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Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex.

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Paper

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The authors' code

MATLAB · 146 lines · 4.8 KB · no license

  1. function generate_Fig1_behavior()
  2. % Reproduces Fig. 1 panels g-h from:
  3. % Marc et al., Nature Communications (2026)
  5. % Panel g : Behavioral Accuracy p(R) across symbolic distances (SD1–SD5)
  6. % Panel h : Correct reaction times (Correct RT, ms)
  7. %
  8. % -------------------------------------------------------------------------
  9. % INPUT FILE
  10. % Fig1_Data.mat
  11. % The file contains:
  12. % Fig1.sdLabels
  13. % Labels for SDs (e.g., {'SD1','SD2','SD3','SD4','SD5'})
  15. % Fig1.panelG.data.monkeyC or .monkeyP
  16. % Accuracy values used to generate panel g
  17. % Dimensions: sessions × SD
  19. % Fig1.panelH.data.monkeyC or .monkeyP
  20. % Correct RT (ms) used to generate panel h
  21. % Dimensions: sessions × SD
  23. % Fig1.panelG.stats
  24. % Fig1.panelH.stats
  25. % Pre-computed statistical results reported in the manuscript.
  27. % ===================== External functions =====================
  28. % This code uses external MATLAB functions for visualization:
  29. %
  30. % 1) brewermap (ColorBrewer colormaps):
  31. % https://nl.mathworks.com/matlabcentral/fileexchange/45208-colorbrewer-attractive-and-distinctive-colormaps
  32. %
  33. % 2) boundedline (for shaded error bars):
  34. % https://nl.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m
  35. %
  36. % If these functions are not available, users can replace them with:
  37. % - custom RGB colors
  38. % - standard MATLAB plotting functions (e.g., plot + fill for error shading)
  40. clc
  41. close all
  42. %% Load data
  43. load('Fig1_Data.mat')
  45. sd = 1:length(Fig1.sdLabels);
  46. SD_labels =Fig1.sdLabels;
  48. WithinDesign = table((sd)','VariableNames',{'SD'});
  50. % NOTE: This uses the external function 'brewermap' :
  51. monkeyColors = brewermap(2,'*Dark2');
  53. % Create figure
  54. Fig1gh = figure(1); set(Fig1gh, 'Name', 'Fig.1g-h: Behavioral Performance across SDs', 'NumberTitle', 'off'); clf;
  56. %% Panel g — Accuracy
  57. subplot(1,2,1); hold on
  59. dataC = Fig1.panelG.data.monkeyC;
  60. dataP = Fig1.panelG.data.monkeyP;
  62. % monkey C
  63. meanC = mean(dataC,1);
  64. semC = std(dataC)/sqrt(size(dataC,1));
  66. % NOTE: Requires the external function 'boundedline'.
  67. boundedline(sd,meanC,semC,'Color',monkeyColors(1,:),'alpha','transparency',0.1,'LineWidth',2)
  68. plot(sd,meanC,'s','Color',monkeyColors(1,:),'MarkerFaceColor',monkeyColors(1,:),'MarkerSize',8,'LineWidth',2)
  70. % monkey P
  71. meanP = mean(dataP,1);
  72. semP = std(dataP)/sqrt(size(dataP,1));
  74. boundedline(sd,meanP,semP,'Color',monkeyColors(2,:), 'alpha','transparency',0.1,'LineWidth',2)
  75. plot(sd,meanP,'s','Color',monkeyColors(2,:), 'MarkerFaceColor',monkeyColors(2,:),'MarkerSize',8,'LineWidth',2)
  77. xlabel('|SD|','FontSize',13);
  78. ylabel('Accuracy p(R)','FontSize',13);
  79. xticks(sd); ylim ([0.65 1]); axis square;
  80. title('Accuracy')
  81. legend({'','monkey C','','','monkey P'}, 'Location','best','Box','off');
  83. % --- Statistics (Accuracy): one-way repeated measures ANOVA
  85. % monkeyC
  86. T_C = array2table(dataC,'VariableNames',SD_labels);
  87. rmC = fitrm(T_C,'SD1-SD5 ~ 1','WithinDesign',WithinDesign);
  88. ranova_C = ranova(rmC);
  90. % monkeyP
  91. T_P = array2table(dataP,'VariableNames',SD_labels);
  92. rmP = fitrm(T_P,'SD1-SD5 ~ 1','WithinDesign',WithinDesign);
  93. ranova_P = ranova(rmP);
  95. text(1,0.68,sprintf('monkey C: F = %.2f, p = %.3g',ranova_C.F(1),ranova_C.pValue(1)),'FontSize',10)
  96. text(1,0.66,sprintf('monkey P: F = %.2f, p = %.3g',ranova_P.F(1),ranova_P.pValue(1)),'FontSize',10)
  99. %% Panel h — Correct RT
  100. subplot(1,2,2); hold on
  102. dataC = Fig1.panelH.data.monkeyC;
  103. dataP = Fig1.panelH.data.monkeyP;
  105. % monkey C
  106. meanC = mean(dataC,1);
  107. semC = std(dataC)/sqrt(size(dataC,1));
  109. boundedline(sd,meanC,semC,'Color',monkeyColors(1,:),'alpha','transparency',0.1,'LineWidth',2)
  110. plot(sd,meanC,'s','Color',monkeyColors(1,:),'MarkerFaceColor',monkeyColors(1,:),'MarkerSize',8,'LineWidth',2)
  112. % monkey P
  113. meanP = mean(dataP,1);
  114. semP = std(dataP)/sqrt(size(dataP,1));
  116. boundedline(sd,meanP,semP,'Color',monkeyColors(2,:),'alpha','transparency',0.1,'LineWidth',2)
  117. plot(sd,meanP,'s','Color',monkeyColors(2,:),'MarkerFaceColor',monkeyColors(2,:),'MarkerSize',8,'LineWidth',2)
  119. xlabel('|SD|','FontSize',13);
  120. ylabel('Correct RT (ms)','FontSize',13);
  121. xticks(sd); ylim([300 500]); axis square;
  123. title('Correct RTs')
  124. legend({'','monkey C','','','monkey P'}, 'Location','best','Box','off');
  126. %% --- Statistics (Reaction Time): one-way repeated measures ANOVA
  128. SD_labels = Fig1.sdLabels;
  129. WithinDesign = table((sd)','VariableNames',{'SD'});
  131. % monkeyC
  132. T_C = array2table(dataC,'VariableNames',SD_labels);
  133. rmC = fitrm(T_C,'SD1-SD5 ~ 1','WithinDesign',WithinDesign);
  134. ranova_C = ranova(rmC);
  136. % monkeyP
  137. T_P = array2table(dataP,'VariableNames',SD_labels);
  138. rmP = fitrm(T_P,'SD1-SD5 ~ 1','WithinDesign',WithinDesign);
  139. ranova_P = ranova(rmP);
  141. text(1,315,sprintf('monkey C: F = %.2f, p = %.3g',ranova_C.F(1),ranova_C.pValue(1)),'FontSize',10)
  142. text(1,305,sprintf('monkey P: F = %.2f, p = %.3g',ranova_P.F(1),ranova_P.pValue(1)),'FontSize',10)
  146. end

generate_Fig1_behavior.m, no license · at the source

Overview

Authors: Isabel Beatrice Marc1, Valentina Giuffrida1,2, Surabhi Ramawat1, Giampiero Bardella1, Stefano Ferraina1, Emiliano Brunamonti1
ORCID iDs: Isabel Beatrice Marc, Valentina Giuffrida, Surabhi Ramawat, Giampiero Bardella, Stefano Ferraina, Emiliano Brunamonti
  1. Department of Physiology and Pharmacology, Sapienza University, Rome, Italy
  2. Behavioral Neuroscience PhD Program, Sapienza University, Rome, Italy
Institutions: Sapienza University of Rome (Italy)
Journal: Nature communications, volume 17, issue 1, article 5336
Dates: received 19 May 2025; accepted 25 March 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71724-1 · PMID 41997959 · PMCID PMC13272969 · OpenAlex W7154724230
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), non-human primate (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity
Keywords: Decision
MeSH: Decision Making*, Memory*, Motor Cortex*, Animals, Local Field Potential Measurement, Macaca mulatta, Male (* major topic)
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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OSF 5jexn

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 18 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: boundedline (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/5jexn/

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: OSF 5jexn
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41467-026-71724-1.

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Data

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Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-71724-1.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 1 keyword, 7 MeSH terms, 73 references.

Cite

This paper

Marc, I. B., Giuffrida, V., Ramawat, S., Bardella, G., Ferraina, S., & Brunamonti, E. (2026). Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex. Nature communications, 17(1), 5336. https://doi.org/10.1038/s41467-026-71724-1

BibTeX

@article{marc2026dynamics,
author = {Marc, Isabel Beatrice and Giuffrida, Valentina and Ramawat, Surabhi and Bardella, Giampiero and Ferraina, Stefano and Brunamonti, Emiliano},
title = {{Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5336},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71724-1},
url = {https://doi.org/10.1038/s41467-026-71724-1},
pmid = {41997959},
pmcid = {PMC13272969}
}

RIS

TY - JOUR
AU - Marc, Isabel Beatrice
AU - Giuffrida, Valentina
AU - Ramawat, Surabhi
AU - Bardella, Giampiero
AU - Ferraina, Stefano
AU - Brunamonti, Emiliano
TI - Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/17
VL - 17
IS - 1
SP - 5336
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71724-1
UR - https://doi.org/10.1038/s41467-026-71724-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-71724-1",
"type": "article-journal",
"title": "Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex",
"container-title": "Nature communications",
"author": [
{
"family": "Marc",
"given": "Isabel Beatrice"
},
{
"family": "Giuffrida",
"given": "Valentina"
},
{
"family": "Ramawat",
"given": "Surabhi"
},
{
"family": "Bardella",
"given": "Giampiero"
},
{
"family": "Ferraina",
"given": "Stefano"
},
{
"family": "Brunamonti",
"given": "Emiliano"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5336",
"DOI": "10.1038/s41467-026-71724-1",
"PMID": "41997959",
"PMCID": "PMC13272969",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71724-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
17
]
]
}
}

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