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A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI.

Code ↔ Paper

5 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 5 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Superficial white matter fiber identification ↔ SWM_identification/network_filtering/train_swm+nonswm.py, lines 87–130 · score 0.91 · cross entropy loss, DataParallel, PyTorch, Adam, GPUs, Optimization
  2. [2] § STAR★Methods › Method details › Construction of whole-brain fiber tractography ↔ data_processing/S7_fib_tracking.sh, the whole file · a weak match · score 0.90 · SD_STREAM, iFOD1, iFOD2, MRtrix, RK4, cropping
  3. [3] § STAR★Methods › Method details › Tractography ablation analysis ↔ data_processing/S7_fib_tracking.sh, the whole file · a weak match · score 0.81 · SD_STREAM, iFOD1, iFOD2, MRtrix, algorithms, masks
  4. [4] § STAR★Methods › Method details › Superficial white matter fiber identification ↔ SWM_identification/S2_predict_netprob_7networks.py, lines 32–48 · score 0.54 · binary classification, neural network, fiber
  5. [5] § STAR★Methods › Method details › Superficial white matter fiber identification ↔ SWM_identification/S6_compute_ifSWM_final.py, lines 8–9 · score 0.51 · adjacent regions, SWM identification, DKT, filtering, Yeo, fibers

Paper

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The authors' code

Shell · 88 lines · 8.1 KB · no license · 2 matches

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It can be read at the source: data_processing/S7_fib_tracking.sh.

Overview

Authors: Yifei He1, Yu Xie1, Hiuying Yip2, Yoonmi Hong3, Ye Wu1
ORCID iDs: Yifei He, Yoonmi Hong, Ye Wu
  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
  2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China
  3. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Institutions: Nanjing University of Science and Technology (China); Tsinghua Shenzhen International Graduate School (China); Tsinghua University (China); University of North Carolina at Chapel Hill (United States)
Journal: iScience, volume 29, issue 7, article 116671
Dates: received 21 January 2026; accepted 18 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116671 · PMID 42436984 · PMCID PMC13355829 · OpenAlex W7167483813
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Machine learning, Smoothing, state filtering, decompositions, Statistics, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: superficial white matter, diffusion MRI, tractography, white matter atlas, functional networks
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Superficial white matter (SWM) supports local cortico-cortical communication. Still, its whole-brain organization remains difficult to characterize in vivo, due to its short length, high curvature, proximity to the gray-white matter interface, and individual variability. Here, we constructed a high-resolution, tractography-derived human SWM atlas using 7T diffusion MRI data from 171 participants in the Human Connectome Project. We combined deterministic and probabilistic tractography, multi-stage clustering, geometric filtering, and a deep-learning classifier trained on expert-informed SWM labels to identify anatomically plausible SWM clusters. The resulting atlas retained approximately 10% of whole-brain streamlines and comprised 643 and 1,403 SWM clusters under Yeo 7- and 17-network parcellations, respectively. Cross-dataset analyses supported reproducible SWM-like tractography patterns. We further provide network-level annotations, Neurosynth-based functional associations, and a TW-dFC-derived uncertainty index as complementary references for interpreting clusters. Together, this work provides a publicly available SWM atlas and processing framework for future studies of white matter connectivity.

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 5 matches between paragraphs and lines of code.

mushroomer1823/swm_atlas

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4808218e8f054cf592b2377262cfdabd992f3fe2, 7 November 2025
Languages: Python (22), Shell (2), MATLAB (1)
Size: 28 files, 25 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, environment (environment.yml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (15 files), pandas (12 files), NiBabel (5 files), DIPY (2 files), h5py (2 files), MRtrix3 (2 files), PyTorch (2 files), scikit-learn (2 files), SciPy (2 files), Statistics and Machine Learning Toolbox (1 file), Nilearn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files, not copied: shown from their source

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At the source: github.com/mushroomer1823/swm_atlas

mushroomer1823/connectomic_cluster

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d53b9f442b3688fba84ae6fd983b1fb91015f845, 16 October 2025
Languages: Shell (14), MATLAB (11), Python (2)
Size: 33 files, 27 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MRtrix3 (16 files), Statistics and Machine Learning Toolbox (8 files), FSL (3 files), ANTs (1 file), h5py (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files, not copied: shown from their source

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At the source: github.com/mushroomer1823/connectomic_cluster

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;
  • 52 scripts, each with its path and the digest of its content;
  • 5 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 and code availability

The tractography-derived SWM atlas generated in this study has been deposited and is publicly available at Science DataBank: https://doi.org/10.57760/sciencedb.28793. The SWM atlas was identified and extracted from a large-scale whole-brain fine-grained fiber cluster dataset resource,51 which has also been deposited and is publicly available at Science DataBank: https://doi.org/10.57760/sciencedb.28989. The SWM atlas resource includes SWM tractograms mapped to MNI152 space before and after hierarchical merging, cluster labels, and the correspondence between clusters under both the Yeo 7- and 17-network parcellation schemes. Data are provided in standard diffusion MRI and neuroimaging formats, including .tck and .nii.gz. Any future data updates related to this study will also be uploaded to the same Science Data Bank repository. Due to file storage limitations, subject-level fiber clusters before hierarchical merging are not included in the online resource but are available from the lead contact upon request. More

Original code used for atlas generation, multi-criteria SWM pathway identification, hierarchical merging, and ICA analyses is available at GitHub: https://github.com/mushroomer1823/swm_atlas. The pipeline was implemented using MRtrix3, MATLAB, and in-house Python scripts and was tested in a high-performance computing environment.

Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.

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

Materials availability

This study did not generate new unique reagents or biological materials. The tractography-derived superficial white matter atlas generated in this study is available as described in the data and code availability section.

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, 5 authors, 5 keywords, 1 funder, 74 references.

Cite

This paper

He, Y., Xie, Y., Yip, H., Hong, Y., & Wu, Y. (2026). A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI. iScience, 29(7), 116671. https://doi.org/10.1016/j.isci.2026.116671

BibTeX

@article{he2026high,
author = {He, Yifei and Xie, Yu and Yip, Hiuying and Hong, Yoonmi and Wu, Ye},
title = {{A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {7},
pages = {116671},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116671},
url = {https://doi.org/10.1016/j.isci.2026.116671},
pmid = {42436984},
pmcid = {PMC13355829}
}

RIS

TY - JOUR
AU - He, Yifei
AU - Xie, Yu
AU - Yip, Hiuying
AU - Hong, Yoonmi
AU - Wu, Ye
TI - A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/06
VL - 29
IS - 7
SP - 116671
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116671
UR - https://doi.org/10.1016/j.isci.2026.116671
LA - en
ER -

CSL-JSON

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