PROJECT SUMMARY/ ABSTRACT Many specifics of the pathological process of Alzheimer’s disease (AD) remain unknown, such as the precise, functional relationship between tau accumulation and cognitive decline as a function of age, as well as other biomarkers that may modify these relationships. Conventional statistical approaches cannot easily answer questions about the relationship between tau and cognition, due to their dynamic relationship, unknown time lags, and complex measurement error structures. Mathematical modeling techniques—commonly used in infectious disease epidemiology and computational biology—are specialized for the study of complex relationships between biological variables, while incorporating prior knowledge about the relevant physiologic system. The proposed project leverages my quantitative expertise from dissertation research on infectious disease, using data from across the age span of AD onset to elucidate the relationship between tau-PET measures and cognition. As more tau-targeting drugs move through the pipeline, it is important to determine the optimal timing and duration of treatment for trial design and for post-approval clinical guidelines. The ideal timing for tau-targeting therapies may depend on factors such as age, amyloid, or vascular burden. Existing and emerging blood- based biomarkers may offer important information about how tau spreads in the brain and the timing of subsequent atrophy and cognitive decline longitudinally. A growing number of studies now perform tau-PET, and including repeated neuroimaging, making it possible for an improved understanding of the dynamics of tau and cognition in relation to other biomarkers. We propose a biologically motivated, mathematical modeling approach to understand how neuroimaging and other biomarkers can be used to better understand Alzheimer’s disease biology. We plan to fit mechanistic models to data from three cohorts across the age span of AD diagnosis: Alzheimer’s Disease Neuroimaging Initiative (ADNI), Longitudinal Early-onset Alzheimer's Disease Study (LEADS), and The 90+ Study. The long- term objective of this research is to improve our understanding of the age-specific pathophysiology of AD, determining the precise relationship between tau and cognition, with the ultimate goal of guiding therapeutic development and trials for AD treatment. The proposed training activities include hands-on research experience, as well as didactics, advanced coursework, and directed readings and mentorship with the primary mentor Professor M. Maria Glymour and co-mentor Professor Gil Rabinovici, MD. Scientific advisors Professors María Corrada (MPI: The 90+ Study; University of California, Irvine), clinical neuropsychologist and Professor Adam Staffaroni, and Professor Roy Anderson (Imperial College London) will also contribute their expertise.

PROJECT NARRATIVE I propose a biologically motivated, mathematical modeling approach to understand how neuroimaging and other biomarkers can be used to better understand Alzheimer’s disease biology. I plan to fit mechanistic models to data from 3 cohorts across the age span of Alzheimer’s disease onset: Longitudinal Early Onset Alzheimer’s Disease Study (LEADS) (ages 40-64), Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (ages 55-90), and The 90+ Study (ages 90+). The long-term objective of this research is to improve our understanding of the age-specific pathophysiology of Alzheimer’s disease, determining the precise relationship between tau and cognition, with the ultimate goal of guiding therapeutic development and trials for Alzheimer’s treatment.