pmcid = {PMC13298929}
}

RIS

TY - JOUR
AU - van Leeuwen, Anina Moira
AU - Welzel, Julius
AU - D’Ascanio, Ilaria
AU - Lang, Charlotte
AU - Vinod, Vaishali
AU - Görrisen, Pia
AU - Geritz, Johanna
AU - Hansen, Clint
AU - Gazit, Eran
AU - Tov, Shahar Siman
AU - Prusak, Revital
AU - Casadei, Ilaria
AU - Contri, Angela
AU - Tampellini, Francesca
AU - Pellicciari, Leonardo
AU - Lopane, Giovanna
AU - Buonaura, Giovanna Calandra
AU - Palmerini, Luca
AU - Zahid, Noman
AU - Mehwish, Mehwish
AU - Ratanapongleka, Mayna
AU - Razee, Husna
AU - von Wegner, Frederic
AU - van Wijk, Bernadette C. M.
AU - Bruijn, Sjoerd M.
AU - Ravi, Deepak K.
AU - Okubo, Yoshiro
AU - Singh, Navrag B.
AU - Brodie, Matthew
AU - La Porta, Fabio
AU - Hausdorff, Jeffrey M.
AU - Maetzler, Walter
AU - van Dieën, Jaap H.
TI - Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/06/25
VL - 21
IS - 6
SP - e0348957
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0348957
UR - https://doi.org/10.1371/journal.pone.0348957
LA - en
ER -

CSL-JSON

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