ArticleImaging neuroscience (Cambridge, Mass.)2025
Hierarchical modelling of crossing fibres in the white matter.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Longitudinal quantitative streamline tractography: robust estimation of white matter connectivity differences.bioRxiv : the preprint server for biology · 2026Article
- DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- TRAMFIX: TRavelling Across Melbourne for FIXel-based analysis (a reproducibility and reliability study).Imaging neuroscience (Cambridge, Mass.)Article
- Mapping the structural connections between the anterior cingulate cortex and the insula/ventrolateral prefrontal cortex.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While diffusion MRI is typically used to estimate microstructural properties of tissue in volumetric elements (voxels), more specificity can be obtained by separately modelling the properties of individual fibre populations within a voxel. In the context of cross-subjects modelling, these fixel-based analyses are usually performed in two stages. Crossing fibre modelling is first performed in each subject to produce fixels, and these are subsequently modelled across subjects following registration and fibre population reassignment. Here, we introduce a new hierarchical framework for fitting crossing fibre models to diffusion MRI data in a population of subjects. This hierarchical setup guarantees that the crossing fibres are consistent by construction and, therefore, comparable across subjects. We propose an expectation-maximisation algorithm to fit the model, which can scale to large number of subjects. This approach produces a template for crossing fibre populations in the white matter which can be used to estimate fibre-specific parameters that are consistent across subjects, hence providing data that are by construction suitable for fixel-based statistical analyses.
Indexed as
Identifiers
What OpenQuestion holds
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.