Abstract #0715

Characterisation of cerebellar microstructure with two-compartment Spherical Mean Technique

Giovanni Savini^1,2, Fulvia Palesi^2,3, Gloria Castellazzi^2,4, Letizia Casiraghi^2,5, Francesco Grussu⁶, Alessandro Lascialfari^1,3, Egidio D'Angelo^2,5, and Claudia AM Gandini Wheeler-Kingshott^5,6,7

¹Department of Physics, University of Milan, Milan, Italy, ²Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, ³Department of Physics, University of Pavia, Pavia, Italy, ⁴Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, ⁵Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ⁶Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom, ⁷Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy

Cerebrum microstructure has been extensively investigated with diffusion-weighted MRI, but little attention has been dedicated to the microstructural characterisation of the cerebellum.

We considered an anatomical parcellation of the cerebellum and fitted a multi-compartment model to diffusion data exploiting the spherical mean technique, which provides parametric maps unconfounded by the underlying fibre orientation distribution. For each region we report average values for multi-compartment parameters (e.g. intra-neurite volume fraction and intrinsic diffusivity) and diffusion tensor metrics.

Multi-compartment metrics more specific to microstructure provide information complementary to diffusion tensor metrics in the cerebellum, thus giving new insights about microstructural correlations between regions.

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