Modeling structural and functional connectomics with cognitive deficits and spatial transcriptomics in a mouse model of tauopathy

Motivation: Magnetic resonance imaging can non-invasively measure structural and functional connectivity in order to investigate the progression of tau pathology. Goal(s): We aim to understand the relationships between structural and functional connectivity with cognitive decline as tau pathology accumulates, as well as the underlying transcriptomic changes. Approach: We model the relationships between learning and memory with structural and functional connectivity in a mouse model of tauopathy. We expand this approach with high resolution spatial transcriptomics. Results: We identify patterns of functional connectivity changes and will model how brain activity and spatial transcriptomic changes contribute to cognitive decline. Impact: We use machine learning to understand the relationships between structural and functional connectivity with cognitive deficits in a mouse model of tauopathy, as well as identify underlying spatial transcriptomic changes to enhance our understanding of the progression of tau pathology.