OSCR

Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth.

Code ↔ Paper

13 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 13 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Reconstruction of structural connectome ↔ dMRIprocessing/Tractseg2Connectome.sh, the whole file · a weak match · score 0.79 · iFOD2, FODs, tcksift2, HSVS, orientation, FreeSurfer
  2. [2] § Methods › Statistical analysis › Developmental alignment with the S-A connectional axis ↔ development_script/4th_changerate_SAcorr/S1st_SAcorr_alongAge_HCPD.Rmd, lines 62–176 · score 0.73 · connectional axis correlation, correlation coefficient, posterior derivative, age point, zero, alignment
  3. [3] § Methods › Reconstruction of structural connectome ↔ dMRIprocessing/Tractseg2Connectome.sh, the whole file · a weak match · score 0.73 · radial search, global tractography, SIFT2, nodes, atlas, weight
  4. [4] § Methods › Statistical analysis › Development of structural connectivity strength in youth ↔ development_script/1st_dataclean/S3rd_combat_gam/neuroHarmonize/neuroHarmonize/harmonizationLearn.py, lines 12–92 · score 0.72 · generalized additive models, smooth term, freedom, splines, optimal, linear
  5. [5] § Methods › Statistical analysis › Correction for multi-site batch effects ↔ development_script/1st_dataclean/S3rd_combat_gam/neuroHarmonize/neuroHarmonize/harmonizationLearn.py, lines 12–92 · score 0.66 · ComBat GAM, smooth term, scanners, harmonize, NonlinearLongitudinalComBat, batch
  6. [6] § Methods › Cognitive assessment ↔ development_script/5th_cognition/S2nd_compositescorePlot_scatterplot_ABCD.Rmd, lines 33–54 · score 0.65 · working memory, fluid cognitive, flanker, inhibitory, composite, score
  7. [7] § Methods › Statistical analysis › Developmental alignment with the S-A connectional axis ↔ gamfunction/gamderivatives.R, the whole file · a weak match · score 0.63 · posterior distribution, posterior derivative, fitted model, coefficient, median, smooth
  8. [8] § Methods › Statistical analysis › Development of structural connectivity strength in youth ↔ gamfunction/gamsmooth.R, lines 52–92 · score 0.60 · parametric bootstrap, full model, simulations, nested, windows, ANOVA
  9. [9] § Methods › Statistical analysis › Development of structural connectivity strength in youth ↔ development_script/2nd_fitdevelopmentalmodel/R3_WithinPersonEffect.R, lines 1–46 · score 0.59 · age_bp, age_wp, threshold, baseline, ABCD, SC
  10. [10] § Methods › Statistical analysis › Associations between structural connectivity strength and higher-order cognition ↔ development_script/5th_cognition/S2nd_compositescorePlot_scatterplot_ABCD.Rmd, lines 33–54 · score 0.59 · working memory, fluid cognition, flexibility, components, composite, score
  11. [11] § Results › Developmental alignment with the S-A connectional axis shifts during youth ↔ development_script/4th_changerate_SAcorr/S1st_SAcorr_alongAge_HCPD.Rmd, lines 62–176 · score 0.54 · credible interval, posterior derivatives, histogram, ribbon, connectional axis, Spearman
  12. [12] § Methods › Reconstruction of structural connectome ↔ development_script/3rd_plotConnectionalAxis/S2nd_generate12fractionSAsurfaceRdBu.m, lines 1–32 · score 0.54 · sensorimotor association, Schaefer, global, cortex, parcellated, nodes
  13. [13] § Methods › Statistical analysis › Associations between structural connectivity strength and higher-order cognition ↔ gamfunction/gamsmooth.R, lines 52–92 · score 0.52 · parametric bootstrap, full model, simulations, magnitude, GAM, correlation

Paper

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

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The authors' code

Shell · 77 lines · 5.2 KB · no license · 2 matches

  1. #!/bin/bash
  2. #SBATCH --nodes=1 # OpenMP requires a single node
  3. #SBATCH -p q_cn
  4. #SBATCH --ntasks=1 # Run a single serial task
  5. #SBATCH --cpus-per-task=4
  6. #SBATCH --mail-user [email hidden]
  7. ##### END OF JOB DEFINITION #####
  9. module load singularity
  10. module load mrtrix3
  11. subj=$1
  13. processedpath=/ibmgpfs/cuizaixu_lab/xuxiaoyu/HCPD/processed/qsiprep
  14. freesurfermri=/ibmgpfs/cuizaixu_lab/xuxiaoyu/HCPD/processed/fmriresults01/${subj:4:14}_V1_MR/T1w/${subj}/mri
  15. echo $subj
  17. #export http_proxy=10.11.100.5:3128
  18. #export HTTP_PROXY=10.11.100.5:3128
  19. #export https_proxy=10.11.100.5:3128
  20. #export HTTPS_PROXY=10.11.100.5:3128
  21. #export ftp_proxy=10.11.100.5:3128
  22. #export FTP_PROXY=10.11.100.5:3128
  23. #export all_proxy=10.11.100.5:3128
  24. #export ALL_PROXY=10.11.100.5:3128
  26. # 1. Convert the orientation of FOD to the Tractseg required orientation
  27. # LPS to LAS
  28. mrconvert ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd.mif.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.mif.gz -stride -1,2,3,4 -force
  30. # 2. Conert .mif to .nii
  31. mrconvert ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.mif.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.nii.gz -force
  33. # 3. Compute the peak image
  34. sh2peaks ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.nii.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2_peak.nii.gz -force
  36. # 4. Prepare T1
  37. mrconvert ${processedpath}/${subj}/qsiprep/${subj}/anat/${subj}_desc-preproc_brain.nii.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/T1w_acpc_dc_restore_brain.nii.gz -stride -1,2,3 -force
  39. # 5. Segment bundle start and end regions
  40. ## TractSeg tracts
  41. TractSeg -i ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2_peak.nii.gz \
  42. -o ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output --output_type tract_segmentation
  44. TractSeg -i ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2_peak.nii.gz \
  45. -o ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output --output_type endings_segmentation
  47. # 6. Tracking the 72 tracts
  48. Tracking -i ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.nii.gz \
  49. -o ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output --track_FODs iFOD2 --nr_fibers 10000
  51. # 7. Merge all the tck to build a global tractography
  52. tckedit ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/FOD_iFOD2_trackings/*.tck ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/All_tracks720k.tck
  54. # 8. Generate sift2 weights
  55. mrconvert ${processedpath}/${subj}/qsirecon/${subj}/anat/${subj}_desc-preproc_space-fsnative_desc-hsvs_5tt.mif ${processedpath}/${subj}/qsirecon/${subj}/anat/${subj}_desc-preproc_space-fsnative_desc-hsvs_5tt_LAS.mif -stride -1,2,3,4 -force
  57. tcksift2 -act ${processedpath}/${subj}/qsirecon/${subj}/anat/${subj}_desc-preproc_space-fsnative_desc-hsvs_5tt_LAS.mif -out_mu ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/sift_mu720k.txt -out_coeffs ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/sift_coeffs720k.txt ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/All_tracks720k.tck ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-wmFODmtnormed_msmtcsd_LAS2.mif.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/sift_720k.txt
  59. # 9. Reconstruct connectome
  60. mrconvert ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-schaefer400_atlas.nii.gz ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-schaefer400_atlas_LAS.nii.gz -stride -1,2,3,4 -force
  62. # 1) inverse node volume
  63. tck2connectome -force -symmetric -nthreads 72 -assignment_radial_search 2 -scale_invnodevol \
  64. -tck_weights_in ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/sift_720k.txt \
  65. ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/All_tracks720k.tck \
  66. ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-schaefer400_atlas_LAS.nii.gz \
  67. ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/Tractseg_schaefer400_SC_invnode.csv \
  68. -out_assignment ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/assignments_720k_nodeS400.csv
  69. # 2) without inverse node volume
  70. tck2connectome -force -symmetric -nthreads 72 -assignment_radial_search 2 \
  71. -tck_weights_in ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/sift_720k.txt \
  72. ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/All_tracks720k.tck \
  73. ${processedpath}/${subj}/qsirecon/${subj}/dwi/${subj}_space-T1w_desc-preproc_desc-schaefer400_atlas_LAS.nii.gz \
  74. ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/Tractseg_schaefer400_SC.csv \
  75. -out_assignment ${processedpath}/${subj}/qsirecon/${subj}/dwi/tractseg_output/assignments_720k_nodeS400.csv

Tractseg2Connectome.sh at commit 3ceade0, no license · at the source

Overview

Authors: Xiaoyu Xu1,2,3, Hang Yang2,3, Jing Cong1,2,3, Haoshu Xu2,3,4, Jason Kai5, Shaoling Zhao2,3, Yang Li2,3, Haochang Shou6, Kangcheng Wang7, Valerie J Sydnor8, Ting Xu5, Fang-Cheng Yeh9, Zaixu Cui2,3
ORCID iDs: Hang Yang, Jason Kai, Haochang Shou, Kangcheng Wang, Valerie J Sydnor, Ting Xu, Fang-Cheng Yeh, Zaixu Cui
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  2. Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
  3. Chinese Institute for Brain Research, Beijing, Beijing, China
  4. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
  5. Child Mind Institute, Center for the Integrative Developmental Neuroscience, New York, NY USA
  6. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
  7. School of Psychology, Shandong Normal University, Jinan, China
  8. Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
  9. Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA USA
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College (China); Beijing Normal University (China); Chinese Institute for Brain Research (China); State Key Laboratory of Cognitive Neuroscience and Learning; Peking University (China); Child Mind Institute (United States); University of Pennsylvania (United States); Shandong Normal University (China); University of Pittsburgh (United States); University of Pittsburgh Medical Center (United States)
Journal: Nature communications, volume 17, issue 1, article 6550
Dates: received 12 November 2025; accepted 30 April 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73072-6 · PMID 42140891 · PMCID PMC13381693 · OpenAlex W7161255464
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, Preprocessing, fMRI & imaging, Machine learning
Keywords: Developmental biology, Development of the nervous system, Diffusion tensor imaging, Cognitive neuroscience
MeSH: Brain*, Connectome*, White Matter*, Adolescent, Child, Cognition, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Neural Pathways, Neurodevelopment, Young Adult (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIDA NIH HHS (U01 DA041093, U01 DA041156, U01 DA050987, U01 DA050988, U01 DA050989, U01 DA041089, U01 DA051039, U01 DA041022, U24 DA041147, U01 DA041025, U01 DA041106, U01 DA051038, U01 DA041117, U01 DA041148, U01 DA051016, U01 DA041028, U01 DA041048, U01 DA041120, U01 DA051018, U01 DA051037, U24 DA041123, U01 DA041134, U01 DA041174); Chinese Academy of Medical Sciences (CAMS) (2025-I2M-XHJC-056); NIMH NIH HHS (U01 MH109589, T32 MH016804); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (T32MH016804); National Natural Science Foundation of China (National Science Foundation of China) (32000760)
Citations: cited by 2 papers (Europe PMC); 107 references in the paper

Abstract

Childhood and adolescence are marked by protracted developmental remodeling of cortico-cortical structural connectivity. However, the spatiotemporal variability of white matter connectivity development across the human connectome and its relevance to cognition and psychopathology remains unclear. Using diffusion MRI data from three independent developmental cohorts spanning youth, we identified a robust divergence in structural connectivity maturation along a predefined sensorimotor-association (S-A) connectional axis during youth (http://connectcharts.cibr.ac.cn). This developmental continuum ranged from early childhood increases in sensorimotor-sensorimotor connectivity strength to late adolescent increases in association-association connectivity strength, with the transition occurring around age 15. The S-A connectional axis also captured spatial variations in the associations between structural connectivity and both higher-order cognition and general psychopathology. Moreover, group-level developmental trajectories of structural connectivity differed by cognitive and psychopathological levels, with psychopathological effects predominantly observed in association connections. These findings delineate a spatiotemporal continuum of structural connectivity development during youth, providing a normative reference for quantifying developmental variability in psychiatric disorders.

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 13 matches between paragraphs and lines of code.

CuiLabCIBR/SCDevelopment

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3ceade07882277428a46cc75eb212c2592be0b3c, 16 June 2026
Languages: R (40), Python (5), Shell (4), MATLAB (2)
Size: 147 files, 51 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (development_script/1st_dataclean/S3rd_combat_gam/neuroHarmonize/pyproject.toml, development_script/1st_dataclean/S3rd_combat_gam/neuroHarmonize/setup.py), 10 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (33 files), mgcv (26 files), psych (15 files), reshape2 (14 files), ggplot2 (11 files), lme4 (4 files), NumPy (4 files), pandas (4 files), cifti-matlab (2 files), MRtrix3 (2 files), neuroCombat (2 files), neuroHarmonize (2 files), rpy2 (2 files), statsmodels (2 files), easystats (1 file), FreeSurfer (1 file), lmerTest (1 file), NiBabel (1 file), patchwork (1 file), QSIPrep (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
52 files
At the source: github.com/CuiLabCIBR/SCDevelopment.git

Zenodo 19549943

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

Code availability

All codes used to perform the analyses in this study107 and the statistical magnitudes derived from analyses can be found at https://github.com/CuiLabCIBR/SCDevelopment.git. To enhance accessibility, we also developed an interactive website (https://connectcharts.cibr.ac.cn/) that allows the broader community to explore and use our developmental charts. All analysis methods are described in the main text and supplementary materials.

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;
  • 51 scripts, each with its path and the digest of its content;
  • 13 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 HCP-Development 2.0 Release data used in this report came from DOI: 10.15154/1520708 via the NDA (https://nda.nih.gov/ccf). The ABCD 5.1 data release used in this report came from DOI: 10.15154/z563-zd24 via the NDA (https://nda.nih.gov/abcd). The fast-track data from the ABCD Study is also available through the NDA. The devCCNP data in the Chinese Cohort is available via the Science Data Bank (10.57760/sciencedb.07478). Data from the EFNY and SAND studies are available under restricted access because data collection is ongoing and the datasets include sensitive human participant information. Researchers may request access to EFNY data by contacting Zaixu Cui and to SAND data by contacting Kangcheng Wang, and by providing a brief research proposal outlining the intended use of the data. Requests will be evaluated based on scientific merit and compliance with ethical standards. Data will be made available to qualified researchers for non-commercial academic research purposes only, subject to a data use agreement. We aim to respond to data access requests within approximately one month. Approved access will be granted for a defined period (e.g. 12 months), with the possibility of extension upon request. We anticipate that these datasets will be made more broadly available after completion of data collection and appropriate processing of sensitive information. Source data are provided with this paper.

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, 13 authors, 4 keywords, 13 MeSH terms, 5 funders, 94 references.

Cite

This paper

Xu, X., Yang, H., Cong, J., Xu, H., Kai, J., Zhao, S., Li, Y., Shou, H., Wang, K., Sydnor, V. J., Xu, T., Yeh, F.-C., & Cui, Z. (2026). Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth. Nature communications, 17(1), 6550. https://doi.org/10.1038/s41467-026-73072-6

BibTeX

@article{xu2026mapping,
author = {Xu, Xiaoyu and Yang, Hang and Cong, Jing and Xu, Haoshu and Kai, Jason and Zhao, Shaoling and Li, Yang and Shou, Haochang and Wang, Kangcheng and Sydnor, Valerie J and Xu, Ting and Yeh, Fang-Cheng and Cui, Zaixu},
title = {{Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6550},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73072-6},
url = {https://doi.org/10.1038/s41467-026-73072-6},
pmid = {42140891},
pmcid = {PMC13381693}
}

RIS

TY - JOUR
AU - Xu, Xiaoyu
AU - Yang, Hang
AU - Cong, Jing
AU - Xu, Haoshu
AU - Kai, Jason
AU - Zhao, Shaoling
AU - Li, Yang
AU - Shou, Haochang
AU - Wang, Kangcheng
AU - Sydnor, Valerie J
AU - Xu, Ting
AU - Yeh, Fang-Cheng
AU - Cui, Zaixu
TI - Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/15
VL - 17
IS - 1
SP - 6550
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73072-6
UR - https://doi.org/10.1038/s41467-026-73072-6
LA - en
ER -

CSL-JSON

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In common: mgcv, FreeSurfer, patchwork, 5 other tools, structural MRI / diffusion, 8 references
[10] doi:10.1002/jcv2.70135 [code]
Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence.
Journal: JCPP advances
In common: psych, easystats, lmerTest, 4 other tools, developmental, 7 references

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