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Retrieving neuronal orientations using 3D scanning SAXS and comparison with diffusion MRI
- Source :
- NeuroImage, Vol 204, Iss , Pp 116214- (2020)
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- While diffusion MRI (dMRI) is currently the method of choice to non-invasively probe tissue microstructure and study structural connectivity in the brain, its spatial resolution is limited and its results need structural validation. Current ex vivo methods employed to provide 3D fiber orientations have limitations, including tissue-distorting sample preparation, small field of view or inability to quantify 3D fiber orientation distributions. 3D fiber orientation in tissue sections can be obtained from 3D scanning small-angle X-ray scattering (3D sSAXS) by analyzing the anisotropy of scattering signals. Here we adapt the 3D sSAXS method for use in brain tissue, exploiting the high sensitivity of the SAXS signal to the ordered molecular structure of myelin. We extend the characterization of anisotropy from vectors to tensors, employ the Funk-Radon-Transform for converting scattering information to real space fiber orientations, and demonstrate the feasibility of the method in thin sections of mouse brain with minimal sample preparation. We obtain a second rank tensor representing the fiber orientation distribution function (fODF) for every voxel, thereby generating fODF maps. Finally, we illustrate the potential of 3D sSAXS by comparing the result with diffusion MRI fiber orientations in the same mouse brain. We show a remarkably good correspondence, considering the orthogonality of the two methods, i.e. the different physical processes underlying the two signals. 3D sSAXS can serve as validation method for microstructural MRI, and can provide novel microstructural insights for the nervous system, given the method’s orthogonality to dMRI, high sensitivity to myelin sheath’s orientation and abundance, and the possibility to extract myelin-specific signal and to perform micrometer-resolution scanning.
Details
- Language :
- English
- ISSN :
- 10959572
- Volume :
- 204
- Issue :
- 116214-
- Database :
- Directory of Open Access Journals
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.438f94dcc8db42eda68ec556320f8f19
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2019.116214