ArticleNeuroImage2018
Modeling topographic regularity in structural brain connectivity with application to tractogram filtering.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Surface-Based Probabilistic Fiber Tracking in Superficial White Matter.IEEE transactions on medical imaging · 2024Article
- Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter.Frontiers in neuroscience · 2024Article
- Article
- FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.Computational diffusion MRI. CDMRI (Workshop) · 2023Article
- Diffusion MRI tractography of the locus coeruleus-transentorhinal cortex connections using GO-ESP.Magnetic resonance in medicine · 2022Article
- Connectivity gradients on tractography data: Pipeline and example applications.Human brain mapping · 2021Article
- A Comparative Study of Diffusion Fiber Reconstruction Models for Pyramidal Tract Branches.Frontiers in neuroscience · 2021Article
- A probabilistic atlas of locus coeruleus pathways to transentorhinal cortex for connectome imaging in Alzheimer's disease.NeuroImage · 2020Article
- Topographic Filtering of Tractograms as Vector Field Flows.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Topographic regularity is an important biological principle in brain connections that has been observed in various anatomical studies. However, there has been limited research on mathematically characterizing this property and applying it in the analysis of in vivo connectome imaging data. In this work, we propose a general mathematical model of topographic regularity for white matter fiber bundles based on previous neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition. The GSGA provides a common set of eigenvectors for the graphs formed by topographic proximity of nearby tracts, which gives rises to the group graph spectral distance, or G
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.