A task-general connectivity model reveals variation in convergence of cortical inputs to functional regions of the cerebellum

While resting-state fMRI studies have provided a broad picture of the connectivity between human neocortex and cerebellum, the degree of convergence of cortical inputs onto cerebellar circuits remains unknown. Does each cerebellar region receive input from a single cortical area or convergent inputs from multiple cortical areas? Here, we use task-based fMRI data to build a range of cortico-cerebellar connectivity models, each allowing for a different degree of convergence. We compared these models by their ability to predict cerebellar activity patterns for novel Task Sets. Models that allow some degree of convergence provided the best predictions, arguing for convergence of multiple cortical inputs onto single cerebellar voxels. Importantly, the degree of convergence varied across the cerebellum with the highest convergence observed in areas linked to language, working memory, and social cognition. These findings suggest important differences in the way that functional subdivisions of the cerebellum support motor and cognitive function.

Automated summary

While resting-state fMRI studies provide an overview of the connectivity between the human neocortex and cerebellum, it is unclear how cortical inputs converge onto cerebellar circuits. This study used task-based fMRI data to build different models of cortico-cerebellar connectivity, and found that models allowing for some degree of convergence provided the best predictions. The degree of convergence varied across the cerebellum, with higher convergence observed in areas related to language, working memory, and social cognition.