ArticleFrontiers in neuroinformatics2017
Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of automated methods to perform white matter tract segmentation.Frontiers in neuroscience · 2024Pooled it
- Insights into temporal and spatial dynamics of short association fiber formation in the human fetal brain.Science advances · 2026Article
- A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI.iScience · 2026Article
- Cross-population white matter atlas creation for concurrent mapping of brain connections in neonates and adults with diffusion MRI tractography.Fundamental research · 2026Article
- Superficial and deep white matter brain alterations discriminate tinnitus in older adults.Brain structure & function · 2026Article
- Atlas-based templates vs. subject-specific tractography: resolving the debate.Brain structure & function · 2025Review
- The relationship of white matter tract orientation to vascular geometry in the human brain.Scientific reports · 2025Article
- The relationship of white matter tract orientation to vascular geometry in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Surface-based and Shape-informed U-fiber Atlasing for Robust Superficial White Matter Connectivity Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.Human brain mapping · 2024Article
- A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations.Scientific data · 2024Article
- Surface-Based Probabilistic Fiber Tracking in Superficial White Matter.IEEE transactions on medical imaging · 2024Article
- Connectivity defines the distinctive anatomy and function of the hand-knob area.Brain communications · 2024Article
- PhyberSIM: a tool for the generation of ground truth to evaluate brain fiber clustering algorithms.Frontiers in neuroscience · 2024Article
- Phybers: a package for brain tractography analysis.Frontiers in neuroscience · 2024Article
- Imaging of the superficial white matter in health and disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.Computational diffusion MRI. CDMRI (Workshop) · 2023Article
- Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation.NeuroImage · 2023Article
- Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features.Brain structure & function · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.
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