OSCR

Home numeracy experiences are associated with number-related brain activity and connectivity in early childhood.

Code ↔ Paper

6 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 6 matches
  1. [1] § Material and methods › fMRI data preprocessing ↔ Nurturing_math_brain/batch.m, lines 1–101 · score 0.63 · ArtRepair, Gaussian, smoothed, realignment, repaired, SPM
  2. [2] § Material and methods › fMRI data preprocessing ↔ NeuralRep_Absolute+RelativeMagnitude/batch.m, lines 161–225 · score 0.62 · unified segmentation, fMRI, coregistration, MNI, voxel
  3. [3] § Material and methods › fMRI data preprocessing ↔ Nurturing_math_brain/batch.m, lines 166–229 · score 0.62 · unified segmentation, fMRI, coregistration, MNI, voxel
  4. [4] § Material and methods › fMRI data preprocessing ↔ NeuralRep_Absolute+RelativeMagnitude/batch.m, lines 1–100 · score 0.61 · ArtRepair, Gaussian, smoothed, realignment, repaired, SPM
  5. [5] § Material and methods › fMRI data analysis ↔ Nurturing_math_brain/batch.m, lines 239–324 · score 0.58 · serial correlations, AR, canonical, onsets, model, regressors
  6. [6] § Material and methods › fMRI data analysis ↔ NeuralRep_Absolute+RelativeMagnitude/batch.m, lines 235–320 · score 0.57 · serial correlations, AR, canonical, onsets, model, regressors

Paper

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The authors' code

MATLAB · 436 lines · 18 KB · no license · 3 matches

  1. function nothing = pre_batch
  2. % pre_batch.m
  3. %
  4. % this is a script that handles all of the
  5. % preprocessing and processing in SPM12 and artrepair. It
  6. % intelligently pulls files off of the SAN and create a logical
  7. % hierarchical directory structure.
  8. %
  9. % Initial script from Ken Roberts
  10. % Adapted and modified by Daniel Weissman, Josh Carp, Jerome Prado and Chris McNorgan
  11. % This last updated version is adapted for handling data organized in the BIDS
  12. % format. It also uses Artrepair
  13. %
  15. % Prerequisites for use:
  17. % 1) SPM12 must be in the matlab path.
  18. % 2) Artrepair scripts must be in the matlab path.
  19. % 3) Nifti tools must be in the matlab path.
  20. % 4) GLM Flex must be in the matlab path.
  22. global CCN;
  24. % load the defaults first, and then do 'load_vars' to allow the
  25. % defaults to be overwritten as specified below.
  27. spm('defaults','fmri');
  28. load_vars;
  30. This is the script that was used to preprocess data and analyze main effects from the manuscript "Nurturing the mathematical brain:
  31. Home numeracy practices are associated with children’s neural responses to Arabic numerals" by Cléa Girard, Thomas Bastelica, Jessica Léone, Justine Epinat-Duclos, Léa Longo & Jérôme Prado
  35. %%%%%%%%%%%%%%%%%User-Defined Variables%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  37. %Here is a list of the possible procedures to perform. These should be specified in c_names in the order they should be performed.
  39. %uncompress_c : Converts original nii.gz files into nii images (necessary for the batch system to work
  40. %clean_c : Removes all preprocessing files
  41. %deface_c : Defaces anatomical images
  42. %expand_c : Converts 4D nii images into 3D nii images and put them in preprocessing folder (necessary for artrepair to work)
  43. %slice_c : implements slice-timing
  44. %realign_c : implememts realignment
  45. %resample_c : Reslices the functional images.
  46. %smooth_c : Spatially smooths the functional images with a Gaussian
  47. %motionregress_c : Removes residual interpolation errors after the realign and reslice operations (from the ArtRepair toolbox)
  48. %global_c : Removes outlier scans (see art_global.m from the Artrepair toolbox)
  49. %coregister_c: coregisters an anatomical and a functional image (not necessary if normalizing functionals independent of the anatomical)
  50. %normalise_c : Normalizes the functionals to the functional or anatomical tenmplate
  51. %fmri_model_c: Estimates brain activity for different conditions/trial types using the general linear model
  52. %fmri_contrasts_c : Generates contrasts between linear combinations of betas within each subject.
  53. %spm_rfx_bch_c : Performs Random-Effects across subjects
  54. %compress_c : Converts original nii images into nii.gz files
  55. %motion_report_c : Generates a text file (motion_report.tsv) that contains information about the amount of movement in each subject/task/run
  56. %ROI_c : Average beta values within Regions of Interest
  58. %c_names = {'uncompress_c' 'clean_c' 'expand_c' 'slice_c' 'realign_c' 'resample_c' 'smooth_c' 'motionregress_c' 'global_c' 'coregister_c' 'normalise_c' 'motion_report_c'}; % preprocessing pipeline with Artrepair
  59. %c_names = {'fmri_model_c' 'fmri_contrasts_c'}; % single-subject analysis pipeline
  60. c_names = {'spm_rfx_bch_c'}; % RFX analysis
  61. %c_names = {'ROI_c'}; % ROI analysis
  62. %c_names = {'clean_c'};
  63. %c_names = {'compress_c'};
  66. %%%%%%%%%%%%%%%%%% DO NOT EDIT THIS SECTION %%%%%%%%%%%%%%%%%%%%%%%%%
  69. for i = 1:length(CCN.all_subjects)
  70. %Load the defaults for this subject with load_vars and then specify any exceptions for this subject
  71. load_vars;
  72. CCN.subject = sprintf('%s', CCN.all_subjects{i});
  74. cd(CCN.work_dir);
  76. %Do each step of preprocessing
  77. for j = 1:length(c_names)
  78. feval(c_names{j})
  79. if find(ismember(c_names, 'spm_rfx_bch_c')), 'quitting', return, end
  80. if find(ismember(c_names, 'spm_rfx_bch_LOSO_c')), 'quitting', return, end
  81. end;
  84. end; % for each subject
  86. return;
  87. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  92. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  93. % This is where all of the options are defined. There are three main
  94. % classes of option.
  95. % - ones that define the directory hierarchy
  96. % - ones that define the preprocessing options
  97. % - ones that define the operation of the preprocessing script
  98. % eg, where to log the results, and where to email if something
  99. % goes wrong
  100. %
  101. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  102. function load_vars
  103. global CCN;
  105. %%%%%%%%%
  106. % Directory Hierarchy
  107. %%%%%%%%
  109. % root local directory for the experiment.
  110. CCN.root_dir = '/crnldata/bbl/NTR/PRE_MATHS_BIDS';
  113. %50 subjects for digits
  114. CCN.all_subjects = {'sub-01' 'sub-02' 'sub-03' 'sub-04' 'sub-05' 'sub-06' 'sub-07' 'sub-09' 'sub-10' 'sub-11' 'sub-12' 'sub-13' 'sub-14' 'sub-15' 'sub-17' 'sub-18' 'sub-19' 'sub-20' 'sub-24' 'sub-25' 'sub-27' 'sub-28' 'sub-29' 'sub-30' 'sub-31' 'sub-32' 'sub-36' 'sub-37' 'sub-38' 'sub-40' 'sub-41' 'sub-43' 'sub-44' 'sub-45' 'sub-46' 'sub-48' 'sub-50' 'sub-54' 'sub-55' 'sub-58' 'sub-59' 'sub-60' 'sub-61' 'sub-64' 'sub-65' 'sub-67' 'sub-68' 'sub-69' 'sub-70' 'sub-72'};
  116. %44 subjects for digits
  117. %CCN.all_subjects = {'sub-01' 'sub-03' 'sub-04' 'sub-05' 'sub-06' 'sub-07' 'sub-09' 'sub-10' 'sub-11' 'sub-12' 'sub-13' 'sub-14' 'sub-17' 'sub-18' 'sub-19' 'sub-20' 'sub-25' 'sub-28' 'sub-29' 'sub-30' 'sub-31' 'sub-32' 'sub-36' 'sub-37' 'sub-38' 'sub-40' 'sub-41' 'sub-43' 'sub-44' 'sub-45' 'sub-46' 'sub-48' 'sub-50' 'sub-55' 'sub-58' 'sub-59' 'sub-60' 'sub-61' 'sub-64' 'sub-65' 'sub-67' 'sub-68' 'sub-69' 'sub-72'};
  119. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  122. %%%%%%%%%%%%%%%%%% DO NOT EDIT THIS SECTION %%%%%%%%%%%%%%%%%%%%%%%%%
  125. % file pattern - describes the name of the files for each step
  126. % contains different values depending on which m file calls it.
  128. CCN.work_dir = CCN.root_dir;
  130. % run pattern - describes the name of the run folders that will be in the preprocessing directory
  131. CCN.run_pattern = 'sub*';
  133. CCN.file_pattern = struct( ...
  134. 'default', 'sub-*.nii', ...
  135. 'slice_c', 'sub-*.nii', ...
  136. 'realign_c', 'asub-*.nii', ...
  137. 'resample_c', 'asub-*.nii', ...
  138. 'normalise_c', 'vmsrasub-*.nii', ...
  139. 'smooth_c', 'rasub-*.nii', ...
  140. 'expand_c', 'sub-*.nii', ...
  141. 'global_c', 'msrasub-*.nii', ...
  142. 'fmri_model_c', 'wvmsrasub-*.nii', ...
  143. 'spm_rfx_bch_c', 'swasub-*.nii');
  146. %
  147. % functional and anatomical codes
  149. % The way these work is that each describes the location of a large number
  150. % of files in changing places. When the code runs, the paths will be formed
  151. % by doing a number of substitutions. Any quantity in square brackets will
  152. % be replaced with the contents of that field in the variable CCN.
  153. % Any regular expression will also be expanded.
  154. CCN.functional_dirs = '[root_dir]/[subject]/func/';
  155. CCN.anatomical_dirs = '[root_dir]/[subject]/anat/';
  156. CCN.functional_files = '[root_dir]/[subject]/func/[file_pattern]';
  157. CCN.anatomical_files = '[root_dir]/[subject]/anat/[file_pattern]';
  158. CCN.first_anat = '[root_dir]/[subject]/anat/sub-*.nii';
  159. CCN.def_anat = '[root_dir]/[subject]/anat/y_sub-*.nii';
  162. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  164. %%%%%%%%%
  165. % Preprocessing options
  166. %%%%%%%%
  169. % slice timing, assumes reference slice of 1.
  170. CCN.TR = 2;
  171. CCN.nslices = 32;
  173. %Specify the order in which the slices were acquired
  174. %1=ascending (1...nslices)
  175. %2=descending (nslices.jp...1)
  176. %3=interleaved odd (1 3 5 7 9.....2 4 6 8 10)
  177. %4=interleaved even (2 4 6 8 10.....1 3 5 7 9)
  178. CCN.seq=4;
  180. % realignment options.
  181. CCN.realign_flags = struct( ...
  182. 'quality', 0.9, ... % between 0 and 1, this is the default
  183. 'fwhm', 5, ... % in mm, this is the default
  184. 'rtm', 0); % use 1 for fMRI registration to mean
  185. CCN.first_func = '[root_dir]/[subject]/func/[subject]_task-Digits_run-01_bold.nii'; % image to use as reference for realignement
  188. % reslice options.
  189. CCN.reslice_flags = struct( ...
  190. 'mask', 1, ...
  191. 'mean', 0, ...
  192. 'interp', 1, ...
  193. 'which', 2);
  195. % coregistration options
  196. CCN.coreg_flags = struct( ...
  197. 'sep', [4 2], ... % optimisation sampling steps (mm)
  198. 'params', [0 0 0 0 0 0], ... % starting estimates (6 elements)
  199. 'cost_fun', 'nmi', ... % cost function string
  200. 'tol', [0.02 0.02 0.02 0.001 0.001 0.001], ... % tolerences for accuracy of each param
  201. 'fwhm', [7 7] ... % smoothing to apply to 256x256 joint histogram
  202. );
  204. % normalization options (if 3 or 4 chosen, only CCN.normalise_wr_flags is read)
  205. CCN.norm=4; % Determine normalisation parameters from: 1= first functional; 2= anatomital; 3= unified segmentation (SPM8 style); 4= unified segmentation (SPM12 style)
  208. CCN.normalise_est_flags = struct( ...
  209. 'smosrc', 8, ... % smoothing of source image (FWHM of Gaussian in mm)
  210. 'smoref', 0, ... % smoothing of template image (defaults to 0).
  211. 'regtype', 'mni', ... % regularisation type for affine registration
  212. ... % See spm_affreg.m (default = 'mni').
  213. 'weight', '', ...
  214. 'cutoff', 30, ... % Cutoff of the DCT bases. Lower values mean more
  215. ... % basis functions are used (default = 30mm).
  216. 'nits', 16, ... % number of nonlinear iterations (default=16).
  217. 'reg', 0.1, ... % amount of regularisation, higher val = less warping,
  218. ... % (default=0.1)
  219. 'wtsrc', 0);
  221. CCN.normalise_wr_flags = struct( ...
  222. 'preserve', 0, ... %
  223. 'bb', [-78 -112 -50; 78 76 85], ... % bounding box
  224. 'vox', [2 2 3.5], ... % voxel size
  225. 'interp', 7, ... % 2nd order bspline interpolation
  226. 'wrap', [0 0 0]); % wrap around edges in x y or z dimensions
  228. % smoothing options
  229. CCN.smooth_kernel = [4 4 7]; % FWHM of Gaussian kernel in mm
  232. %% ArtRepair options
  233. CCN.z_thresh = 3; % global mean intensity outliers in std (see art_global.m line 110 for explanations)
  234. CCN.mv_thresh = 2; % allowable motion within a TR (see art_global.m line 116 for explanations)
  235. CCN.MVMTTHRESHOLD = 4; % motion threshold (see function art_clipmvmt)
  237. %%%%%%%%%
  238. % Model specification options
  239. %%%%%%%%
  241. % is the onset vector specified in scans or seconds? ('scans' or 'secs')
  242. %the first scan (TR) is always numbered 0.
  243. CCN.model.units = 'secs';
  245. % specify the basis set- the choices are the strings below:
  246. % 'hrf', 'hrf (with time derivative)', 'hrf (with time and dispersion derivatives)',
  247. % 'Fourier set', 'Fourier set (Hanning)', 'Gamma functions', 'Finite Impulse Response'
  248. CCN.model.basis = 'hrf';
  250. %Specify the number of seconds or scans to be modeled
  251. %This variable applies when modeling with a canonical HRF or a Finite
  252. %Impulse Response Model
  253. CCN.model.length = 24;
  255. %Specify the the number of time points to be modeled if FIR
  256. %This variable is automatically set and overwritten when NOT using an FIR
  257. CCN.model.order = 12;
  259. %Specify fMRI_T --how many bins to subdivide each TR into
  260. %DEFAULT=16;
  261. CCN.model.fmri_T = 16;
  263. %Specify fMRI_TO --reference slice
  264. %DEFAULT=1;
  265. CCN.model.fmri_T0 = 1;
  267. %Specify if you want to orthogonalize the regressors in the design matrix
  268. %1=yes
  269. %0=no
  270. CCN.model.orthogonalize = 1;
  272. %Specify if you want to include the motion regressors in the design matrix
  273. %1=yes
  274. %0=no
  275. CCN.model.motion = 0;
  278. % Model Volterra interactions? (2=yes, 1=no)
  279. % the number actually corresponds as the order of the Volterra
  280. % interactions, with 1 and 2 being the only options.
  281. %Entering 2 would allow you to model interactions between trial types
  282. CCN.model.volterra = 1;
  284. % Global intensity normalisation ('Scaling' or 'None')
  285. CCN.model.global_sc = 'None';
  287. % specify the hi-pass cutoff in seconds (number or 'inf', vector for
  288. % sessions, default = 128 sec)
  289. CCN.model.hpf = 128;
  291. % Correct for serial correlations? ('none' | 'AR(1)')
  292. CCN.model.ser_corr = 'AR(1)';
  294. % name of realignment parameters file?
  295. % (one of these should be in every run folder)
  296. CCN.model.rp_name = 'rp_*';
  299. % specify the file that has all of the onsets and covariates
  300. % (expandable)
  302. CCN.model.spec_file = '[root_dir]/model_spec.m';
  305. % Scale factor for NaN thresholding applied in fmri_model_c
  306. % A value of 1 has no effect
  307. CCN.model.thresh_factor = 1;
  309. % specify the place to construct the model (to put the SPM.mat and so
  310. % forth) (expandable)
  311. CCN.model.model_dir = '[root_dir]/[subject]/analysis';
  314. % if you want to do contrasts for each subject, this variable should contain the filename
  315. % of a valid contrast_spec file.
  317. CCN.model.contrast_spec_file = '[root_dir]/create_contrasts.m';
  319. %Specify whether you want to add new contrasts or overwrite old contrasts so that new ones are
  320. %numbered starting with 2 (the F-contrast for effects of interest -
  321. %con0001 - won't be overwritten)
  322. %0=add new contrasts to existing ones
  323. %1=overwrite existing contrasts with new ones
  324. CCN.OverwriteContrasts = 1;
  327. %%%%%%%%%
  328. % Random Effects Analyses
  329. %%%%%%%%
  331. % RFX parameters
  333. CCN.dept = 0; %% Assume Dependence? 0=no or 1=yes
  334. CCN.var = 1; %% Assume Unequal Variance? 0=no or 1=yes
  335. CCN.tm.tm_none = 1; %% No Threshold Masking
  336. CCN.im = 0; %% Implicit Masking 1 = yes, 0 = no
  337. CCN.em = {''}; %% Explicit Masks list mask files e.g. {'/autofs/space/plato_002/users/APS_MATLAB/spm8/templates/epi.nii,1'};
  338. CCN.g_omit = 1; %% Global Calculation: 1 is the default setting
  339. CCN.gmsca.gmsca_no = 1; %% No Grand Mean Scaling.
  340. CCN.glonorm = 1; %% Global Normalization. 1 = No. 2 = Proportional 3 = ANCOVA
  343. % which test to perform?
  344. % 1=one-sample t-test (one group)
  345. % 2=two-sample t-test (two groups)
  346. % 3=paired t-test (two sessions)
  347. % 4=one-way between subjects ANOVA (up to 6 groups)
  348. % 5=one-way repeated measures ANOVA (up to 6 sessions)
  349. % 6=2X2 between subjects ANOVA
  350. % 7=2X2 within subjects ANOVA
  351. % 8=2X2 mixed subjects ANOVA
  352. % 9=Multiple regression
  354. CCN.rfx.ttest = 1 ;
  356. % GROUP VECTOR IF TEST 2 to 5
  357. % This is ignored if one-sample t-test is selected above. Otherwise, the
  358. % vector should specify which subject belongs to which group (the subject list is in CCN.all_subjects).
  359. % Two-sample and paired t-tests require a vector in which each subject can have two values (1 or 2 depending on the group)
  360. % one-way between subjects and repeated measures ANOVA can have up to 6 levels
  361. % The subject order does not matter for two-sample t-test and between subject ANOVA
  362. % For paired t-test and repeated measures ANOVA, the order matters: the first subject in each group is considered the same repeated measure, and so on
  364. CCN.rfx.groups = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ]; %ELEMENTARY vs COLLEGE
  367. % GROUP VECTOR IF TEST 6 to 8.
  368. % Each vector represents one factor
  369. % (between-subject factor for tests 6 and 8 and within-subject factor
  370. % for test 7). Withing each vector, each level (1 or 2) needs to be specified. Fill the rest with 0s.
  371. CCN.rfx.groups_1 = [1 1 1 1 1 1 1 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0];
  372. CCN.rfx.groups_2 = [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 2 2 2];
  374. % COVARIATE VECTORS IF TEST 9.
  375. % Each vector represents one covariate (up
  376. % to 6). If there is less than 6 covariates, leave an empty vector
  377. % (e.g., CCN.rfx.covar_6 = [])
  381. CCN.rfx.covar_1 = [];
  383. CCN.rfx.covar_2 = [];
  385. CCN.rfx.covar_3 = [];
  387. CCN.rfx.covar_4 = [];
  389. CCN.rfx.covar_5 = [];
  391. CCN.rfx.covar_6 = [];
  395. % which contrasts to analyze? Either indicate a vector of contrats
  396. % (e.g.CCN.rfx.contrasts = [9];) or that you want to run all of the
  397. % contrasts (i.e., CCN.rfx.contrasts = 'all';)
  399. CCN.rfx.contrasts = [1];
  401. % where to do the analysis
  402. CCN.rfx.rfx_dir = '[root_dir]/RFX/digits';
  405. %%%%%%%%%%%%%%%%%%% ROI ANALYSES %%%%%%%%%%%%%%%%%%%%%%
  407. CCN.ROI.dir = '[root_dir]/ROIs_analysis'; % directory where the ROI can be found and where the results will be stored
  408. CCN.ROI.file = 'IPS'; % name of the ROI (this should be a nifti file located in the CCN.ROI.dir directory)
  411. %%%%%%%%%%%%%%%%%DO NOT EDIT BELOW THIS LINE%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  413. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  414. %
  415. % Deletes files specified by a certain string.
  416. %
  417. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  418. function delete_many_files(file_spec)
  419. global CCN;
  421. my_files = expand_path(file_spec);
  423. fprintf('Deleting %d files for subject %s \r\n %s\r\n', length(my_files), CCN.subject, file_spec)
  425. for i = 1:length(my_files)
  426. delete(my_files{i});
  427. if mod(i,100) == 0
  428. fprintf('\r\n %d ', i);
  429. elseif mod(i,10) == 0
  430. fprintf(' %d', i);
  431. else
  432. %nothing
  433. end;
  434. end;
  436. return;

batch.m at commit 197f0da, no license · at the source

Overview

Authors: Cléa Girard1, Léa Longo2, Hannah Chesnokova2, Justine Epinat-Duclos2, Jérôme Prado2
  1. Laboratoire de Psychologie et Neurocognition (LPNC), CNRS UMR 5105, Université Grenoble Alpes, Grenoble, France
  2. Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028—CNRS UMR5292, Université de Lyon, Bron, France
Institutions: Centre National de la Recherche Scientifique (France); Laboratoire de Psychologie et NeuroCognition (France); Université Grenoble Alpes (France); Inserm (France); Centre de Recherche en Neurosciences de Lyon (France)
Journal: NPJ science of learning, volume 11, issue 1, article 31
Dates: received 3 September 2025; accepted 16 March 2026; published online 3 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41539-026-00419-5 · PMID 41932931 · PMCID PMC13223258 · OpenAlex W7148923000
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), developmental (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Neuroscience, Psychology
Topic: Cognitive and developmental aspects of mathematical skills (Statistics and Probability, Mathematics), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-17- CE28-0014)
Citations: not cited yet (Europe PMC); 103 references in the paper

Abstract

Children vary widely in numerical knowledge before school entry, and these early differences predict later achievement. Although home numeracy experiences relate to young children’s skills, it remains unclear how early experiences shape neural systems supporting number processing at the start of formal schooling. Using fMRI, we measured brain activity during passive perception of digits (vs. letters) in 37 five-year-olds. Parents reported the frequency of home numeracy practices and engaged in a free play session allowing us to quantify number talk. Children showed digit-specific activity in the left intraparietal sulcus (IPS). Across families, higher home numeracy experiences were associated with lower digit-related activity in several regions, including the IPS, but with stronger functional connectivity between the left IPS and other regions. Our results suggest that home numeracy experiences may support early number-related brain networks by enhancing connectivity and reducing local processing demands, illustrating how home experiences influence the developing learning brain.

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 6 matches between paragraphs and lines of code.

OSF vuar8

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (88), Python (2)
Size: 143 files, 90 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (17 files), SPM (16 files), Statistics and Machine Learning Toolbox (2 files), NumPy (2 files), PsychoPy (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
90 files
At the source: osf.io/vuar8

BBL-lab/BBL-batch-system

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 197f0daeffe524906a251ed08d4e8b9b0fef9711, 10 October 2021
Languages: MATLAB (130)
Size: 143 files, 130 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (42 files), Tools for NIfTI and ANALYZE image (MATLAB) (17 files), Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
131 files
At the source: github.com/BBL-lab/BBL-batch-system

Code availability

The Matlab scripts used to analyze the fMRI data are available via the OSF at https://osf.io/vuar8. The scripts are also available on GitHub (https://github.com/BBL-lab/BBL-batch-system).

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

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;
  • 220 scripts, each with its path and the digest of its content;
  • 6 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

The pre-registration, questionnaires, fMRI task, behavioral data, and individual beta maps that support the findings of this study are available via the OSF at https://osf.io/vuar8/.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 1 funder, 81 references.

Cite

This paper

Girard, C., Longo, L., Chesnokova, H., Epinat-Duclos, J., & Prado, J. (2026). Home numeracy experiences are associated with number-related brain activity and connectivity in early childhood. NPJ science of learning, 11(1), 31. https://doi.org/10.1038/s41539-026-00419-5

BibTeX

@article{girard2026home,
author = {Girard, Cléa and Longo, Léa and Chesnokova, Hannah and Epinat-Duclos, Justine and Prado, Jérôme},
title = {{Home numeracy experiences are associated with number-related brain activity and connectivity in early childhood}},
journal = {NPJ science of learning},
year = {2026},
month = apr,
volume = {11},
number = {1},
pages = {31},
publisher = {Nature Publishing Group},
issn = {2056-7936},
doi = {10.1038/s41539-026-00419-5},
url = {https://doi.org/10.1038/s41539-026-00419-5},
pmid = {41932931},
pmcid = {PMC13223258}
}

RIS

TY - JOUR
AU - Girard, Cléa
AU - Longo, Léa
AU - Chesnokova, Hannah
AU - Epinat-Duclos, Justine
AU - Prado, Jérôme
TI - Home numeracy experiences are associated with number-related brain activity and connectivity in early childhood
T2 - NPJ science of learning
J2 - NPJ Sci Learn
PY - 2026
DA - 2026/04/03
VL - 11
IS - 1
SP - 31
SN - 2056-7936
PB - Nature Publishing Group
DO - 10.1038/s41539-026-00419-5
UR - https://doi.org/10.1038/s41539-026-00419-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41539-026-00419-5",
"type": "article-journal",
"title": "Home numeracy experiences are associated with number-related brain activity and connectivity in early childhood",
"container-title": "NPJ science of learning",
"author": [
{
"family": "Girard",
"given": "Cléa"
},
{
"family": "Longo",
"given": "Léa"
},
{
"family": "Chesnokova",
"given": "Hannah"
},
{
"family": "Epinat-Duclos",
"given": "Justine"
},
{
"family": "Prado",
"given": "Jérôme"
}
],
"container-title-short": "NPJ Sci Learn",
"volume": "11",
"issue": "1",
"page": "31",
"DOI": "10.1038/s41539-026-00419-5",
"PMID": "41932931",
"PMCID": "PMC13223258",
"ISSN": "2056-7936",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41539-026-00419-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
3
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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