Keywords: Alzheimer's Disease, Brain Connectivity, Machine Learning, Diffusion Tensor Imaging, Tauopathy, Transcriptomics
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.
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