OSCR

Brain structural and functional connectivity converge prenatally but diverge after birth.

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
  1. [1] § Materials and methods › Non-negative matrix factorization ↔ run_opnmf_stability_analysis.m, lines 1–27 · score 0.85 · negative Matrix Factorization, row represented, Silhouette Coefficient, Adjusted Rand, quantifies, bootstrap
  2. [2] § Materials and methods › Association with cognitive outcomes at 18 months ↔ run_behavioral_association_analysis.m, lines 20–81 · score 0.81 · behavior associations, max statistic, scan age, cognition, language, motor
  3. [3] § Results › SF coupling at birth is associated with later developmental outcomes ↔ run_behavioral_association_analysis.m, lines 20–81 · score 0.79 · expressive language, assessment scores, maternal postnatal, max statistic, motor, cognitive
  4. [4] § Materials and methods › Non-negative matrix factorization ↔ run_opnmf_stability_analysis.m, lines 73–98 · score 0.50 · NMF components, Silhouette Coefficient

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 250 lines · 8 KB · CC-BY-4.0 · 2 matches

  1. function results = run_opnmf_stability_analysis(cfg)
  2. % RUN_OPNMF_STABILITY_ANALYSIS
  3. % Evaluate model order and parcel-level clustering stability for orthogonal
  4. % projective non-negative matrix factorization (opNMF).
  5. %
  6. % This script was prepared for public code sharing. It uses generic input and
  7. % output paths and does not include private file paths or project-specific
  8. % identifiers.
  9. %
  10. % Expected input:
  11. % A numeric matrix with rows representing subjects/scans and columns
  12. % representing brain regions/parcels. Values should be parcel-wise
  13. % structure-function coupling (SFC) estimates or another non-negative
  14. % feature matrix after preprocessing.
  15. %
  16. % Main outputs:
  17. % 1. Mean silhouette coefficient for each candidate factor number k.
  18. % 2. Bootstrap stability quantified by the adjusted Rand index (ARI).
  19. % 3. Region-wise cluster assignments for each candidate k.
  20. %
  21. % Dependencies:
  22. % - MATLAB Statistics and Machine Learning Toolbox, for silhouette().
  23. % - opnmf_mem.m, for orthogonal projective NMF.
  24. %
  25. % Author: <replace with author name>
  26. % License: <replace with selected license, e.g., MIT>
  27. % Repository/DOI: <replace after Zenodo archiving>
  29. %% 1. Configure paths and analysis parameters
  30. if nargin < 1 || isempty(cfg)
  31. cfg = default_config();
  32. end
  34. rng(cfg.random_seed, 'twister');
  36. if ~exist(cfg.output_dir, 'dir')
  37. mkdir(cfg.output_dir);
  38. end
  40. input_file = fullfile(cfg.input_dir, cfg.input_filename);
  41. assert(exist(input_file, 'file') == 2, 'Input file not found: %s', input_file);
  43. %% 2. Load parcel-wise SFC matrix
  44. % The input matrix should have dimensions: subjects/scans x regions.
  45. sfc_matrix = readmatrix(input_file, 'TreatAsMissing', cfg.missing_values);
  47. [n_subjects, n_regions] = size(sfc_matrix);
  48. assert(n_subjects > 1 && n_regions > 1, ...
  49. 'Input matrix must contain more than one subject/scan and more than one region.');
  51. if any(~isfinite(sfc_matrix(:)))
  52. error(['The input matrix contains NaN or Inf values. ', ...
  53. 'Please remove or impute missing values before running opNMF.']);
  54. end
  56. %% 3. Shift values to satisfy the non-negativity requirement of NMF
  57. % NMF requires non-negative input. For correlation-like SFC values, adding a
  58. % constant shift can be used when values are bounded within a known range.
  59. % Set cfg.nonnegative_shift = 0 if the input matrix is already non-negative.
  60. analysis_matrix = sfc_matrix + cfg.nonnegative_shift;
  62. if any(analysis_matrix(:) < 0)
  63. error(['The analysis matrix still contains negative values after applying ', ...
  64. 'cfg.nonnegative_shift. Increase the shift or use another ', ...
  65. 'non-negative transformation before NMF.']);
  66. end
  68. %% 4. Transpose matrix for region-wise opNMF clustering
  69. % opNMF is applied to a regions x subjects/scans matrix so that each row is a
  70. % brain region and each column is an observation.
  71. mat = analysis_matrix';
  73. %% 5. Run opNMF across candidate model orders
  74. k_values = cfg.k_range(:)';
  75. n_k = numel(k_values);
  77. classification = zeros(n_k, n_regions);
  78. silhouette_mean = zeros(n_k, 1);
  79. reconstruction_error = zeros(n_k, 1);
  81. for k_idx = 1:n_k
  82. k = k_values(k_idx);
  84. % W contains region x factor weights; H contains factor x observation
  85. % weights. The maximum loading of each row of W is used to assign each
  86. % region to one opNMF component.
  87. [W, H] = opnmf_mem(mat, k, [], cfg.n_opnmf_runs);
  89. [~, labels] = max(W, [], 2);
  90. classification(k_idx, :) = labels';
  92. % Calculate the mean silhouette coefficient across regions.
  93. s = silhouette(mat, labels);
  94. silhouette_mean(k_idx) = mean(s(~isnan(s)));
  96. % Optional diagnostic: relative Frobenius reconstruction error.
  97. reconstruction_error(k_idx) = norm(mat - W * H, 'fro') / norm(mat, 'fro');
  98. end
  100. %% 6. Assess bootstrap stability of region-wise classification
  101. n_bootstraps = cfg.n_bootstraps;
  102. bootstrap_sample_size = round(n_subjects * cfg.bootstrap_fraction);
  103. bootstrap_sample_size = max(2, min(bootstrap_sample_size, n_subjects));
  105. classification_bootstrap = zeros(n_regions, n_bootstraps, n_k);
  106. bootstrap_subject_indices = cell(n_bootstraps, n_k);
  108. for k_idx = 1:n_k
  109. k = k_values(k_idx);
  111. for b = 1:n_bootstraps
  112. subject_idx = randperm(n_subjects, bootstrap_sample_size);
  113. bootstrap_subject_indices{b, k_idx} = subject_idx;
  115. [W_boot, ~] = opnmf_mem(mat(:, subject_idx), k, [], cfg.n_opnmf_runs);
  116. [~, labels_boot] = max(W_boot, [], 2);
  118. classification_bootstrap(:, b, k_idx) = labels_boot;
  119. end
  120. end
  122. %% 7. Calculate adjusted Rand index between full-sample and bootstrap labels
  123. ari = zeros(n_bootstraps, n_k);
  125. for k_idx = 1:n_k
  126. labels_full = classification(k_idx, :)';
  128. for b = 1:n_bootstraps
  129. labels_boot = classification_bootstrap(:, b, k_idx);
  130. ari(b, k_idx) = adjusted_rand_index(labels_boot, labels_full);
  131. end
  132. end
  134. mean_ari = mean(ari, 1);
  135. std_ari = std(ari, 0, 1);
  137. %% 8. Save results
  138. summary_table = table(k_values', silhouette_mean, reconstruction_error, ...
  139. mean_ari', std_ari', ...
  140. 'VariableNames', {'k', 'mean_silhouette', 'relative_reconstruction_error', ...
  141. 'mean_adjusted_rand_index', 'std_adjusted_rand_index'});
  143. writetable(summary_table, fullfile(cfg.output_dir, 'opnmf_model_order_summary.csv'));
  145. results = struct();
  146. results.config = cfg;
  147. results.n_subjects = n_subjects;
  148. results.n_regions = n_regions;
  149. results.k_values = k_values;
  150. results.classification = classification;
  151. results.classification_bootstrap = classification_bootstrap;
  152. results.bootstrap_subject_indices = bootstrap_subject_indices;
  153. results.silhouette_mean = silhouette_mean;
  154. results.reconstruction_error = reconstruction_error;
  155. results.ari = ari;
  156. results.mean_ari = mean_ari;
  157. results.std_ari = std_ari;
  158. results.summary_table = summary_table;
  160. save(fullfile(cfg.output_dir, 'opnmf_stability_results.mat'), 'results');
  162. end
  164. function cfg = default_config()
  165. % DEFAULT_CONFIG
  166. % Default settings for the opNMF model-order and stability analysis.
  168. project_dir = pwd;
  170. cfg = struct();
  171. cfg.input_dir = fullfile(project_dir, 'data');
  172. cfg.output_dir = fullfile(project_dir, 'results', 'opnmf_stability');
  174. % Use a generic file name for public release. Replace this with the actual
  175. % input file name locally, but avoid exposing private project paths or
  176. % sensitive dataset identifiers in public code if not necessary.
  177. cfg.input_filename = 'sfc_matrix_subjects_by_regions.txt';
  179. % Candidate numbers of opNMF components.
  180. cfg.k_range = 2:10;
  182. % Number of repeated initializations or internal opNMF runs, depending on the
  183. % implementation of opnmf_mem.m.
  184. cfg.n_opnmf_runs = 4;
  186. % Bootstrap settings.
  187. cfg.n_bootstraps = 100;
  188. cfg.bootstrap_fraction = 0.50;
  190. % Additive shift to ensure non-negative input. For SFC values bounded between
  191. % -1 and 1, a shift of 1 maps them to [0, 2].
  192. cfg.nonnegative_shift = 1;
  194. % Reproducibility.
  195. cfg.random_seed = 1;
  197. % Missing value strings recognized by readmatrix(). Numeric NaNs are checked
  198. % after loading and should be handled before NMF.
  199. cfg.missing_values = {'NaN', 'NA'};
  201. end
  203. function ari = adjusted_rand_index(labels_a, labels_b)
  204. % ADJUSTED_RAND_INDEX
  205. % Compute the adjusted Rand index between two partitions.
  206. %
  207. % This local implementation avoids requiring an external rand_index()
  208. % function and makes the script easier to reuse.
  210. labels_a = labels_a(:);
  211. labels_b = labels_b(:);
  213. valid_idx = isfinite(labels_a) & isfinite(labels_b);
  214. labels_a = labels_a(valid_idx);
  215. labels_b = labels_b(valid_idx);
  217. n = numel(labels_a);
  218. if n < 2
  219. ari = NaN;
  220. return;
  221. end
  223. [~, ~, labels_a] = unique(labels_a);
  224. [~, ~, labels_b] = unique(labels_b);
  226. contingency = accumarray([labels_a, labels_b], 1);
  227. row_sums = sum(contingency, 2);
  228. col_sums = sum(contingency, 1);
  230. sum_comb_cells = sum(comb2(contingency(:)));
  231. sum_comb_rows = sum(comb2(row_sums));
  232. sum_comb_cols = sum(comb2(col_sums));
  233. total_comb = comb2(n);
  235. expected_index = (sum_comb_rows * sum_comb_cols) / total_comb;
  236. max_index = 0.5 * (sum_comb_rows + sum_comb_cols);
  238. denominator = max_index - expected_index;
  239. if abs(denominator) < eps
  240. ari = 1;
  241. else
  242. ari = (sum_comb_cells - expected_index) / denominator;
  243. end
  245. end
  247. function y = comb2(x)
  248. % Number of unordered pairs that can be selected from x elements.
  249. y = x .* (x - 1) ./ 2;
  250. end

run_opnmf_stability_analysis.m, under CC-BY-4.0 · at the source

Overview

Authors: Ruoke Zhao1, Mingyang Li1, Yuqi Zhang1, Ruike Chen1, Chenglin Ning1, Zhiyong Zhao2, Zhihan Yan3, Meihao Wang3,4, Dan Wu1,2,3
ORCID iDs: Ruoke Zhao, Dan Wu
  1. College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
  2. Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents’ Health and Diseases, Hangzhou, China
  3. Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
  4. Key Laboratory of Intelligent Medical Imaging of Wenzhou, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Institutions: Zhejiang University (China); Children's Hospital of Zhejiang University (China); Wenzhou Medical University (China); Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (China); First Affiliated Hospital of Wenzhou Medical University (China)
Journal: PLoS biology, volume 24, issue 9, article e3003927
Dates: received 30 October 2025; accepted 15 July 2026; published online 9 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003927 · PMID 42715242 · PMCID PMC13557396 · OpenAlex W7168019031
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), human (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Graphs, Machine learning, fMRI & imaging
MeSH: Brain*, Child, Preschool, Connectome, Female, Humans, Infant, Infant, Newborn, Magnetic Resonance Imaging, Male, Nerve Net, Neurodevelopment (* major topic)
Journal subjects: Biology and Life Sciences, Developmental Biology, Neonates, Neuroscience, Cognitive Science, Cognitive Psychology, Language, Psychology, Social Sciences, Brain Mapping, Brain Morphometry, Diffusion Magnetic Resonance Imaging, Medicine and Health Sciences, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Computer and Information Sciences, Software Engineering, Preprocessing, Engineering and Technology, Connectomics, Anatomy, Nervous System, Neuroanatomy, Neural Networks, Functional Magnetic Resonance Imaging, Physical Sciences, Mathematics, Discrete Mathematics, Combinatorics, Permutation
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Science and Technology of the People’s Republic of China (2021ZD0200202); National Natural Science Foundation of China (32427802, U24A20754)
Citations: not cited yet (Europe PMC); 105 references in the paper

Abstract

Brain anatomical architecture supports its functional activity and complex cognitive processes. However, how the structure–function (SF) relationship establishes and develops during early life, as well as its underlying mechanisms, remain largely unclear. To address these questions, we leveraged multimodal MRI data from two large-scale public databases, the developing Human Connectome Project (dHCP) and the Baby Connectome Project (BCP), to characterize the spatiotemporal dynamics of SF coupling from the perinatal period to toddlerhood. Our results revealed that SF coupling at birth exhibited a spatial variation along the sensorimotor-association cortical axis. During the perinatal period (26–44 postmenstrual weeks), SF coupling strengthened drastically and followed three distinct developmental trajectories across the cortex, with sensorimotor and visual areas showing the fastest growth and the earliest plateau. After birth, SF coupling shifted toward a weakening pattern across the cortex during infancy and toddlerhood (1–28 months). These developmental changes of SF coupling were more strongly associated with the maturation of functional connectivity, which first converged toward the local structural architecture prenatally and then diverged postnatally through the expansion of global inter-modular pathways. Furthermore, SF coupling at birth, the developmental transition point, exhibited a significant association with individual differences in cognition and language outcomes at 18 months of age. Collectively, these findings offer valuable insights into the organizational principles underlying structural and functional network development during early life as well as the complex evolving relationship between them.

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 4 matches between paragraphs and lines of code.

Zenodo 21316924

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: MATLAB (3), R (1)
Size: 4 files, 4 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: GIfTI library for MATLAB (1 file), Statistics and Machine Learning Toolbox (1 file), mgcv (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.21316924

brainlife/BCT

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: b7f59ebcf0b4f4979ab3914e30c61eaf345b11d5, 16 December 2020
Languages: MATLAB (134)
Size: 148 files, 134 scripts
Software Heritage: not archived
Found in: the text, “Network metrics”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brain Connectivity Toolbox (22 files), Statistics and Machine Learning Toolbox (5 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
136 files
At the source: github.com/brainlife/BCT

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;
  • 138 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.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

The raw data used in this study were obtained from publicly available databases. The dHCP dataset can be accessed at https://biomedia.github.io/dHCP-release-notes/, and the HCP dataset at https://db.humanconnectome.org/. The BCP dataset is available via the NIH Data Archive (NDA, Study ID: 2848). The numerical data underlying the figure panels are provided in S1 Data. Custom code used for data analysis is available on Zenodo at: https://doi.org/10.5281/zenodo.21316924.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 11 MeSH terms, 2 funders, 103 references.

Cite

This paper

Zhao, R., Li, M., Zhang, Y., Chen, R., Ning, C., Zhao, Z., Yan, Z., Wang, M., & Wu, D. (2026). Brain structural and functional connectivity converge prenatally but diverge after birth. PLoS biology, 24(9), e3003927. https://doi.org/10.1371/journal.pbio.3003927

BibTeX

@article{zhao2026brain,
author = {Zhao, Ruoke and Li, Mingyang and Zhang, Yuqi and Chen, Ruike and Ning, Chenglin and Zhao, Zhiyong and Yan, Zhihan and Wang, Meihao and Wu, Dan},
title = {{Brain structural and functional connectivity converge prenatally but diverge after birth}},
journal = {PLoS biology},
year = {2026},
month = sep,
volume = {24},
number = {9},
pages = {e3003927},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003927},
url = {https://doi.org/10.1371/journal.pbio.3003927},
pmid = {42715242},
pmcid = {PMC13557396}
}

RIS

TY - JOUR
AU - Zhao, Ruoke
AU - Li, Mingyang
AU - Zhang, Yuqi
AU - Chen, Ruike
AU - Ning, Chenglin
AU - Zhao, Zhiyong
AU - Yan, Zhihan
AU - Wang, Meihao
AU - Wu, Dan
TI - Brain structural and functional connectivity converge prenatally but diverge after birth
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/09/09
VL - 24
IS - 9
SP - e3003927
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003927
UR - https://doi.org/10.1371/journal.pbio.3003927
LA - en
ER -

CSL-JSON

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"title": "Brain structural and functional connectivity converge prenatally but diverge after birth",
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"family": "Zhao",
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