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An abstract relational map emerges in the human medial prefrontal cortex with consolidation.

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 · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Method Details › fMRI data acquisition and pre-processing ↔ utilities/freesurfer/ssbloch.m, the whole file · a weak match · score 0.88 · echo planar imaging, field inhomogeneities, flip angle, coil, transverse, TE
  2. [2] § Method Details › fMRI data acquisition and pre-processing ↔ Scripts/RSA_analyses/utilities/rsaToolbox/+rsa/+fmri/spatiallySmooth4DfMRI_mm.m, the whole file · a weak match · score 0.71 · Gaussian kernel, fMRI, mm, monitor, box, slices
  3. [3] § Quantification and Statistical Analysis › fMRI data analysis: Representational Similarity Analysis ↔ irrelDist_vs_mPfcSwitchStrength.m, the whole file · a weak match · score 0.70 · beta maps, switch trials, mPFC, irrelevant distances, MNI, scanning
  4. [4] § Quantification and Statistical Analysis › fMRI data analysis: Repetition suppression ↔ irrelDist_vs_mPfcSwitchStrength.m, the whole file · a weak match · score 0.67 · parametric modulators, switch trials, SPM, onsets, preceding, regressor
  5. [5] § Quantification and Statistical Analysis › Representations of the relevant and irrelevant graphs ↔ rsa/rsaAlonWrapper.m, lines 84–188 · score 0.66 · FWE correction, Brodmann, peak, entorhinal, Activations, ROIs
  6. [6] § Results › Representations of both the relevant and irrelevant graphs in the MTL ↔ rsa/volMniRsa/collapseRdm_roi.m, the whole file · a weak match · score 0.61 · diagonal elements, symmetric matrix, distance matrices, RDM, ROI, RSA
  7. [7] § Results › Representations of both the relevant and irrelevant graphs in the MTL ↔ rsa/volMniRsa/collapseRdm1324_roi.m, the whole file · a weak match · score 0.61 · diagonal elements, symmetric matrix, distance matrices, ROI, RSA, RDM
  8. [8] § Quantification and Statistical Analysis › fMRI data analysis: Representational Similarity Analysis ↔ utilities/runRSA_withinCluster.m, the whole file · a weak match · score 0.59 · MNI space, correlation distances, cluster, GLM, betas, RSA
  9. [9] § Results › Subjects learn and flexibly switch between two graphs with the same structure ↔ Scripts/Figure2.m, lines 244–376 · score 0.57 · Wilcoxon signed rank, 1–2, dz, Cohen, CI, correlated
  10. [10] § Quantification and Statistical Analysis › Representations of the relevant and irrelevant graphs ↔ utilities/surfing/python/afni_surface_alphasim.py, lines 430–473 · score 0.52 · uncorrected threshold, survived, anatomical, temporal, cluster, masks

Paper

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

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The authors' code

MATLAB · 100 lines · 5.8 KB · no license · 2 matches

  1. clear
  2. close all
  4. spmPath = '/vols/Scratch/abaram/MATLAB/spm12';
  5. addpath(spmPath);
  7. root='/home/fs0/mgarvert/scratch/ManyMaps/imagingData';
  9. subs = {'1','2','3','4','5','6','7','8','9','10','11','12','13','14',...
  10. '16','19','20','21','22','23','24','25'};
  12. for s = 1:length(subs)
  13. subStr = ['Subj_' subs{s}];
  14. for session = 1:2
  15. sessionStr = ['session_' num2str(session)];
  16. load(strtrim(ls(['/vols/Scratch/mgarvert/ManyMaps/scan_1.1/datafiles/Subj_',subs{s},'/*_session_',num2str(session),'/data_',subs{s},'_',num2str(session),'.mat'])))
  17. for r = 1:4
  18. runStr = ['run_' num2str(r)];
  20. d = data.scan{r}.objDiff;
  21. objTr = data.scan{r}; % object trials
  23. % get irrelevant distance, indexed in object trials
  25. % Exclude first trial: doesn't have a preceding object
  26. ix = 2:length(objTr.map); % ignore first trial because it doesn't have a preceding object
  27. curr_obj = data.scan{r}.seq(1,ix);
  28. prev_obj = data.scan{r}.seq(1,ix-1);
  29. curr_obj(curr_obj>17) = curr_obj(curr_obj>17) -17; % subtract 17 if context 2
  30. prev_obj(prev_obj>17) = prev_obj(prev_obj>17) -17; % subtract 17 if context 2
  31. for i = 1:length(curr_obj) % go through all the objects
  32. m = objTr.map(i);
  33. dRel(i) = data.map{m,m}(curr_obj(i),prev_obj(i)); % compute the distance between the two stimuli on the irrelevant$
  34. dIrrel(i) = data.map{m,mod(m,2)+1}(curr_obj(i),prev_obj(i)); % compute the distance between the two stimuli on the irrelev$
  35. end
  36. % add back the first trial so that the trial indeces match
  37. dRel = [nan dRel];
  38. dIrrel = [nan dIrrel];
  40. % get switch trials, indexed in Obj trials
  41. switchTrials = find([false objTr.map(2:end) ~= objTr.map(1:end-1)]);
  42. stayTrials = find([false objTr.map(2:end) == objTr.map(1:end-1)]);
  44. stayWithPrecedingSwitchInds = stayTrials(ismember(stayTrials-1,switchTrials));
  45. stayWithoutPrecedingSwitchInds = setdiff(stayTrials,stayWithPrecedingSwitchInds);
  46. switchWithSuccedingStayInds = switchTrials(ismember(switchTrials+1,stayTrials)); % same as stayWithPrecedingSwitch-1
  47. irrelDistInValidStayTrials = dIrrel(stayWithPrecedingSwitchInds);
  49. % load onsets of all objects
  50. onsetsAllObj = load(fullfile(root,subStr,sessionStr,'behaviour','EVs',[subStr '_' sessionStr '_' runStr],'allObjects.txt'));
  52. % get onsets of valid stay trials
  53. onsetsStayWithPrecedingSwitch = onsetsAllObj(stayWithPrecedingSwitchInds,:);
  54. stayWithPrecedingSwitch_irrelDist = onsetsStayWithPrecedingSwitch;
  55. stayWithPrecedingSwitch_irrelDist(:,3) = irrelDistInValidStayTrials;
  57. onsetsStayWithoutPrecedingSwitch = onsetsAllObj(stayWithoutPrecedingSwitchInds,:);
  59. % get all non valid stay trials onsets
  60. % get parameteric modulator of mPFC signal in preceding switch
  61. % trials
  63. % get mask
  64. mask = 'spmT_switch_stay_session_2_0001_mask_1p5_mPFC'; %switchMinusStay from design_322
  65. mask_file = fullfile(root,'masks',[mask, '.nii']);
  66. mask_nii = spm_vol(mask_file);
  67. mask_data = spm_read_vols(mask_nii);
  69. % single-trial betas spmDir
  70. spmDirMni = fullfile(root,subStr,sessionStr,'1stLevel','design_401_noSmooth','MNI');
  71. nRegsPerRun = 144; % including nuisance regressors - choiceTrials, buttonPress, 6*motionParams
  72. stayWithPrecedingSwitch_switchMPfc = stayWithPrecedingSwitch_irrelDist; % get the onsets and the 3-col structure
  73. for iValidStay = 1:length(stayWithPrecedingSwitchInds)
  74. % for each valid stay trial, load single-trial beta map of the
  75. % preceding switch trial
  76. betaNum = (r-1)*nRegsPerRun + switchWithSuccedingStayInds(iValidStay);
  77. betaNumStr = sprintf('%0*d', 4, betaNum);
  78. betaFile = fullfile(spmDirMni,['MNI_beta_' betaNumStr '.nii']);
  79. beta_nii = spm_vol(betaFile);
  80. beta_data = spm_read_vols(beta_nii);
  81. meanSignalInMask = mean(beta_data(mask_data ~= 0),'omitnan');
  82. % update in 3-col regressor
  83. stayWithPrecedingSwitch_switchMPfc(iValidStay,3) = meanSignalInMask;
  84. end
  85. % caculate the interaction regressor of the two regressors
  86. % get the 3-col structure and onsets
  87. stayWithPrecedingSwitch_switchMPfc_X_irrelDis = stayWithPrecedingSwitch_switchMPfc;
  88. % demean the original regs and calculate interaction
  89. stayWithPrecedingSwitch_switchMPfc_X_irrelDis(:,3) = (stayWithPrecedingSwitch_switchMPfc(:,3)-mean(stayWithPrecedingSwitch_switchMPfc(:,3))) .* (stayWithPrecedingSwitch_irrelDist(:,3)-mean(stayWithPrecedingSwitch_irrelDist(:,3)));
  92. % save 3-col regressor txt files
  93. dlmwrite(fullfile(root,subStr,sessionStr,'behaviour','EVs',[subStr '_' sessionStr '_' runStr],'stayWithPrecedingSwitch_switchMPfc.txt'), stayWithPrecedingSwitch_switchMPfc, 'delimiter', ' ');
  94. dlmwrite(fullfile(root,subStr,sessionStr,'behaviour','EVs',[subStr '_' sessionStr '_' runStr],'stayWithPrecedingSwitch_irrelDist.txt'), stayWithPrecedingSwitch_irrelDist, 'delimiter', ' ');
  95. dlmwrite(fullfile(root,subStr,sessionStr,'behaviour','EVs',[subStr '_' sessionStr '_' runStr],'stayWithPrecedingSwitch_switchMPfc_X_irrelDis.txt'), stayWithPrecedingSwitch_switchMPfc_X_irrelDis, 'delimiter', ' ');
  96. dlmwrite(fullfile(root,subStr,sessionStr,'behaviour','EVs',[subStr '_' sessionStr '_' runStr],'stayWithoutPrecedingSwitch.txt'), onsetsStayWithPrecedingSwitch, 'delimiter', ' ');
  98. end
  99. end
  100. end

irrelDist_vs_mPfcSwitchStrength.m at commit a2b92bb, no license · at the source

Overview

Authors: Alon B Baram1, Hamed Nili1,2, Ines Barreiros1, Veronika Samborska1, Timothy EJ Behrens1,3, Mona M Garvert1,4
ORCID iDs: Alon B Baram, Mona M Garvert
  1. Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DA, UK
  2. Institute for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany
  3. Sainsbury Wellcome Centre for Neural Circuits and Behaviour, UCL, 25 Howland St, London W1T 4JG, UK
  4. Junior professorship of Neuroscience, Faculty of Human Sciences, Julius-Maximilians-University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
Institutions: John Radcliffe Hospital (United Kingdom); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); University Medical Center Hamburg-Eppendorf (Germany); Sainsbury Wellcome Centre (United Kingdom); University College London (United Kingdom); University of Würzburg (Germany)
Journal: Current biology : CB, volume 36, issue 13, pages 3315-3325.e4
Dates: published online 24 June 2026; in print 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.cub.2026.05.068 · PMID 42341750 · PMCID PMC7619258 · OpenAlex W4403329510
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.10.11.617652
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Statistics, Machine learning, fMRI & imaging
Keywords: fMRI, abstraction, Consolidation, Schemas, Relational Representation, Cognitive Maps, Medial Pfc
MeSH: Learning*, Prefrontal Cortex*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (203147/Z/16/Z, 219627/Z/19/Z, 203139/Z/16/Z); European Research Council (101221509); Gatsby Charitable Foundation; Universität Würzburg; James S. McDonnell Foundation
Citations: cited by 2 papers (Europe PMC); 74 references in the paper
Research resources: Matlab RRID:SCR_001622, FSL RRID:SCR_002823, Python RRID:SCR_008394

Abstract

Understanding the structure of a problem, such as the relationships between stimuli, supports fast learning and flexible reasoning. Rodent work has suggested that the abstraction of structure away from sensory details occurs over the course of multiple days in the cortex. However, direct evidence of such explicit relational representations in humans is scarce, and it is unclear whether they emerge on similar timescales. Here, we combine a graph-learning paradigm with functional magnetic resonance imaging (fMRI) to look for such a relational map in the human brain. We first trained participants on two associative graphs with the same structure. We then scanned participants twice while they used this knowledge, with several days between scanning sessions. Using fMRI repetition suppression, we found an abstract relational representation in the medial prefrontal cortex (mPFC) that emerged across the two scanning sessions. This finding was also replicated using representational similarity analysis (RSA). These results shed new light on how neural representations organizing relational knowledge change with time.

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

alonbaram2/many-maps-alon

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a2b92bb483103c4a0c43d9cae9e1815a636ef3db, 19 May 2026
Languages: MATLAB (902), C/C++ (89), C++ (45), Python (17), C (16), Shell (14)
Size: 1,510 files, 1,083 scripts
Software Heritage: not archived
Found in: the resources table
Holds: tests, documentation
Not found: README, license file, CITATION.cff, environment file, continuous integration
Tools: Statistics and Machine Learning Toolbox (94 files), SPM (82 files), FreeSurfer (57 files), FSL (17 files), NumPy (11 files), Image Processing Toolbox (10 files), GIfTI library for MATLAB (6 files), NiBabel (5 files), Signal Processing Toolbox (2 files), SciPy (2 files), AFNI (1 file), Optimization Toolbox (1 file), Matplotlib (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1,083 files
At the source: github.com/alonbaram2/many-maps-alon

alonbaram2/CurrentBiology2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7885bc7d22f64171565e4bdf6e323ebdd26e0e62, 28 May 2026
Languages: MATLAB (249), Jupyter (3), C (3)
Size: 1,225 files, 255 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, documentation, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: Statistics and Machine Learning Toolbox (36 files), SPM (21 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), seaborn (3 files), FSL (2 files), GIfTI library for MATLAB (2 files), SciPy (2 files), statsmodels (2 files), FreeSurfer (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
256 files
At the source: github.com/alonbaram2/CurrentBiology2026

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;
  • 1,338 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 Availability Statement

This study did not generate any new material contributions.

The subject-specific neuroimaging data reported in this study cannot be deposited in a public repository because consent for public data sharing was not obtained at the time of data collection due to the ethics guidelines at the time (2017). Summary statistics describing these data, pseudonymized behavioral data, and all original code have been deposited at https://github.com/alonbaram2/CurrentBiology2026. Any additional information required to reanalyze the data reported in this paper is available from one of the corresponding authors upon request.

Reproduced under the paper's license (CC BY), 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 2, 28 September 2026

  • Publisher: n/a → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 9 MeSH terms, 5 funders, 74 references, 3 RRIDs.

Cite

This paper

Baram, A. B., Nili, H., Barreiros, I., Samborska, V., Behrens, T. E., & Garvert, M. M. (2026). An abstract relational map emerges in the human medial prefrontal cortex with consolidation. Current biology : CB, 36(13), 3315-3325.e4. https://doi.org/10.1016/j.cub.2026.05.068

BibTeX

@article{baram2026abstract,
author = {Baram, Alon B and Nili, Hamed and Barreiros, Ines and Samborska, Veronika and Behrens, Timothy EJ and Garvert, Mona M},
title = {{An abstract relational map emerges in the human medial prefrontal cortex with consolidation}},
journal = {Current biology : CB},
year = {2026},
month = jun,
volume = {36},
number = {13},
pages = {3315--3325.e4},
publisher = {Elsevier BV},
issn = {0960-9822},
doi = {10.1016/j.cub.2026.05.068},
url = {https://doi.org/10.1016/j.cub.2026.05.068},
pmid = {42341750},
pmcid = {PMC7619258}
}

RIS

TY - JOUR
AU - Baram, Alon B
AU - Nili, Hamed
AU - Barreiros, Ines
AU - Samborska, Veronika
AU - Behrens, Timothy EJ
AU - Garvert, Mona M
TI - An abstract relational map emerges in the human medial prefrontal cortex with consolidation
T2 - Current biology : CB
J2 - Curr Biol
PY - 2026
DA - 2026/06/24
VL - 36
IS - 13
SP - 3315
EP - 3325.e4
SN - 0960-9822
PB - Elsevier BV
DO - 10.1016/j.cub.2026.05.068
UR - https://doi.org/10.1016/j.cub.2026.05.068
LA - en
ER -

CSL-JSON

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