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Abstract #0185

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

Elizabeth Hipskind1, Lindsay Fadel1, Steen Pedersen2, Caitlyn Ortiz2, Jonathan Romero2, Sydney Fox2, Tina Manirambona2, Md Abul Hassan Samee2, and Robia Pautler2
1Neuroscience, Baylor College of Medicine, Houston, TX, United States, 2Integrative Physiology, Baylor College of Medicine, Houston, TX, United States

Synopsis

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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