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Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization.

Code ↔ Paper

8 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 8 matches
  1. [1] § Results › A two-phase adaptation is observed in the EMG activity of individual muscles ↔ Codes/Figure6.m, lines 56–160 · score 0.83 · EDC pre, EDC post, FDS pre, FDS post, EMG profile, Cross correlation
  2. [2] § Methods › Data analysis › Joint kinematics analysis ↔ Codes/Figure13.m, lines 1–19 · score 0.78 · kinematic coupling, MCP angle, joint angles, wrist angle, refinement, tenodesis
  3. [3] § Results › Distinct neural implementations of a compensatory tenodesis strategy ↔ Codes/Figure13.m, lines 1–19 · score 0.62 · MCP angle, wrist angle, coupling, refinement, aggregated, tenodesis
  4. [4] § Results › Adaptation occurs through modulating the activation of stable muscle synergies ↔ Codes/Figure10.m, lines 1–16 · score 0.54 · Top row, Bottom row, cross correlated, post surgery, flexor, extensor
  5. [5] § Results › Adaptation occurs through modulating the activation of stable muscle synergies ↔ Codes/Figure9.m, lines 1–16 · score 0.54 · Top row, Bottom row, Cross correlations, post surgery, flexor, extensor
  6. [6] § Results › Adaptation occurs through modulating the activation of stable muscle synergies ↔ Codes/Figure9.m, lines 1–16 · score 0.53 · Top row, Bottom row, cross correlated, post surgery, flexor, extensor
  7. [7] § Results › Adaptation occurs through modulating the activation of stable muscle synergies ↔ Codes/Figure10.m, lines 1–16 · score 0.53 · Top row, Bottom row, Cross correlations, post surgery, flexor, extensor
  8. [8] § Results › Adaptation occurs through modulating the activation of stable muscle synergies ↔ Codes/Figure8.m, lines 1–14 · score 0.52 · spatial weights, Temporal activation, secondary Synergies, Cosine, Figure 8

Paper

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

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The authors' code

MATLAB · 226 lines · 7.6 KB · MIT · 2 matches

  1. % =========================================================================
  2. % SCRIPT: Figure13.m (Tenodesis Coupling)
  3. %
  4. % PURPOSE:
  5. % Generates Figure 13: Tenodesis Coupling Analysis (Wrist vs MCP Angles).
  6. % Analyze kinematic coupling refinement in Monkey B over 11 sessions.
  7. %
  8. % LAYOUT (AllDays Option):
  9. % - Facet Grid: 11 individual day plots arranged in rows.
  10. % - Combined Plot: One large plot aggregating all days (Bottom Right).
  11. %
  12. % INPUT FILES:
  13. % - MP-joint_angles.xlsx (MCP Data)
  14. % - MP-wrist_angles.xlsx (Wrist Data)
  15. %
  16. % AUTHOR: Roland Philipp
  17. % =========================================================================
  19. clear; clc; close all;
  21. %% 1. CONFIGURATION & PATHS
  22. % -------------------------------------------------------------------------
  23. % --- DYNAMIC PATH SETUP ---
  24. scriptPath = fileparts(mfilename('fullpath'));
  25. if isempty(scriptPath), scriptPath = pwd; end % Fallback for running sections
  26. baseDir = fileparts(scriptPath);
  28. fprintf('Detected Base Directory: %s\n', baseDir);
  30. % Input Directory
  31. dataDir = fullfile(baseDir, 'Data', 'kinematics');
  32. fileMCP = 'MP-joint_angles.xlsx';
  33. fileWrist = 'MP-wrist_angles.xlsx';
  35. % Output Directory
  36. outFigDir = fullfile(baseDir, 'outputFigures_Fig13');
  37. if ~exist(outFigDir, 'dir'), mkdir(outFigDir); end
  39. % Verify Data
  40. if ~exist(dataDir, 'dir')
  41. error('Data folder not found at: %s\n(Did you download the ''Data'' folder from GitHub?)', dataDir);
  42. end
  44. % Analysis Settings
  45. plotOption = 'AllDays'; % 'LandmarkDays' or 'AllDays'
  46. saveFig = true;
  48. % Day Mappings
  49. allDayLabels = [-4, 22, 36, 44, 49, 50, 55, 56, 58, 62, 63];
  50. colors = parula(length(allDayLabels));
  52. %% 2. DATA LOADING
  53. % -------------------------------------------------------------------------
  54. fprintf('Loading Kinematic Data...\n');
  55. try
  56. mcpData = readmatrix(fullfile(dataDir, fileMCP));
  57. wristData = readmatrix(fullfile(dataDir, fileWrist));
  58. catch ME
  59. error('Data load failed: %s\nCheck paths in %s', ME.message, dataDir);
  60. end
  62. % Check Dimensions (Expect 20 trials x 11 days)
  63. if ~isequal(size(mcpData), [20, 11])
  64. warning('Unexpected data dimensions. Expected 20x11, got %dx%d.', size(mcpData));
  65. end
  67. %% 3. SETUP PLOT LAYOUT
  68. % -------------------------------------------------------------------------
  69. if strcmp(plotOption, 'AllDays')
  70. dayIndices = 1:11;
  71. nPlots = 11;
  72. % Layout: 3 Rows x 5 Cols.
  73. % Rows 1-2 fill normally. Row 3 fills 3 slots. Combined plot takes 2x2 at bottom right.
  74. nRows = 3; nCols = 5;
  75. combIdx = [9, 10, 14, 15]; % Combined plot spans these subplot indices
  77. else % LandmarkDays
  78. dayIndices = [1, 2, 3, 4, 5, 11];
  79. nPlots = 6;
  80. nRows = 2; nCols = 3;
  81. combIdx = [];
  82. end
  84. % Determine Global Axis Limits (for consistent scaling)
  85. selWrist = wristData(:, dayIndices);
  86. selMCP = mcpData(:, dayIndices);
  88. wRange = range(selWrist(:)); mRange = range(selMCP(:));
  89. limWrist = [min(selWrist(:)) - 0.1*wRange, max(selWrist(:)) + 0.1*wRange];
  90. limMCP = [min(selMCP(:)) - 0.1*mRange, max(selMCP(:)) + 0.1*mRange];
  92. %% 4. GENERATE FIGURE
  93. % -------------------------------------------------------------------------
  94. fig = figure('Name', ['Tenodesis Coupling: ' plotOption], 'Color', 'w', 'WindowState', 'maximized');
  95. sgtitle(['Refinement of Tenodesis Coupling (' plotOption ')'], 'FontSize', 14, 'FontWeight', 'bold');
  97. % --- A. Plot Individual Days (Facets) ---
  98. for i = 1:nPlots
  99. colIdx = dayIndices(i);
  101. % Determine Subplot Position (Custom 5+3+3 logic for AllDays)
  102. if strcmp(plotOption, 'AllDays')
  103. if i <= 5, spIdx = i; % Row 1
  104. elseif i <= 8, spIdx = i; % Row 2 (Left)
  105. else, spIdx = i + 2; % Row 3 (Left, skip combined slots)
  106. end
  107. else
  108. spIdx = i;
  109. end
  111. ax = subplot(nRows, nCols, spIdx);
  113. % Call Helper to Plot Scatter & Fit
  114. plotDayFit(ax, wristData(:, colIdx), mcpData(:, colIdx), ...
  115. colors(i,:), allDayLabels(i), limWrist, limMCP);
  116. end
  118. % --- B. Plot Combined Summary (Bottom Right) ---
  119. if strcmp(plotOption, 'AllDays') && ~isempty(combIdx)
  120. axComb = subplot(nRows, nCols, combIdx);
  121. hold(axComb, 'on');
  123. for i = 1:nPlots
  124. scatter(axComb, wristData(:, dayIndices(i)), mcpData(:, dayIndices(i)), ...
  125. 30, colors(i,:), 'filled', 'MarkerFaceAlpha', 0.6);
  126. end
  128. title(axComb, 'All Days Combined', 'FontSize', 11);
  129. xlabel(axComb, 'Wrist Angle (deg)', 'FontSize', 9);
  130. ylabel(axComb, 'MCP Angle (deg)', 'FontSize', 9);
  131. xlim(axComb, limWrist); ylim(axComb, limMCP);
  132. grid(axComb, 'off'); set(axComb, 'TickDir', 'out', 'FontSize', 9);
  134. % Colorbar Setup
  135. colormap(axComb, colors);
  136. cb = colorbar(axComb);
  137. cb.Label.String = 'Days Relative to Surgery';
  138. cb.Ticks = linspace(1/(2*nPlots), 1 - 1/(2*nPlots), nPlots);
  139. cb.TickLabels = allDayLabels(dayIndices);
  140. cb.FontSize = 9;
  141. end
  143. %% 5. SAVE FIGURE (Updated for SVG Compatibility)
  144. % -------------------------------------------------------------------------
  145. if saveFig
  146. fNameBase = fullfile(outFigDir, ['Tenodesis_' plotOption]);
  148. % A4 Dimensions (cm)
  149. A4_W = 29.7; A4_H = 21.0; Margin = 1.5;
  151. set(fig, 'PaperUnits', 'centimeters', 'PaperOrientation', 'landscape');
  152. set(fig, 'PaperSize', [A4_W, A4_H]);
  154. % Calc optimal position keeping screen aspect ratio
  155. scrPos = get(fig, 'Position'); % Pixels
  156. aspRatio = scrPos(3) / scrPos(4);
  158. availW = A4_W - 2*Margin;
  159. availH = A4_H - 2*Margin;
  161. if (availW / availH) > aspRatio
  162. figH = availH; figW = figH * aspRatio;
  163. else
  164. figW = availW; figH = figW / aspRatio;
  165. end
  167. leftPos = (A4_W - figW) / 2;
  168. botPos = (A4_H - figH) / 2;
  170. set(fig, 'PaperPosition', [leftPos, botPos, figW, figH]);
  172. % Save .fig (Editable)
  173. saveas(fig, [fNameBase '.fig']);
  175. % Save .png (High Res Raster)
  176. exportgraphics(fig, [fNameBase '.png'], 'Resolution', 300);
  178. % Save Vector (Robust Method)
  179. try
  180. print(fig, [fNameBase '.svg'], '-dsvg', '-painters');
  181. fprintf('Saved SVG: %s\n', [fNameBase '.svg']);
  182. catch
  183. exportgraphics(fig, [fNameBase '.pdf'], 'ContentType', 'vector');
  184. fprintf('SVG failed. Saved PDF instead: %s\n', [fNameBase '.pdf']);
  185. end
  187. fprintf('Figure saved to: %s\n', outFigDir);
  188. end
  190. %% ========================================================================
  191. % HELPER FUNCTIONS
  192. % =========================================================================
  193. function plotDayFit(ax, xData, yData, col, dayLabel, xLim, yLim)
  194. % Plots Scatter points and Linear Regression line
  196. scatter(ax, xData, yData, 50, col, 'filled', 'MarkerFaceAlpha', 0.7);
  197. hold(ax, 'on');
  199. % Linear Regression (Fit)
  200. [p, ~] = polyfit(xData, yData, 1);
  201. xFit = linspace(min(xData), max(xData), 10);
  202. yFit = polyval(p, xFit);
  204. plot(ax, xFit, yFit, 'k-', 'LineWidth', 1.5);
  206. % Calc R-squared
  207. corrMat = corrcoef(xData, yData);
  208. if numel(corrMat) > 1 && ~any(isnan(corrMat(:)))
  209. r2 = corrMat(1,2)^2;
  210. else
  211. r2 = NaN;
  212. end
  214. % Formatting
  215. title(ax, sprintf('Day %d', dayLabel), 'FontSize', 11);
  216. xlabel(ax, 'Wrist (deg)', 'FontSize', 9);
  217. ylabel(ax, 'MCP (deg)', 'FontSize', 9);
  218. xlim(ax, xLim); ylim(ax, yLim);
  220. text(ax, 0.05, 0.9, sprintf('R^2 = %.2f', r2), ...
  221. 'Units', 'normalized', 'FontSize', 9, 'FontWeight', 'bold');
  223. grid(ax, 'off');
  224. set(ax, 'FontSize', 9, 'TickDir', 'out');
  225. hold(ax, 'off');
  226. end

Figure13.m at commit e881e84, under MIT · at the source

Overview

Authors: Roland Philipp1,2, Yuki Hara3, Naohito Ohta1,2, Naoki Uchida1,2, Tomomichi Oya1,4, Tetsuro Funato2, Kazuhiko Seki1
ORCID iDs: Roland Philipp, Yuki Hara, Tetsuro Funato, Kazuhiko Seki
  1. National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan
  2. University of ElectroCommunications, Graduate School of Informatics and Engineering, Department of Mechanical and Intelligent Systems Engineering, Tokyo, Japan
  3. National Center of Neurology and Psychiatry, Department of Orthopaedic Surgery, Tokyo, Japan
  4. Western Institute for Neuroscience, University of Western Ontario, London, Canada
Institutions: University of Electro-Communications (Japan); National Center of Neurology and Psychiatry (Japan); Western University (Canada)
Journal: eLife, volume 14, article RP108684
Dates: published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108684 · PMID 42318877 · PMCID PMC13282115 · OpenAlex W4415935311
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Spectral & time-frequency, Statistics, Machine learning, Physiology & signal measures
Keywords: Other
MeSH: Adaptation, Physiological*, Central Nervous System*, Muscle, Skeletal*, Animals, Fingers, Hand Strength, Macaca, Movement, Tendons (* major topic)
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (19H05724, 23H05488, 26250013, 15K21754, 26120003, 24K21313, 19H01092); National Science Foundation (2113096); Japan Agency for Medical Research and Development (JP24gm0010009)
Citations: not cited yet (Europe PMC); 85 references in the paper
Research resources: MATLAB RRID:SCR_001622, DeepLabCut RRID:SCR_021391, DS8R RRID:SCR_024845

Abstract

The central nervous system (CNS) can effectively control body movements despite environmental changes. While much is known about adaptation to external environmental changes, less is known about responses to internal bodily changes. This study investigates how the CNS adapts to long-term alterations in the musculoskeletal system using a tendon transfer model in nonhuman primates (Macaca fuscata). We surgically relocated finger flexor and extensor muscles to examine how the CNS adapts its strategy for finger movement control by measuring muscle activities during grasping tasks. Two months post-surgery, the monkeys demonstrated significant recovery of grasping function despite the initial disruption. Our findings suggest a two-phase CNS adaptation process: an initial phase enabling function with the transferred muscles, followed by a later phase abandoning this enabled function and restoring a control strategy that, while potentially less conflicted than the maladaptive state, resembled the original pattern, possibly representing a ‘good enough’ solution. These results highlight a multi-phase CNS adaptation process with distinct time constants in response to sudden bodily changes, offering potential insights into understanding and treating movement disorders.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

animalmodel/Philipp_eLife_2025

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e881e84137e7888d44db129aba12492575c9f659, 16 June 2026
Languages: MATLAB (20)
Size: 1,153 files, 20 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files), Signal Processing Toolbox (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
22 files
At the source: github.com/animalmodel/Philipp_eLife_2025

Zenodo 18030926

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.18030926

The paper's code and data availability statement is in the Data section.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 8 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

All data and custom MATLAB code used to generate the figures in this study are available at the GitHub repository: https://github.com/animalmodel/Philipp_eLife_2025 (copy archived at Philipp and Kosugi, 2026). For long-term preservation, the code and dataset (including large EMG and Synergy matrices) have been archived at Zenodo (DOI: https://doi.org/10.5281/zenodo.18030926).

The following dataset was generated:

Philipp R, Kosugi AK. 2026. animalmodel/Philipp_eLife_2025: Code and data completion. Zenodo.

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, pages, dates, 7 authors, 1 keyword, 9 MeSH terms, 3 funders, 83 references, 3 RRIDs.

Cite

This paper

Philipp, R., Hara, Y., Ohta, N., Uchida, N., Oya, T., Funato, T., & Seki, K. (2026). Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization. eLife, 14, RP108684. https://doi.org/10.7554/elife.108684

BibTeX

@article{philipp2026multi,
author = {Philipp, Roland and Hara, Yuki and Ohta, Naohito and Uchida, Naoki and Oya, Tomomichi and Funato, Tetsuro and Seki, Kazuhiko},
title = {{Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization}},
journal = {eLife},
year = {2026},
month = jun,
volume = {14},
pages = {RP108684},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108684},
url = {https://doi.org/10.7554/elife.108684},
pmid = {42318877},
pmcid = {PMC13282115}
}

RIS

TY - JOUR
AU - Philipp, Roland
AU - Hara, Yuki
AU - Ohta, Naohito
AU - Uchida, Naoki
AU - Oya, Tomomichi
AU - Funato, Tetsuro
AU - Seki, Kazuhiko
TI - Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/06/19
VL - 14
SP - RP108684
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108684
UR - https://doi.org/10.7554/elife.108684
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.108684",
"type": "article-journal",
"title": "Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization",
"container-title": "eLife",
"author": [
{
"family": "Philipp",
"given": "Roland"
},
{
"family": "Hara",
"given": "Yuki"
},
{
"family": "Ohta",
"given": "Naohito"
},
{
"family": "Uchida",
"given": "Naoki"
},
{
"family": "Oya",
"given": "Tomomichi"
},
{
"family": "Funato",
"given": "Tetsuro"
},
{
"family": "Seki",
"given": "Kazuhiko"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP108684",
"DOI": "10.7554/elife.108684",
"PMID": "42318877",
"PMCID": "PMC13282115",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.108684",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
19
]
]
}
}

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