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Initial organization and progressive expansion of the math-responsive brain network during the first school years.

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The authors' code

Python · 136 lines · 5.8 KB · no license

  2. import nilearn
  3. import nilearn.image
  4. import nilearn.plotting
  5. import nilearn.glm
  6. import nilearn.glm.first_level
  7. import nilearn.reporting
  8. from matplotlib import pyplot as plt
  9. import numpy as np
  10. from nilearn.glm.first_level import make_first_level_design_matrix
  11. from nilearn.plotting import plot_design_matrix
  12. from nilearn.glm.first_level import FirstLevelModel
  13. from nilearn.glm.second_level import SecondLevelModel
  14. from nilearn.image import threshold_img
  15. from nilearn.glm import threshold_stats_img
  16. from nilearn import datasets
  17. from nilearn import surface
  18. from matplotlib.colors import ListedColormap, LinearSegmentedColormap
  19. from nilearn import plotting, image
  20. from matplotlib import cm
  21. from nilearn.interfaces.bids import save_glm_to_bids
  22. from nilearn.glm.second_level import make_second_level_design_matrix
  23. from nilearn.surface import vol_to_surf
  24. import pandas as pd
  25. from scipy.stats import norm
  26. import pickle
  27. from nilearn.reporting import get_clusters_table
  28. from sklearn.preprocessing import PolynomialFeatures
  29. from pathlib import Path
  30. import json
  31. from tqdm import tqdm
  33. # Path for the first-level contrasts
  34. deriv_root = Path("../Data/derivatives/firstlevel")
  35. task = "sentences"
  36. view = ["lateral","posterior","medial"]
  37. contrastAll = ["maths_vs_nonmath","social_vs_nonsocial"]
  38. z_thresh = norm.isf(0.001)
  39. sessions = ["01","02","03"]
  41. #____________________________________________________________________________________________________
  42. # Compute the second level model
  43. # Figure 2A : Activations for math vs. non-math sentences (top row)
  44. # Figure S5A: Activations for social vs. non-social sentences (top row)
  45. #____________________________________________________________________________________________________
  47. for contrastName in tqdm(contrastAll, total = 2, desc = "Contrasts"):
  49. # ---- 1. Collect subject-level maps ----
  50. contrast_maps = []
  51. for ses in sessions:
  52. contrast_maps.extend(
  53. deriv_root.glob(
  54. f"sub-*/ses-{ses}/func/"
  55. f"*desc-{contrastName}_stat-es.nii.gz"
  56. )
  57. )
  58. contrast_maps = sorted(contrast_maps)
  60. # ---- 2. Compute the second-level design matrix ----
  61. design_matrix = pd.DataFrame({
  62. "intercept": [1] * len(contrast_maps)
  63. })
  65. # ---- 3. Compute second-level analysis ----
  66. second_level_model = SecondLevelModel(smoothing_fwhm=8)
  67. second_level_model = second_level_model.fit(contrast_maps,
  68. design_matrix = design_matrix)
  70. # ---- 4. Compute contrast ----
  71. zmap = second_level_model.compute_contrast(second_level_contrast = "intercept",
  72. output_type="z_score")
  73. zmap_corrected, threshold1 = threshold_stats_img(zmap, alpha=0.01,
  74. height_control="fdr",
  75. two_sided=True)
  77. # ---- 5. Plotting in surface ----
  78. nilearn.plotting.plot_img_on_surf(zmap_corrected,
  79. views=view,
  80. hemispheres=['left', 'right'],
  81. colorbar=True,
  82. title = "1st Level : " + contrastName,
  83. threshold = z_thresh,
  84. inflate = True)
  85. nilearn.plotting.show()
  87. # ---- 5. Plotting in volumes (z-axis) ----
  88. nilearn.plotting.plot_stat_map(zmap_corrected, colorbar=True,
  89. threshold = z_thresh,
  90. title = "1st Level : " + contrastName,
  91. display_mode = "z",
  92. cut_coords = [-20, -10, 0, 10, 20, 30, 40, 50, 60])
  93. nilearn.plotting.show()
  95. #____________________________________________________________________________________________________
  96. # In each period separately
  97. # Figure 2A : Activations for math vs. non-math sentences (bottom row)
  98. # Figure S5A: Activations for social vs. non-social sentences (bottom row)
  99. # #____________________________________________________________________________________________________
  101. for ses in sessions:
  102. for contrastName in tqdm(contrastAll, total=2, desc="Contrasts"):
  104. # ---- 1. Collect subject-level maps ----
  105. contrast_maps = []
  106. contrast_maps.extend(
  107. deriv_root.glob(
  108. f"sub-*/ses-{ses}/func/"
  109. f"*desc-{contrastName}_stat-es.nii.gz"))
  110. contrast_maps = sorted(contrast_maps)
  112. design_matrix = pd.DataFrame({
  113. "intercept": [1] * len(contrast_maps)
  114. })
  116. # ---- 2. Compute second-level analysis ----
  117. second_level_model = SecondLevelModel(smoothing_fwhm=8)
  118. second_level_model = second_level_model.fit(contrast_maps,
  119. design_matrix=design_matrix)
  121. # ---- 3. Compute contrast ----
  122. zmap = second_level_model.compute_contrast(second_level_contrast="intercept")
  123. zmap_corrected, threshold1 = threshold_stats_img(zmap, alpha=0.1,
  124. height_control="fdr",
  125. two_sided=True)
  126. # ---- 4. Plotting in surface ----
  127. nilearn.plotting.plot_img_on_surf(zmap_corrected,
  128. views=view,
  129. hemispheres=['left', 'right'],
  130. colorbar=True,
  131. title = "1st Level : " + contrastName + ", Period: " + ses,
  132. threshold = z_thresh,
  133. vmax = 6.5,
  134. inflate = True)
  135. nilearn.plotting.show()

secondLevelEstimation.py, no license · at the source

Overview

Authors: Théo Morfoisse1,2, Séverine Becuwe2, Marie Palu2, Cassandra Potier-Watkins1, Ghislaine Dehaene-Lambertz2, Stanislas Dehaene1,2
ORCID iDs: Marie Palu, Ghislaine Dehaene-Lambertz, Stanislas Dehaene
  1. Chair of Experimental Cognitive Psychology, Collège de France, Paris 75005, France
  2. Cognitive NeuroImaging Unit, Commissariat à l’Energie atomique et aux énergies alternatives, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette 91191, France
Institutions: Collège de France (France); Université Paris-Saclay (France)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 27, article e2602515123
Dates: received 23 January 2026; accepted 19 May 2026; published online 2 July 2026; in print 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2602515123 · PMID 42391394 · PMCID PMC13342980 · OpenAlex W7167060396
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Statistics, fMRI & imaging
Keywords: cognitive development, mathematical cognition, longitudinal study, education, fMRI
MeSH: Brain*, Child Development*, Mathematics*, Nerve Net*, Brain Mapping, Child, Child, Preschool, Cognition, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male (* major topic)
Journal subjects: Biological Sciences, Neuroscience
Topic: Cognitive and developmental aspects of mathematical skills (Statistics and Probability, Mathematics), according to OpenAlex
Funding: ERC (ERC-2022-ADG101095866); France 2030 program (ANR-23-IAHU-0010)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

The neural mechanisms by which the developing brain acquires higher mathematical concepts from elementary intuitions remain poorly understood. Through a large-scale longitudinal functional MRI study of children from preschool through first and second grade, we tracked how neural responses to mathematical and nonmathematical statements change in the first 2 y of formal schooling, and we used these data to evaluate several theories of developmental change. Before school, when listening to math statements, children already engaged an adult-like cortical network, with partial specialization for geometry. Over the first 2 y of school, we observed an overall increase in math-related activation, a small recruitment of additional neural territory, reduced activation for facts that get better known, and a small overall increase in the dimensionality of representational space. fMRI responses to individual sentences suggest that these mechanisms, particularly in left inferior frontal gyrus and bilateral intraparietal sulcus, all contribute to children’s growing mastery of mathematical concepts.

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

Repository

Its files are read in the Code ↔ Paper reader above.

OSF 5kvd9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (3)
Size: 238 files, 3 scripts
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), Nilearn (3 files), NumPy (3 files), pandas (3 files), SciPy (3 files), scikit-learn (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/5kvd9/overview?view_only=52f336a5f13f446eb569ada6c798cf31

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;
  • 3 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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, Materials, and Software Availability

All code used for data preprocessing, statistical analyses, and figure generation is available on the Open Science Framework (OSF) at https://osf.io/5kvd9/overview?view_only=52f336a5f13f446eb569ada6c798cf31 (92). All other data are included in the manuscript and/or SI Appendix (http://www.pnas.org/lookup/doi/10.1073/pnas.2602515123#supplementary-materials).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 13 MeSH terms, 2 funders, 84 references.

Cite

This paper

Morfoisse, T., Becuwe, S., Palu, M., Potier-Watkins, C., Dehaene-Lambertz, G., & Dehaene, S. (2026). Initial organization and progressive expansion of the math-responsive brain network during the first school years. Proceedings of the National Academy of Sciences of the United States of America, 123(27), e2602515123. https://doi.org/10.1073/pnas.2602515123

BibTeX

@article{morfoisse2026initial,
author = {Morfoisse, Théo and Becuwe, Séverine and Palu, Marie and Potier-Watkins, Cassandra and Dehaene-Lambertz, Ghislaine and Dehaene, Stanislas},
title = {{Initial organization and progressive expansion of the math-responsive brain network during the first school years}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = jul,
volume = {123},
number = {27},
pages = {e2602515123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2602515123},
url = {https://doi.org/10.1073/pnas.2602515123},
pmid = {42391394},
pmcid = {PMC13342980}
}

RIS

TY - JOUR
AU - Morfoisse, Théo
AU - Becuwe, Séverine
AU - Palu, Marie
AU - Potier-Watkins, Cassandra
AU - Dehaene-Lambertz, Ghislaine
AU - Dehaene, Stanislas
TI - Initial organization and progressive expansion of the math-responsive brain network during the first school years
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/07/02
VL - 123
IS - 27
SP - e2602515123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2602515123
UR - https://doi.org/10.1073/pnas.2602515123
LA - en
ER -

CSL-JSON

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