The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: a multiparametric MRI study

Background Cerebellar involvement is not comprehensively studied from an MRI point of view in multiple sclerosis (MS). We aimed to quantify cerebellar damage and identify predictors of physical disability and cognitive dysfunction in MS patients, and to characterize patients with cerebellar disability. Methods In this prospective study, 164 (89 relapsing-remitting and 75 progressive) MS patients and 53 healthy controls were enrolled. Subjects underwent 3T MRI with sequences for assessing lesions and atrophy in cerebellum, supratentorial brain, brainstem and cervical cord. Cerebellar peduncle diffusion-tensor metrics were also derived. Random forest models identified MRI predictors of Expanded Disability Status Scale (EDSS) score and cognition z-score. Hierarchical clustering was applied on MRI metrics in patients with cerebellar disability. Results In MS patients, predictors of higher EDSS score (out-of-bag-R-2 = 0.83) were: lower cord grey matter (GM) and global areas, brain volume, GM volume (GMV), cortical GMV, cerebellum lobules I-IV and vermis GMV; and higher cord GM and brainstem lesion volume (LV). Predictors of lower cognition z-score (out-of-bag-R-2 = 0.25) were: higher supratentorial and superior cerebellar peduncle LV; and lower brain, thalamus and basal ganglia volumes, GMV, cerebellum lobule VIIIb and Crus II GMV. In patients with cerebellar disability, we found three clusters with homogenous MRI metrics: patients with high brain lesion volumes (including cerebellar peduncles), those with marked cerebellum GM atrophy and patients with severe cord damage. Conclusions Damage to cerebellum GM and connecting structures has a relevant role in explaining cognitive dysfunction and physical disability in MS. Data-driven MRI clustering might improve our knowledge of MRI-clinical correlations.

Automated summary

This study confirmed the relevance of cerebellar gray matter and connecting structures damage in explaining cognitive dysfunction and physical disability in multiple sclerosis patients. Data-driven MRI clustering analysis can improve our understanding of the correlation between MRI findings and clinical manifestations.