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Exploring the Sensitivity Limits of Neuronal Current Imaging With MRI and MEG in the Human Brain.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and Methods › MRI Data Analysis ↔ Analysis_MRI/Trento_pipeline.m, lines 36–75 · score 0.73 · processing pipeline, block duration, FSL, baseline, cutoffs, realignment
  2. [2] § Materials and Methods › MRI Data Analysis ↔ Analysis_MRI/Trento_pipeline.m, lines 36–75 · score 0.70 · high resolution, RFR procedure, FSL, baseline, cutoff, regression
  3. [3] § Materials and Methods › MRI Data Analysis ↔ Analysis_MRI/postproc.m, lines 23–76 · score 0.61 · high pass filter, SVarM, SPM, breathing, regression, power

Paper

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The authors' code

MATLAB · 208 lines · 8.2 KB · MIT · 2 matches

  1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  2. % RFR_Trento_Pipeline
  3. %
  4. % This script performs the RFR analysis on a set of subjects' data.
  5. %
  6. % Runing this file will:
  7. % 1. Define pre-processing parameters
  8. % 2. Select the subject(s) to analyse
  9. % For each subject:
  10. % 3. Prepare data structure according to Trento acquisition
  11. % 3.1. Import functional and anatomical data
  12. % 3.2. (IF) Realignment
  13. % 3.3. (IF) Segmentation of the anatomical data and corregistration
  14. % 4. Run different metric analysis (RFR, SVarM, GLM, ICA)
  15. % 5. Call for statistical analysis pipelines
  16. %
  17. % Warning!
  18. % This pipeline is tailored to run in the Trento dataset.
  19. % Feel free to adjust it to the needs of your own acquisition!
  20. %
  21. % Authors:
  22. % Milena Capiglioni
  23. %
  24. % Date: started 2024/07,
  25. % 2025/02: unified RFR pipeline (check with notes)
  26. % 2025/08: added ICA denoising option
  27. %
  28. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  30. clear; clc; close all;
  32. % Add necessary paths
  33. addpath('/home/user/matlab/PostProcessing/Trento/functions')
  34. addpath('/home/user/matlab/PostProcessing/Trento/Analysis_MRI/')
  36. %% 1. Define processing parameters
  38. % Define functional sequences to analyze with the RFR procedure
  39. func_seq = {'NEMO_36_Hz_alll'}; %
  41. % Define the name of the anat sequence
  42. t1_seq = '001_csMP2RAGE_1mm_iso_Lambda^0';
  44. % Pre-processing parameters
  45. realignment_mode = 'none'; % 'none' (no realignment) // 'spm' // 'fsl'
  46. all_slices = 1;
  47. coreg.cor = 1; % non-linear cor of high resolution
  48. coreg.tool = 'fsl'; % tool for cor
  49. ICA_denoise = 0; % requires manual intervention
  50. DL_segment = 1;
  52. % RFR parameters
  53. regression.type = 0; % 0=baseline // 1=baseline+hrf // 2=baseline+hrf+mov
  54. filter.cutoff = 0.25;
  55. filter.type = 1;
  57. % Debug images are printed at each step of the processing pipeline
  58. debug = 0;
  60. % Deal with prohibited options TODO
  61. if strcmp(realignment_mode,'none') && regression.type == 2
  62. error('You cannot regress movement parameters without performing realignment')
  63. end
  65. % Acquisition parameters
  66. dummy_scans = 12;
  68. % Stimulation parameters
  69. stim.number_blocks = 24;
  70. stim.block_duration = 16;
  71. stim.freq = 1/(stim.block_duration); % Hz
  72. stim.one_side_duration = stim.block_duration * stim.number_blocks/2;
  74. % Set the starting path where data is
  75. cd('/str/data/Analysis/Trento/Analysis_paper/MR_measurements/');
  77. %% 2. Select the subject(s) to analyse
  79. % multiple_subjects = input('Do you want to analyze more than one subject? 0=NO 1=YES: ');
  80. multiple_subjects = 1;
  81. if multiple_subjects == 0 % Select single subject
  82. % work_dir_aux = strcat(uigetdir(path,'Select subject folder'),filesep);
  83. work_dir_aux = '/str/data/Analysis/Trento/Analysis_paper/MR_measurements/v_20230508_01_PG_LUCR/';
  84. aux = strsplit(work_dir_aux,filesep);
  85. work_dir_aux = strcat(strjoin(aux(1:end-2),filesep),filesep);
  86. subjects.name = aux{end-1};
  87. else % Select folder were all the subject are
  88. work_dir_aux = strcat(uigetdir(path,'Select folder with subjects'),filesep);
  89. cd(work_dir_aux);
  90. files = dir(work_dir_aux);
  91. aux = contains({files.name},{'v_20'})&[files.isdir];
  92. subjects = files(aux);
  93. end
  95. clear aux multiple_subjects files
  97. %% For each subject, run pipelines
  99. for sub = 12
  101. work_dir = strcat(work_dir_aux,subjects(sub).name,filesep);
  102. disp(['Initiating processing for Subject ', num2str(sub), '/', num2str(length(subjects)), ': ',subjects(sub).name, ' ' ])
  103. cd(work_dir);
  105. all_folders = dir;
  106. aux = ismember({all_folders.name},func_seq)&[all_folders.isdir];
  107. func_folders = all_folders(aux);
  108. if isempty(func_folders)
  109. disp(['Subject', subjects(sub).name, ' does not have any sequence of interest']);
  110. continue;
  111. end
  113. for f = 1:length(func_folders)
  115. % 3 - Prepare data structure according to Trento acquisition
  116. % Create output folder
  117. if coreg.cor == 0
  118. txt_cor = '';
  119. else
  120. txt_cor = ['-cor' coreg.tool];
  121. end
  122. if ICA_denoise == 0
  123. txt_ICA = '';
  124. else
  125. txt_ICA = '-ICA';
  126. end
  127. if strcmp(realignment_mode,'none')
  128. txt_real = '';
  129. else
  130. txt_real = ['-real' realignment_mode];
  131. end
  132. out_dir = [work_dir 'Analysis' filesep 'Corregistration_test' filesep func_folders(f).name [txt_real '-reg' num2str(regression.type) '-hpf' num2str(filter.type) '-' num2str(filter.cutoff) txt_cor txt_ICA '-TEST_data'] filesep];
  134. if ~exist(out_dir,'dir')
  135. mkdir(out_dir);
  136. elseif ~isempty(dir([out_dir '*.mat']))
  137. continue;
  138. end
  140. % Read functional data
  141. func_data_path = [work_dir func_folders(f).name filesep];
  142. func_nii_path = create_nii__(func_data_path,[out_dir 'preproc' filesep 'func' filesep],'name','func','dummy_scans',dummy_scans);
  144. % Perform realigment of functional data
  145. [data_path, regression.movement_params, frame_disp] = Realign_func(func_nii_path,realignment_mode,all_slices,1); % TODO: define if this changes the func_path or if it is selected later!
  147. % Read anatomical data
  148. anat_folder = [out_dir 'preproc' filesep 'anat' filesep]; % Folder where the anatomy data will be
  149. anat_hr = [anat_folder 'T1w_norm.nii']; % Define name that the anatomy should have
  150. anat_hr_seg = [anat_folder 'T1w_norm_seg.nii']; % Define name that the segmented anatomy will have
  151. if ~(exist(anat_folder,'dir') && exist(anat_hr,'file') && exist(anat_hr_seg,'file') )
  152. mkdir(anat_folder);
  153. if DL_segment
  154. try
  155. model = 0; % 0 = v0 -> Desikan-Killiany , 1 = v7 -> Destrieux atlas
  156. DLdirect_path = DLdirect_subject_space_Trento(work_dir,t1_seq,model); % Run DL+DiReCT on anatomical image or obtain path to segmented data
  157. catch
  158. disp('Subject space DL segmentation failed, procesing with standard DL pipeline');
  159. waitforbuttonpress;
  160. DLdirect_path = DLdirect(work_dir,t1_seq); % Run DL+DiReCT on anatomical image or obtain path to segmented data
  161. end
  162. system(['cp ' DLdirect_path 'T1w_norm.nii ' anat_folder]);
  163. system(['cp ' DLdirect_path 'T1w_norm_seg.nii ' anat_folder]);
  164. else
  165. [anat_hr, anat_hr_seg, vc_mask] = Segment_anatomy(t1_seq, anat_folder)
  166. end
  167. end
  169. % Corregister to anatomical image
  170. [seg_data_path] = Corregister_func_anat_3(data_path,anat_hr,anat_hr_seg,coreg.cor,coreg.tool);
  172. % Separate between left and right stimulation
  173. func{1}.path = data_path;
  174. [func{1}.path,func{2}.path] = nii_split(data_path);
  175. func{1}.txt = 'Side1_Stim_Left';
  176. func{2}.txt = 'Side2_Stim_Right';
  178. if ICA_denoise
  179. numIC = '';
  180. func{1}.path = melodic_ICA(func{1}.path, seg_data_path, stim, numIC);
  181. func{2}.path = melodic_ICA(func{2}.path, seg_data_path, stim, numIC);
  182. end
  184. % Split movement parameters and assign to regression
  185. if regression.type == 2
  186. [movement_path_1,movement_path_2] = separate_table(regression.movement_params);
  187. end
  189. var_val = {func_folders(f).name,t1_seq,realignment_mode,all_slices,dummy_scans}';
  190. var_name = {'func_seq:';'t1_seq:';'pre_proc:';'all_slices:';'dummy_scans:'};
  191. T = table(var_val,'RowNames',var_name);
  192. writetable(T,[out_dir,'preproc/func/preprocessing_params.txt'],'Delimiter','\t','WriteRowNames',true)
  194. %% Metrics calculation
  196. % RFR & SVarM
  197. outdir_post = [out_dir 'postproc' filesep];
  198. regression.movement_params = movement_path_1;
  199. postproc(func{1}.path,seg_data_path,stim,filter,regression,[outdir_post 'side1' filesep]);
  200. regression.movement_params = movement_path_2;
  201. postproc(func{2}.path,seg_data_path,stim,filter,regression,[outdir_post 'side2' filesep]);
  203. %% Statistical analysis
  204. % metrics(func{1}.path,seg_data_path,outdir_post,stim,'side1',func_folders(f).name);
  205. % metrics(func{2}.path,seg_data_path,outdir_post,stim,'side2',func_folders(f).name);
  207. end
  208. end

Trento_pipeline.m at commit 545472e, under MIT · at the source

Overview

Authors: Milena Capiglioni1,2, Davide Tabarelli3,4,5, Stefano Tambalo3,6,7, Federico Turco1, Roland Wiest1, Jorge Jovicich3
ORCID iDs: Milena Capiglioni, Davide Tabarelli, Stefano Tambalo, Federico Turco, Roland Wiest, Jorge Jovicich
  1. Support Center for Advanced Neuroimaging (SCAN), Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern Bern Switzerland
  2. High‐Field MR Center Max Planck Institute for Biological Cybernetics Tübingen Germany
  3. Center for Mind and Brain Sciences (CIMeC) University of Trento Rovereto Italy
  4. Department of Physics University of Trento Trento Italy
  5. Trento Institute for Fundamental Physics and Applications (TIFPA‐INFN), University of Trento Trento Italy
  6. Department of Physics University of Torino Torino Italy
  7. Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
Institutions: University Hospital of Bern (Switzerland); Max Planck Institute for Biological Cybernetics (Germany); University of Trento (Italy); Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications (Italy); University of Turin (Italy)
Journal: Human brain mapping, volume 47, issue 11, article e70624
Dates: received 19 February 2026; accepted 30 July 2026; published online 7 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70624 · PMID 42565248 · PMCID PMC13449028 · OpenAlex W7201830678
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), MEG (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Source localization, fMRI & imaging, Physiology & signal measures
Keywords: MEG, multimodal imaging, neuroelectrical oscillations, non‐BOLD fMRI, spin‐lock fMRI
MeSH: Brain*, Brain Mapping*, Magnetic Resonance Imaging*, Magnetoencephalography*, Adult, Female, Humans, Male, Phantoms, Imaging, Photic Stimulation, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: International Society for Magnetic Resonance in Medicine 2025 ISMRM–Gates Knowledge Exchange Fellowship (Research Exchange Grant 2022); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (CRSII5‐180365)
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Conventional BOLD‐fMRI relies on hemodynamic responses that are temporally and spatially indirect markers of neural activity. Developing alternative contrasts, sensitive to neuroelectrical phenomena, is a critical challenge in brain imaging. Spin‐lock (SL) fMRI has shown promise in phantom studies for detecting magnetic field changes associated with neuronal activity, but its in‐vivo sensitivity and practicality remain unclear. This study evaluated whether SL contrast can effectively detect and localize human neuronal activation, benchmarked against complementary functional modalities, magnetoencephalography (MEG) and 3T BOLD‐fMRI, to assess the sensitivity of MR‐based neuronal current imaging. Thirteen healthy young volunteers underwent SL‐based imaging during 8 Hz visual stimulation, along with BOLD and MEG acquisitions. Subjects viewed quadrant‐checkerboard stimuli to elicit localized cortical responses. Two balanced SL contrast mechanisms, rotary excitation (REX) and stimulus‐induced rotary saturation (SIRS), were employed. Postprocessing targeted stimulus‐locked signal fluctuations using a regression‐filtering‐rectification strategy. Phantom experiments tested sensitivity and analysis pipeline performance. MEG revealed robust stimulus‐locked responses in the occipital cortex, with estimated local magnetic field amplitudes of ~0.07 nT. Conventional BOLD‐fMRI confirmed reliable hemodynamic activation. In contrast, neither balanced REX nor balanced SIRS produced consistent stimulus‐related activation in vivo. Phantom experiments subsequently yielded detection thresholds of 0.2 nT for REX and 0.6 nT for SIRS, exceeding the MEG‐estimated physiological field amplitudes. Under the present experimental conditions, the tested spin‐lock fMRI implementations did not achieve sufficient sensitivity for reliable in vivo detection of neuronal magnetic fields at 3T. Phantom and MEG‐based estimates indicate that physiological field amplitudes in the visual cortex lie below current detection limits. These findings establish quantitative constraints on direct neuronal current imaging with MRI and provide a benchmark for future methodological developments aimed at bridging electrophysiology and functional MRI.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

milecap/MRIMEG_pipelines

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 545472e08820a630e644fc0430e6e40aa2483450, 8 August 2026
Languages: MATLAB (22)
Size: 24 files, 22 scripts
Software Heritage: not archived
Found in: the text, “MRI Data Analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (6 files), SPM (6 files), dcm2niix (4 files), FSL (4 files), Tools for NIfTI and ANALYZE image (MATLAB) (4 files), Statistics and Machine Learning Toolbox (2 files), FieldTrip (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
24 files
At the source: github.com/milecap/MRIMEG_pipelines

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 3 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 presented in this study will be made available upon request to the corresponding author. The source code for data analysis will be made publicly available upon publication at: https://github.com/milecap/MRIMEG_pipelines.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 2 funders, 33 references.

Cite

This paper

Capiglioni, M., Tabarelli, D., Tambalo, S., Turco, F., Wiest, R., & Jovicich, J. (2026). Exploring the Sensitivity Limits of Neuronal Current Imaging With MRI and MEG in the Human Brain. Human brain mapping, 47(11), e70624. https://doi.org/10.1002/hbm.70624

BibTeX

@article{capiglioni2026exploring,
author = {Capiglioni, Milena and Tabarelli, Davide and Tambalo, Stefano and Turco, Federico and Wiest, Roland and Jovicich, Jorge},
title = {{Exploring the Sensitivity Limits of Neuronal Current Imaging With MRI and MEG in the Human Brain}},
journal = {Human brain mapping},
year = {2026},
month = aug,
volume = {47},
number = {11},
pages = {e70624},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70624},
url = {https://doi.org/10.1002/hbm.70624},
pmid = {42565248},
pmcid = {PMC13449028}
}

RIS

TY - JOUR
AU - Capiglioni, Milena
AU - Tabarelli, Davide
AU - Tambalo, Stefano
AU - Turco, Federico
AU - Wiest, Roland
AU - Jovicich, Jorge
TI - Exploring the Sensitivity Limits of Neuronal Current Imaging With MRI and MEG in the Human Brain
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/08/01
VL - 47
IS - 11
SP - e70624
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70624
UR - https://doi.org/10.1002/hbm.70624
LA - en
ER -

CSL-JSON

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