Multicomponent diffusion analysis reveals microstructural alterations in spinal cord of a mouse model of amyotrophic lateral sclerosis ex vivo

Abstract

The microstructure changes associated with degeneration of spinal axons in amyotrophic lateral sclerosis (ALS) may be reflected in altered water diffusion properties, potentially detectable with diffusion-weighted (DW) MRI. Prior work revealed the classical mono-exponential model fails to precisely depict decay in DW signal at high b-values. In this study, we aim to investigate signal decay behaviors at ultra-high b-values for non-invasive assessment of spinal cord alterations in the transgenic SOD1 G93A mouse model of ALS. A multiexponential diffusion analysis using regularized non-negative least squares (rNNLS) algorithm was applied to a series of thirty DW MR images with b-values ranging from 0 to 858,022 s/ mm 2 on ex vivo spinal cords of transgenic SOD1 G93A and age-matched control mice. We compared the distributions of measured diffusion coefficient fractions between the groups. The measured diffusion weighted signals in log-scale showed non-linear decay behaviors with increased b-values. Faster signal decays were observed with diffusion gradients applied parallel to the long axis of the spinal cord compared to when oriented in the transverse direction. Multiexponential analysis at the lumbar level in the spinal cord identified ten subintervals. A significant decrease of diffusion coefficient fractions was found in the ranges of [1.63×10 −8 ,3.70×10 −6 ] mm 2 /s (P = 0.0002) and of [6.01×10 −6 ,4.20×10 −5 ] mm 2 /s (P = 0.0388) in SOD1 G93A mice. Anisotropic diffusion signals persisted at ultra-high b-value DWIs of the mouse spinal cord and multiexponential diffusion analysis offers the potential to evaluate microstructural alterations of ALS-affected spinal cord non-invasively.

Molecular and microstructural biomarkers of neuroplasticity in neurodegenerative disorders through preclinical and diffusion magnetic resonance imaging studies

“…Hydrogen nuclei (consisting of a single proton) is a natural choice for analytical MRI techniques in biological samples, due to its abundance in water and fat components. Thus, MRI provides a unique opportunity to non-invasively quantify the diffusional characteristics of a wide range of macro and microstructural processes in the context of different neurological diseases (Gao et al, 2020;Gatto and Weissmann, 2020;Gatto et al, 2015;. Then, a probabilistic model to describe the motion of proton particles undergoing diffusion can be built, quantifying the fraction of particles that will move up to a certain distance within a time frame and creating a likelihood that can describe a particular diffusivity behavior based on the particle's displacement.…”

Identifying potential imaging markers for diffusion property changes in a mouse model of amyotrophic lateral sclerosis: Application of the continuous time random walk model to ultrahigh b‐value diffusion‐weighted MR images of spinal cord tissue

“…Data were retrieved from the Harvard Dataverse (https://doi.org/10.7910/DVN/8CZSXY). 30 Seven male SOD1 G93A ‐B6SJL mice (RRID: IMSR_JAX:002726, The Jackson Laboratory) and eight age‐matched, wild‐type male control mice at an early symptomatic stage (90 to 100 postnatal days) were analyzed by MRI immediately after being euthanized and without performing fixation. All MRI was performed on a 9.4‐T Agilent MRI scanner (Agilent, Santa Clara, CA, USA) equipped with a gradient coil with a maximum gradient strength of 1000 mT/m, a maximum slew rate of 7690 T/m/s, and a 39‐mm birdcage quadrature RF coil (Rapid, Germany).…”

Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system

“…Another equally valid regularization paradigm used in the literature is L1-norm or sparsity regularization. The curvature regularization approach was chosen because of its analytic solvability, its justifiable assumption on the resulting spectrum and its successful use in the literature (32,(45)(46)(47)(48)(49)(50)(51)(52).…”

“…Accepted for publication Mar 08, 2021. doi: 10.21037/qims- View this article at: http://dx.doi.org/10.21037/qims-20-1360 number of major diffusion compartments actually present in the tissue, and offers a means for their quantification during (patho)physiological changes (43). It has been successfully employed for multicomponent diffusion analysis of microstructural alterations in multicellular tumor spheroids, bone marrow, the central nervous system, and liver as well as in the parenchyma of healthy organs of the upper abdomen (32,(45)(46)(47)(48)(49)(50)(51)(52).…”