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Functional Specialization of the Visual Word Form Area During Word Reading: A Multimodal Neuroimaging Study.

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 · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Experiment 1 › MRI data analysis › Structural connectivity analysis. ↔ DTI/script-pyd/step3_topup_eddy.sh, the whole file · a weak match · score 0.56 · b0 images, TOPUP, susceptibility, EDDY, FSL, space · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Experiment 1 › MRI data analysis › Structural connectivity analysis. ↔ DTI/script-qj/step3_topup_eddy.sh, the whole file · a weak match · score 0.56 · b0 images, TOPUP, susceptibility, EDDY, FSL, space · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Experiment 1 › MRI data analysis › Activation analysis in the language network localizer task. ↔ RSA/single_event_model_ls2.m, lines 1 to 61 · score 0.53 · motion parameters, events, modeled, onsets, regressors · Report a mismatch
  4. [4] § MATERIALS AND METHODS › Experiment 2 › MRI data analysis › Representational similarity analysis. ↔ RSA/single_event_model_ls2.m, lines 1 to 61 · score 0.53 · ridge regression, squares, beta, modeled, RSA · Report a mismatch

Paper

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The authors' code

MATLAB · 280 lines · 9.4 KB · no license · 2 matches

  1. function out = single_event_model_ls2(featdir,write_std_image,use_raw, fsldir)
  2. % Usage: single_event_model_ls2(featdir,write_std_image,use_raw)
  3. % Takes a feat directory, and runs a ls and ls with 1 at a time
  4. % estimation on the filtered_func_data.img file within.
  5. %
  6. % CURRENTLY THERE IS NO OPTION TO STANDARIZE YOUR DATA AND R/L SWAP
  7. % PROBABLY WILL HAPPEN YOU MUST USE FSLORIENT -FORCERADIOLOGICAL TO FIX THIS IF IT
  8. % HAPPENS!!!
  9. %
  10. % write_std_image - CURRENTLY TURNED OFF set to 1 to create standard space files
  11. % use_raw - set to 1 to use raw data rather than filtered_func_data
  12. %
  13. % Returns a 4D nifti file of beta estimates for least squares and one
  14. % at a time least squares
  15. %
  16. % Jeanette Mumford 12/16/2010:
  17. % Adapted from single_event_model.m from the ridge regression code
  21. if ~exist('write_std_image')
  22. write_std_image=0;
  23. end
  25. if (~exist('use_raw')) | use_raw==0
  26. use_raw=0;
  27. raw_flag='';
  28. end
  29. if use_raw==1,
  30. raw_flag='_raw';
  31. end
  33. save_pth=[featdir,'/stats'];
  35. design = read_fsl_design2(featdir);
  37. %load onsets
  39. onscond=[];
  40. ons=[];
  41. condnames={};
  42. goodconds=[];
  44. for evnum=1:design.evs_orig % ignore the last regressor
  45. condnames(evnum)={eval(sprintf('design.evtitle%d',evnum))};
  46. % condnames{evnum}=design.evtitle{evnum};
  47. onsetf_loop=sprintf('design.custom%d', evnum);
  48. ons_loop=load(eval(onsetf_loop));
  49. % added by RP, 11/8/09
  50. % skips over single-column onset files (e.g., motion parameters)
  51. if size(ons_loop,2)==3,
  52. goodconds=[goodconds evnum];
  53. ons_loop=ons_loop(ons_loop(:,3)~=0,:);
  54. ons=[ons;ons_loop];
  55. onscond=[onscond ones(1,size(ons_loop,1))*evnum];
  56. end
  58. end
  59. goodconds=unique(goodconds);
  61. nruns = length(ons);
  64. %load in nuisance regressors. NOTE THIS IS TAILORED FOR THIS ANALYSIS
  65. % nuisance=read_design_mat([featdir, '/design.mat']);
  66. % nuisance=nuisance(:, [3 4]);
  68. %load mask
  69. maskf = strcat(featdir, '/mask.nii.gz');
  70. maskfile = load_nii_zip(maskf);
  71. mask = maskfile.img;
  73. %import data and convert from int16 into double
  74. if use_raw,
  75. %I need to add a catch if .fsf has .nii.gz extension already
  76. filenamecheck=dir(design.feat_files{1});
  77. if sum(filenamecheck.name((end-5):end)=='nii.gz')~=6
  78. dataf=strcat(design.feat_files{1},'.nii.gz');
  79. else,
  80. dataf=strcat(design.feat_files{1});
  81. end
  83. else,
  84. dataf = strcat(featdir, '/filtered_func_data.nii.gz');
  85. end
  87. datafile = load_nii_zip(dataf);
  88. data = datafile.img;
  89. data = double(data);
  92. TR=design.tr;
  93. ntp=design.npts;
  94. onsets = round((ons(:,1)+TR)/TR);
  96. %create a 2D matrix of data
  97. data2D=zeros(ntp, sum(mask(:)));
  98. for i=1:ntp
  99. dat_loop=data(:,:,:,i);
  100. data2D(i,:)=dat_loop(mask==1);
  101. end
  105. %single event regressors
  106. X_single = zeros(ntp,length(onsets));
  107. hrf = spm_hrf(TR);
  108. trial = zeros(length(onsets),ntp+length(hrf)-1);
  111. for t = 1:length(onsets)
  113. ssf = zeros(1,ntp);
  114. ssf(onsets(t))=1;
  115. trial(t,:) = conv(ssf,hrf);
  116. X_single(:,t) = trial(t,1:ntp)';
  118. end
  120. %X_single=[X_single, nuisance];
  121. X_single=[X_single];
  122. % We need to HP filter the design. This is an approximation to what
  123. % FSL does. Note the data are already HP filtered and I'm (Jeanette)
  124. % assuming that this approximation is close enough to what has been
  125. % done to the data. The reason I'm not starting with the original
  126. % is because I would like to retain the smoothing and scaling that was
  127. % done to filtered_func_data
  129. cut=design.paradigm_hp/design.tr;
  130. sigN2=(cut/(sqrt(2)))^2;
  131. K=toeplitz(1/sqrt(2*pi*sigN2)*exp(-[0:(ntp-1)].^2/(2*sigN2)));
  132. K=spdiags(1./sum(K')', 0, ntp,ntp)*K;
  134. H = zeros(ntp,ntp); % Smoothing matrix, s.t. H*y is smooth line
  135. X = [ones(ntp,1) (1:ntp)'];
  136. for k = 1:ntp
  137. W = diag(K(k,:));
  138. Hat = X*pinv(W*X)*W;
  139. H(k,:) = Hat(k,:);
  140. end
  142. F=eye(ntp)-H;
  144. X_single_hp=F*X_single;
  147. nparam=size(X_single, 2);
  148. nt=size(X_single, 1);
  149. beta_maker=zeros(nparam, nt);
  151. %This yields the final design for least squares.
  152. ncolX=size(X_single_hp, 2);
  153. for i=1:ncolX
  154. X_single_hp(:,i)=X_single_hp(:,i)-mean(X_single_hp(:,i));
  155. end
  157. %create the design matrix for the 1 at a time model (LS-S)
  158. %Gui, note that I put all other trials into a single regressor.
  159. %Although I have tried a separate regressor for each trial type,
  160. %it didn't work well BUT the data I was using had 4 trial
  161. %types. It might be okay with 2 trial types. There is some
  162. %commented out code below that could be edited for this
  163. %purpose.
  164. for i=1:nparam
  165. %des_loop=[X_single_hp(:,i), sum(X_single_hp, 2)-X_single_hp(:,i), nuisance];
  166. des_loop=[X_single_hp(:,i), sum(X_single_hp, 2)-X_single_hp(:,i)];
  167. %hp filter
  168. %des_loop=F*des_loop;
  169. %mean center
  170. des_loop=des_loop-kron(ones(length(des_loop),1),mean(des_loop));
  171. beta_maker_loop=pinv(des_loop);
  172. beta_maker(i,:)=beta_maker_loop(1,:);
  173. end
  176. %This is code you can edit if you want to break up the "other
  177. %trial" regressor into two or more (here I had 4)
  178. %for i=1:nparam
  179. % keep_loop=ones(size(condlist));
  180. % keep_loop(i)=0;
  181. % conlist_loop=condlist(keep_loop==1);
  182. % X_omit=X_single(:, keep_loop==1);
  183. % des_loop=[X_single(:,i), sum(X_omit(:,conlist_loop==1),2), sum(X_omit(:,conlist_loop==2),2), sum(X_omit(:,conlist_loop==3),2), sum(X_omit(:,conlist_loop==4),2), nuisance];
  184. %
  185. % des_loop=F*des_loop;
  186. % des_loop=des_loop-kron(ones(length(des_loop),1),mean(des_loop));
  187. %
  188. % beta_maker_loop=pinv(des_loop);
  189. % beta_maker(i,:)=beta_maker_loop(1,:);
  190. %end
  194. beta_est2D=beta_maker*data2D;
  196. beta_lm2D=pinv(X_single_hp)*data2D;
  197. %put it into a 4D data set
  199. beta_est=data(:,:, :, 1:nparam)*0;
  200. beta_est_lm=data(:,:, :, 1:nparam)*0;
  202. for i=1:nparam
  203. data_loop=beta_est(:, :, :, 1)*0;
  204. data_loop(mask==1)=beta_est2D(i,:);
  206. data_loop_lm=beta_est(:, :, :, 1)*0;
  207. data_loop_lm(mask==1)=beta_lm2D(i, :);
  209. beta_est(:,:,:,i)=data_loop;
  210. beta_est_lm(:,:,:,i)=data_loop_lm;
  211. end
  218. %Save data
  219. fprintf('\nSaving data...\n');
  221. %use datafile as a template for 4D beta_est data
  222. % You'll need to edit this to reflect the path where you want to
  223. % save your results
  224. mat_file = strcat(featdir, '/reg/example_func2standard.mat');
  225. std_ref=sprintf('%s/data/standard/MNI152_T1_2mm.nii.gz', ...
  226. getenv('FSLDIR'))
  228. for condition=goodconds,
  229. condition_ons=find(onscond==condition);
  230. fprintf('writing RR image for %s...\n',condnames{condition});
  231. fname_beta_est = sprintf('%s/pe%dls_one_at_time%s.nii',save_pth,condition,raw_flag);
  232. beta_est_nii = datafile; %uses 'datafile' as a templat
  233. beta_est_nii.img = beta_est(:,:,:,condition_ons); %setting the image data
  234. beta_est_nii.hdr.dime.dim(5)=length(condition_ons); %Replace 204 with number of onsets=nruns
  235. beta_est_nii.hdr.dime.datatype=16; % how the computer stores the data
  236. beta_est_nii.hdr.dime.bitpix=32; %how the computer stores the data
  237. save_untouch_nii(beta_est_nii, fname_beta_est); %finally saving the data
  238. system(sprintf('gzip -f %s',fname_beta_est));
  241. condition_ons=find(onscond==condition);
  242. fprintf('writing RR image for %s...\n',condnames{condition});
  243. fname_beta_ls_est = sprintf('%s/pe%dls_all%s.nii',save_pth,condition,raw_flag);
  244. beta_est_nii = datafile; %uses 'datafile' as a templat
  245. beta_est_nii.img = beta_est_lm(:,:,:,condition_ons); %setting the image data
  246. beta_est_nii.hdr.dime.dim(5)=length(condition_ons); %Replace 204 with number of onsets=nruns
  247. beta_est_nii.hdr.dime.datatype=16; % how the computer stores the data
  248. beta_est_nii.hdr.dime.bitpix=32; %how the computer stores the data
  249. save_untouch_nii(beta_est_nii, fname_beta_ls_est); %finally saving the data
  250. system(sprintf('gzip -f %s',fname_beta_ls_est));
  254. %flips the image so it is correct in fslview
  255. %system('echo FSLOUTPUTTYPE=NIFTI_GZ')
  256. %system(sprintf(['%s/bin/fslorient -forceradiological %s.gz'], fsldir,fname_beta_est));
  257. %system(sprintf(['%s/bin/fslorient -forceradiological %s.gz'], fsldir,fname_beta_ls_est));
  259. %if write_std_image,
  261. % system(sprintf(['%s/bin/fslconf/fsl.sh'], fsldir));
  263. %fprintf('writing standard space image for %s...\n',condnames{condition});
  264. %std_beta_est = sprintf('%s/pe%dls_one_at_time%s_std.nii',save_pth, ...
  265. % condition,raw_flag);
  266. %system(sprintf(['%s/bin/applyxfm4D %s.gz %s %s %s -singlematrix'], fsldir,fname_beta_est, std_ref,std_beta_est, mat_file));
  268. %fprintf('writing standard space image for %s...\n',condnames{condition});
  269. %std_beta_ls_est = sprintf('%s/pe%dls_all%s_std.nii',save_pth, ...
  270. % condition,raw_flag);
  271. %system(sprintf(['%s/bin/applyxfm4D %s.gz %s %s %s -singlematrix'], fsldir, fname_beta_ls_est, std_ref,std_beta_ls_est, mat_file));
  273. %end
  276. end

single_event_model_ls2.m, no license · at the source

Overview

Authors: Lala Gu1,2,3, Huiling Li1,4, Jing Qu1,5, Jingyu Yang1,2,3, Xin Fu1,2,3, Rui Hu1,2,3, Leilei Mei1,2,3
ORCID iDs: Leilei Mei
  1. Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, Guangzhou, China
  2. Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
  3. School of Psychology, South China Normal University, Guangzhou, China
  4. School of Psychology, Guizhou Normal University, Guiyang, China
  5. Key Laboratory of Behavioral and Mental Health of Gansu, Northwest Normal University, Lanzhou, China
Institutions: South China Normal University (China); Guizhou Normal University (China); Northwest Normal University (China)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.225
Dates: received 4 June 2025; accepted 13 November 2025; published online 26 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.225 · PMID 41971749 · PMCID PMC13065096 · OpenAlex W4416589463
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/n9kqj
Status: code verified (how the code is found and verified)
Categories: fMRI (modality)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging, Machine learning
Keywords: visual word form area, word reading, neural representation, connectivity-biased hypothesis, fMRI
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (32271098, 2024B0303390003, 32571226); South China Normal University (2024B0303390003); Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515011023)
Citations: cited by 1 paper (Europe PMC); 83 references in the paper

Abstract

The visual word form area (VWFA) has been consistently identified as a crucial structure in word reading, and its function differs across subregions. Nevertheless, the functional roles of its subregions and their functional origins remain controversial. Here, we adopted multimodal neuroimaging techniques (i.e., task-state fMRI, resting-state fMRI, and diffusion MRI) combined with representational similarity analysis to investigate the functional role of VWFA subregions and the brain circuitry supporting their function in two experiments. Results revealed respective roles of the posterior and anterior VWFA subregions in visual and semantic processing, which is consistent with their respective connectivity to orthographic and semantic networks. In addition, processing demands modulated the neural representations of high-level linguistic information in the VWFAs. These convergent findings elucidated the local neural computations in the VWFAs and their cooperative mechanism with distant brain regions related to language processing, jointly providing multimodal neuroimaging evidence for the connectivity-biased hypothesis.

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

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OSF n9kqj

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Shell (57), MATLAB (12)
Size: 201 files, 69 scripts
Software Heritage: not checked
Found in: “Code and Data Availability Statements”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (39 files), Statistics and Machine Learning Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), SPM (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
69 files
At the source: osf.io/n9kqj/?view_only=8768b3b808b94d149d9843615df35b35

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 69 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.

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The materials, statistical code, and data are available at OSF (https://osf.io/n9kqj/?view_only=8768b3b808b94d149d9843615df35b35).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 3 funders, 81 references.

Cite

This paper

Gu, L., Li, H., Qu, J., Yang, J., Fu, X., Hu, R., & Mei, L. (2026). Functional Specialization of the Visual Word Form Area During Word Reading: A Multimodal Neuroimaging Study. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.225. https://doi.org/10.1162/nol.a.225

BibTeX

@article{gu2026functional,
author = {Gu, Lala and Li, Huiling and Qu, Jing and Yang, Jingyu and Fu, Xin and Hu, Rui and Mei, Leilei},
title = {{Functional Specialization of the Visual Word Form Area During Word Reading: A Multimodal Neuroimaging Study}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {7},
pages = {NOL.a.225},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.225},
url = {https://doi.org/10.1162/nol.a.225},
pmid = {41971749},
pmcid = {PMC13065096}
}

RIS

TY - JOUR
AU - Gu, Lala
AU - Li, Huiling
AU - Qu, Jing
AU - Yang, Jingyu
AU - Fu, Xin
AU - Hu, Rui
AU - Mei, Leilei
TI - Functional Specialization of the Visual Word Form Area During Word Reading: A Multimodal Neuroimaging Study
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/03/26
VL - 7
SP - NOL.a.225
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.225
UR - https://doi.org/10.1162/nol.a.225
LA - en
ER -

CSL-JSON

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"id": "10.1162/nol.a.225",
"type": "article-journal",
"title": "Functional Specialization of the Visual Word Form Area During Word Reading: A Multimodal Neuroimaging Study",
"container-title": "Neurobiology of language (Cambridge, Mass.)",
"author": [
{
"family": "Gu",
"given": "Lala"
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{
"family": "Li",
"given": "Huiling"
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{
"family": "Qu",
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{
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"given": "Leilei"
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],
"container-title-short": "Neurobiol Lang (Camb)",
"volume": "7",
"page": "NOL.a.225",
"DOI": "10.1162/nol.a.225",
"PMID": "41971749",
"PMCID": "PMC13065096",
"ISSN": "2641-4368",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/nol.a.225",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
26
]
]
}
}

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