Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Methods › fMRI Data Analysis and Statistical Assessment ↔ Analysis/D/D2_cluster_detection_4D.m, the whole file · a weak match · score 0.62 · cluster forming threshold, bwconncomp, connected, maps, temporal, voxels
[2] § Methods › fMRI Data Analysis and Statistical Assessment ↔ Analysis/C/C12_glm_1stLevel.m, lines 126–139 · score 0.56 · auto correlation, threshold, filtering, temporal, model, fMRI
[3] § Methods › fMRI Data Analysis and Statistical Assessment ↔ Analysis/C/C01_first_level_batch_BIDS_function.m, lines 127–152 · score 0.55 · high pass filter, regressor, realignment, beta, correlation, detrending
[4] § Methods › Experimental Procedure ↔ Acquisition/experimental_script.m, lines 21–87 · score 0.52 · stimulus slice, parallel, triggers, log, stimulator, volume

Paper

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The authors' code

MATLAB · 75 lines · 2.2 KB · no license · 1 match

function [maxArea, keepers, L, file_4D, CC, info] = D2_cluster_detection_4D(dir_2nd, cluster_thresh_p, n2, min_size, num_bins, this_sign)
% cluster_detection_4D searches for connected components throughout many 3D
% NIFTIs that belong to statistical maps created through F-contrasts
% OUTPUT:
% maxArea: Voxel/Time-point count of the biggest cluster in this analysis
% keepers: 4D map of all the clusters resulting from this analysis
% under the assigned parameters (see input)
% L: 4D labels of all clusters resulting from the analysis
% INPUT:
% dir_2nd: directory containg all the 2nd level F-maps
% cluster_thresh_p: cluster-forming threshold as a p-value
% n2: second degrees of freedom of the F-test
% min_size: cluster size cutoff for the output "keepers"
% num_bins: number of temporal bins or number of F-maps to
% include
% cluster_thresh_p = 0.001; % like uncorrected
n1 = 1; % df 1: each F-map separatly
% cluster_thresh_F = tinv(1-cluster_thresh_p,n2);
cluster_thresh_T = tinv(1-cluster_thresh_p,n2); % t-contrasts: one - and one +
if this_sign == -1
con_nums = 1:num_bins;
else
con_nums = (num_bins+1):num_bins*2;
end
counter = 0;
for sz = con_nums
counter = counter + 1;
if sz < 10
t_str = [dir_2nd filesep 'spmT_000' num2str(sz) '.nii'];
elseif sz < 100
t_str = [dir_2nd filesep 'spmT_00' num2str(sz) '.nii'];
else
t_str = [dir_2nd filesep 'spmT_0' num2str(sz) '.nii'];
end
if counter == 1
temp = niftiread(t_str);
file_4D = zeros([size(temp), num_bins]);
clear temp
end
file_4D(:,:,:,counter) = niftiread(t_str);
end
info = niftiinfo(t_str);
binary_4D = zeros(size(file_4D));
binary_4D(file_4D>cluster_thresh_T) = 1;
CC = bwconncomp(binary_4D);
L = labelmatrix(CC);
prop = regionprops(CC,"Area");
[maxArea,maxIdx] = max([prop.Area]);
[cluster_sizes, sortingIdx] = sort([prop.Area], 'descend'); % so that B = A(I)
kickIdx = sortingIdx(cluster_sizes<min_size);
idx = setdiff(1:CC.NumObjects,kickIdx);
keepers = cc2bw(CC,ObjectsToKeep=idx);
end

D2_cluster_detection_4D.m at commit a5aaaaa, no license · at the source

Overview

Authors: Sara Wesolek¹, Till Nierhaus¹, Dirk Ostwald², Felix Blankenburg¹

ORCID iDs: Dirk Ostwald

Institutions: Freie Universität Berlin (Germany); Otto-von-Guericke-Universität Magdeburg (Germany)

Journal: Human brain mapping, volume 47, issue 4, article e70507

Dates: received 5 September 2025; accepted 7 March 2026; published online 16 March 2026; in print March 2026

Type: Research article · Language: English

License: CC BY-NC

Identifiers: DOI 10.1002/hbm.70507 · PMID 41834676 · PMCID PMC13093736 · OpenAlex W7136503066

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), stroke (population), systems (subfield)

Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: 4D fMRI, finite impulse response models, fMRI, S1, S2, somatosensory cortex

MeSH: Brain*, Brain Mapping*, Evoked Potentials, Somatosensory*, Magnetic Resonance Imaging*, Somatosensory Cortex*, Adult, Cerebrovascular Circulation, Female, Humans, Image Processing, Computer-Assisted, Male, Oxygen, Time Factors, Young Adult (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

The blood oxygen level‐dependent (BOLD) signal has been instrumental in characterizing brain activity. While the spatial resolution of fMRI continues to improve, relatively few methods have focused on enhancing and leveraging temporal resolution to investigate the spatiotemporal dynamics of the hemodynamic response. In this study, we applied a reordering method to achieve ultra‐high temporal resolution (60 ms) in data acquired during a somatosensory stimulation paradigm. We then used a finite impulse response model (FIR) for each participant (N = 31) to preserve the temporal dynamics in the statistical analysis. At the group level, we employed an ANOVA combined with 4D nonparametric permutation testing to identify significant signal changes in time across the whole brain. Our results characterize the hemodynamic response in terms of both its temporal and spatial patterns and reveal distinct differences in response shapes within the somatosensory system. This method introduces a time‐resolved approach to BOLD signal analysis, drawing inspiration from grand‐average techniques commonly used in EEG research.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Neurocomputation-and-Neuroimaging-Unit/Somatosensory_Evoked_BOLD-signals

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: a5aaaaad6231789201e40d870b23439e42009691, 16 January 2026

Languages: MATLAB (29)

Size: 33 files, 29 scripts

Software Heritage: not archived

Found in: the text, “fMRI Data Analysis and Statistical Assessment”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: SPM (16 files), Image Processing Toolbox (6 files), Statistics and Machine Learning Toolbox (4 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), Psychtoolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

30 files

Acquisition/config_io.m, MATLAB, 13 lines
Acquisition/experimental_script.m, MATLAB, 165 lines, 1 match
Acquisition/test_double.m, MATLAB, 16 lines
Analysis/A/A0_dicom2bids_batch.m, MATLAB, 156 lines
Analysis/A/A1_dicm2bids.m, MATLAB, 300 lines
Analysis/B/B0_prepro_SEBs_Batch_function.m, MATLAB, 408 lines
Analysis/B/B1_Realignment_all_runs.m, MATLAB, 49 lines
Analysis/B/B2_detrending_lmgs.m, MATLAB, 546 lines
Analysis/B/B3_calculate_SEBS.m, MATLAB, 60 lines
Analysis/B/B4_coregister_est.m, MATLAB, 40 lines
Analysis/B/B5_segmentation.m, MATLAB, 59 lines
Analysis/B/B6_normalization_run.m, MATLAB, 42 lines
Analysis/B/B7_smoothing_run.m, MATLAB, 35 lines
Analysis/C/C01_first_level_batch_BIDS_function.m, MATLAB, 267 lines, 1 match
Analysis/C/C02_second_level_batch_BIDS.m, MATLAB, 71 lines
Analysis/C/C03_2nd_level_shuffle_function.m, MATLAB, 41 lines
Analysis/C/C12_glm_1stLevel.m, MATLAB, 153 lines, 1 match
Analysis/C/C13_contrast_1stLevel.m, MATLAB, 37 lines
Analysis/C/C14_1st_level_FIR.m, MATLAB, 139 lines
Analysis/C/C21_2ndLevel_OneSampleTest.m, MATLAB, 89 lines
Analysis/C/C22_2ndLevel_1wayANOVA.m, MATLAB, 70 lines
Analysis/C/C31_1st_level_FIR_shuff.m, MATLAB, 78 lines
Analysis/D/D0_permutation_statistics_batch.m, MATLAB, 395 lines
Analysis/D/D1_contrasts_function.m, MATLAB, 31 lines
Analysis/D/D2_cluster_detection_4D.m, MATLAB, 75 lines, 1 match
Analysis/D/ROI_on_FIR.m, MATLAB, 158 lines
Analysis/D/ROI_on_GLM.m, MATLAB, 69 lines
Analysis/D/get_region_from_anatomy.m, MATLAB, 61 lines
Analysis/D/get_region_from_anatomy_HCPex.m, MATLAB, 27 lines
README.md, Text, 30 lines

At the source: github.com/Neurocomputation-and-Neuroimaging-Unit/Somatosensory_Evoked_BOLD-signals

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
29 scripts, each with its path and the digest of its content;
4 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

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Reproduced under the paper's license (CC BY-NC), 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 14 MeSH terms, 39 references.

Cite

This paper

Wesolek, S., Nierhaus, T., Ostwald, D., & Blankenburg, F. (2026). Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution. Human brain mapping, 47(4), e70507. https://doi.org/10.1002/hbm.70507

BibTeX

@article{wesolek2026somatosensory, author = {Wesolek, Sara and Nierhaus, Till and Ostwald, Dirk and Blankenburg, Felix}, title = {{Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution}}, journal = {Human brain mapping}, year = {2026}, month = mar, volume = {47}, number = {4}, pages = {e70507}, publisher = {Wiley}, issn = {1065-9471}, doi = {10.1002/hbm.70507}, url = {https://doi.org/10.1002/hbm.70507}, pmid = {41834676}, pmcid = {PMC13093736} }

RIS

TY - JOUR AU - Wesolek, Sara AU - Nierhaus, Till AU - Ostwald, Dirk AU - Blankenburg, Felix TI - Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution T2 - Human brain mapping J2 - Hum Brain Mapp PY - 2026 DA - 2026/03/01 VL - 47 IS - 4 SP - e70507 SN - 1065-9471 PB - Wiley DO - 10.1002/hbm.70507 UR - https://doi.org/10.1002/hbm.70507 LA - en ER -

CSL-JSON

{ "id": "10.1002/hbm.70507", "type": "article-journal", "title": "Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution", "container-title": "Human brain mapping", "author": [ { "family": "Wesolek", "given": "Sara" }, { "family": "Nierhaus", "given": "Till" }, { "family": "Ostwald", "given": "Dirk" }, { "family": "Blankenburg", "given": "Felix" } ], "container-title-short": "Hum Brain Mapp", "volume": "47", "issue": "4", "page": "e70507", "DOI": "10.1002/hbm.70507", "PMID": "41834676", "PMCID": "PMC13093736", "ISSN": "1065-9471", "publisher": "Wiley", "URL": "https://doi.org/10.1002/hbm.70507", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 1 ] ] } }

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