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Distinct roles of brain network flexibility in motor learning across age.

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Paper

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The authors' code

MATLAB · 83 lines · 3.1 KB · no license

  2. clc;
  3. clear all;
  4. close all;
  6. load("## PATH ## \Fig2_new_data.mat");
  8. % default figure properties
  9. set(0,'DefaultAxesTitleFontWeight','normal');
  10. set(0,'defaultAxesFontSize',12);
  12. %aesthetics
  13. tgtclrs=parula(8);
  14. grpclrs=parula(4);
  15. grpclrs([1 2 3],:) = [grpclrs(1,:); grpclrs(3,:); grpclrs(2,:)];
  16. gray=[0.8 0.8 0.8];
  17. white=[1 1 1];
  18. black_line=[0 0 0];
  20. %% Plot group learning curve plot FIGURE 2A
  21. h(1)=figure; hold on
  22. set(gcf,'PaperPositionMode','auto','units','inches','pos',[5 3 5.5 3.63]);
  24. grpdata(1)=shadedErrorBar(1:length(learningcurve.YA),nanmean(learningcurve.YA,1),nanstderr(learningcurve.YA),{'-','LineWidth',1.5,'color',grpclrs(1,:)},1);
  25. grpdata(2)=shadedErrorBar(1:length(learningcurve.OA),nanmean(learningcurve.OA,1),nanstderr(learningcurve.OA),{'-','LineWidth',1.5,'color',grpclrs(2,:)},1);
  27. ylim([0.8 1.8]);
  28. xline(11,'--k');
  29. xline(30,'--k');
  30. xline(40.5,'--k');
  31. rectangle('position',[30 0.5 10.5 1.5],'facecolor',white,'edgecolor',white);
  33. xlabel('Cycle (5 trials)');
  34. ylabel('Displacement');
  35. legend([grpdata(1).mainLine,grpdata(2).mainLine],...
  36. {'Young','Old'},'location','northeast');
  37. legend('boxoff');
  39. %% Plot Learning rate(Exponaton fitting) FIGURE 2B
  40. h(2)=figure; hold on
  42. young_learning_rate=LearningRate_Aftereffect.learningRate(1:23);
  43. eld_learning_rate=LearningRate_Aftereffect.learningRate(24:end);
  45. set(gcf,'paperpositionmode','auto','units','inches','pos',[5 5 3 3.63])
  46. bar(1,mean(young_learning_rate),'facecolor',gray);
  47. bar(2,mean(eld_learning_rate),'facecolor',gray);
  48. errbar(1-.1,mean(young_learning_rate),nanstderr(young_learning_rate),'k','linewidth',1.5);
  49. errbar(2-.1,mean(eld_learning_rate),nanstderr(eld_learning_rate),'k','linewidth',1.5);
  51. plot(.2*rand(length(young_learning_rate),1)+1,young_learning_rate,'.','markersize',12,'markerfacecolor',grpclrs(1,:),...
  52. 'markeredgecolor',grpclrs(1,:));
  54. plot(.2*rand(length(eld_learning_rate),1)+2,eld_learning_rate,'.','markersize',12,'markerfacecolor',grpclrs(2,:),...
  55. 'markeredgecolor',grpclrs(2,:));
  56. ylim([-0.5 5]);
  57. set(gca,'xtick',[1 2],'xticklabel', {'Young','Old'},'xticklabelrotation',45)
  58. ylabel('Learning rate (Slope)')
  60. %% Plot Aftereffect FIGURE 2C
  61. h(3)=figure; hold on
  63. young_aftereffect=LearningRate_Aftereffect.Aftereffect(1:23);
  64. eld_aftereffect=LearningRate_Aftereffect.Aftereffect(24:end);
  66. set(gcf,'paperpositionmode','auto','units','inches','pos',[5 5 3 3.63])
  67. bar(1,mean(young_aftereffect),'facecolor',gray);
  68. bar(2,mean(eld_aftereffect),'facecolor',gray);
  69. errbar(1-.1,mean(young_aftereffect),nanstderr(young_aftereffect),'k','linewidth',1.5);
  70. errbar(2-.1,mean(eld_aftereffect),nanstderr(eld_aftereffect),'k','linewidth',1.5);
  72. plot(.2*rand(length(young_aftereffect),1)+1,young_aftereffect,'.','markersize',12,'markerfacecolor',grpclrs(1,:),...
  73. 'markeredgecolor',grpclrs(1,:));
  75. plot(.2*rand(length(eld_aftereffect),1)+2,eld_aftereffect,'.','markersize',12,'markerfacecolor',grpclrs(2,:),...
  76. 'markeredgecolor',grpclrs(2,:));
  77. ylim([-1 3]);
  78. set(gca,'xtick',[1 2],'xticklabel', {'Young','Old'},'xticklabelrotation',45)
  79. ylabel('Aftereffect')
  83. %% end

Figure2_new.m, no license · at the source

Overview

Authors: Kazumasa Uehara1,2,3, Makoto Hagihara3,4, Keiichi Kitajo3,4
ORCID iDs: Kazumasa Uehara, Makoto Hagihara, Keiichi Kitajo
  1. Neural Information Dynamics Laboratory, Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan
  2. Human-Centered AgriTech Co-Creation Center (HAC3), Toyohashi University of Technology, Toyohashi, Japan
  3. Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan
  4. Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies, SOKENDAI, Okazaki, Japan
Institutions: Toyohashi University of Technology (Japan); National Institute for Physiological Sciences (Japan); The Graduate University for Advanced Studies, SOKENDAI (Japan)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1359
Dates: received 27 February 2026; accepted 11 August 2026; published online 9 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1359 · PMID 42724499 · PMCID PMC13559766 · OpenAlex W4403854764
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.10.29.620762
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Graphs, Complexity, Physiology & signal measures
Keywords: motor learning, brain network flexibility, electroencephalogram, aging
MeSH: Aging*, Brain*, Learning*, Nerve Net*, Psychomotor Performance*, Adult, Aged, Electroencephalography, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Topic: Muscle activation and electromyography studies (Biomedical Engineering, Engineering), according to OpenAlex
Funding: JSPS KAKENHI (JP19K20103); grant of OML Project by the National Institutes of Natural Sciences (OML032401); Japan Science and Technology Corporation (JPMJPF2502)
Citations: not cited yet (Europe PMC); 123 references in the paper

Abstract

Motor learning is a lifelong process, from infancy through old age. Acquiring new motor actions through repetitive practice requires adjusting motor output in response to sensory input and integrating them to facilitate learning. For this to occur, the central nervous system must flexibly predict and adapt to the dynamic interplay between sensory inputs and motor outputs. Although overall brain function changes with age, it remains unclear how flexible brain networks, reflecting moment-to-moment reconfiguration of large-scale brain networks, influence motor learning ability with aging. To address this, we designed a visuomotor learning task involving both younger and older adults and quantitatively assessed brain network flexibility, leveraging multichannel electroencephalography (EEG) in humans. We found age-group differences in motor learning properties, brain network flexibility, and their neural relationships. In younger adults, the learning aftereffect was associated with brain network flexibility during motor preparation in the learning task. However, this association was not observed in older adults. Together, our findings suggest that brain network flexibility during motor preparation was critical for acquiring and maintaining new motor actions in younger adults, but that this coupling was attenuated with aging.

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

Repository

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

OSF 3fc94

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: MATLAB (5)
Size: 10 files, 5 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), shadedErrorBar (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.17605/OSF.IO/3FC94

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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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 and Code Availability

The source data and code underlying all main figures and Supplementary figures have been deposited in the Open Science Framework (OSF) via the following link https://doi.org/10.17605/OSF.IO/3FC94. Other data, including large raw continuous EEG data and behavior data, are available upon request from the corresponding authors.

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, pages, dates, 3 authors, 4 keywords, 13 MeSH terms, 3 funders, 123 references.

Cite

This paper

Uehara, K., Hagihara, M., & Kitajo, K. (2026). Distinct roles of brain network flexibility in motor learning across age. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1359. https://doi.org/10.1162/imag.a.1359

BibTeX

@article{uehara2026distinct,
author = {Uehara, Kazumasa and Hagihara, Makoto and Kitajo, Keiichi},
title = {{Distinct roles of brain network flexibility in motor learning across age}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = sep,
volume = {4},
pages = {IMAG.a.1359},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1359},
url = {https://doi.org/10.1162/imag.a.1359},
pmid = {42724499},
pmcid = {PMC13559766}
}

RIS

TY - JOUR
AU - Uehara, Kazumasa
AU - Hagihara, Makoto
AU - Kitajo, Keiichi
TI - Distinct roles of brain network flexibility in motor learning across age
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/09/09
VL - 4
SP - IMAG.a.1359
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1359
UR - https://doi.org/10.1162/imag.a.1359
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1359",
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"author": [
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"family": "Uehara",
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}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1359",
"DOI": "10.1162/imag.a.1359",
"PMID": "42724499",
"PMCID": "PMC13559766",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1359",
"language": "en",
"issued": {
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