3 years ago

Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis.

Andrey Chuhutin, Brian Hansen, Agnieszka Wlodarczyk, Trevor Owens, Noam Shemesh, Sune Nørhøj Jespersen

Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel disease biomarkers and provides access to microstructural parameters. DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and the animal models of such diseases. In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability. Thirteen EAE spinal cords of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with 5 control specimen. The diffusion weighted data were acquired together with high resolution T2* images. Diffusion weighted data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results show strong relationship between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity, similar to what was found in other hypomyelinating MS models and in patients. However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability. These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and MS mechanisms.

Publisher URL: http://arxiv.org/abs/1811.03620

DOI: arXiv:1811.03620v1

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