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A densely sampled fMRI dataset for investigating food valuation.

Code ↔ Paper

6 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 6 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › fMRI data preprocessing › Functional data preprocessing ↔ fig2cdef_EstimateGLMs.m, the whole file · a weak match · score 0.74 · motion correction, brain mask, preprocessed BOLD, head, timeseries, thresholding
  2. [2] § Methods › fMRI data preprocessing › Functional data preprocessing ↔ f00_fMRIPrep.sh, the whole file · a weak match · score 0.61 · FreeSurfer, fMRIPrep, derivatives, temporal
  3. [3] § Technical Validation › Replication of previous findings ↔ fig2cdef_EstimateGLMs.m, the whole file · a weak match · score 0.60 · motion correction parameters, HRF, SPM, head, fMRI, FD
  4. [4] § Technical Validation › Replication of previous findings ↔ fig2cdef_DefineGLM4.m, the whole file · a weak match · score 0.54 · Valuation phase, GLM4, fat, protein, Feedback, duration
  5. [5] § Data Records ↔ fig2cdef_DefineGLM1.m, the whole file · a weak match · score 0.51 · events.tsv, food_run, behavioral, onsets, Feedback, durations
  6. [6] § Data Records ↔ fig2cdef_DefineGLM2.m, the whole file · a weak match · score 0.51 · events.tsv, food_run, behavioral, onsets, Feedback, durations

Paper

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

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The authors' code

MATLAB · 86 lines · 4.8 KB · no license · 2 matches

  1. function fig2cdef_EstimateGLMs(dir_root_target)
  2. % dir_root_target: the root directory for the data analysis (should be changed for your environment)
  3. %%
  4. close all; clc;
  5. spm_get_defaults('stats.maxmem', 8 * 1024^3); % メモリの拡張（8GB）
  6. spm('Defaults', 'fMRI'); % Initialise SPM
  7. spm_jobman('initcfg'); % Initialise cfg_util
  8. dir_root = dir_root_target;
  10. subs_list = {'001','002','003','004','005','006','007','008','009','010','011','012','013','014','015','016','017','018','019','020','021','022','023','024','025','026','027','028','029','030','031'}; % List of the subjects
  11. sess_list = {'01','02','03'}; % List of the sessions
  12. runs_list_tmp = {'01','02','03','04'}; % List of the runs
  13. fd_thr = 0.9; head_radius = 50; % Censoring parameter
  14. glm_name_list = {'glm_1','glm_2','glm_3','glm_4'};
  16. for i = 1:length(subs_list) % Loop over the subjects
  18. %%%%% Setting directories
  19. dir_sub = [dir_root,'/sub-',subs_list{i}]; disp(['%%%%% Subject ',subs_list{i},' %%%%%'])
  20. dir_data = [dir_sub,'/spm_data'];
  22. for m = 1:length(glm_name_list) % Loop over the models
  24. dir_model = [dir_sub,'/glm_model/',glm_name_list{m}];
  26. %%%%%%%%%% Model Specification %%%%%%%%%%
  27. matlabbatch{1}.spm.stats.fmri_spec.dir = {dir_model};
  28. matlabbatch{1}.spm.stats.fmri_spec.timing.units = 'secs';
  29. matlabbatch{1}.spm.stats.fmri_spec.timing.RT = 0.8;
  30. matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t = 60;
  31. matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t0 = 30;
  32. cnt = 0;
  33. for s = 1:length(sess_list) % Loop over the sessions
  35. %%%%% Deal with the missing data in sub-016_ses-03
  36. if isequal(subs_list{i},'016') && isequal(sess_list{s},'03'), runs_list = {'01','02'}; else runs_list = runs_list_tmp; end
  38. for j = 1:length(runs_list)
  39. data_fMRI = cellstr(spm_select('FPList',dir_data,['s_sub-',subs_list{i},'_ses-',sess_list{s},'_task-food_run-',runs_list{j},'_space-MNI152NLin2009cAsym_desc-preproc_bold.nii'])); disp(data_fMRI)
  40. cnt = cnt + 1;
  41. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).scans = data_fMRI;
  42. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).cond = struct('name', {}, 'onset', {}, 'duration', {}, 'tmod', {}, 'pmod', {});
  43. model_name_tmp = [glm_name_list{m},'_ses_',sess_list{s},'_run_',runs_list{j}];
  44. model_name_tmp = fullfile(dir_model, strcat(model_name_tmp, '.mat')); disp(model_name_tmp)
  45. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).multi = cellstr(model_name_tmp);
  46. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).regress = struct('name', {}, 'val', {});
  48. % Addition of the motion correction parameters
  49. confounds = tdfread([dir_data,'/sub-',subs_list{i},'_ses-',sess_list{s},'_task-food_run-',runs_list{j},'_desc-confounds_timeseries.tsv']);
  50. data_MC = [confounds.trans_x, confounds.trans_y, confounds.trans_z, confounds.rot_x, confounds.rot_y, confounds.rot_z];
  51. writematrix(data_MC, [dir_data,'/rp_sub-',subs_list{i},'_ses-',sess_list{s},'_task-food_run-',runs_list{j},'_desc-confounds_timeseries.txt'], 'Delimiter', 'tab');
  52. file_MC = [dir_data,'/rp_sub-',subs_list{i},'_ses-',sess_list{s},'_task-food_run-',runs_list{j},'_desc-confounds_timeseries.txt'];
  53. rp = load(file_MC);
  54. [X, prop_censored] = get_FD_from_rp(rp, fd_thr, head_radius); disp(prop_censored)
  55. reg_file = fullfile(dir_data, sprintf('multi_reg_food_run0%s.txt',num2str(cnt)));
  56. writematrix(X, reg_file, 'Delimiter','\t');
  57. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).multi_reg = {reg_file};
  59. matlabbatch{1}.spm.stats.fmri_spec.sess(cnt).hpf = 128; %%%%%
  60. end
  62. end
  64. matlabbatch{1}.spm.stats.fmri_spec.fact = struct('name', {}, 'levels', {});
  65. matlabbatch{1}.spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
  66. matlabbatch{1}.spm.stats.fmri_spec.volt = 1;
  67. matlabbatch{1}.spm.stats.fmri_spec.global = 'None';
  68. matlabbatch{1}.spm.stats.fmri_spec.mthresh = 0; %%%%%%%%%%
  69. fname_mask = fullfile(dir_data,['sub-', subs_list{i}, '_space-MNI152NLin2009cAsym_desc-brain_mask.nii']);
  70. disp(['Mask file: ', fname_mask])
  71. matlabbatch{1}.spm.stats.fmri_spec.mask = {fname_mask};
  72. matlabbatch{1}.spm.stats.fmri_spec.cvi = 'AR(1)';
  74. %%%%%%%%%% Model Estimation %%%%%%%%%%
  75. disp('Estimated model:');
  76. disp(cellstr(fullfile(dir_model,'SPM.mat')))
  77. matlabbatch{2}.spm.stats.fmri_est.spmmat = cellstr(fullfile(dir_model,'SPM.mat'));
  78. matlabbatch{2}.spm.stats.fmri_est.write_residuals = 0;
  79. matlabbatch{2}.spm.stats.fmri_est.method.Classical = 1;
  81. spm_jobman('run', matlabbatch); % exerting the processing!
  82. clear matlabbatch
  84. end
  86. end

fig2cdef_EstimateGLMs.m at commit cb25ddf, no license · at the source

Overview

Authors: Michiyo Sugawara1, Yoko Mano2, Yuhi Aoki1, Koki Nakaya1, Yuma Matsuda1, Asako Toyama2, Shinsuke Suzuki1,3
ORCID iDs: Michiyo Sugawara
  1. Graduate School of Social Data Science, Hitotsubashi University,Kunitachi, Japan
  2. Hitotsubashi Institute for Advanced Study, Hitotsubashi University,Kunitachi, Japan
  3. Centre for Brain, Mind and Markets, The University of Melbourne,Melbourne, Australia
Institutions: Hitotsubashi University (Japan); The University of Melbourne (Australia)
Journal: Scientific data, volume 13, issue 1, article 957
Dates: received 20 January 2026; accepted 22 April 2026; published online 27 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07323-y · PMID 42045250 · PMCID PMC13323986 · OpenAlex W7155807894
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Statistics, fMRI & imaging
MeSH: Food*, Magnetic Resonance Imaging*, Prefrontal Cortex*, Reward*, Brain, Humans, Ventral Striatum (* major topic)
Journal subjects: Data Descriptor
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (JP22K21357)
Citations: cited by 1 paper (Europe PMC); 64 references in the paper
Research resources: RRID:SCR_001847, RRID:SCR_002438, 39 RRID:SCR_002502, RRID:SCR_002823, distributed with ANTs 2.6.242 RRID:SCR_004757, RRID:SCR_008796

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

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

szkshnsk/A-densely-sampled-fMRI-dataset-for-investigating-food-valuation

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: cb25ddf9a7a57e9c6309a45b5579ff67558d8c58, 20 January 2026
Languages: MATLAB (14), Shell (2)
Size: 17 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (4 files), Statistics and Machine Learning Toolbox (2 files), FSL (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
17 files
At the source: github.com/szkshnsk/A-densely-sampled-fMRI-dataset-for-investigating-food-valuation

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/s41597-026-07323-y.

Tracing map

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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/s41597-026-07323-y.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 MeSH terms, 1 funder, 63 references, 6 RRIDs.

Cite

This paper

Sugawara, M., Mano, Y., Aoki, Y., Nakaya, K., Matsuda, Y., Toyama, A., & Suzuki, S. (2026). A densely sampled fMRI dataset for investigating food valuation. Scientific data, 13(1), 957. https://doi.org/10.1038/s41597-026-07323-y

BibTeX

@article{sugawara2026densely,
author = {Sugawara, Michiyo and Mano, Yoko and Aoki, Yuhi and Nakaya, Koki and Matsuda, Yuma and Toyama, Asako and Suzuki, Shinsuke},
title = {{A densely sampled fMRI dataset for investigating food valuation}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {957},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07323-y},
url = {https://doi.org/10.1038/s41597-026-07323-y},
pmid = {42045250},
pmcid = {PMC13323986}
}

RIS

TY - JOUR
AU - Sugawara, Michiyo
AU - Mano, Yoko
AU - Aoki, Yuhi
AU - Nakaya, Koki
AU - Matsuda, Yuma
AU - Toyama, Asako
AU - Suzuki, Shinsuke
TI - A densely sampled fMRI dataset for investigating food valuation
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/27
VL - 13
IS - 1
SP - 957
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07323-y
UR - https://doi.org/10.1038/s41597-026-07323-y
LA - en
ER -

CSL-JSON

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"title": "A densely sampled fMRI dataset for investigating food valuation",
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"author": [
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"family": "Sugawara",
"given": "Michiyo"
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{
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"PMCID": "PMC13323986",
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