MODELING FUNCTIONAL CONNECTIVITY WITH LEARNING AND MEMORY IN A MOUSE MODEL OF ALZHEIMER'S DISEASE Abbreviated title (50 characters): Modeling Functional Brain Connectivity and Memory

LIndsay Fadel Elizabeth Hipskind Steen Pedersen Jonathan Romero Caitlyn J Ortiz Eric Shin Hassan Samee ^* Robia G. Pautler ^*

• Baylor College of Medicine, Houston, Texas, United States

Functional connectivity (FC) is a metric of how different brain regions interact with each other. Although there have been some studies correlating learning and memory with FC, there have not yet been, to date, studies that use machine learning (ML) to explain how FC changes can be used to explain behavior not only in healthy mice but also in mouse models of Alzheimer’s Disease (AD). Here, we investigated changes in FC and their relationship to learning and memory in controls and the APP/PS1 mouse model of AD at 3-, 6-, and 10-months of age. Using resting state functional Magnetic Resonance Imaging (rs-fMRI) in awake, unanesthetized mice, we assessed FC between 30 brain regions. In the APP/PS1 mice, we identified a pattern of hyperconnectivity across all three time points, with 47 hyperconnected regions at 3 months, 46 at 6 months, and 84 at 10 months. Notably, FC changes were also observed in the Default Mode Network (DMN), exhibiting a loss of hyperconnectivity over time. ML models were then used to define interactions between neuroimaging readouts and learning and memory performance, providing novel insights into how FC changes explain spatial learning and memory performance. These ML models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline. Additionally, ML may provide a means to begin to understand how FC translates into learning and memory performance.