The cognitive cerebellum: linking microstructure to cognitive functions in a healthy population

Background: The cerebellum is recognized for its role in motor control. However, it also plays a crucial part in modulating circuits involved in cognition and affect. While studies conducted on people with cerebellar disorders highlight both structural and functional links with cognition, research on cerebellar structure in the healthy population remains sparse. To better clarify the cerebellum's role and operational mode in cognition, this multi-scale study explores the relationship between cognitive functions and cerebellar macrostructure and microstructure in healthy individuals. Macrostructural analysis focused on grey matter (GM) and white matter (WM) volumes, while microstructural evaluation used fractional anisotropy (FA) values. Methods: Using a large normative cohort, the study examined cerebellar GM and WM volumes in 151 participants and FA in 83 participants. Cerebellar GM and WM volumes and FA values were correlated voxel-wise against the following cognitive domains: long-term memory, abstract reasoning, language-related executive functions, processing speed, and impulsive decision-making. Results: Significant positive correlations were found between FA in specific cerebellar regions and long-term memory (p = 0.030), abstract reasoning (p = 0.048), and language-related executive functions (p = 0.043). Additionally, cerebellar FA values negatively correlated in several clusters with reaction time (p = 0.001; p = 0.026; p = 0.045), indicating faster processing speed with higher FA. No significant associations were found between cerebellar GM/WM volumes and cognitive performance after Family Wise Error correction, except for a positive correlation between WM and reaction time (p = 0.023). Discussion: These findings highlight the cerebellum's microstructure role in cognition. FA may reflect the efficiency of communication between cerebellar and cortical regions, thus allowing the cerebellum to improve cognitive performance by updating internal models and correcting discrepancies between predictions and outcomes.