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Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins.

Code ↔ Paper

21 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 21 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Comparisons between FOCA and conventional FC ↔ s9_3_NeonatalAgePrediction_SVR_FOCA_dHCP.m, lines 1–24 · score 0.95 · predict chronological age, predictive validity, neonatal period, Pearson correlation, birth interval, prediction accuracy
  2. [2] § Materials and methods › Comparisons between FOCA and conventional FC ↔ s9_4_NeonatalAgePrediction_SVR_FC_dHCP.m, lines 1–23 · score 0.94 · predictive validity, FC features, chronological age, Pearson correlation, birth interval, prediction accuracy
  3. [3] § Materials and methods › Developmental refinement and neurodevelopmental outcomes of internetwork covariance during the early postnatal period ↔ s8_3_behavior_prediction_neonatal_FOCA_GLM.m, lines 1–21 · score 0.94 · neonatal internetwork covariance, Bayley III, scan birth intervals, multiple linear regression, dependent variables, FOCA profiles
  4. [4] § Materials and methods › Convergent and divergent functional network topography in adults ↔ s5_hierarchical_clustering_and_modular_summary.m, lines 1–23 · score 0.92 · inter cluster correlations, modular organization, distance metric, MATLAB R2020b, Hierarchical clustering, intra
  5. [5] § Materials and methods › Generating individualized functional networks based on the TM method ↔ s1_1_generate_group_templates.m, lines 29–48 · score 0.90 · somato cognitive action, cingulo opercular, dorsal attention, anterolateral, retrosplenial, dorsolateral
  6. [6] § Materials and methods › Developmental refinement and neurodevelopmental outcomes of internetwork covariance during the early postnatal period ↔ s8_1_neonatal_FOCA_GAM_analysis.R, lines 1–42 · score 0.90 · age related trajectories, early postnatal period, mass univariate, scan birth intervals, network pair, GAMs
  7. [7] § Results › The spatial alignment of functional topography encodes brain development at birth and predicts neurodevelopmental outcomes at 18 months ↔ s8_3_behavior_prediction_neonatal_FOCA_GLM.m, lines 1–21 · score 0.89 · Bayley III, scan birth intervals, behavioural scores, multiple linear regression, predictive accuracy, FOCA profiles
  8. [8] § Materials and methods › Developmental refinement and neurodevelopmental outcomes of internetwork covariance during the early postnatal period ↔ s8_2_age_effects_neonatal_FOCA_GAM.m, lines 1–20 · score 0.86 · age related trajectories, early postnatal period, mass univariate, internetwork covariance, GAMs, FDR
  9. [9] § Results › The spatial alignment of functional topography encodes brain development at birth and predicts neurodevelopmental outcomes at 18 months ↔ s8_1_neonatal_FOCA_GAM_analysis.R, lines 1–42 · score 0.84 · mass univariate GAMs, early postnatal period, scan birth intervals, network pair, foot, PMA
  10. [10] § Results › Comparison between FOCA and conventional FC ↔ s9_4_NeonatalAgePrediction_SVR_FC_dHCP.m, lines 1–23 · score 0.80 · scan birth intervals, chronological age, Prediction accuracy, fold cross validation, held, FC
  11. [11] § Results › Comparison between FOCA and conventional FC ↔ s9_3_NeonatalAgePrediction_SVR_FOCA_dHCP.m, lines 1–24 · score 0.78 · scan birth intervals, chronological age, Prediction accuracy, fold cross validation, held, GSR
  12. [12] § Results › Individualized spatial topography of positive and negative connections in adult functional networks ↔ s1_1_generate_group_templates.m, lines 29–48 · score 0.76 · somato cognitive action, cingulo opercular, dorsal attention, parietal, MSC03, salience
  13. [13] § Materials and methods › Convergent and divergent functional network topography in adults ↔ s3_compute_foca_matrix.m, lines 1–22 · score 0.75 · negative correlations reflected, Positive correlations, topographic maps, network pairs, individualized functional topography, FOCA matrix
  14. [14] § Materials and methods › Internetwork covariance constrained by fundamental organizational features of the brain ↔ s6_brain_axes_prediction_FOCA.m, lines 1–23 · score 0.75 · feature alignment matrix, fold cross validation, linear regression, FOCA matrix, neurobiological, fundamental
  15. [15] § Results › Distinct spatial coordination of functional network topographies in neonatal brain ↔ s7_1_hierarchical_clustering_dHCP_comparison.m, lines 47–67 · score 0.74 · DMN Ant, DMN Dor, neonatal FOCA matrix, hierarchical clusters, V5, FP
  16. [16] § Results › Individualized spatial topography of positive and negative connections in adult functional networks ↔ s2_generate_individual_topography.m, lines 35–92 · score 0.66 · individualized topographic maps, cortical vertices, Individualized networks, S2, functional networks, matching
  17. [17] § Results › The spatial alignment of functional topography encodes brain development at birth and predicts neurodevelopmental outcomes at 18 months ↔ s8_2_age_effects_neonatal_FOCA_GAM.m, lines 1–20 · score 0.65 · mass univariate GAMs, early postnatal period, trajectories, FOCA matrix, age, cluster
  18. [18] § Materials and methods › Convergent and divergent functional network topography in neonates ↔ s7_1_hierarchical_clustering_dHCP_comparison.m, lines 1–19 · score 0.63 · hierarchical clustering solution, adult derived, FOCA matrix, neonatal, topographic, networks
  19. [19] § Materials and methods › Internetwork covariance constrained by fundamental organizational features of the brain ↔ s6_brain_axes_prediction_FOCA.m, lines 1–23 · score 0.61 · cortical neurobiological, internetwork covariation, fundamental, human, brain
  20. [20] § Results › Convergent and divergent coordination of functional network topography in adult brain ↔ s5_hierarchical_clustering_and_modular_summary.m, lines 1–23 · score 0.53 · hierarchical clustering, inter cluster, intra, FOCA matrix, distance, topographic
  21. [21] § Materials and methods › Participants and data acquisition ↔ mutualinfofromnetworks.sh, the whole file · a weak match · score 0.50 · Midnight Scan Club, head, motion

Paper

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The authors' code

MATLAB · 101 lines · 4 KB · MIT · 2 matches

  1. %% ================================================================
  2. % Script: NeonatalAgePrediction_SVR_FOCA.m
  3. % Purpose:
  4. % Validate predictive validity of FOCA (Functional Orthogonal
  5. % Connectivity Architecture) features in predicting chronological
  6. % age during the neonatal period using linear SVR.
  7. %
  8. % Description:
  9. % - FOCA features (GSR-preprocessed, individual-level dHCP data)
  10. % are used as predictors.
  11. % - Sex, mean FD, and scan–birth intervals are regressed out
  12. % from both FOCA features and target (chronological age).
  13. % - A 10-fold cross-validation linear SVR model is trained to
  14. % predict postmenstrual age at scan.
  15. % - Prediction accuracy is defined as the Pearson correlation
  16. % between predicted and actual age in held-out folds.
  17. %
  18. % Reference:
  19. % Zhao et al., "Convergent and divergent functional topographies
  20. % of individualized human brain network and their developmental origins"
  21. % ================================================================
  23. clc; clear;
  24. addpath('./dependencies');
  26. %% ---------------------- Path Settings ----------------------------
  27. PredictionFolder = './results/AgePrediction';
  28. FeatureFolder = './data/dHCP/FOCA/';
  29. AgeInfoFile = './data/dHCP/PredictionAgeScore.mat';
  30. GroupMaskFile = './data/dHCP/Group_FC_dHCP_GSR.mat';
  32. FoldQuantity = 10;
  33. Pre_Method = 'Normalize';
  34. C_Parameter = 1;
  35. Permutation_Flag = 0;
  37. %% ---------------------- Load FOCA Features -----------------------
  38. disp('--- Loading FOCA (individual-level, dHCP GSR) features ---');
  39. load([FeatureFolder 'FOCA_Individual_dHCP_GSR.mat']);
  40. FOCA_Individual = FOCA_Individual_dHCP_GSR;
  42. % Remove subjects with zero maps
  43. subject_sum = sum(FOCA_Individual, 2);
  44. valid_idx = find(subject_sum ~= 0);
  45. FOCA_Individual = FOCA_Individual(valid_idx, :);
  47. % Replace any placeholder values (e.g., 1) with 0
  48. FOCA_Individual(FOCA_Individual == 1) = 0;
  49. clear subject_sum FOCA_Individual_dHCP_GSR
  51. %% ---------------------- Load Age and Covariates ------------------
  52. disp('--- Loading age and covariates ---');
  53. load(AgeInfoFile, 'PredictionAgeScore');
  55. Age = PredictionAgeScore.scan_age(valid_idx);
  56. Sex = double(cellfun(@(x) x, PredictionAgeScore.sex(valid_idx, 2)));
  57. MeanFD = PredictionAgeScore.MeanFD(valid_idx);
  58. ScanBirthInterval = PredictionAgeScore.scan_age(valid_idx) - PredictionAgeScore.birth_age(valid_idx);
  59. Covariates = [Sex, MeanFD, ScanBirthInterval];
  61. %% ---------------------- Regress Out Covariates -------------------
  62. disp('--- Regressing covariates from age and FOCA features ---');
  63. % Remove covariate effects from age
  64. stats = regstats(Age, Covariates, 'linear');
  65. Age_resid = stats.beta(1) + stats.r;
  67. % Load group-level FC to mask positive (retain negative) connections
  68. load(GroupMaskFile, 'uCi_Group_FC');
  69. neg_mask = (uCi_Group_FC <= 0);
  70. FOCA_Individual(:, ~neg_mask(:)') = 0;
  72. % Regress covariates from each FOCA feature
  73. for j = 1:size(FOCA_Individual, 2)
  74. [b,~,~,~,~] = regress(FOCA_Individual(:, j), [ones(size(Covariates,1),1), Covariates]);
  75. FOCA_Individual(:, j) = FOCA_Individual(:, j) - Covariates*b(2:end);
  76. end
  78. clear b stats j
  80. %% ---------------------- Run SVR Prediction -----------------------
  81. disp('--- Running 10-fold SVR prediction (FOCA) ---');
  82. ResultantFolder = [PredictionFolder '/SVR_Age_FOCA_dHCP_GSR_10CV/'];
  83. if ~exist(ResultantFolder, 'dir')
  84. mkdir(ResultantFolder);
  85. end
  87. Prediction = SVR_NFolds_Sort( ...
  88. FOCA_Individual, Age_resid, FoldQuantity, ...
  89. Pre_Method, C_Parameter, Permutation_Flag, ResultantFolder);
  91. fprintf('Prediction Results:\n');
  92. fprintf(' r = %.3f\n', Prediction.r_value);
  93. fprintf(' p = %.3e\n', Prediction.p_value);
  95. % Save prediction results
  96. save([ResultantFolder 'Prediction_FOCA_dHCP_GSR.mat'], ...
  97. 'Prediction', 'FOCA_Individual', 'Age_resid');
  99. %% ---------------------- Display Summary --------------------------
  100. disp('--- FOCA (dHCP GSR) age prediction complete ---');
  101. disp(['r = ' num2str(Prediction.r_value) ', p = ' num2str(Prediction.p_value)]);

s9_3_NeonatalAgePrediction_SVR_FOCA_dHCP.m at commit 19949c6, under MIT · at the source

Overview

Authors: Jianlong Zhao1,2,3, Yu Zhai1,3, Yuehua Xu1,2,3, Lianglong Sun1,2,3, Tengda Zhao1,2,3
ORCID iDs: Jianlong Zhao
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
  3. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
Institutions: Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning
Journal: Psychoradiology, volume 6, article kkag013
Dates: received 19 November 2025; accepted 4 February 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/psyrad/kkag013 · PMID 42039375 · PMCID PMC13103294 · OpenAlex W4416364583
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.18.689167
Status: code verified
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: functional network, negative connectivity, functional topography, cortical hierarchy, neonatal brain
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (319456)
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

Background: The human brain is intrinsically organized as canonical functional networks with distinct spatial topographies. While precision functional mapping studies have delineated individualized topographies of single networks, the spatial coordination among these networks and its developmental origin remains largely unknown.

Methods: Utilizing three well-established task-free functional magnetic resonance imaging (fMRI) datasets encompassing both conventional and densely sampled scans across neonatal and adult cohorts, we proposed functional topography covariance analysis (FOCA), a novel framework that quantifies convergent and divergent spatial alignments across individualized functional networks and further delineated their internetwork relationships, neurobiological basis, ontogenetic layouts, and cognitive outcomes.

Results: In adults, FOCA consistently revealed self-clustered and gradient-distributed functional hierarchies characterized by convergent couplings within primary systems and divergent couplings in higher-order systems. Such pattern was well predicted by fundamental neurobiological attributes, especially aerobic glycolysis. In a large public neonatal cohort, FOCA matrix exhibited adult-inverted hierarchical couplings and prominent changes in auditory and action-mode networks, driven primarily by redistributions of negative couplings. Moreover, neonatal FOCA profiles in the primary visual system significantly predicted neurodevelopmental outcomes at 18 months. Finally, compared with conventional functional connectivity, FOCA demonstrated greater robustness to the global signal and higher sensitivity to the maturation of negative couplings.

Conclusions: These findings highlight the critical role of negative functional connectivity and deepen our understanding of the cooperative–competitive interactions among functional systems and their developmental origins.

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 21 matches between paragraphs and lines of code.

zhaohuaxishi1/Convergent_Divergent_FOCA_Development

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 19949c692e9ac547326de9b0efe420b568c216b9, 3 November 2025
Languages: MATLAB (32), R (1)
Size: 93 files, 33 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (9 files), FieldTrip (3 files), mgcv (1 file), nlme (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
35 files
At the source: github.com/zhaohuaxishi1/Convergent_Divergent_FOCA_Development

BioMedIA/dhcp-structural-pipeline

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: aa214f285fb6dd919125584dd2476fc447dffb59, 15 November 2020
Languages: C++ (721), C/C++ (470), C (21), Shell (17), CUDA (14), Python (4), JavaScript (1)
Size: 1,662 files, 1,248 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (Dockerfile)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FSL (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
1,250 files
At the source: github.com/BioMedIA/dhcp-structural-pipeline

DCAN-Labs/compare_matrices_to_assign_networks

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4e32763d80a14697e9dab92b90287632ef655a84, 6 March 2026
Languages: MATLAB (57), Shell (3)
Size: 89 files, 60 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cifti-matlab (35 files), Statistics and Machine Learning Toolbox (11 files), FieldTrip (9 files), Parallel Computing Toolbox (2 files), Signal Processing Toolbox (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
62 files
At the source: github.com/DCAN-Labs/compare_matrices_to_assign_networks

ZaixuCui/Pattern_Regression_Clean

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: ccd4f3c556bf81f882966636eef5d22f108fea8a, 2 November 2021
Languages: MATLAB (19), Python (13)
Size: 33 files, 32 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (13 files), SciPy (13 files), scikit-learn (12 files), SPM (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
33 files
At the source: github.com/ZaixuCui/Pattern_Regression_Clean

Code availability

Software packages used in this manuscript include HCP pipeline (https://github.com/Washington-University/HCPpipelines/releases/), dHCP structural pipeline (https://github.com/BioMedIA/dhcp-structural-pipeline), dHCP functional pipeline (https://git.fmrib.ox.ac.uk/seanf/dhcp-neonatal-fmri-pipeline/-/tree/master), Connectome Workbench (https://www.humanconnectome.org/software/connectome-workbench), cifti-matlab toolbox v2 (https://github.com/Washington-University/cifti-matlab/), SPM12 toolbox (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), GRETNA toolbox v2.0.0 (https://www.nitrc.org/projects/gretna/), Template matching v1.0 (https://github.com/DCAN-Labs/compare_matrices_to_assign_networks), MSM (https://github.com/ecr05/MSM_HOCR/releases), LIBSVM (3.25) (https://www.csie.ntu.edu.tw/∼cjlin/libsvm/ (https://www.csie.ntu.edu.tw/~cjlin/libsvm/)), Support Vector Regression (https://github.com/ZaixuCui/Pattern_Regression_Clean), and R 4.0.3 (https://www.r-project.org), Matlab 2020b (https://www.mathworks.com). The codes used in this study are available at github: https://github.com/zhaohuaxishi1/Convergent_Divergent_FOCA_Development.

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

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,373 scripts, each with its path and the digest of its content;
  • 21 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

All data required for reproducing our findings are publicly available, including the individualized functional networks, group- and individual-level functional topography, FOCA matrix, and the data for visualizing main figures. They are stored in a publicly accessible cloud repository (github: https://github.com/zhaohuaxishi1/Convergent_Divergent_FOCA_Development). The MSC data are publicly available at https://openneuro.org/datasets/ds000224. For the HCP dataset, raw image scans are publicly available at https://www.humanconnectome.org/. Source data are provided with this paper. For the dHCP dataset, raw image scans are publicly available at https://nda.nih.gov/.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 1 funder, 67 references.

Cite

This paper

Zhao, J., Zhai, Y., Xu, Y., Sun, L., & Zhao, T. (2026). Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins. Psychoradiology, 6, kkag013. https://doi.org/10.1093/psyrad/kkag013

BibTeX

@article{zhao2026convergent,
author = {Zhao, Jianlong and Zhai, Yu and Xu, Yuehua and Sun, Lianglong and Zhao, Tengda},
title = {{Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins}},
journal = {Psychoradiology},
year = {2026},
month = apr,
volume = {6},
pages = {kkag013},
publisher = {Oxford University Press},
issn = {2634-4416},
doi = {10.1093/psyrad/kkag013},
url = {https://doi.org/10.1093/psyrad/kkag013},
pmid = {42039375},
pmcid = {PMC13103294}
}

RIS

TY - JOUR
AU - Zhao, Jianlong
AU - Zhai, Yu
AU - Xu, Yuehua
AU - Sun, Lianglong
AU - Zhao, Tengda
TI - Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins
T2 - Psychoradiology
J2 - Psychoradiology
PY - 2026
DA - 2026/04/23
VL - 6
SP - kkag013
SN - 2634-4416
PB - Oxford University Press
DO - 10.1093/psyrad/kkag013
UR - https://doi.org/10.1093/psyrad/kkag013
LA - en
ER -

CSL-JSON

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