OSCR

Distorting anatomy to test MEG models and metrics.

Code ↔ Paper

7 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 7 matches · 4 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Implementation ↔ coreg_subject_distort.m, the whole file · a weak match · score 0.73 · surface mesh, single shell, FreeSurfer, cortical surface, vertex, distortion · Report a mismatch
  2. [2] § Results › Simulated data ↔ spm_distort_mesh_example.m, lines 161 to 263 · score 0.61 · distort mesh, distorted surfaces, Free energy, matching, EBB, spm · Report a mismatch
  3. [3] § Methods › Diffeomorphic modeling ↔ +brca/@model/model.m, lines 1 to 64 · score 0.60 · latent coordinates, principal component, posterior, residual, covariance, modeling · Report a mismatch
  4. [4] § Methods › Implementation ↔ surfandlf_distort.m, the whole file · a weak match · score 0.58 · single shell, random seed, vertex, field, surface, distortion · Report a mismatch
  5. [5] § Methods › Parameter space ↔ surfandlf_distort.m, the whole file · a weak match · score 0.56 · vertex distances, random seeds, deviation, mm, components, trajectories · Report a mismatch
  6. [6] § Methods › Parameter space ↔ spm_distort_mesh_example.m, lines 70 to 74 · score 0.53 · 8–100, cortical surface, scores, mesh, components, distorted · Report a mismatch
  7. [7] § Methods › Inversion schemes ↔ invert_group_matched.m, the whole file · a weak match · score 0.50 · Cross validation, source reconstruction, Inversion · Report a mismatch

Paper

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The authors' code

MATLAB · 313 lines · 11 KB · no license · 2 matches

  1. %% DEMO SCRIPT TO RUN LOPEZ ET AL. REVISION December 2025 distortion paper
  2. %% GRB December 2025
  4. %% Run code for subject 1
  6. clear all;
  7. close all;
  9. DISTORTANAT=1; %% distort anatomies (only need to do this once)
  12. addpath('D:\spm'); %% spm directory
  13. addpath('D:\spm_distort'); %% directory with distort code from github
  14. rootdir='C:\Users\gbarnes\Documents\jimmytest\'; %% where the downloaded data sits
  16. spm('defaults','eeg');
  17. spm_jobman('initcfg');
  19. [v,r]=spm('ver'); %% Code is currently in the 2025 development version of spm (r=0.0); but not the 2025 'release' version
  20. %% next year (2026) it will be in both versions.
  21. if (str2num(v(4:end))>=26) | (str2num(r)==0),
  22. fprintf('SPM version OK\n')
  23. else
  24. error('Need to use current (>March 2025) development version of SPM : https://github.com/spm/spm')
  25. end;
  28. %% set up the directory structure
  29. datadirectory=fullfile(rootdir,'data');
  30. mri_dir=fullfile(datadirectory,'mri');
  32. outputdirectory=fullfile(rootdir,'output')
  33. mkdir(outputdirectory);
  35. %% where the PCA template for the diffeomorphic code sits
  36. PCAtemplate=fullfile(spm('Dir'),'tpm','shp','Template_0.nii');
  38. %% load in information on the 8 subjects
  39. subjects=create_anon_subject_structure();
  42. %%%% FILES OF AVERAGED MEG DATA AND Time-Frequency WINDOWS
  43. allfname=strvcat('mpinstr_rcinstr_Tafdf.mat','mpdots_rcinstr_Tafdf.mat','mprcresp_Tafdf.mat');
  44. %% inversion choices
  45. allwoi=[ 0 500; -2500 -2000;-200 300]; %% approx where evoked responses sit in these files
  46. invmethods={'EBB','IID','GS'}; %% SPM inversion algorithms
  47. foi=[5 90]; %% frequency window in Hz
  48. patch_size=0.6; %% extent of source (arb units) on cortical surface
  49. n_temp_modes=16; %% number of temporal modes
  50. Nblocks=1; pctest=0; %% no cross validation
  51. swap_subj=[];
  52. %% for cross validation: Nblocks=10; pctest=10; for example
  56. %%%%%%%%%%%%%%%%%%%%% DEFINE PARAMETERS %%%%%%%%%%%%%%%%%%%%
  57. subind=1; %% individual subject data to look at (1-8)
  58. fileind=1; %% file to look at
  60. if length(subind)*length(fileind)>1,
  61. error('Only set up to deal with one subject and one dataset in this example code')
  62. % NB although simple to modify if needed
  63. end;
  65. Npoints=17; %% points on trajectory (17 in paper)
  66. RandSeeds=[1:8]; %% seed which sets the trajectory (runs from 1 to 8 in paper)
  67. %% RandSeeds=[3] to run just one representative seed (faster)
  68. RunClean='Yes'; %% if 'No' already have all surfaces worked out
  70. %% distortion parameters
  71. Zmax=3; %% amount of distortion per component (as Z score)
  72. DistortIndices=[8 100]; %% which PCAs to distort (as in paper)
  73. HeadModel='Single Shell'; %% head model to use
  74. loc_surface='white'; %% cortical surface to use white (in paper) or pial
  79. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% PROCESSING %%%%%%%%%%%%%%%%%%
  81. allFvals=[];
  82. allR2=[];
  83. allcrosserr=[];
  84. allfiles=[];
  85. gainmatfiles={};
  88. if DISTORTANAT,
  89. %% loop through the list of random seeds- each seed defines a trajectory
  90. for rs=1:length(RandSeeds),
  91. RandSeed=RandSeeds(rs);
  93. %% load in average evoked dataset
  94. fname = fullfile(datadirectory,'meg',subjects(subind).subj_id,deblank(allfname(fileind,:)));
  95. D = spm_eeg_load(fname);
  97. %% Decide on the cortical surface to use for this subject (freesurfer/laMEG output)
  98. subj_surf_dir=fullfile(datadirectory, 'surf',sprintf('%s-synth',...
  99. subjects(subind).subj_id),'surf');
  100. %%%% FROM COREG SUBJ ERF
  101. fprintf('\n Using original cortex %s \n',sprintf('%s.ds.gii', loc_surface))
  102. tmp_loc_surface=gifti(fullfile(subj_surf_dir, sprintf('%s.ds.gii', loc_surface)));
  103. %% make new surface without the surface normals (just faces and vertices)..
  104. ds_loc_surface=[]; ds_loc_surface.vertices=tmp_loc_surface.vertices;
  105. ds_loc_surface.faces=tmp_loc_surface.faces; ds_loc_surface.mat=tmp_loc_surface.mat;
  106. fprintf('Removing orienation info from original surface (if it exists)');
  107. surf_name=sprintf('ori_norm_%s_loc.gii', loc_surface);
  108. full_surf_name=fullfile(subj_surf_dir, surf_name);
  109. save(gifti(ds_loc_surface),full_surf_name);
  111. %% now make a unique file name based on datafile and surface
  112. subj_outputdirectory=fullfile(outputdirectory,subjects(subind).subj_id);
  113. mkdir(subj_outputdirectory);
  114. [a1,b1,c1]=spm_fileparts(fname)
  115. coreg_fname=fullfile(subj_outputdirectory, sprintf('loc_%s_%s_norm.mat',loc_surface,b1))
  116. %% make copy of original fname and coregister this file to the chosen surface
  117. fprintf('\n Running initial coreg')
  118. coreg_fname=coreg_subject_distort(subjects(subind), deblank(allfname(fileind,:)),full_surf_name,coreg_fname,HeadModel,datadirectory);
  119. fprintf('\n Now running distortion')
  120. %% now make some distorted brians (distorted brain)
  121. %% for each brain and brian compute a lead-field
  122. [aM,aL]=surfandlf_distort(coreg_fname,RandSeed,HeadModel,Npoints,Zmax,DistortIndices,RunClean);
  124. %% now compute distances of brians to brain.
  125. M0=gifti(aM{1}.brain); %% get original cortex
  126. for i=1:Npoints,
  127. M1=gifti(aM{1}.brians{i});
  128. d0=M1.vertices-M0.vertices;
  129. d1(i)=mean(sqrt(dot(d0',d0'))); %% mean vertex-vertex distance to M0
  130. end;% for i
  133. %% now quantify how much the lead fields for the distorted surfaces differ from the original
  134. %% first LF is the original
  135. G=load(aL{1}.gainmat{1}); %% original lead fields
  136. L0=G.G;
  137. diffL=[];
  138. for i=1:Npoints,
  139. G=load(aL{1}.gainmat{i+1});
  140. L1=G.G;
  141. diffL(i)=mean(dot(L1-L0,L1-L0));
  142. end; % for i
  145. %%&%%%%%%%%%%%%%%%%% Now run inversion models %%%%%%%%%%%
  146. %%% trajectory includes the new brians and the original brain
  148. %% get a list of the lead-field files
  149. gainmatfiles(rs,1:Npoints+1)=aL{1}.gainmat;
  150. spmfilename=aL{1}.Dc{1};
  151. end; % for rs
  152. save([rootdir filesep 'distortwkspace.mat']); % keep useful info for this subject
  153. else
  154. load([rootdir filesep 'distortwkspace.mat']); % load info on distorted surfaces
  155. end; % if DISTORTANAT
  158. INVDISTORT=1; %% run inversions on the distorted anatomies (only needs to be set first time it is run)
  161. %% make a copy (MAY NOT BE NECESSARY)
  162. [a1,b1,c1]=spm_fileparts(spmfilename);
  163. newspmfilename=[a1 filesep b1 '_copy' '.mat']
  164. Dorig=spm_eeg_load(spmfilename);
  165. Dorig.copy(newspmfilename)
  166. Dnew=spm_eeg_load(newspmfilename); %% new spmfilename is a copy of Dorig
  169. idstr=sprintf('w%d-%d_f%d-%d_Nb%d_pc%d_pt%d_Nt%d%s',round(allwoi(fileind,1)),round(allwoi(fileind,2)),...
  170. round(foi(1)),round(foi(2)),Nblocks,pctest,patch_size*10,n_temp_modes,swap_subj);
  171. fprintf('\n id is %s\n',idstr)
  173. if INVDISTORT, %% run inversions on these surfaces
  174. everyresults=[];
  175. for rs=1:length(RandSeeds),
  176. RandSeed=RandSeeds(rs);
  177. gmfiles=strvcat(gainmatfiles{rs,1:Npoints+1});
  179. spatialmodesname=fullfile(a1, sprintf('Grpmod_%s_seed%03dNb%d.mat',idstr,RandSeed,Npoints));
  180. [F_vals,R2,VE,crosserr,allresultsfiles]=invert_group_matched(Dnew,gmfiles,spatialmodesname,invmethods,allwoi(fileind,:),foi,idstr,Nblocks,pctest,patch_size,n_temp_modes)
  181. allFvals(rs,:,:)=F_vals;
  182. allR2(rs,:,:)=R2;
  183. allVE(rs,:,:)=VE;
  184. allcrosserr(rs,:,:)=crosserr;
  185. everyresults=strvcat(everyresults,allresultsfiles);
  186. end;
  187. save([rootdir filesep 'inv_distortwkspace.mat']); % keep useful info for this subject
  188. else %% DONT REDO INVERSION JUST LOAD
  189. load([rootdir filesep 'inv_distortwkspace.mat']); % load info on distorted surfaces
  190. end; % INVDISTORT
  194. markerstr={'d','o','s'}
  195. colstr={'b','r','g'}
  196. mspairs=strvcat('bd','ro','gs');
  197. Lw=4; %% line width
  198. Ms=10; %% marker size
  200. figure; %% part 1 of figure for slides
  201. for im1=1:numel(invmethods),
  202. [shortF,shortdist]=simplify_dist_metric(allFvals(:,:,im1),[0 d1]);
  203. hold on;
  204. plotSeed=8; %% shows local maximum
  205. h01=plot(shortdist,squeeze(shortF(plotSeed,:)),colstr{im1});
  206. [dum,ind]=max(squeeze(shortF(plotSeed,:)));
  207. h1=plot(shortdist(ind),shortF(plotSeed,ind),mspairs(im1,:))
  208. set(h01,'LineWidth',Lw);
  209. set(h1(1),'LineWidth',Lw);
  210. set(h1(1),'MarkerSize',Ms);
  211. end;
  213. xlabel('Distortion (mm)')
  214. ylabel('Free Energy')
  215. set(gca,'Fontsize',18);
  218. Nseed=length(RandSeeds)
  220. figure; hold on;
  221. for im1=1:numel(invmethods),
  222. plotSeed=[1:Nseed]; %% show all seeds
  223. [shortF,shortdist]=simplify_dist_metric(allFvals(:,:,im1),[0 d1]);
  224. [dum,ind]=max(squeeze(shortF(plotSeed,:))');
  225. for f=1:length(ind)
  227. h1=plot(shortdist(ind(f)),shortF(plotSeed(f),ind(f))',mspairs(im1,:))
  229. set(h1,'LineWidth',Lw);
  230. set(h1,'MarkerSize',Ms);
  231. end;
  232. end;
  234. hold on;
  235. msumdist=zeros(numel(invmethods),1);
  236. for im1=1:numel(invmethods),
  238. [shortF,shortdist]=simplify_dist_metric(allFvals(:,:,im1),[0 d1]);
  239. [dum,ind]=max(squeeze(shortF(plotSeed,:))');
  240. msumdist(im1)=0;
  241. for f=1:length(ind),
  242. msumdist(im1)=msumdist(im1)+shortdist(ind(f));
  243. end;
  244. msumdist(im1)=msumdist(im1)/length(ind);
  245. ax=axis;
  246. h=plot(msumdist(im1),ax(3),mspairs(im1,:))
  247. set(h,'Markersize',Ms*2)
  248. set(h,'LineWidth',Lw)
  249. end;
  251. xlabel('Distortion (mm)')
  252. ylabel('Free Energy')
  253. set(gca,'Fontsize',18);
  254. axis('tight')
  258. rs=8; %% seed to plot
  259. RandSeed=RandSeeds(rs);
  260. invmethod_example={'EBB'}
  261. gmfiles=strvcat(gainmatfiles{rs,1:Npoints+1});
  262. spatialmodesname=fullfile(a1, sprintf('Grpmod_%s_seed%03dNb%d.mat',idstr,RandSeed,Npoints));
  263. [F_vals,R2,VE,crosserr,allresultsfiles,Mall]=invert_group_matched(Dnew,gmfiles,spatialmodesname,invmethod_example,allwoi(fileind,:),foi,idstr,Nblocks,pctest,patch_size,n_temp_modes);
  265. %% PLOT SOME REAL DATA AND INVERSIONS
  266. figure;
  267. megind=setdiff(Dnew.indchantype('MEG'),Dnew.badchannels)
  268. data=squeeze(D(megind,:,1));
  269. plot(D.time,data)
  270. set(gca,'Fontsize',18);
  271. xlabel('Time (s)')
  272. ylabel('Field (fT)')
  274. ind0=1;
  275. indmaxdist=Npoints+1;
  276. J0=squeeze(Mall(ind0,1,:,:))*data;
  277. Jmax=squeeze(Mall(indmaxdist,1,:,:))*data;
  279. rmsJ0=std(J0');
  280. rmsJmax=std(Jmax');
  281. rmsJtot=rmsJ0+rmsJmax;
  282. [dum,maxsurfind]=max(rmsJtot);
  284. M0=Dnew.inv{1}.mesh.tess_mni;
  285. M0.vertices=M0.vert;
  286. M0.faces=M0.face;
  287. M0i=spm_mesh_inflate(gifti(M0));
  288. figure;
  289. h=trisurf(M0i.faces,M0i.vertices(:,1),M0i.vertices(:,2),M0i.vertices(:,3),rmsJ0)
  290. set(h,'EdgeColor','none');
  291. F_valsrel=F_vals-min(F_vals);
  292. colorbar;
  293. title(sprintf('F rel=%3.2f',F_valsrel(ind0)))
  294. xlabel('x'); ylabel('y');zlabel('z');
  295. set(gca,'Fontsize',18)
  296. caxis([0 0.065])
  297. figure;
  298. h=trisurf(M0i.faces,M0i.vertices(:,1),M0i.vertices(:,2),M0i.vertices(:,3),rmsJmax)
  299. set(h,'EdgeColor','none')
  300. title(sprintf('F rel=%3.2f',F_valsrel(indmaxdist)))
  301. xlabel('x'); ylabel('y');zlabel('z');
  302. set(gca,'Fontsize',18)
  303. caxis([0 0.065])
  304. colorbar;
  306. figure;
  307. h=plot(D.time,J0(maxsurfind,:),D.time,Jmax(maxsurfind,:))
  308. set(h,'Linewidth',3)
  309. legend('True','Distorted')
  310. title(sprintf('At MNI %3.2f %3.2f %3.2f',M0.vertices(maxsurfind,1),M0.vertices(maxsurfind,2),M0.vertices(maxsurfind,3)));
  311. set(gca,'FontSize',18)

spm_distort_mesh_example.m at commit 9f16cc0, no license · at the source

Overview

Authors: José David López1,2, Yael Balbastre3,4, John Ashburner3, Alberto Mariola3, James J. Bonaiuto5,6, Gareth Barnes3
ORCID iDs: Gareth Barnes
  1. Engineering Faculty, Universidad de Antioquia UDEA, Medellín, Colombia
  2. Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
  3. Department of Imaging Neuroscience, UCL Institute of Neurology, University College London, London, United Kingdom
  4. Department of Experimental Psychology, University College London, London, United Kingdom
  5. Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
  6. Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France
Institutions: Trinity College Dublin (Ireland); Universidad de Antioquia (Colombia); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); Lyon 1 Université (France); Centre National de la Recherche Scientifique (France); Université de Lyon (France); Institut des Sciences Cognitives Marc Jeannerod (France)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1189
Dates: received 1 April 2025; accepted 5 March 2026; published online 30 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1189 · PMID 41924033 · PMCID PMC13037658 · OpenAlex W7135089158
Open access: diamond, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), MEG (modality)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: MEG/EEG brain imaging, diffeomorphic modeling, brain anatomy
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Colombian MinCiencias (1222-852-69927); MRC and Spinal Research Charity through the ERA-NET Neuron (MR/R000050/1); Royal Society (NIF\R1\232460); Wellcome Trust (226793/Z/22/Z, 203147/Z/16/Z); BBSRC (BB/M009645/1)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

The magnetoencephalographic and electroencephalographic (M/EEG) source reconstruction problem is an ill-posed model inversion, so it must be constrained by imposing biologically and physically plausible assumptions. Different M/EEG source reconstruction methods entail different assumptions about the underlying current distribution, yet all produce subjectively plausible current estimates. This work aims to develop an objective method that can be used to test any M/EEG analysis pathway. We make use of advances in diffeomorphic brain shape modeling to construct a set of parametrically deformable cortical surfaces that are representative of the population. These deformed (surrogate) brains provide a quantifiable parametric distortion from the ground-truth anatomy. If the current flow giving rise to the MEG data were generated on the true cortical manifold, MEG current estimates should be selective of the true anatomy. We show in simulation and with empirical data how the correct reconstruction assumptions depend closely on the true anatomy. We present a method to quantify the performance of MEG source reconstruction algorithms (and metrics of fit) in terms of millimeters of distortion.

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 7 matches between paragraphs and lines of code.

balbasty/BRCA

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 0fdeba175a3f5b362e805364340d8a7865c59d60, 3 December 2025
Languages: MATLAB (58)
Size: 61 files, 58 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
60 files
At the source: github.com/balbasty/BRCA

barnesgr123/spm_distort

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 9f16cc0ddd12f8343ef6d802628075a944fa2777, 5 March 2026
Languages: MATLAB (6)
Size: 12 files, 6 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (4 files), GIfTI library for MATLAB (3 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/barnesgr123/spm_distort/

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;
  • 64 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Data and Code Availability

The code is part of the current development version of SPM (https://github.com/spm/spm). The deformation templates will be automatically downloaded the first time the algorithm is used. The code to train the Bayesian Residual Component Analysis model is available at https://github.com/balbasty/BRCA. The MEG-evoked response data, individual MRIs, and cortical surfaces are available here: https://figshare.com/s/e3ff829753a76bcfda22. The additional code to reproduce the simulations and single-subject analyses can be found at https://github.com/barnesgr123/spm_distort/ and within the latest version of SPM. The complete dataset, originally from Bonaiuto, Meyer, et al. (2018) and Little et al. (2018), can be found at https://osf.io/eu6nx.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 3 keywords, 5 funders, 46 references.

Cite

This paper

López, J. D., Balbastre, Y., Ashburner, J., Mariola, A., Bonaiuto, J. J., & Barnes, G. (2026). Distorting anatomy to test MEG models and metrics. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1189. https://doi.org/10.1162/imag.a.1189

BibTeX

@article{lopez2026distorting,
author = {López, José David and Balbastre, Yael and Ashburner, John and Mariola, Alberto and Bonaiuto, James J. and Barnes, Gareth},
title = {{Distorting anatomy to test MEG models and metrics}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1189},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1189},
url = {https://doi.org/10.1162/imag.a.1189},
pmid = {41924033},
pmcid = {PMC13037658}
}

RIS

TY - JOUR
AU - López, José David
AU - Balbastre, Yael
AU - Ashburner, John
AU - Mariola, Alberto
AU - Bonaiuto, James J.
AU - Barnes, Gareth
TI - Distorting anatomy to test MEG models and metrics
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/30
VL - 4
SP - IMAG.a.1189
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1189
UR - https://doi.org/10.1162/imag.a.1189
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1189",
"type": "article-journal",
"title": "Distorting anatomy to test MEG models and metrics",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "López",
"given": "José David"
},
{
"family": "Balbastre",
"given": "Yael"
},
{
"family": "Ashburner",
"given": "John"
},
{
"family": "Mariola",
"given": "Alberto"
},
{
"family": "Bonaiuto",
"given": "James J."
},
{
"family": "Barnes",
"given": "Gareth"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1189",
"DOI": "10.1162/imag.a.1189",
"PMID": "41924033",
"PMCID": "PMC13037658",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1189",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
30
]
]
}
}

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