UC Berkeley

Anatomical, neuropsychological, and neuroimaging research has revealed extensive connectivity between most of the cerebral cortex and the cerebellum, pointing to the involvement of the human cerebellum in a broad range of functions beyond its traditional association with motor control. In a series of studies, I used functional magnetic resonance imaging, neuropsychology, and behavior to investigate how cortico-cerebellar connectivity contributes to cognition. In Chapter 1, I leveraged a multi-domain task battery to develop a comprehensive functional map of the human cerebellum, and in Chapter 2, I adopted a machine learning approach to build models of cortico-cerebellar connectivity to determine how this functional map is constrained by cortical input patterns. In Chapter 3, I showed that there is no selective impairment of the cerebellum in semantic prediction processing, and in Chapter 4, I demonstrated that the architecture of cortico-cerebellar connectivity changes over the course of learning for dynamic action prediction tasks. I hope that findings from this work can be incorporated by future studies to better understand how cortico-cerebellar networks are impacted in neurological disease and developmental disorders that affect the cerebellum.