OSCR

Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions.

Code ↔ Paper

19 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 19 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Experiments › Multivariate pattern analyses (MVPA) ↔ code/behavResp/plotBehavAccu_mainExpt.m, lines 1 to 16 · score 0.76 · hdFw, hdPt, huFw, huPt, decoding experiments · Report a mismatch
  2. [2] § Methods › Experiments › Directional motion decoding experiments ↔ code/behavResp/plotBehavAccu_mainExpt.m, lines 1 to 16 · score 0.74 · hdFw, hdPt, huFw, huPt, decoding · Report a mismatch
  3. [3] § Methods › Experiments › MT/MST visual motion localizer ↔ subfun/design/expDesign.m, the whole file · a weak match · score 0.74 · right hemifield, radial motion, fixation cross, MST localizer, randomized, angle · Report a mismatch
  4. [4] § Methods › Experiments › Directional motion decoding experiments ↔ code/behavResp/plotAccu.m, lines 1 to 15 · score 0.73 · HdFw, HdPt, HuFw, HuPt · Report a mismatch
  5. [5] § Methods › Experiments › MT/MST visual motion localizer ↔ subfun/defaults/checkParameters.m, lines 1 to 107 · score 0.72 · radial motion, fixation cross, motion stimuli, opposite, hemifields, aperture · Report a mismatch
  6. [6] § Methods › Experiments › Multivariate pattern analyses (MVPA) ↔ code/behavResp/plotAccu.m, lines 1 to 15 · score 0.69 · HdFw, HdPt, HuFw, HuPt · Report a mismatch
  7. [7] § Methods › Experiments › Visual motion localizer ↔ subfun/defaults/checkParameters.m, lines 1 to 107 · score 0.68 · static dots, fixation cross, motion stimulus, visual motion localizer, configurations, ISI · Report a mismatch
  8. [8] § Methods › Experiments › Multi-dimensional scaling ↔ code/calcSearchlight/step1RunSearchlight.m, lines 1 to 138 · score 0.66 · pinky thumb, finger wrist, visual horizontal, visual vertical, map · Report a mismatch
  9. [9] § Methods › Experiments › Multivariate pattern analyses (MVPA) ↔ code/calcSearchlight/step1RunSearchlightAcrossHandAnat.m, lines 1 to 147 · score 0.61 · pinky thumb, finger wrist, HDfw, HDpt, HUfw, HUpt · Report a mismatch
  10. [10] § Methods › Experiments › fMRI preprocessing ↔ code/src/a_localizer_analysis.m, lines 85 to 148 · score 0.60 · IXI549Space, fMRI, preprocessed, slice, BidSpm, smoothing · Report a mismatch
  11. [11] § Methods › Experiments › Tactile motion localizer ↔ TactileMotionLocaliser2.m, lines 81 to 189 · score 0.59 · inter stimulus interval, ISI, IBI, duration, tactile motion, eyes · Report a mismatch
  12. [12] § Methods › Experiments › Visual motion localizer ↔ TactileMotionLocaliser2.m, lines 81 to 189 · score 0.58 · inter stimulus interval, ISI, IBI, press, fixation, blocks · Report a mismatch
  13. [13] § Methods › Experiments › fMRI preprocessing ↔ code/src/runAnalysis.m, lines 14 to 43 · score 0.56 · skull strip, segmentation, anatomic, smoothing, FWHM · Report a mismatch
  14. [14] § Methods › Stimuli › Tactile stimuli ↔ FoR2.m, lines 4 to 41 · score 0.56 · auditory cues, eyes closed, Iqra, instructed, stimuli, Tactile · Report a mismatch
  15. [15] § Methods › Experiments › Searchlight analyses ↔ code/calcSearchlight/step4CreateContrastMaps.m, the whole file · a weak match · score 0.53 · Individual accuracy maps, searchlight, sphere, smoothed, FWHM, voxel · Report a mismatch
  16. [16] § Methods › Experiments › Cross-modal decoding ↔ code/calcSearchlight/step1RunCrossModalSearchlight.m, lines 32 to 175 · score 0.53 · cross validation, cross modal, demeaned, training, motion direction, modality · Report a mismatch
  17. [17] § Methods › Experiments › Cross-modal decoding ↔ code/calcSearchlight/step1RunSearchlightCrossModalAnatBoth.m, lines 25 to 165 · score 0.53 · cross validation, cross modal, demeaned, training, motion direction, modality · Report a mismatch
  18. [18] § Methods › Experiments › Tactile motion localizer ↔ setParameters.m, lines 1 to 122 · score 0.51 · tactile stimulation, asked, ISI, IBI, radial, duration · Report a mismatch
  19. [19] § Methods › Stimuli › Tactile stimuli ↔ setParameters.m, lines 1 to 122 · score 0.51 · auditory cues, monitored, Iqra, instructed, eyes, MRI · Report a mismatch

Paper

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The authors' code

MATLAB · 169 lines · 7.1 KB · no license · 2 matches

  1. % plot accuracies
  2. %reads the excel sheet "behav Accu" which contains the behav accu
  3. %subject-wise for all the tasks
  5. % NOTE THE ORDER OF SUBJECTS IN ALL THE TABLES
  6. subList ={'001','002','003','004','005','006','007','008',...
  7. '009','010','011',...
  8. '013','014','015','016','017',...
  9. 'pil001','pil002','pil004','pil005'};
  11. tab=readtable("behavAccu.xlsx");
  13. %to plot the main decoding experiments
  14. a=tab.visVer(1:20); b= tab.visHor(1:20);
  15. c=tab.huPt(1:20); d=tab.huFw(1:20);
  16. e=tab.hdPt(1:20); f=tab.hdFw(1:20);
  18. %%
  19. %settings for plots
  20. shape='o';
  21. %%set the size of the shape
  22. % sz=50;
  23. %%set the width of the edge of the markers
  24. LineWidthMarkers=1.5;
  25. %%set the width of the edge of the mean line
  26. LineWidthMean=5;
  27. %%set the length of the mean line
  28. LineLength=0.4; %the actual length will be the double of this value%%%set the transparency of the markers
  29. Transparency=1;%0.7;
  30. %%set the color for each condition in RGB (and divide them by 256 to be matlab compatible)
  31. lightGreen=[105 170 153]/256; % Light green
  32. darkGreen=[24 96 88]/256;% Dark green
  33. lightOrange=[255 158 74]/256; % Light Orange
  34. darkOrange=[208 110 48]/256;% Dark Orange
  35. lightPurple=[198 131 239]/256;% Light Purple
  36. darkPurple=[121 57 195]/256; %Dark Purple
  37. Col_A=[105 170 153]/256; %
  38. Col_B=[24 96 88]/256;%
  39. Col_C=[255 158 74]/256; %
  40. Col_D=[208 110 48]/256;%
  41. Col_E=[198 131 239]/256;%
  42. Col_F=[121 57 195]/256; %
  43. Colors=[Col_A;Col_B;Col_C;Col_D;Col_E;Col_F];
  44. black = [0 0 0];
  45. white = [1 1 1];
  46. gray = (black + white)/2;%[0.7, 0.7, 0.7];%
  47. darkGray = [0.25, 0.25, 0.25];
  48. %%set the font styles
  49. FontName='Avenir'; %set the style of the labels
  50. FontSize=17; %%set the size of the labels
  52. %%
  53. % % set the density_distance for clustering (all the values that are
  54. % % within the density distance will be plotted as one marker, the size
  55. % % of the marker will increase according to the number of values that it
  56. % % represents)
  57. density_distance=-0.1; %if not density clustering put -0.1 here. With a value >0 you will have a baloon dot plot
  59. %%%%set jittering (normally to be used when the density clustering is not implemented)
  60. jitterAmount=0.07;% for no jittering put 0 here (otherwise try 0.15/ to be adjusted according to the data)
  62. %%set the size of the markers that represent one value
  63. starting_size=400; % in the density_plot this will be the smallest marker size (= 1 sub), in the jittered plot all the markers will be of this size
  64. %%
  65. figure(1)
  67. model_series = [
  68. mean(a), mean(b);...
  69. mean(c), mean(d);...
  70. mean(e), mean(f)];
  72. model_error = [
  73. std(a)/sqrt(length(a)),std(b)/sqrt(length(b));...
  74. std(c)/sqrt(length(c)),std(d)/sqrt(length(d));...
  75. std(e)/sqrt(length(e)),std(f)/sqrt(length(f))];
  77. % b = bar(model_series, 'grouped');
  78. bplot = bar(model_series, 'grouped','FaceColor','flat', 'LineWidth',LineWidthMean/2,'BarWidth', 0.9 );
  80. bplot(1).EdgeColor = darkGray;
  81. bplot(2).EdgeColor = darkGray;
  83. bplot(1).CData(1,:) = 'w';%lightGreen;%'w';%Colors(2,:); % group 1 1st bar
  84. bplot(1).CData(2,:) = 'w';%lightGreen;%'w';%Colors(2,:); % group 1 2nd bar
  85. bplot(1).CData(3,:) = 'w';%lightGreen;%'w';%Colors(2,:); % group 1 3rd bar
  87. bplot(2).CData(1,:) = 'w';%lightOrange;%'w';%Colors(4,:); % group 2 1st bar
  88. bplot(2).CData(2,:) = 'w';%lightOrange;%'w';%Colors(4,:); % group 2 2nd bar
  89. bplot(2).CData(3,:) = 'w';%lightOrange;%'w';%Colors(4,:); % group 2 3rd bar
  91. % Calculate the number of groups and number of bars in each group
  92. [ngroups,nbars] = size(model_series);
  94. % Get the x coordinate of the bars
  95. x = nan(nbars, ngroups);
  96. for i = 1:nbars
  97. x(i,:) = bplot(i).XEndPoints;
  98. end
  99. hold on
  102. % % Plot connected lines
  103. for iiSub=1:length(subList)
  104. hold on
  105. plot([x(1,1), x(2,1)],[a(iiSub), b(iiSub)],'color',gray);%,'LineWidth',LineWidthMean/3 )
  106. hold on
  107. plot([x(1,2), x(2,2)],[c(iiSub), d(iiSub)],'color',gray);%,'LineWidth',LineWidthMean/3)
  108. hold on
  109. plot([x(1,3), x(2,3)],[e(iiSub), f(iiSub)],'color',gray);%,'LineWidth',LineWidthMean/3)
  111. end
  113. sz=40;
  115. %to plot dots in a single line
  116. scatter(repmat(x(1,1), length(a), 1),a,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  117. scatter(repmat(x(2,1), length(b), 1),b,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  119. scatter(repmat(x(1,2), length(c), 1),c,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  120. scatter(repmat(x(2,2), length(d), 1),d,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  122. scatter(repmat(x(1,3), length(e), 1),e,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  123. scatter(repmat(x(2,3), length(f), 1),f,sz,shape,'MarkerEdgeColor',gray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers);
  126. % %to plot dots spread within the bar
  127. % [data_density,sizeN]=compute_density(a,density_distance, starting_size);
  128. % scatter(repmat(x(1,1), length(a), 1),data_density(:),sz,sizeN,shape,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  129. % [data_density,sizeN]=compute_density(b,density_distance, starting_size);
  130. % scatter(repmat(x(2,1), length(b), 1),data_density(:),sz,shape,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  131. %
  132. % [data_density,sizeN]=compute_density(c,density_distance, starting_size);
  133. % scatter(repmat(x(1,2), length(c), 1),data_density(:),sz,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  134. % [data_density,sizeN]=compute_density(d,density_distance, starting_size);
  135. % scatter(repmat(x(2,2), length(d), 1),data_density(:),sz,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  136. %
  137. % [data_density,sizeN]=compute_density(e,density_distance, starting_size);
  138. % scatter(repmat(x(1,3), length(e), 1),data_density(:),sz,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  139. % [data_density,sizeN]=compute_density(f,density_distance, starting_size);
  140. % scatter(repmat(x(2,3), length(f), 1),data_density(:),sz,'MarkerEdgeColor',darkGray,'MarkerEdgeAlpha',Transparency,'LineWidth',LineWidthMarkers,'jitter','on', 'jitterAmount', jitterAmount);
  142. % Plot the errorbars
  143. err = errorbar(x',model_series,model_error,'color',darkGreen,'linestyle','none','LineWidth',LineWidthMean/2);
  144. err(1).Color = darkGray;
  145. err(2).Color = darkGray;
  146. %
  148. hold on
  149. %plot chance level
  150. % yline(0.5, ':','color', 'k','LineWidth',LineWidthMean/2)
  151. hold off
  153. ylim([0.35 1.06])
  154. % legend({'anat-HDPT-HUPT-vs-HDFW-HUFW','ext-HUPT-HDFW-vs-HDPT-HUFW'},'Location','northeastoutside')
  155. % legend box off
  156. xlabel('Tasks')
  157. ylabel('Performance Accuracy')
  158. xticklabels({'Visual','Hand Up', 'Hand Down'})
  159. title('Behavioural task')
  161. %format plot
  162. set(gcf,'color','w');
  163. ax=gca;
  165. set(ax,'FontName',FontName,'FontSize',FontSize, 'FontWeight','bold',...
  166. 'LineWidth',2.5,'TickDir','out', 'TickLength', [0,0],...
  167. 'FontSize',FontSize);
  168. box off

plotBehavAccu_mainExpt.m at commit 7586f28, no license · at the source

Overview

Authors: Iqra Shahzad1, Ceren Battal1,2, Filippo Cerpelloni1,3, Alice Van Audenhaege1, André Mouraux1, Olivier Collignon1,4
ORCID iDs: Iqra Shahzad, Ceren Battal, Alice Van Audenhaege, Olivier Collignon
  1. Institute of Neuroscience (IoNS), UCLouvain,Brussels, Belgium
  2. Faculty of Psychology and Educational Sciences, University of Coimbra,Coimbra, Portugal
  3. Brain and Cognition, Leuven Brain Institute, KU Leuven,Leuven, Belgium
  4. The Sense Innovation and Research Center,Lausanne, Switzerland
Institutions: UCLouvain (Belgium); University of Coimbra (Portugal); KU Leuven (Belgium); The Sense Innovation and Research Center (Switzerland)
Journal: Nature communications, volume 17, issue 1, article 2625
Dates: received 10 May 2025; accepted 24 February 2026; published online 20 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70537-6 · PMID 41862471 · PMCID PMC13004924 · OpenAlex W7139019987
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Perception, Sensory processing
MeSH: Frontal Lobe*, Motion Perception*, Parietal Lobe*, Touch*, Touch Perception*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Tactile and Sensory Interactions (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 86011 (multiTOUCH). It was also funded in parts by a Mandat d’Impulsion Scientifique awarded to OC, the Belgian Excellence of Science (EOS) program (Project No. 30991544) awarded to OC, and a Flagship ERA-NET grant SoundSight (FRS-FNRS PINT-MULTI R.8008.19) awarded to OC. IS was funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 86011 (multiTOUCH). OC is a senior research associate at the Belgian National Fund for Scientific Research (FRS-FNRS)
Citations: cited by 1 paper (Europe PMC); 111 references in the paper
Research resources: RRID:SCR_007037

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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

iqrashahzad14/visualLocalizer

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1c73f4aec13dfffd1adb5d9bc8c561931fb23b84, 2 August 2022
Languages: MATLAB (17)
Size: 39 files, 17 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, tests, continuous integration, documentation
Not found: CITATION.cff, environment file
Tools: Psychtoolbox (3 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
19 files
At the source: github.com/iqrashahzad14/visualLocalizer

iqrashahzad14/tactileLocalizer

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 5c08fce107a043f8976589eaf4a1966e20ed349a, 2 August 2022
Languages: MATLAB (19)
Size: 57 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, tests, continuous integration
Not found: license file, CITATION.cff, environment file, documentation
Tools: Psychtoolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
20 files
At the source: github.com/iqrashahzad14/tactileLocalizer

iqrashahzad14/mtMstLocalizer

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: abe607df6a01bd480d2914ac82d2f30c6755cd5a, 2 August 2022
Languages: MATLAB (30)
Size: 53 files, 30 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, environment (requirements.txt), tests, continuous integration, documentation
Tools: Psychtoolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
32 files
At the source: github.com/iqrashahzad14/mtMstLocalizer

iqrashahzad14/fMRI_FoR_expt

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 8358ca86de68255ef369717942b95c5197d8afd2, 2 August 2022
Languages: MATLAB (25)
Size: 70 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, continuous integration, documentation
Not found: CITATION.cff, environment file, tests
Tools: Psychtoolbox (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
27 files
At the source: github.com/iqrashahzad14/fMRI_FoR_expt

iqrashahzad14/visTac_fMRI_paper

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7586f288ab42d92be6e09ccc0f69b6fda8503e09, 9 December 2025
Languages: MATLAB (87), R (9), Shell (5), Python (1)
Size: 152 files, 102 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (code/requirements.txt), continuous integration, documentation
Not found: license file, CITATION.cff, tests
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (24 files), Statistics and Machine Learning Toolbox (13 files), CoSMoMVPA (9 files), SPM (9 files), emmeans (4 files), ggplot2 (4 files), lmerTest (4 files), reshape2 (4 files), easystats (2 files), DataLad (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
103 files
At the source: github.com/iqrashahzad14/visTac_fMRI_paper

Zenodo 18225976

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (24 files), Statistics and Machine Learning Toolbox (13 files), CoSMoMVPA (9 files), SPM (9 files), emmeans (4 files), ggplot2 (4 files), lmerTest (4 files), reshape2 (4 files), easystats (2 files), DataLad (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
103 files
At the source:

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:

Read it in the paper: doi.org/10.1038/s41467-026-70537-6.

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:

  • 6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 295 scripts, each with its path and the digest of its content;
  • 19 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

Datasets cited

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:

Read it in the paper: doi.org/10.1038/s41467-026-70537-6.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 13 MeSH terms, 1 funder, 108 references, 1 RRID.

Cite

This paper

Shahzad, I., Battal, C., Cerpelloni, F., Van Audenhaege, A., Mouraux, A., & Collignon, O. (2026). Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions. Nature communications, 17(1), 2625. https://doi.org/10.1038/s41467-026-70537-6

BibTeX

@article{shahzad2026aligned,
author = {Shahzad, Iqra and Battal, Ceren and Cerpelloni, Filippo and Van Audenhaege, Alice and Mouraux, André and Collignon, Olivier},
title = {{Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {2625},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70537-6},
url = {https://doi.org/10.1038/s41467-026-70537-6},
pmid = {41862471},
pmcid = {PMC13004924}
}

RIS

TY - JOUR
AU - Shahzad, Iqra
AU - Battal, Ceren
AU - Cerpelloni, Filippo
AU - Van Audenhaege, Alice
AU - Mouraux, André
AU - Collignon, Olivier
TI - Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/20
VL - 17
IS - 1
SP - 2625
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70537-6
UR - https://doi.org/10.1038/s41467-026-70537-6
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70537-6",
"type": "article-journal",
"title": "Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions",
"container-title": "Nature communications",
"author": [
{
"family": "Shahzad",
"given": "Iqra"
},
{
"family": "Battal",
"given": "Ceren"
},
{
"family": "Cerpelloni",
"given": "Filippo"
},
{
"family": "Van Audenhaege",
"given": "Alice"
},
{
"family": "Mouraux",
"given": "André"
},
{
"family": "Collignon",
"given": "Olivier"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "2625",
"DOI": "10.1038/s41467-026-70537-6",
"PMID": "41862471",
"PMCID": "PMC13004924",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70537-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
20
]
]
}
}

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[1] doi:10.7554/elife.79370 [code]
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