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An evolutionary conserved neural mechanism for interpersonal coordination in primates.

Code ↔ Paper

10 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 10 matches · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Monkey electrophysiological recordings and data analysis › Neural recording ↔ Monkey_Scripts.zip/Fig4C_LFP_PowerSpectra_acrossConditions.m, the whole file · a weak match · score 0.97 · post hoc, 13–30 Hz, 31–60 Hz, 61–100 Hz, 8–12 Hz, High Gamma
  2. [2] § MATERIALS AND METHODS › Monkey electrophysiological recordings and data analysis › Neural recording ↔ Monkey_Scripts.zip/Fig5A_LFP_PowerSpectra_acrossCoordLevels.m, the whole file · a weak match · score 0.97 · post hoc, 13–30 Hz, 31–60 Hz, 61–100 Hz, 8–12 Hz, High Gamma
  3. [3] § RESULTS › A neural mechanism for interpersonal coordination ↔ Monkey_Scripts.zip/Fig4C_LFP_PowerSpectra_acrossConditions.m, the whole file · a weak match · score 0.90 · 13–30 Hz, 31–60 Hz, 61–100 Hz, 8–12 Hz, high Gamma, low Gamma
  4. [4] § RESULTS › A neural mechanism for interpersonal coordination ↔ Monkey_Scripts.zip/Fig5A_LFP_PowerSpectra_acrossCoordLevels.m, the whole file · a weak match · score 0.89 · 13–30 Hz, 31–60 Hz, 61–100 Hz, 8–12 Hz, high Gamma, low Gamma
  5. [5] § MATERIALS AND METHODS › Monkey electrophysiological recordings and data analysis › Neural recording ↔ Monkey_Scripts.zip/Fig4E_5C_LFP_Decoding.m, lines 84–127 · score 0.83 · randomly shuffled, ms bins, cross validation, LIBSVM, splits, trained
  6. [6] § MATERIALS AND METHODS › Behavioral data analysis (humans and monkeys) › Behavioral analysis #3: Behavioral adjustments leading to better interpersonal coordination in JOINT action ↔ Human_Scripts.zip/Fig2Ba_Bb_Bc_Regressions_Human.R, the whole file · a weak match · score 0.65 · mixed models, random slope, random intercept, Regressions, variables, humans
  7. [7] § MATERIALS AND METHODS › Behavioral data analysis (humans and monkeys) › Behavioral analysis #3: Behavioral adjustments leading to better interpersonal coordination in JOINT action ↔ Monkey_Scripts.zip/Fig2Bd_Be_Bf_Monkeys_Regressions.R, the whole file · a weak match · score 0.65 · mixed models, random slope, random intercept, Regressions, variables, monkeys
  8. [8] § MATERIALS AND METHODS › Behavioral data analysis (humans and monkeys) › Behavioral analysis #2: Behavioral adjustments in JOINT action ↔ Monkey_Scripts.zip/Fig2Bd_Be_Bf_Monkeys_Regressions.R, the whole file · a weak match · score 0.63 · mixed models, random slope, random intercept, variables, monkeys, behavioral
  9. [9] § MATERIALS AND METHODS › Behavioral data analysis (humans and monkeys) › Behavioral analysis #2: Behavioral adjustments in JOINT action ↔ Human_Scripts.zip/Fig2Ba_Bb_Bc_Regressions_Human.R, the whole file · a weak match · score 0.63 · mixed models, random slope, random intercept, variables, monkeys, behavioral
  10. [10] § MATERIALS AND METHODS › Monkey electrophysiological recordings and data analysis › Neural recording ↔ Monkey_Scripts.zip/Fig4B_LFP_TimeFrequency.m, the whole file · a weak match · score 0.55 · complex Morlet wavelets, power, LFP, Figure 4

Paper

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The authors' code

MATLAB · 96 lines · 2.9 KB · CC-BY-4.0 · 2 matches

  1. clear; clc;
  2. load('...\LFPdata_Fig4C')
  3. %%
  5. figure()
  6. set(gcf,'Position',[-1151 411 1145 483])
  7. tiledlayout(1,2)
  8. xTickLabels = {'Delta (1-4 Hz)', 'Theta (4-7 Hz)', 'Alpha (8-12 Hz)', 'Beta (13-30 Hz)', 'low-Gamma (31-60 Hz)', 'high-Gamma (61-100 Hz)'};
  9. xTickValues=1:6;
  11. for mkID=1:2
  13. switch mkID
  14. case 1, mkName='D';
  15. case 2, mkName='P';
  16. end
  17. nxt=nexttile;
  18. for cond=1:3
  20. switch cond
  21. case 1, condName='solo';
  22. case 2, condName='obs';
  23. case 3, condName='ja';
  24. end
  25. fieldMk=['mk',num2str(mkID)];
  27. for bands=1:6
  28. powerDataAllTr=LFP.(condName).(fieldMk).power.(['band',num2str(bands)])(:,:);
  30. yAllTr(bands)=mean(powerDataAllTr,'omitnan');
  31. errAllTr(bands)=std(powerDataAllTr,'omitnan')./sqrt(size(rmmissing(powerDataAllTr),1));
  33. end
  35. if strcmp(condName,'ja')
  36. errorbar(1:6,yAllTr,errAllTr,'.','Color','red')
  37. hold on
  38. plt(1)=plot(1:6, yAllTr, '-','Color','red','LineWidth',2);
  39. elseif strcmp(condName,'solo')
  40. errorbar(1:6,yAllTr,errAllTr,'.','Color','blue')
  41. hold on
  42. plt(1)=plot(1:6, yAllTr, '-','Color','blue');
  43. elseif strcmp(condName,'obs')
  44. errorbar(1:6,yAllTr,errAllTr,'.','Color',[.7 .7 .7])
  45. hold on
  46. plt(1)=plot(1:6, yAllTr, '-','Color',[.7 .7 .7]);
  47. end
  49. xlim([0.5 6.5])
  50. ylim([-2 7])
  51. title(['Mk ',mkName])
  52. xticks(xTickValues);
  53. xticklabels(xTickLabels);
  54. ylabel('Power (db)')
  55. clc
  56. box off;
  58. end
  59. end
  61. for mkID=1:2
  63. fieldMk=['mk',num2str(mkID)];
  65. for bands=1:6
  67. powerDataSolo=LFP.solo.(fieldMk).power.(['band',num2str(bands)])(:,:);
  68. powerDataObs=LFP.obs.(fieldMk).power.(['band',num2str(bands)])(:,:);
  69. powerDataJa=LFP.ja.(fieldMk).power.(['band',num2str(bands)])(:,:);
  71. grpAn = [zeros(1,length(powerDataSolo))';ones(1,length(powerDataObs))';2*ones(1,length(powerDataJa))'];
  72. [p,t,stats,terms]=anovan([powerDataSolo;powerDataObs;powerDataJa],grpAn,'display','Off');
  73. postHoc=multcompare(stats,'display','off',"CriticalValueType","bonferroni");
  75. nexttile(mkID);
  76. hold on
  77. if p<0.05
  79. idx=postHoc(:,6)<=0.05;
  80. yLim=ylim;
  81. for i=1:3
  82. switch i
  83. case 1, if idx(i), text(bands-0.1,yLim(2)+0.1-i/3,'S-O*','FontSize',10); end
  84. case 2
  85. if idx(i)
  86. text(bands-0.1,yLim(2)+0.1-i/3,'S-J*','FontSize',10);
  87. end
  88. case 3, if idx(i), text(bands-0.1,yLim(2)+0.1-i/3,'O-J*','FontSize',10); end
  89. end
  90. end
  91. end
  93. end
  94. end
  96. sgtitle('Power Spectra across Conditions')

Fig4C_LFP_PowerSpectra_acrossConditions.m, under CC-BY-4.0 · at the source

Overview

Authors: Lucia Maria Sacheli1,2, Stefano Grasso3, Laura Zapparoli1,2, Eros Quarta3, Fabiana Esposito1, Marika Mariano1, Margherita Adelaide Musco1, Carlo Toneatto1, Gianpaolo Basso4,5, Stefano Ferraina3, Alexandra Battaglia-Mayer3, Eraldo Paulesu1,2
ORCID iDs: Lucia Maria Sacheli, Stefano Grasso, Laura Zapparoli, Eros Quarta, Marika Mariano, Margherita Adelaide Musco, Carlo Toneatto, Gianpaolo Basso, Stefano Ferraina, Alexandra Battaglia-Mayer, Eraldo Paulesu
  1. Department of Psychology and Milan Center for Neuroscience (NeuroMi), University of Milano-Bicocca, Milan 20126, Italy
  2. fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan 20157, Italy
  3. Department of Physiology and Pharmacology, Sapienza University of Rome, Rome 00185, Italy
  4. School of Medicine and Surgery, University of Milano-Bicocca, Milan 20126, Italy
  5. Department of Neuroscience, Fondazione IRCCS San Gerardo dei Tintori, Monza (MB) 20900, Italy
Institutions: IRCCS Ospedale Galeazzi - Sant'Ambrogio (Italy); University of Milano-Bicocca (Italy); Sapienza University of Rome (Italy)
Journal: Science advances, volume 12, issue 16, article eaea1927
Dates: received 30 June 2025; accepted 13 March 2026; published online 17 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aea1927 · PMID 41996495 · PMCID PMC13089334 · OpenAlex W7154744124
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), extracellular electrophysiology (units, LFP) (modality), human (organism), non-human primate (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, fMRI & imaging
MeSH: Biological Evolution*, Brain*, Animals, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Motor Cortex (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: Ministero dell&apos;Università e della Ricerca-NextGenerationEU (PNRR M4.C2.I1.1, CUP B53D23014490006, CUP H53D23004200006); Ministero dell&apos;Universit? e della Ricerca (2022B9NZC4); Young Researcher 2024 (CUP B83C24003140006)
Citations: cited by 2 papers (Europe PMC); 65 references in the paper

Abstract

Interpersonal coordination is fundamental to social evolution. We investigate its phenomenology and neural underpinnings in humans and macaques, examining the behavioral adaptations required for mutual coordination during motor interactions and the extent to which the underlying brain mechanisms are shared across species. Using a common interpersonal coordination paradigm, we conducted functional magnetic resonance imaging (fMRI) in humans and intracranial local field potential (LFP) recordings in macaques. Despite between-species behavioral discrepancies, both monkeys and humans coordinate through adjustments that proved to be based on proactive adaptation of motor planning and execution. Evidence from fMRI and time-resolved decoding analysis of LFPs converged to show modulation of premotor brain activity associated with interpersonal coordination and its effectiveness. Moreover, a dynamic sequential coding emerged, whereby the action context is represented early during planning, and coordination features near movement onset. Our findings reveal an evolutionarily conserved cortical architecture across primates that supports cooperative motor behavior.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

readout.info

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Neural recording”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: readout.info/

Zenodo 18743721

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 6 files
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (13 files), ggplot2 (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
23 files
At the source: doi.org/10.5281/zenodo.18743721

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;
  • 23 scripts, each with its path and the digest of its content;
  • 10 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, code, and materials availability

This study did not generate new materials. All data and code needed to evaluate and reproduce the results in the paper are available at the following link: https://doi.org/10.5281/zenodo.18743721.

Reproduced under the paper's license (CC BY-NC), 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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 9 MeSH terms, 3 funders, 59 references.

Cite

This paper

Sacheli, L. M., Grasso, S., Zapparoli, L., Quarta, E., Esposito, F., Mariano, M., Musco, M. A., Toneatto, C., Basso, G., Ferraina, S., Battaglia-Mayer, A., & Paulesu, E. (2026). An evolutionary conserved neural mechanism for interpersonal coordination in primates. Science advances, 12(16), eaea1927. https://doi.org/10.1126/sciadv.aea1927

BibTeX

@article{sacheli2026evolutionary,
author = {Sacheli, Lucia Maria and Grasso, Stefano and Zapparoli, Laura and Quarta, Eros and Esposito, Fabiana and Mariano, Marika and Musco, Margherita Adelaide and Toneatto, Carlo and Basso, Gianpaolo and Ferraina, Stefano and Battaglia-Mayer, Alexandra and Paulesu, Eraldo},
title = {{An evolutionary conserved neural mechanism for interpersonal coordination in primates}},
journal = {Science advances},
year = {2026},
month = apr,
volume = {12},
number = {16},
pages = {eaea1927},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aea1927},
url = {https://doi.org/10.1126/sciadv.aea1927},
pmid = {41996495},
pmcid = {PMC13089334}
}

RIS

TY - JOUR
AU - Sacheli, Lucia Maria
AU - Grasso, Stefano
AU - Zapparoli, Laura
AU - Quarta, Eros
AU - Esposito, Fabiana
AU - Mariano, Marika
AU - Musco, Margherita Adelaide
AU - Toneatto, Carlo
AU - Basso, Gianpaolo
AU - Ferraina, Stefano
AU - Battaglia-Mayer, Alexandra
AU - Paulesu, Eraldo
TI - An evolutionary conserved neural mechanism for interpersonal coordination in primates
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/04/17
VL - 12
IS - 16
SP - eaea1927
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aea1927
UR - https://doi.org/10.1126/sciadv.aea1927
LA - en
ER -

CSL-JSON

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