Multimodal spinal cord analysis in inherited cerebellar ataxias

• Grant number: 18/16296-0
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: February 11, 2019
• End date: August 10, 2019
• Field of knowledge: Health Sciences - Medicine - Medical Radiology
• Principal Investigator: Marcondes Cavalcante Franca Junior
• Grantee: Thiago Junqueira Ribeiro de Rezende
• Supervisor: Julien Cohen-Adad
• Host Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: École Polytechnique de Montréal, Canada
• Associated to the scholarship: 17/13102-7 - Characterization and Comparison of Bayesian and Deep Learning based Methods for Cerebellar Segmentation, BP.PD

Abstract

Inherited cerebellar ataxias (ICA) represent a heterogeneous group of neurodegenerative disorders that share cerebellar ataxia as the core clinical finding. Pathologically, this group of diseases is characterized by severe neuronal loss in the cerebellum and brainstem. Cervical spinal cord atrophy is a hallmark of these disorders. This is indeed confirmed by quantitative neuroimaging data that have been largely used in the brain for several decades now. In contrast, spinal cord imaging in research and clinics is still largely underutilized, because it is difficult to acquire good quality data due to the numerous artifacts and the small cross-sectional size of the spinal cord. However, recent MRI advances that include high field devices, coil design and post-processing techniques now enable reliable quantitative spinal cord analyses. In this scenario, the Spinal Cord Toolbox (SCT) is a software package specifically designed to process spinal cord multimodal MRI data and includes state-of-the-art tools, which have been validated. We strongly believe that this new technique would provide reliable measures of cross-sectional areas and diffusivity parameters for the main tracts of spinal cord, enabling us to better understand the pathogenesis of ICAs.