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

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.