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

Overview

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

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.

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Data

Datasets cited

• humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in the resources table

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.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 74 references.

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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

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