ArticleNeuroImage2023
Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI.
Article in NeuroImage, 2023. 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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Who cites it
4 citing papers in PubMed.
- Microscopy-informed structural connectivity mapping in the in vivo human brain via domain adaptation.bioRxiv : the preprint server for biology · 2026Article
- Non-parametric prediction of brain MRI microstructure using transfer learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- DIMOND: DIffusion Model OptimizatioN with Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain.PLoS biology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.
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