Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain

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Salo , R A , Belevich , I , Jokitalo , E , Gröhn , O & Sierra , A 2021 , ' Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain ' , NeuroImage , vol. 225 , 117529 . https://doi.org/10.1016/j.neuroimage.2020.117529

Title: Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain
Author: Salo, Raimo A.; Belevich, Ilya; Jokitalo, Eija; Gröhn, Olli; Sierra, Alejandra
Other contributor: University of Helsinki, Electron Microscopy
University of Helsinki, Electron Microscopy

Date: 2021-01-15
Language: eng
Number of pages: 15
Belongs to series: NeuroImage
ISSN: 1053-8119
DOI: https://doi.org/10.1016/j.neuroimage.2020.117529
URI: http://hdl.handle.net/10138/326441
Abstract: Validation and interpretation of diffusion magnetic resonance imaging (dMRI) requires detailed understanding of the actual microstructure restricting the diffusion of water molecules. In this study, we used serial block-face scanning electron microscopy (SBEM), a three-dimensional electron microscopy (3D-EM) technique, to image seven white and grey matter volumes in the rat brain. SBEM shows excellent contrast of cellular membranes, which are the major components restricting the diffusion of water in tissue. Additionally, we performed 3D structure tensor (3D-ST) analysis on the SBEM volumes and parameterised the resulting orientation distributions using Watson and angular central Gaussian (ACG) probability distributions as well as spherical harmonic (SH) decomposition. We analysed how these parameterisations described the underlying orientation distributions and compared their orientation and dispersion with corresponding parameters from two dMRI methods, neurite orientation dispersion and density imaging (NODDI) and constrained spherical deconvolution (CSD). Watson and ACG parameterisations and SH decomposition captured well the 3D-ST orientation distributions, but ACG and SH better represented the distributions due to its ability to model asymmetric dispersion. The dMRI parameters corresponded well with the 3D-ST parameters in the white matter volumes, but the correspondence was less evident in the more complex grey matter. SBEM imaging and 3D-ST analysis also revealed that the orientation distributions were often not axially symmetric, a property neatly captured by the ACG distribution. Overall, the ability of SBEM to image diffusion barriers in intricate detail, combined with 3D-ST analysis and parameterisation, provides a step forward toward interpreting and validating the dMRI signals in complex brain tissue microstructure.
Subject: Diffusion MRI
Brain microstructure
Structure tensor
3D electron microscopy
Fiber orientation distribution
NEURITE ORIENTATION DISPERSION
STRUCTURE TENSOR ANALYSIS
HISTOLOGICAL VALIDATION
TISSUE
RESOLUTION
DENSITY
BALL
TRACTOGRAPHY
ARCHITECTURE
ANISOTROPY
1182 Biochemistry, cell and molecular biology
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