OSCR

Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.

Code ↔ Paper

18 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 18 matches · 6 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 › EEG data processing ↔ scriptTemplates/eegSetParameters.m, the whole file · a weak match · score 0.83 · Pre stimulus, pass band, virtual channel, Hanning, DPSS, taper
  2. [2] § Materials and methods › EEG data processing ↔ scriptTemplates/eegProcessData.m, lines 190–268 · score 0.80 · EEG electrode positions, FEM model, janus3D, SimBio, FieldTrip, MRI
  3. [3] § Materials and methods › Combined EEG–fMRI analysis ↔ fmriRegAnalysis/a_confound_and_task_regressors.m, lines 7–38 · score 0.71 · button presses, task regressors, parametric modulation, alarm, blink, onset
  4. [4] § Materials and methods › Data processing ↔ getToolboxes.sh, lines 59–125 · score 0.67 · FreeSurfer, MRICron, FSL, Workbench, SPM12, FieldTrip
  5. [5] § Materials and methods › Combined EEG–fMRI analysis ↔ fmriRegAnalysis/a2_confound_and_hrf_regressors.m, lines 7–44 · score 0.61 · button presses, parametric modulation, alarm, blink, onset, regressors
  6. [6] § Materials and methods › fMRI motion correction and co-registration ↔ vol2mask.py, lines 359–440 · score 0.60 · Volume Masker, brain mask, TM, affine
  7. [7] § Materials and methods › Data acquisition ↔ scriptTemplates/fmriCorrectFieldDistortion.sh, the whole file · a weak match · score 0.56 · partial brain, consecutive volumes, fMRI, scan, block, EEG
  8. [8] § Results › Behavioural and intermediate results ↔ fmriRegAnalysis/final_plots.m, lines 831–904 · score 0.55 · 0.1–0.8 s, 50–70 Hz, 8–14 Hz, stimulus onset, hemisphere, 0.2 s
  9. [9] § Materials and methods › Combined EEG–fMRI analysis ↔ scriptTemplates/eegVirtualChannelOptSpacePower.m, lines 117–261 · score 0.53 · sliding window, virtual channel, TF, transformed, power, EEG
  10. [10] § Materials and methods › Experimental procedure ↔ fmriRegAnalysis/a2_confound_and_hrf_regressors.m, lines 7–44 · score 0.53 · button press, stimulus onset, hit, alarm, fixation, blinking
  11. [11] § Materials and methods › Experimental procedure ↔ fmriRegAnalysis/a_confound_and_task_regressors.m, lines 7–38 · score 0.53 · button press, stimulus onset, hit, alarm, fixation, blinking
  12. [12] § Materials and methods › Data acquisition ↔ scriptTemplates/fmriCoregistration.m, the whole file · a weak match · score 0.53 · recursive boundary, inverted, displacement, frame, mapping, volumes
  13. [13] § Results › Combined EEG–fMRI analyses ↔ scriptTemplates/eegFmriRegression_old.m, lines 51–110 · score 0.52 · fMRI regressors, SPM, 0.8 s, CSF, deep, superficial
  14. [14] § Materials and methods › Data acquisition ↔ scriptTemplates/fmriCorrectFieldDistortion.sh, the whole file · a weak match · score 0.52 · consecutive blocks, inverted, distortion, fMRI, field, pause
  15. [15] § Materials and methods › pRF mapping ↔ scriptTemplates/eegPrepareFreesurferOutput.sh, lines 41–79 · score 0.52 · pial surface, volumetric, inflated, FreeSurfer, anatomical
  16. [16] § Materials and methods › pRF mapping ↔ scriptTemplates/fmriRetGUI2ROIs.sh, the whole file · a weak match · score 0.51 · FreeSurfer, Freeview, inflated, atlas, mapping, V2
  17. [17] § Materials and methods › fMRI motion correction and co-registration ↔ python_scripts/fmriMotionCorrection.py, the whole file · a weak match · score 0.51 · ANTs, motion, rigid, transformation, mask, laminar
  18. [18] § Materials and methods › Combined EEG–fMRI analysis ↔ scriptTemplates/paper_figures_compute.m, lines 374–481 · score 0.50 · 50–70 Hz, 8–14 Hz, tailed, aros, FOI, bins

Paper

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

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The authors' code

MATLAB · 276 lines · 11 KB · MIT · 2 matches

  1. function R_all_blocks = a_confound_and_task_regressors(parameters, delete_files)
  2. %% R_out = a_confound_and_task_regressors(mainpath, subject, delete_files)
  3. % mainpath = '/project/3018037.01/Experiment3.2_ERC/AnalysisFolder/scripts/scriptTemplates/..';
  5. tc_struct2ws('caller', parameters.main);
  7. %% setup parameters
  8. tc_struct2ws('caller', parameters.paths);
  9. tc_struct2ws('caller', parameters.experiment);
  10. tc_struct2ws('caller', parameters.convolution);
  12. fmri_high_pass_freq = 0.006;
  14. % regressors (name | selected trials | onset time rel to stim | duration | modulation | is parameter modulation)
  15. config_nuis_and_task_regs_fieldnames = {'name', 'trial_sel', 'onset', 'duration', 'modulation', 'isparmod'};
  16. config_nuis_and_task_regs = {...
  17. {'left'}, {'left', 'no_odd_balls', 'no_artifacts', 'no_false_alarm', 'no_blinks'}, {0}, {stim_duration}, {1}, {false}; ... % task regressor left
  18. {'right'}, {'right', 'no_odd_balls', 'no_artifacts', 'no_false_alarm', 'no_blinks'}, {0}, {stim_duration}, {1}, {false}; ... % task regressor right
  19. {'fix_dim'}, {}, {time_fix_change}, {-time_fix_change}, {1}, {false}; ... % fixation change before stimulus onset
  20. {'correct_target_L'}, {'left', 'hit', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % correct odd ball
  21. {'correct_target_R'}, {'right', 'hit', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % correct odd ball
  22. {'incorrect_standard_L'}, {'left', 'false_alarm', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % false alarm
  23. {'incorrect_standard_R'}, {'right', 'false_alarm', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % false alarm
  24. {'incorrect_target_L'}, {'left', 'miss', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % missed odd ball
  25. {'incorrect_target_R'}, {'right', 'miss', 'no_blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % missed odd ball
  26. {'blink_L'}, {'left', 'blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % blink trial
  27. {'blink_R'}, {'right', 'blinks', 'no_artifacts'}, {0}, {stim_duration}, {1}, {false}; ... % blink trial
  28. {'art_L'}, {'left', 'artifacts', 'no_blinks'}, {0}, {stim_duration}, {1}, {false}; ... % artifact trial
  29. {'art_R'}, {'right', 'artifacts', 'no_blinks'}, {0}, {stim_duration}, {1}, {false}; ... % artifact trial
  30. {'button_press'}, {'response'}, {'rt'}, {0}, {1}, {false}; ... % response button press
  31. {'rt_pmod_L'}, {'left', 'response'}, {'rt'}, {'mean_rt'}, {'rt'}, {true}; ... % response time
  32. {'rt_pmod_R'}, {'right', 'response'}, {'rt'}, {'mean_rt'}, {'rt'}, {true}}; % response time
  34. % convert to struct
  35. config_nuis_and_task_regs = cell2struct(config_nuis_and_task_regs', config_nuis_and_task_regs_fieldnames)';
  37. % convolution parameters for regressors
  38. config_conv = parameters.convolution;
  40. %% delete files if indicated
  41. if delete_files
  42. for block = 1:num_blocks
  43. delete(sprintf(design_matrix_file, block))
  44. end
  45. end
  46. %% get preprocessed data
  47. load(MRI_data_file, 'data');
  49. avg_white_matter = squeeze(mean(data.data(logical(data.maskwhite),:,:), 1));
  50. avg_not_gray_not_white_matter = squeeze(mean(data.data(logical(data.masknotgrayorwhite),:,:), 1));
  51. realign_params = squeeze(data.transvecs);
  53. %% make data noise regressors
  55. % model average white matter data
  56. avg_signal_over_blocks = mean(avg_white_matter,2); % avg over blocks
  57. avg_signal_over_blocks = reshape(avg_signal_over_blocks, num_volumes_per_trial, []); % reshape to consecutive volume per row
  58. avg_signal_over_blocks = mean(avg_signal_over_blocks, 2)'; % avg over columns
  59. T1_avg_white_drop_off = repmat(avg_signal_over_blocks, 1, num_scans_per_block / num_volumes_per_trial); % repmat to extend to number of volumes
  61. % normalize signal drop off between 0 and 1
  62. T1_avg_white_drop_off = double(T1_avg_white_drop_off);
  63. T1_avg_white_drop_off = T1_avg_white_drop_off - min(T1_avg_white_drop_off);
  64. T1_avg_white_drop_off = T1_avg_white_drop_off ./ max(T1_avg_white_drop_off);
  66. % model residuals for white matter drop off
  67. betas = [T1_avg_white_drop_off' * ones(1, num_blocks) ones(num_scans_per_block,1)] \ avg_white_matter;
  69. white_matter_signal_res = avg_white_matter -...
  70. [T1_avg_white_drop_off' * ones(1, num_blocks) ones(num_scans_per_block,1)] * betas;
  72. not_gray_not_white_matter_signal_res = avg_not_gray_not_white_matter -...
  73. [T1_avg_white_drop_off' * ones(1, num_blocks) ones(num_scans_per_block,1)] * betas;
  75. % normalize between 0 and 1
  76. white_matter_signal_res = white_matter_signal_res - ...
  77. ones(num_scans_per_block, 1) * min(white_matter_signal_res, [], 1);
  79. white_matter_signal_res = white_matter_signal_res ./ ...
  80. (ones(num_scans_per_block, 1) * max(white_matter_signal_res, [], 1));
  82. not_gray_not_white_matter_signal_res = not_gray_not_white_matter_signal_res - ...
  83. ones(num_scans_per_block, 1) * min(not_gray_not_white_matter_signal_res, [], 1);
  85. not_gray_not_white_matter_signal_res = not_gray_not_white_matter_signal_res ./ ...
  86. (ones(num_scans_per_block, 1) * max(not_gray_not_white_matter_signal_res, [], 1));
  88. % model realignment parameters
  89. decomposed_params = zeros(size(realign_params));
  90. for parameter = 1: size(realign_params, 1)
  91. for block = 1:num_blocks
  92. stats_rp = regstats(realign_params(parameter, :, block), T1_avg_white_drop_off, 'linear', {'beta', 'r'});
  93. decomposed_params(parameter, :, block) = stats_rp.r + stats_rp.beta(1);
  94. end
  95. end
  97. % compute first derivative
  98. decomposed_params = permute(decomposed_params, [2, 1, 3]);
  99. derivative_params = cat(1, zeros(1, size(decomposed_params, 2), num_blocks), diff(decomposed_params, [], 1));
  101. % model first volume separately from others due to scanner pause within
  102. % trial
  103. ders1 = zeros(size(derivative_params));
  104. ders23 = ders1;
  105. ders1(1:3:end, :, :) = derivative_params(1:3:end, :, :);
  107. sel = setdiff(1:num_scans_per_block,1:3:num_scans_per_block);
  108. ders23(sel, :, :) = derivative_params(sel, :, :);
  110. all_realignment_parameters = cat(2, ...
  111. decomposed_params,...
  112. decomposed_params .^ 2,...
  113. ders1,...
  114. ders23);
  116. %% make filter regs
  117. % initiate time vector
  118. time_vec = pseudo_TR:pseudo_TR:pseudo_TR * num_pseudo_volumes;
  119. time_end_2pi = time_vec(end) ./ 2 ./ pi;
  121. % find number of regressors
  122. num_regressors = 1:ceil(fmri_high_pass_freq / (1 / time_vec(end)));
  124. % construct sines and cosines
  125. cosines = cos(num_regressors' * (time_vec / time_end_2pi))';
  126. sines = sin(num_regressors' * (time_vec / time_end_2pi))';
  128. % make filter struct
  129. filt.regs=[cosines(mid_volume_indices,:) sines(mid_volume_indices,:)];
  130. filt.requested_freq=fmri_high_pass_freq;
  131. filt.real_freq=num_regressors(end) / time_vec(end);
  133. %% nuisance regressors
  134. nuis_names = vertcat(config_nuis_and_task_regs.name);
  136. % extract from EEG data: stim onset relative to scanner trigger before
  137. % stimulus, values of received triggers, reaction time values relative to
  138. % stimulus onset and sampling rate
  139. [...
  140. all_stim_onset_rel_to_A1, ...
  141. all_trigger_values_vec, ...
  142. all_reaction_times,...
  143. sampling_rate] = tc_get_event_times_from_eeg(EEG_parameters, scanner_trigger, skip_first_N_scanner_triggers_eeg, response_trigger);
  145. % apply slice time correction to regressors
  146. all_stim_onset_rel_to_A1 = all_stim_onset_rel_to_A1 + mark_offset + slicetime_offset;
  148. % extract trial type (e.g. odd ball or left or response, etc) and indices
  149. % of responses themselves
  150. trial_types = tc_get_events_from_trigger_values(...
  151. all_trigger_values_vec, ... % from above
  152. left_triggers,... % triggers for left stimuli
  153. right_triggers, ... % triggers for right stimuli
  154. [left_triggers(1), right_triggers(1)], ... % non odd balls
  155. 99, ... % code for response trigger value
  156. all_reaction_times, ... % reaction times for invalid eresponse detection
  157. sampling_rate, ... % sampling rate to convert reaction time
  158. stim_duration); % stimulus duration to exclude responses that were hereafter
  160. % only select valid reaction times
  161. all_valid_reaction_times = tc_get_reaction_times(parameters);
  163. % attach blink trial data
  164. load(blinks_file, 'no_blinks')
  165. trial_types.blinks = ~no_blinks;
  166. trial_types.no_blinks = no_blinks;
  168. % attach artifact trial data
  169. load(art_file, 'sel_trls')
  170. no_artifacts = sel_trls;
  171. trial_types.artifacts = ~no_artifacts;
  172. trial_types.no_artifacts = no_artifacts;
  174. % make reaction time a vector of length trial number
  175. reaction_time_vec = zeros(size(trial_types.hit));
  176. reaction_time_vec(trial_types.response) = all_valid_reaction_times;
  178. % construct regressors
  179. nuis_regs = {};
  181. % loop over regressor configuration
  182. for conf_reg = config_nuis_and_task_regs
  184. % concat logical statements to select trials
  186. % multiple selection criteria
  187. if length(conf_reg.trial_sel) > 1
  188. trial_sel = trial_types.(conf_reg.trial_sel{1});
  189. for trial_type = conf_reg.trial_sel(2:end)
  190. trial_sel = trial_sel & trial_types.(trial_type{1});
  191. end
  193. % single selection criteria
  194. elseif length(conf_reg.trial_sel) == 1
  195. trial_sel = trial_types.(conf_reg.trial_sel{1});
  197. % all trials
  198. else
  199. trial_sel = ones(size(trial_types.left));
  200. end
  202. % extract stimulus onsets
  203. rel_to_A1 = all_stim_onset_rel_to_A1(trial_sel);
  205. % determine stimulus onset offset (e.g. for change in fixation)
  206. onset_setoff = conf_reg.onset;
  208. % determine whether single or multiple parameter setup. In the later
  209. % case it has to be determined according to specifications
  210. if ischar(onset_setoff{1})
  211. switch onset_setoff{1}
  212. case 'rt'
  213. onset_setoff = reaction_time_vec(trial_sel);
  214. end
  215. else
  216. onset_setoff = onset_setoff{1};
  217. end
  219. % see above
  220. duration = conf_reg.duration;
  221. if ischar(duration{1})
  222. switch duration{1}
  223. case 'mean_rt'
  224. duration = mean(reaction_time_vec(trial_sel));
  225. end
  226. else
  227. duration = duration{1};
  228. end
  230. % see above
  231. pmod = conf_reg.modulation;
  232. if ischar(pmod{1})
  233. switch pmod{1}
  234. case 'rt'
  235. pmod = reaction_time_vec(trial_sel);
  236. end
  237. else
  238. pmod = pmod{1};
  239. end
  241. % append regressor
  242. nuis_regs = cat(1, nuis_regs, {[...
  243. rel_to_A1 + onset_setoff, ...
  244. ones(size(rel_to_A1)) .* duration,...
  245. ones(size(rel_to_A1)) .* pmod,...
  246. block_vec(trial_sel)]});
  247. end
  249. %% convolve regressors
  250. nuis_regs_conv = tc_conv_regs(nuis_regs, nuis_names, config_conv, mid_volume_indices);
  252. %% make design matrix
  253. is_parmod_vec = cell2mat([config_nuis_and_task_regs.isparmod])';
  255. R_all_blocks = [];
  257. for block = 1: num_blocks
  259. is_not_parmod = ~is_parmod_vec(cellfun(@(x) logical(sum(strcmpi(x, [nuis_regs_conv.(['b' num2str(block)]).names]))), nuis_names));
  261. R = double([...
  262. nuis_regs_conv.(['b' num2str(block)]).task(:, is_not_parmod),...
  263. nuis_regs_conv.(['b' num2str(block)]).par,...
  264. white_matter_signal_res(:, block),...
  265. not_gray_not_white_matter_signal_res(:, block),...
  266. all_realignment_parameters(:, :, block),...
  267. T1_avg_white_drop_off',...
  268. filt.regs...
  269. ]);
  271. R_all_blocks.(['b' num2str(block)]) = R;
  272. save(sprintf(design_matrix_file, block), 'R')
  273. end
  274. R_all_blocks.config_nuis_and_task_regs = config_nuis_and_task_regs;
  275. R_all_blocks.config_conv = config_conv;
  276. save(strrep(design_matrix_file, 'B%d', 'fmri'), 'R_all_blocks')

a_confound_and_task_regressors.m at commit f076311, under MIT · at the source

Overview

Authors: Tommy Clausner1,2, José P Marques2, René Scheeringa1,2, Mathilde Bonnefond1,3
ORCID iDs: Tommy Clausner, José P Marques, René Scheeringa, Mathilde Bonnefond
  1. Lyon Neuroscience Research Center, Computation, Cognition and Neurophysiology COPHY, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1 Lyon France
  2. Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen Netherlands
  3. INRIA, Computation, Cognition and Neurophysiology COPHY Bron France
Institutions: Lyon 1 Université (France); Radboud University Nijmegen (Netherlands); Institut national de recherche en sciences et technologies du numérique (France)
Journal: eLife, volume 14, article RP108408
Dates: published online 3 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108408 · PMID 42689526 · PMCID PMC13541303 · OpenAlex W4415107794
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fMRI (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Source localization, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: laminar fMRI, EEG, oscillations, alpha, gamma, visual features, Human
MeSH: Electroencephalography*, Magnetic Resonance Imaging*, Visual Cortex*, Visual Perception*, Brain Mapping, Female, Humans, Male (* major topic)
Journal subjects: Neuroscience
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (10.3030/716862); Fondation pour la Recherche Médicale (FDT202106013010); Labex Cortex (ANR-10-LABX-0042)
Citations: cited by 1 paper (Europe PMC); 117 references in the paper

Abstract

The role of cortical oscillations in brain function has been extensively debated, resulting in a variety of theoretical frameworks. Using interleaved simultaneous electroencephalography–functional magnetic resonance imaging, we examined the layer-specific relationship between oscillatory activity and visual processing. We could demonstrate that γ band activity positively correlates with feature-specific signals in superficial layers, but we were able to report a deep layer contribution as well. In addition, we could demonstrate that α band power not only correlates negatively with the feature-unspecific BOLD signal but is related to feature-specific BOLD as well. Lower frequency α was predominantly related to feature-unspecific superficial layer BOLD, while upper frequency α was found to be related to feature-specific BOLD in superficial and deep layers. We conclude that the role of α band oscillations extends beyond widespread inhibition and might be involved in active stimulus processing on the level of visual features.

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 18 matches between paragraphs and lines of code.

TommyClausner/laminarfMRIv2

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f076311ff3dd4fa924067657247603e111f615fe, 10 August 2026
Languages: MATLAB (119), Shell (72), Python (10)
Size: 470 files, 201 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (44 files), Statistics and Machine Learning Toolbox (39 files), FSL (25 files), FreeSurfer (16 files), Tools for NIfTI and ANALYZE image (MATLAB) (11 files), ANTs (10 files), Matplotlib (7 files), NumPy (6 files), SPM (6 files), pandas (5 files), seaborn (5 files), Image Processing Toolbox (3 files), h5py (2 files), Signal Processing Toolbox (2 files), scikit-image (2 files), SciPy (2 files), statsmodels (2 files), NiBabel (1 file), Pingouin (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
203 files
At the source: github.com/TommyClausner/laminarfMRIv2

Zenodo 7211757

License: other-open
State: the link answers, verified on 26 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: DIPY (1 file), Matplotlib (1 file), NiBabel (1 file), NumPy (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
3 files
At the source:

tommyclausner/mri-volume-masker-3000-tm

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 28edfc8cef53197f6725c285bf914b13ddcd0305, 16 October 2022
Languages: Python (1)
Size: 7 files, 1 script
Software Heritage: not checked
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: DIPY (1 file), Matplotlib (1 file), NiBabel (1 file), NumPy (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
3 files
At the source: github.com/TommyClausner/MRI-Volume-Masker-3000-TM/tree/v1.0.0

The paper's code and data availability statement is in the Data section.

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  • 3 repositories 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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Data

No dataset and no data link were found in the paper.

Data availability

All raw eye tracking, EEG and fMRI data, as well as all derivatives have been deposited at https://doi.org/10.34973/w2z2-2c91 but are not fully publicly available due to data protection regulations. The data are available to all bona fide researchers via the Radboud Data Repository (https://data.ru.nl/login) upon agreement to the Data User Agreement, version RU-HD-SU-1.0 (https://data.ru.nl/dua/RU-HD-SU-1.0). The code for the analyses presented in this paper is openly accessible at https://github.com/TommyClausner/laminarfMRIv2 (copy archived at Clausner, 2026).

The following dataset was generated:

ClausnerT MarquesJ ScheeringaR BonnefondM 2026Frequency and Laminar Profile of Feature Specific and nonspecific Neural Activity in Human Visual Cortex Revealed by Interleaved EEG-fMRIRadboud Data Repository10.34973/w2z2-2c91PMC1354130342689526

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 7 keywords, 8 MeSH terms, 3 funders, 109 references.

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Clausner, T., Marques, J. P., Scheeringa, R., & Bonnefond, M. (2026). Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI. eLife, 14, RP108408. https://doi.org/10.7554/elife.108408

BibTeX

@article{clausner2026frequency,
author = {Clausner, Tommy and Marques, José P and Scheeringa, René and Bonnefond, Mathilde},
title = {{Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI}},
journal = {eLife},
year = {2026},
month = sep,
volume = {14},
pages = {RP108408},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108408},
url = {https://doi.org/10.7554/elife.108408},
pmid = {42689526},
pmcid = {PMC13541303}
}

RIS

TY - JOUR
AU - Clausner, Tommy
AU - Marques, José P
AU - Scheeringa, René
AU - Bonnefond, Mathilde
TI - Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/09/03
VL - 14
SP - RP108408
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108408
UR - https://doi.org/10.7554/elife.108408
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI",
"container-title": "eLife",
"author": [
{
"family": "Clausner",
"given": "Tommy"
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"given": "José P"
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{
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"given": "René"
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{
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"given": "Mathilde"
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],
"container-title-short": "Elife",
"volume": "14",
"page": "RP108408",
"DOI": "10.7554/elife.108408",
"PMID": "42689526",
"PMCID": "PMC13541303",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.108408",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
3
]
]
}
}

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