OSCR

Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Micro- and macrostructural MRI extraction ↔ behavioural/MRI/QC/anat/3.QC_SPM12_cat_TIV.R, lines 1–75 · score 0.77 · cerebrospinal fluid, Intracranial Volume, CSF, WM, TIV, GM
  2. [2] § Methods › Micro- and macrostructural MRI extraction ↔ Functions/ForCluster_cat12_Segmenting.m, the whole file · a weak match · score 0.76 · T1 weighted MRI, CAT12 toolbox, SPM, written, MATLAB, segmentation
  3. [3] § Data Record ↔ anonymize/deface.sh, the whole file · a weak match · score 0.59 · NIfTI, nii.gz, defaced, anat, BIDS
  4. [4] § Methods › Functional MRI preprocessing and statistical analysis ↔ Functions/FMRI_Preprocess_BidsFormat.m, lines 1–49 · score 0.58 · SPM, coregistered, realigned, unwarped, smoothed, slice
  5. [5] § Methods › Functional MRI preprocessing and statistical analysis ↔ Functions/Neurolingua_STATS.m, lines 1–55 · score 0.57 · GLM, SPM, onsets, Regression, event, slice
  6. [6] § Methods › Demographic variables ↔ behavioural/4.1_DBcombined_QCd.R, lines 474–513 · score 0.55 · Vocational Training, Baccalaureate, Postgraduate, educational, variables
  7. [7] § Data Record ↔ Functions/FMRI_Preprocess_BidsFormat.m, lines 107–183 · score 0.53 · nii.gz, smoothing, MNI, preprocessed, func, anat

Paper

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

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The authors' code

MATLAB · 183 lines · 11 KB · no license · 2 matches

  1. function FMRI_Preprocess_BidsFormat(path_subj,subject,sequence)
  2. %--------------------------------------------------------------------------
  3. % FMRI_Preprocess_BidsFormat
  4. % Author: Ileana Quinones (BCBL), with contributions from Inigo Diez and Laura de Frutos-Sagastuy
  5. % Project: Neurolingua
  6. % Date: 15/04/2024
  7. % Location: Donostia - San Sebastian, Spain
  9. % ------------------------ OVERVIEW -------------------------
  10. % This function performs preprocessing of fMRI data in BIDS format using SPM12.
  11. % It includes the following steps: slice timing, realignment/unwarping, coregistration,
  12. % normalization, detrending, and smoothing (FWHM = 6 & 8).
  13. % The function relies on pre-configured SPM matlabbatch templates.
  15. % ----------------------- DEPENDENCIES ----------------------
  16. % - SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/)
  17. % - JSONLab toolbox for reading BIDS .json metadata
  18. % - Preconfigured .mat batch templates:
  19. % Template_SliceTiming.mat
  20. % Template_RealignUnwarp.mat
  21. % Template_Coregister.mat
  22. % Template_Normalise_T1_EstWrite.mat
  23. % Template_NormaliseWriteT1.mat
  24. % Template_Smooth.mat
  25. % - Additional functions: cspm_lmgs_2010b (for detrending)
  27. % ----------------------- REQUIREMENTS -----------------------
  28. % SPM version 12 (https://www.fil.ion.ucl.ac.uk/spm/)
  29. % - To use this function, data must follow the structure defined by BIDS
  30. % - For each participant you should have 'tb_*' and 'y_tb*' images derived from structural T1 segmentation
  32. % ----------------------- INPUTS -----------------------
  33. % path_subj: Path to the BIDS dataset
  34. % filter = text file including the selected participants or subject identifiers (e.g., 'sub-01');
  35. % sequence = structure defining the functional sequences to run seperated by semicolumn (e.g., {'ep2dboldPinelBasque','ep2dboldPinelSpanish'});
  37. % ----------------------- EXAMPLE -----------------------
  38. % ForCluster_PreprocessingFMRI('Subjects.txt','/bcbl/home/public/Neurolang_BIDS/',{'ep2dboldPinelBasque', 'ep2dboldPinelSpanish'})
  40. % ----------------------- SEQUENCING STEPS -----------------------
  41. % 1) Slice Timing
  42. % 2) Realign-Unwarp
  43. % 3) Coregister
  44. % 4) Normalise
  45. % 5) 4D to 3D conversion before detrending
  46. % 6) Detrending: Corrects the drift
  47. % 7) 3D to 4D conversion and the subsequent deletion of temporal files
  48. % 8) Smooth
  49. %--------------------------------------------------------------------------
  51. % --------- Starting the loop per participant per functional run ----------
  52. sequence_gz = cellstr(spm_select('FPList',[path_subj,filesep,subject,filesep,'func'],['^',subject,'.*',sequence,'.*\.nii.gz$']));
  53. if ~isempty(sequence_gz{1})
  54. for sess = 1:length(sequence_gz)
  55. indiv_seq = gunzip(sequence_gz{sess});
  56. img = spm_vol(indiv_seq);
  57. if size(img{1},1) > 80
  58. index = strfind(indiv_seq{1},filesep);
  59. seq_name = indiv_seq{1}(index(end) + 1 : end - 4);
  60. % --------- Extracting info from the json file for the slice timing -----------
  61. fid = fopen(spm_select('FPList',[path_subj,filesep,subject,filesep,'func'],[seq_name,'.json$']));
  62. raw = fread(fid,inf); % Reading the contents
  63. str = char(raw'); % Transformation
  64. fclose(fid); % Closing the file
  65. json = loadjson(str); % Using the jsondecode function to parse JSON from string
  67. % --------- Slice Timing -----------
  68. matlabbatch = load ('Template_SliceTiming.mat');
  69. matlabbatch = matlabbatch.matlabbatch;
  70. matlabbatch{1}.spm.temporal.st.scans{1} = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^',seq_name,'.*\.nii$'],Inf));
  71. matlabbatch{1}.spm.temporal.st.nslices = length(json.SliceTiming);
  72. matlabbatch{1}.spm.temporal.st.tr = json.RepetitionTime;
  73. matlabbatch{1}.spm.temporal.st.ta = json.RepetitionTime - json.RepetitionTime/length(json.SliceTiming);
  74. matlabbatch{1}.spm.temporal.st.so = json.SliceTiming;
  75. matlabbatch{1}.spm.temporal.st.refslice = ceil(length(json.SliceTiming)/2);
  76. spm_jobman('run',matlabbatch);
  78. % ------------ Realign_Unwarp --------------
  79. matlabbatch = load('Template_RealignUnwarp.mat');
  80. matlabbatch = matlabbatch.matlabbatch;
  81. matlabbatch{1}.spm.spatial.realignunwarp.data.scans = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^a',seq_name,'.*\.nii$'],Inf));
  82. matlabbatch{1}.spm.spatial.realignunwarp.data.pmscan = '';
  83. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.quality = 0.9;
  84. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.sep = 4;
  85. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.fwhm = 5;
  86. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.rtm = 0;
  87. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.einterp = 2;
  88. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.ewrap = [0 0 0];
  89. matlabbatch{1}.spm.spatial.realignunwarp.eoptions.weight = '';
  90. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.basfcn = [12 12];
  91. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.regorder = 1;
  92. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.lambda = 100000;
  93. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.jm = 0;
  94. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.fot = [4 5];
  95. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.sot = [];
  96. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.uwfwhm = 4;
  97. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.rem = 1;
  98. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.noi = 5;
  99. matlabbatch{1}.spm.spatial.realignunwarp.uweoptions.expround = 'Average';
  100. matlabbatch{1}.spm.spatial.realignunwarp.uwroptions.uwwhich = [2 1];
  101. matlabbatch{1}.spm.spatial.realignunwarp.uwroptions.rinterp = 4;
  102. matlabbatch{1}.spm.spatial.realignunwarp.uwroptions.wrap = [0 0 0];
  103. matlabbatch{1}.spm.spatial.realignunwarp.uwroptions.mask = 1;
  104. matlabbatch{1}.spm.spatial.realignunwarp.uwroptions.prefix = 'u';
  105. spm_jobman('run', matlabbatch);
  107. % ---------- Batch modification Coregister -------------
  108. matlabbatch = load('Template_Coregister.mat');
  109. matlabbatch = matlabbatch.matlabbatch;
  110. T1img = cellstr(spm_select('FPList',[path_subj,filesep,subject,filesep,'anat'],'^sub-.*UNIT1.*\.nii.gz$')); %3d image
  111. if size(T1img,1) == 1 && isempty(T1img{:})
  112. SPM_folder = which('SPM');
  113. ImgRef = cellstr(spm_select('FPList',[SPM_folder(1:end-5),filesep,'tpm'],'^EPI\.nii$'));
  114. elseif size(T1img,1)==1 && ~isempty(T1img{:})
  115. ind = 1;
  116. gunzip(T1img)
  117. ImgRef = cellstr(spm_select('FPList',[path_subj,filesep,subject,filesep,'anat'],'^sub-.*T1.*\.nii$'));
  118. sep = strfind(T1img{ind},filesep);
  119. else
  120. cat_T1 = dir([path_subj,filesep,subject,filesep,'anat',filesep,'report',filesep,'cat_*sub-*T1*.mat']);
  121. ICR = zeros(size(T1img,1),1);
  122. for i = 1:size(T1img,1)
  123. load([path_subj,filesep,subject,filesep,'anat',filesep,'report',filesep,cat_T1(i).name],'S');
  124. ICR(i,1) = S.qualitymeasures.ICR;
  125. end
  126. ind = find(ICR==max(ICR));
  127. gunzip(T1img{ind});
  128. sep = strfind(T1img{ind},filesep);
  129. ImgRef = cellstr(spm_select('FPList',[path_subj,filesep,subject,filesep,'anat'],['^',T1img{ind}(sep(end)+1:end-7),'\.nii$']));
  130. end
  131. matlabbatch{1,1}.spm.spatial.coreg.estimate.ref = ImgRef;
  132. matlabbatch{1,1}.spm.spatial.coreg.estimate.source = cellstr(spm_select('FPList',[path_subj,filesep,subject,filesep,'func'],['^meanua',seq_name,'.*\.nii$']));
  133. matlabbatch{1,1}.spm.spatial.coreg.estimate.other = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^ua',seq_name,'.*\.nii$'],Inf));
  134. spm_jobman('run', matlabbatch);
  136. % ----------- Normalise -----------
  137. if size(T1img,1) == 1 && isempty(T1img{:})
  138. matlabbatch = load ('Template_Normalise_T1_EstWrite.mat');
  139. matlabbatch = matlabbatch.matlabbatch;
  140. matlabbatch{1}.spm.spatial.normalise.estwrite.subj.vol = ImgRef;
  141. matlabbatch{1}.spm.spatial.normalise.estwrite.subj.resample = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^ua',seq_name,'.*\.nii$'],Inf));
  142. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasreg = 0.0001;
  143. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasfwhm = 60;
  144. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.tpm = {'/bcbl/home/public/Neurolang_BIDS/MRI/scripts/Preprocessing/Toolbox/spm12/tpm/TPM.nii'};
  145. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.affreg = 'mni';
  146. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.reg = [0 0.001 0.5 0.05 0.2];
  147. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.fwhm = 0;
  148. matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.samp = 3;
  149. matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.bb = [-84 -120 -72; 84 84 96];
  150. matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.vox = [2 2 2];
  151. matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.interp = 4;
  152. matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.prefix = 'w';
  153. else
  154. matlabbatch = load ('Template_NormaliseWriteT1.mat');
  155. matlabbatch = matlabbatch.matlabbatch;
  156. matlabbatch{1}.spm.spatial.normalise.write.subj.def = cellstr(spm_select('FPList',[path_subj,filesep,'derivatives',filesep,'CAT12',filesep,'remaining',filesep,subject,filesep,'anat',filesep,'mri'],['^y_',T1img{ind}(sep(end)+1:end-7),'\.nii$']));
  157. matlabbatch{1}.spm.spatial.normalise.write.subj.resample = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^ua',seq_name,'.*\.nii$'],Inf));
  158. matlabbatch{1}.spm.spatial.normalise.write.woptions.bb = [-84 -120 -72; 84 84 96];
  159. matlabbatch{1}.spm.spatial.normalise.write.woptions.vox = [2 2 2];
  160. matlabbatch{1}.spm.spatial.normalise.write.woptions.interp = 4;
  161. matlabbatch{1}.spm.spatial.normalise.write.woptions.prefix = 'w';
  162. end
  163. spm_jobman('run', matlabbatch);
  165. % ---------- Detrending ------------
  166. Images = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^wua',seq_name,'.*\.nii$'],Inf));
  167. cspm_lmgs_2010b(Images);
  169. % ----------- Smooth using a FWHM of 8 -----------
  170. matlabbatch = load ('Template_Smooth.mat');
  171. matlabbatch = matlabbatch.matlabbatch;
  172. matlabbatch{1}.spm.spatial.smooth.data = cellstr(spm_select('ExtFPList',[path_subj,filesep,subject,filesep,'func'],['^dwua',seq_name,'.*\.nii$'],Inf));
  173. matlabbatch{1}.spm.spatial.smooth.fwhm = [8 8 8];
  174. matlabbatch{1}.spm.spatial.smooth.prefix = 's8';
  175. spm_jobman('run', matlabbatch);
  176. % ----------- Smooth using a FWHM of 6 -----------
  177. matlabbatch{1}.spm.spatial.smooth.fwhm = [6 6 6];
  178. matlabbatch{1}.spm.spatial.smooth.prefix = 's6';
  179. spm_jobman('run', matlabbatch);
  180. end
  181. end
  182. end
  183. end

FMRI_Preprocess_BidsFormat.m, no license · at the source

Overview

Authors: Ileana Quiñones1,2,3, Amaia Carrión-Castillo1,3, Iñigo Diez-Zabala2,4, Laura de Frutos-Sagastuy1,4, Brendan Costello1,3, David Carcedo1, Lucía Manso-Ortega1,4, Maksim Slivka5, Abraham Sánchez1,4, Anique Schüller1,4, César Caballero-Gaudes1,3, Pedro M. Paz-Alonso1,3, Manuel Carreiras1,3,4
ORCID iDs: Ileana Quiñones, Amaia Carrión-Castillo, Iñigo Diez-Zabala, Maksim Slivka, Pedro M. Paz-Alonso, Manuel Carreiras
  1. Basque Center on Cognition, Brain, and Language (BCBL),20009 Donostia-San Sebastian, Spain
  2. Biogipuzkoa Health Research Institute,20014 Donostia-San Sebastian, Spain
  3. IKERBASQUE. Basque Foundation for Science,48009 Bilbao, Spain
  4. University of the Basque Country, UPV/EHU,48940 Bilbao, Spain
  5. Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo,0313 Oslo, Norway
Institutions: Basque Center on Cognition, Brain and Language (Spain); Biogipuzkoa Health Research Institute (Spain); Ikerbasque (Spain); University of the Basque Country (Spain); University of Oslo (Norway)
Journal: Scientific data, volume 13, issue 1, article 1315
Dates: received 10 July 2025; accepted 30 April 2026; published online 6 June 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07423-9 · PMID 42251037 · PMCID PMC13575113 · OpenAlex W7163760708
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), methods / tools (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Language, Databases
MeSH: Comprehension*, Multilingualism*, Adolescent, Adult, Aged, Aged, 80 and over, Brain, Female, Humans, Linguistics, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Young Adult (* major topic)
Journal subjects: Data Descriptor
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación) (Ramón y Cajal Fellowship RYC2022-035533-I (I.Q.), Ramon y Cajal Fellowship RYC2022-035511-I (A.C.-C.), via the BCBL Severo Ochoa excellence accreditation CEX2020-001010-S); H2020 Marie Skłodowska‐Curie Actions (grant agreement No. 101027016 (A.C.-C.)); Eusko Jaurlaritza (Basque Government) (BERC 2022-2025 program)
Citations: not cited yet (Europe PMC); 49 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 b6ur5

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (26), MATLAB (7), Shell (6)
Size: 52 files, 39 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (20 files), cowplot (12 files), ggplot2 (12 files), Plotly (4 files), SPM (4 files), ggpubr (3 files), psych (3 files), lme4 (2 files), lmerTest (2 files), Parallel Computing Toolbox (2 files), CAT12 (1 file), FSL (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
39 files
At the source: osf.io/b6ur5/

Code availability statement

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Read it in the paper: doi.org/10.1038/s41597-026-07423-9.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s41597-026-07423-9.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 15 MeSH terms, 3 funders, 44 references.

Cite

This paper

Quiñones, I., Carrión-Castillo, A., Diez-Zabala, I., de Frutos-Sagastuy, L., Costello, B., Carcedo, D., Manso-Ortega, L., Slivka, M., Sánchez, A., Schüller, A., Caballero-Gaudes, C., Paz-Alonso, P. M., & Carreiras, M. (2026). Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults. Scientific data, 13(1), 1315. https://doi.org/10.1038/s41597-026-07423-9

BibTeX

@article{quinones2026unraveling,
author = {Quiñones, Ileana and Carrión-Castillo, Amaia and Diez-Zabala, Iñigo and de Frutos-Sagastuy, Laura and Costello, Brendan and Carcedo, David and Manso-Ortega, Lucía and Slivka, Maksim and Sánchez, Abraham and Schüller, Anique and Caballero-Gaudes, César and Paz-Alonso, Pedro M. and Carreiras, Manuel},
title = {{Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults}},
journal = {Scientific data},
year = {2026},
month = jun,
volume = {13},
number = {1},
pages = {1315},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07423-9},
url = {https://doi.org/10.1038/s41597-026-07423-9},
pmid = {42251037},
pmcid = {PMC13575113}
}

RIS

TY - JOUR
AU - Quiñones, Ileana
AU - Carrión-Castillo, Amaia
AU - Diez-Zabala, Iñigo
AU - de Frutos-Sagastuy, Laura
AU - Costello, Brendan
AU - Carcedo, David
AU - Manso-Ortega, Lucía
AU - Slivka, Maksim
AU - Sánchez, Abraham
AU - Schüller, Anique
AU - Caballero-Gaudes, César
AU - Paz-Alonso, Pedro M.
AU - Carreiras, Manuel
TI - Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/06/06
VL - 13
IS - 1
SP - 1315
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07423-9
UR - https://doi.org/10.1038/s41597-026-07423-9
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41597-026-07423-9",
"type": "article-journal",
"title": "Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults",
"container-title": "Scientific data",
"author": [
{
"family": "Quiñones",
"given": "Ileana"
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{
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},
{
"family": "Diez-Zabala",
"given": "Iñigo"
},
{
"family": "de Frutos-Sagastuy",
"given": "Laura"
},
{
"family": "Costello",
"given": "Brendan"
},
{
"family": "Carcedo",
"given": "David"
},
{
"family": "Manso-Ortega",
"given": "Lucía"
},
{
"family": "Slivka",
"given": "Maksim"
},
{
"family": "Sánchez",
"given": "Abraham"
},
{
"family": "Schüller",
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},
{
"family": "Caballero-Gaudes",
"given": "César"
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{
"family": "Paz-Alonso",
"given": "Pedro M."
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{
"family": "Carreiras",
"given": "Manuel"
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],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "1315",
"DOI": "10.1038/s41597-026-07423-9",
"PMID": "42251037",
"PMCID": "PMC13575113",
"ISSN": "2052-4463",
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"URL": "https://doi.org/10.1038/s41597-026-07423-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
6
]
]
}
}

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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-74565-0 [code]
The functional neurobiology of dispositions towards negative emotions.
Journal: Nature communications
In common: psych, Parallel Computing Toolbox, FSL, 5 other tools, 1 reference
[2] doi:10.1038/s41593-026-02363-4 [code]
Cortical thickness changes precede high levels of amyloid by at least 7 years.
Journal: Nature neuroscience
In common: lmerTest, lme4, cowplot, 3 other tools, author Maksim Slivka
[3] doi:10.1038/s41467-026-72916-5 [code]
Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age.
Journal: Nature communications
In common: lmerTest, SPM, lme4, 4 other tools, 3 references
[4] doi:10.1093/braincomms/fcag121 [code]
Anterior insular co-activation patterns associated with stress markers in chronic primary pain.
Journal: Brain communications
In common: psych, lmerTest, Plotly, 5 other tools
[5] doi:10.7554/elife.103846 [code]
Overt visual attention modulates decision-related signals in the frontal cortex.
Journal: eLife
In common: Parallel Computing Toolbox, lmerTest, FSL, 5 other tools
[6] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: psych, lmerTest, FSL, 5 other tools
[7] doi:10.1007/s00701-026-06927-y
Digital MULTIMAP: a standardization of objects and actions naming task in a french population.
Journal: Acta neurochirurgica
In common: 2 authors
[8] doi:10.1093/cercor/bhag113 [code]
Long-term reliability and stability of parameterized resting state EEG: evidence from a five-year follow-up.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: psych, lmerTest, lme4, 4 other tools, methods / tools
[9] doi:10.1371/journal.pone.0353990 [code]
Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language.
Journal: PloS one
In common: psych, lmerTest, lme4, 3 other tools, 1 reference
[10] doi:10.1073/pnas.2603114123 [code]
The human hippocampus can pattern separate memories by meaning.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: psych, lmerTest, FSL, 4 other tools

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