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Cortico-pallidal beta dynamics underlie impaired turning in Parkinson's disease.

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Paper

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The authors' code

MATLAB · 117 lines · 5.4 KB · no license

  1. close all
  2. clear all
  3. clc
  5. %% Testing datasets
  6. % This code has been tested with a set of datasets under
  7. % ~/Box/UCSF-RCS_Test_Datasets_Analysis-rcs-data-Code/../Power/...
  8. % Access to this folder is managed and restricted to UCSF employees under
  9. % (https://ucsf.box.com/s/bolhachjv80rhywa5h0r9peo73mz3003)
  10. %
  11. % This example code can be run with a benchotp dataset that is shared for any user under
  12. % https://ucsf.box.com/s/9bte1t8s4il7rr0ot4egwsae1exl5y7i
  14. %% This code assumes you have run ProcessRCS and/or DEMO_LoadRCS.m
  15. % Select file
  16. [fileName,pathName] = uigetfile('AllDataTables.mat');
  18. % Load file
  19. disp('Loading selected .mat file')
  20. load([pathName fileName])
  22. % Create unified table with selected data streams -- use timeDomain data as
  23. % time base
  24. dataStreams = {timeDomainData, AccelData, PowerData, FFTData, AdaptiveData};
  25. [combinedDataTable] = createCombinedTable(dataStreams,unifiedDerivedTimes,metaData);
  27. %% Example how to compare streamed power from device and 'off line' calculated power, given
  28. % - predefined fftSettings
  29. % - predefined powerSettings
  31. % Comparing 'off line' power data with the RCS streamed for default fft and power settings
  32. if ~isempty(powerSettings)
  34. % Here we get the power calculated 'off-line' using the time domain
  35. % for each power channel that have been streamed
  36. [combinedPowerTable, powerTablesBySetting] = getPowerFromTimeDomain(combinedDataTable,fftSettings, powerSettings, metaData,2);
  37. idxPowerCalc = ~isnan(combinedPowerTable.Power_Band1);
  38. idxPowerRCS = ~isnan(combinedDataTable.Power_Band1);
  40. % plot the result, comparing actual Streamed power with 'off line'
  41. figure, hold on, legend show, set(gca,'FontSize',20)
  42. title('RC+S band power: on-board vs off-line comparison')
  43. plot(combinedDataTable.localTime(idxPowerRCS),...
  44. combinedDataTable.(['Power_Band',num2str(1)])(idxPowerRCS),...
  45. 'Marker','*','MarkerSize',1,'Linewidth',1,...
  46. 'DisplayName',['on-board, band(Hz) = ',powerSettings.powerBands(1).powerBinsInHz{1}])
  48. % plot(combinedPowerTable.localTime(idxPowerCalc),...
  49. % combinedPowerTable.(['Power_Band',num2str(1)])(idxPowerCalc),...
  50. % 'Marker','o','MarkerSize',5,'LineWidth',2,...
  51. % 'DisplayName',['Calculated Power Band, Bins(Hz) = ',powerSettings.powerBands(1).powerBinsInHz{1}])
  53. % Here we calculate equivalent power series from a selected
  54. % time domain channel (1, 2, 3 or 4) for a chosen power band [X,Y]Hz
  55. [newPowerFromTimeDomain, newSettings] = calculateNewPower(combinedDataTable, fftSettings, powerSettings, metaData, 1, [8 12.2]);
  56. idxPowerNewCalc = ~isnan(newPowerFromTimeDomain.calculatedPower);
  58. % plot the result
  59. plot(newPowerFromTimeDomain.localTime(idxPowerNewCalc),...
  60. newPowerFromTimeDomain.calculatedPower(idxPowerNewCalc),...
  61. 'Marker','o','MarkerSize',1,'LineWidth',2,...
  62. 'DisplayName',['off-line, band(Hz) = ',newSettings.powerSettings.powerBands.powerBinsInHz])
  64. ylabel('Power (millivolts^2)')
  65. end
  67. %% Example how to create a power output just based on time domain signal and desired fft and power settings, e.g.
  68. % provided power was not sense and/or streame and/or you want to define
  69. % power band limits given a different fftSize than default used during recording session
  71. % Reset powerSettings to avoid table
  72. powerSettings = [];
  73. newfftSettings = fftSettings;
  74. % take default sampling rate - a must
  75. currentTDsampleRate = fftSettings.TDsampleRates;
  77. % Choose new fft parameters and frequency band between these options
  78. % fft interval: 50 to 50000 ms
  79. % fft size: 64, 256, 1024
  80. % windowLoad ('100% Hann', '50% Hann', '25% Hann')
  81. % freqBand:[0 to samplingRate/2]
  82. newfftSettings.fftConfig.interval = 50;
  83. newfftSettings.fftConfig.size = 256;
  84. newfftSettings.fftConfig.windowLoad = '100% Hann';
  85. freqBand = [20, 23];
  87. % Determine fftBins
  88. numBins = newfftSettings.fftConfig.size/2;
  89. binWidth = (currentTDsampleRate/2)/numBins;
  90. lowerBins = (0:numBins-1)*binWidth;
  91. fftBins = lowerBins + binWidth/2; % Bin center
  93. % Create a powerSettings structure based on chosen parameters
  94. powerSettings.fftConfig.interval = newfftSettings.fftConfig.interval;
  95. powerSettings.fftConfig.size = newfftSettings.fftConfig.size;
  96. powerSettings.powerBands.fftBins = fftBins;
  98. % Determine indeces of frquency bins corresponsing and add to power settings structure
  99. idxBinsA = find(powerSettings.powerBands.fftBins>freqBand(1));
  100. idxBinsB = find(powerSettings.powerBands.fftBins<freqBand(2));
  101. powerSettings.powerBands.indices_BandStart_BandStop(1,1) = idxBinsA(1);
  102. powerSettings.powerBands.indices_BandStart_BandStop(1,2) = idxBinsB(end);
  104. % Calculate equivalent device power given the new fft and power settings
  105. [newPower, newSettings] = calculateNewPower(combinedDataTable, newfftSettings, powerSettings, metaData, 1, freqBand);
  106. idxPowerNewCalc = ~isnan(newPower.calculatedPower);
  108. % Plot the results
  109. % figure, hold on, legend show, set(gca,'FontSize',15)
  110. plot(newPower.localTime(idxPowerNewCalc),...
  111. newPower.calculatedPower(idxPowerNewCalc),...
  112. 'Marker','s','MarkerSize',1,'LineWidth',2,...
  113. 'DisplayName',['off-line, band(Hz) = ',newSettings.powerSettings.powerBands.powerBinsInHz])
  115. % END: remember this are only examples of use
  116. % If you find errors while using this code or want to help further develop
  117. % it, feel free to contact [email hidden] or [email hidden]

DEMO_CalculatePowerRCS.m at commit e04baae, no license · at the source

Overview

Authors: Poojan D. Shukla1, Jessica E. Bath2, Kenneth H. Louie1, Hamid Fekri Azgomi1, Rithvik Ramesh1, Eleni Patelaki1, Jacob H. Marks1, Jannine P. Balakid1, Doris D. Wang1
  1. Department of Neurological Surgery, University of California,San Francisco, CA USA
  2. Department of Physical Therapy and Rehabilitation Science, University of California,San Francisco, CA USA
Institutions: University of California, San Francisco (United States)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 219
Dates: received 12 December 2025; accepted 26 May 2026; published online 6 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01421-9 · PMID 42251056 · PMCID PMC13575165 · OpenAlex W4416754096
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.25.25340849
Status: code verified
Categories: Parkinson's (population), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Physiology & signal measures
Keywords: Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: NIH (R01NS130183); Michael J. Fox Foundation for Parkinson’s Research (18649); Burroughs Wellcome Fund; UCSF Catalyst Grant; Tianqiao and Chrissy Chen Institute
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Turning while walking is one of the most common yet complex human movements, and it is frequently impaired in Parkinson’s disease (PD), leading to falls and loss of independence. The neural mechanisms underlying this difficulty remain unclear. Using chronically implanted devices that simultaneously record and stimulate the brain, we repeatedly measured activity in the motor cortex and basal ganglia of five individuals with PD during natural walking and turning. Successful turns were marked by reduced beta-band activity and flexible communication between cortical and pallidal regions, whereas impaired turns showed excessive beta synchrony that rigidly constrained movement. Medication and deep brain stimulation improved turning through distinct circuit mechanisms, dopamine suppressing abnormal pallidal beta activity and its communication with the cortex, and stimulation restoring cortical flexibility. These findings reveal how dynamic cortical–basal ganglia interactions may enable complex movement and establish circuit targets for adaptive brain stimulation to reduce falls in PD.

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

Repository

Its files are read in the Code ↔ Paper reader above.

openmind-consortium/Analysis-rcs-data

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e04baae07f56f73d265daa1d0f1b887607930dd2, 22 January 2026
Languages: MATLAB (129), C (21), C/C++ (5), C++ (1)
Size: 265 files, 156 scripts
Software Heritage: not archived
Found in: the text, “Experimental setup, data collection, and data al”
Holds: README, tests, documentation
Not found: license file, CITATION.cff, environment file, continuous integration
Tools: Signal Processing Toolbox (2 files), Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
157 files
At the source: github.com/openmind-consortium/Analysis-rcs-data

Code availability

The MATLAB and Python scripts used in this study can be made available upon reasonable request to the corresponding author.

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

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;
  • 156 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

The data, MATLAB, and Python scripts used in this study can be made available upon reasonable request to the corresponding author. All patient confidentiality and disclosure standards must be adhered to.

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, 9 authors, 2 keywords, 5 funders, 70 references.

Cite

This paper

Shukla, P. D., Bath, J. E., Louie, K. H., Fekri Azgomi, H., Ramesh, R., Patelaki, E., Marks, J. H., Balakid, J. P., & Wang, D. D. (2026). Cortico-pallidal beta dynamics underlie impaired turning in Parkinson's disease. NPJ Parkinson's disease, 12(1), 219. https://doi.org/10.1038/s41531-026-01421-9

BibTeX

@article{shukla2026cortico,
author = {Shukla, Poojan D. and Bath, Jessica E. and Louie, Kenneth H. and Fekri Azgomi, Hamid and Ramesh, Rithvik and Patelaki, Eleni and Marks, Jacob H. and Balakid, Jannine P. and Wang, Doris D.},
title = {{Cortico-pallidal beta dynamics underlie impaired turning in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = jun,
volume = {12},
number = {1},
pages = {219},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01421-9},
url = {https://doi.org/10.1038/s41531-026-01421-9},
pmid = {42251056},
pmcid = {PMC13575165}
}

RIS

TY - JOUR
AU - Shukla, Poojan D.
AU - Bath, Jessica E.
AU - Louie, Kenneth H.
AU - Fekri Azgomi, Hamid
AU - Ramesh, Rithvik
AU - Patelaki, Eleni
AU - Marks, Jacob H.
AU - Balakid, Jannine P.
AU - Wang, Doris D.
TI - Cortico-pallidal beta dynamics underlie impaired turning in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/06/06
VL - 12
IS - 1
SP - 219
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01421-9
UR - https://doi.org/10.1038/s41531-026-01421-9
LA - en
ER -

CSL-JSON

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"container-title": "NPJ Parkinson's disease",
"author": [
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"family": "Shukla",
"given": "Poojan D."
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