ArticleImaging neuroscience (Cambridge, Mass.)2025
Free water elimination tractometry for aging brains.
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.
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4 citing papers in PubMed.
- Reproducibility and Reliability of Free-Water-Corrected Diffusion Tensor Imaging of the Brain: Revisited.Human brain mapping · 2026Article
- Tractometry-Based Quantification of Along-Tract White-Matter Hemispheric Asymmetry in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2026Article
- What needs to be standardized for reliable, reproducible, and robust tractography?GigaScience · 2026Review
- Free water elimination tractometry reveals local and remote white matter alterations in diffuse gliomas.Journal of neuro-oncology · 2025Article
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Abstract
Tractometry of diffusion-weighted magnetic resonance imaging (dMRI) non-invasively quantifies tissue properties of brain connections. It is widely used in aging studies but could be less reliable in aging brains due to increased white matter free water. We demonstrate that computational free water elimination (FWE) and multi-shell multi-tissue (MSMT) modeling both increase the reliability and accuracy of tractometry in a large (n = 396) cohort of older adults (65-103 y.o.). We found substantial improvements in reliability in a split-half comparison at every stage of the pipeline: estimation of voxel-level fiber orientation distribution functions, delineation of major pathway trajectories, and assessment of tissue properties along the pathways. FWE in particular provided better tractography yield, that included more coverage of areas of leukoaraiosis. However, tractometry was strongly predictive of Fazekas scores, which assess the extent of white matter hyperintensity (WMH) burden, regardless of method. This indicates that increased WMH burden is associated with global changes to white matter. By sub-sampling a multi-shell dataset, we demonstrated that these findings generalize to single-shell data, which is important for many datasets where only one
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