Keywords: Small Animals, Alzheimer's Disease, resting state fMRI, Functional Connectivity, Spatial Proteomics
Motivation: Alzheimer's Disease (AD) is a devastating neurodegenerative disease that affects 50 million people worldwide.
Goal(s): Advancements in neuroimaging techniques that can detect deficits prior to significant accumulation of pathology and cognitive decline would aid in early detection, diagnosis, and possible therapeutic intervention.
Approach: Resting state fMRI studies have identified early functional connectivity (FC) deficits in memory-related brain regions, preceding cognitive impairment, making it a promising clinical marker for AD.
Results: Using an AD mouse model, our study has identified unique patterns of FC changes across disease progression. Using machine learning we are working to establish relationships between neuroimaging and cognitive and proteomic changes.
Impact: This study aims to uncover dynamic alterations in functional connectivity and the link to cognitive and proteomic changes using a mouse model of Alzheimer's Disease. These findings offer potential insights for early detection and diagnostic advancements in Alzheimer's Disease research.
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