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Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence.

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The authors' code

Python · 137 lines · 5.1 KB · no license

  1. from scipy.io import loadmat
  2. import scipy.io as scio
  3. import numpy as np
  4. from sklearn.linear_model import Lasso
  5. from sklearn.metrics import r2_score
  6. import time
  8. # import setting
  9. alpha_list = 0.01
  10. bootstrap_num = 1000
  11. list_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/bootstrap_list.mat'
  12. list_data = loadmat(list_path)
  13. list_data = list_data['bootstrap_list']
  14. list_data = np.array(list_data)
  15. # import cognition
  16. y_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Cognition_output/Cognition.mat'
  17. y_data = loadmat(y_path)
  18. y_data = y_data['Cognition']
  19. y_data_all = np.array(y_data)
  21. start_time = time.time()
  22. # region
  23. X_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Region_input/Region_input.mat'
  24. X_data = loadmat(X_path)
  25. X_data = X_data['Region_input']
  26. X_data_all = np.array(X_data)
  27. for beh_ind in range(8):
  28. All_para = []
  29. All_r2 = []
  30. # bootstrap
  31. for i in range(bootstrap_num):
  32. first_index = list_data[:, i]
  33. X_data = X_data_all[first_index, :]
  34. indices = np.random.choice(range(len(X_data)), size=len(X_data), replace=True)
  35. X_data = X_data[indices, :]
  36. y_bootstrap = y_data_all[first_index, :]
  37. y_bootstrap = y_bootstrap[indices, :]
  38. y_data = y_bootstrap[:, beh_ind]
  39. # start
  40. model = Lasso(alpha=alpha_list, precompute=True, max_iter=100000, tol=1e-5, selection='random')
  41. model.fit(X_data, y_data)
  42. # parameter
  43. coefficients = model.coef_
  44. All_para.append(coefficients)
  45. # r2
  46. y_pred = model.predict(X_data)
  47. r2 = r2_score(y_data, y_pred)
  48. All_r2.append(r2)
  49. # save
  50. All_para = np.array(All_para)
  51. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Region/All_para_%s.mat' % (beh_ind+1)
  52. scio.savemat(save_path, {'All_para': All_para})
  53. All_r2 = np.array(All_r2)
  54. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Region/All_r2_%s.mat' % (beh_ind+1)
  55. scio.savemat(save_path, {'All_r2': All_r2})
  56. end_time = time.time()
  57. elapsed_time = end_time - start_time
  58. print(elapsed_time)
  60. start_time = time.time()
  61. # connection
  62. X_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Connection_input/Connection_input.mat'
  63. X_data = loadmat(X_path)
  64. X_data = X_data['Connection_input']
  65. X_data_all = np.array(X_data)
  66. for beh_ind in range(8):
  67. All_para = []
  68. All_r2 = []
  69. # bootstrap
  70. for i in range(bootstrap_num):
  71. first_index = list_data[:, i]
  72. X_data = X_data_all[first_index, :]
  73. indices = np.random.choice(range(len(X_data)), size=len(X_data), replace=True)
  74. X_data = X_data[indices, :]
  75. y_bootstrap = y_data_all[first_index, :]
  76. y_bootstrap = y_bootstrap[indices, :]
  77. y_data = y_bootstrap[:, beh_ind]
  78. # start
  79. model = Lasso(alpha=alpha_list, precompute=True, max_iter=100000, tol=1e-5, selection='random')
  80. model.fit(X_data, y_data)
  81. # parameter
  82. coefficients = model.coef_
  83. All_para.append(coefficients)
  84. # r2
  85. y_pred = model.predict(X_data)
  86. r2 = r2_score(y_data, y_pred)
  87. All_r2.append(r2)
  88. # save
  89. All_para = np.array(All_para)
  90. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Connection/All_para_%s.mat' % (beh_ind+1)
  91. scio.savemat(save_path, {'All_para': All_para})
  92. All_r2 = np.array(All_r2)
  93. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Connection/All_r2_%s.mat' % (beh_ind+1)
  94. scio.savemat(save_path, {'All_r2': All_r2})
  95. end_time = time.time()
  96. elapsed_time = end_time - start_time
  97. print(elapsed_time)
  99. start_time = time.time()
  100. # hub
  101. X_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Hub_input/Hub_input.mat'
  102. X_data = loadmat(X_path)
  103. X_data = X_data['Hub_input']
  104. X_data_all = np.array(X_data)
  105. for beh_ind in range(8):
  106. All_para = []
  107. All_r2 = []
  108. # bootstrap
  109. for i in range(bootstrap_num):
  110. first_index = list_data[:, i]
  111. X_data = X_data_all[first_index, :]
  112. indices = np.random.choice(range(len(X_data)), size=len(X_data), replace=True)
  113. X_data = X_data[indices, :]
  114. y_bootstrap = y_data_all[first_index, :]
  115. y_bootstrap = y_bootstrap[indices, :]
  116. y_data = y_bootstrap[:, beh_ind]
  117. # start
  118. model = Lasso(alpha=alpha_list, precompute=True, max_iter=100000, tol=1e-5, selection='random')
  119. model.fit(X_data, y_data)
  120. # parameter
  121. coefficients = model.coef_
  122. All_para.append(coefficients)
  123. # r2
  124. y_pred = model.predict(X_data)
  125. r2 = r2_score(y_data, y_pred)
  126. All_r2.append(r2)
  127. # save
  128. All_para = np.array(All_para)
  129. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Hub/All_para_%s.mat' % (beh_ind+1)
  130. scio.savemat(save_path, {'All_para': All_para})
  131. All_r2 = np.array(All_r2)
  132. save_path = '/home/users/jyan/JYan/First_Project_Result/New_241127/First_step/Hub/All_r2_%s.mat' % (beh_ind+1)
  133. scio.savemat(save_path, {'All_r2': All_r2})
  134. end_time = time.time()
  135. elapsed_time = end_time - start_time
  136. print(elapsed_time)

Bootstrap_1000.py at commit d062e00, no license · at the source

Overview

Authors: Jiadong Yan1,2, Yasser Iturria-Medina1,3, Gleb Bezgin1, Paule Joanne Toussaint1,2, Ke Xie1, Liang He1,2, Judy Chen1, Kirsten Hilger4, Erhan Genç5, Alan C Evans1,2, Sherif Karama2,6
ORCID iDs: Jiadong Yan, Yasser Iturria-Medina, Gleb Bezgin, Paule Joanne Toussaint, Ke Xie, Kirsten Hilger, Alan C Evans, Sherif Karama
  1. Montreal Neurological Institute, McGill University, Montreal, QC Canada
  2. McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC Canada
  3. Ludmer Centre for NeuroInformatics and Mental Health, Montreal, QC Canada
  4. Department of Psychology I, Würzburg University, Würzburg, Germany
  5. Neuroimaging and Interindividual Differences, Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, Dortmund, Germany
  6. Douglas Mental Health University Institute, McGill University, Montreal, QC Canada
Institutions: McGill University (Canada); Ludmer Centre for Neuroinformatics and Mental Health (Canada); University of Würzburg (Germany); TU Dortmund University (Germany); Leibniz Research Centre for Working Environment and Human Factors (Germany); Douglas Mental Health University Institute (Canada)
Journal: Communications biology, volume 9, issue 1, article 584
Dates: received 30 August 2025; accepted 26 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09831-4 · PMID 41820538 · PMCID PMC13121603 · OpenAlex W7135042256
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Graphs, Machine learning, fMRI & imaging, Preprocessing
Keywords: Intelligence, Machine learning, Development of the nervous system
MeSH: Brain*, Cognition*, Adolescent, Child, Female, Humans, Intelligence, Magnetic Resonance Imaging, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (HI 2185/1); China Scholarship Council; Canada First Research Excellence Fund; National Institutes of Health (7u01da041117-03, U01D A041174, 5u01da051016-05, 3u01da041089-07s1, 5u01da041022-10, 7u01da041174-02, 5u01da041048-10, 5u01da041120-10, 5u01da051018-05, 5u01da051037-05, U01DA04 1025, 5u24da041123-10, 5u01da041028-10, 5u01da041134-10, 5u01da041106-10, 5u01da050989-05, 3u01da051039-02s1, 5u01da041093-08, 5u01da050987-05, 3u01da041148-08s1, 3u24da041147-07s2, 5u01da051038-05, 5u01da041025-08, 5u01da041156-10, U01DA04 1120, 5u01da050988-05, U01DA041093, U01DA041028, U01DA041117, U01DA 041134, U01DA050988, U01DA051016, U01DA051018, U01DA041022, U24DA041123, U01DA050989, U01DA041148, U01DA041089, U01DA051038, U01DA041106, U01DA051037, U01 DA041156, U01DA050987, U24DA041147, U01DA051039, U01DA041048); Canadian Institutes of Health Research (U01DA041089, U01DA041148, U01DA041117, U01DA051016, U01DA041120, U01DA051018, U01DA041156, U01DA051038, U01DA041106, U01DA041022, U01DA050988, U01DA041093, U01DA041134, U24DA041123, U01DA051037, U01DA041048, U01DA041028, U01DA041174, U01DA050989, U01DA051039, U01DA050987, U01DA041025, U24DA041147)
Citations: cited by 2 papers (Europe PMC); 109 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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

JDYan/Brain-Cognition-Association

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d062e009021a96b153661c017109000b9d47048d, 21 August 2025
Languages: MATLAB (2), Python (1)
Size: 3 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), NumPy (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/JDYan/Brain-Cognition-Association

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-09831-4.

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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-09831-4.

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, issue, pages, dates, 11 authors, 3 keywords, 9 MeSH terms, 5 funders, 101 references.

Cite

This paper

Yan, J., Iturria-Medina, Y., Bezgin, G., Toussaint, P. J., Xie, K., He, L., Chen, J., Hilger, K., Genç, E., Evans, A. C., & Karama, S. (2026). Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence. Communications biology, 9(1), 584. https://doi.org/10.1038/s42003-026-09831-4

BibTeX

@article{yan2026comprehensive,
author = {Yan, Jiadong and Iturria-Medina, Yasser and Bezgin, Gleb and Toussaint, Paule Joanne and Xie, Ke and He, Liang and Chen, Judy and Hilger, Kirsten and Genç, Erhan and Evans, Alan C and Karama, Sherif},
title = {{Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence}},
journal = {Communications biology},
year = {2026},
month = mar,
volume = {9},
number = {1},
pages = {584},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09831-4},
url = {https://doi.org/10.1038/s42003-026-09831-4},
pmid = {41820538},
pmcid = {PMC13121603}
}

RIS

TY - JOUR
AU - Yan, Jiadong
AU - Iturria-Medina, Yasser
AU - Bezgin, Gleb
AU - Toussaint, Paule Joanne
AU - Xie, Ke
AU - He, Liang
AU - Chen, Judy
AU - Hilger, Kirsten
AU - Genç, Erhan
AU - Evans, Alan C
AU - Karama, Sherif
TI - Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/03/12
VL - 9
IS - 1
SP - 584
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09831-4
UR - https://doi.org/10.1038/s42003-026-09831-4
LA - en
ER -

CSL-JSON

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