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Principal Investigator  
Principal Investigator's Name: Sarah Ackley
Institution: UCSF
Department: Epidemiology and Biostatistics
Proposed Analysis: Tau aggregates (neurofibrillary tangles) and amyloid beta oligomers and plaques are key biomarkers of sporadic Alzheimer’s disease (AD). Amyloid-targeting drugs have been largely unsuccessful; treatments have been costly and resulted in only a handful of potentially effective drugs, none of which have progressed to FDA approval. A key challenge in trial design has been difficulty in determining who would benefit from therapy and when in the disease progression patients should receive treatment. As more tau-targeting drugs move through the pipeline, it will be important to determine the optimal timing and duration of treatment for trial design and for post-approval clinical guidelines. With the introduction of positron emission tomography with tau ligands (tau-PET), it is now possible to observe tau accumulation in vivo. A growing number of cohorts now perform tau-PET, and some have performed repeated neuroimaging. However, despite the growing availability of tau-PET data, it is difficult to gain insight into complex biological systems using conventional statistical and causal inference approaches. Many specifics of the pathological process of AD remain unknown, for example heterogeneities in timing, rates, and topography of tau accumulation and the precise, functional relationship between tau accumulation and cognitive decline. Mathematical modeling techniques—commonly used in infectious disease epidemiology and computational biology—allow for the study of complex relationships between biological variables, while incorporating prior knowledge about the relevant physiologic system. As a PhD epidemiologist with extensive training in mathematical modeling, I propose a mechanistic, biological modeling approach to understand how neuroimaging and other biomarkers can be used to better understand AD biology, improve trial design, and optimally target drug treatment and timing. The long-term objective of my research is to improve our understanding of the pathophysiology of AD, including the spread of tau and the connections between amyloid, tau, and cognition, with the goal of guiding therapeutic development and trials for AD treatment. The proximal goal of this proposal is to leverage tau-PET data in longitudinal cohorts to determine precisely how and when tau accumulation results in cognitive decline. My central hypothesis is that there are important nonlinear and threshold effects and heterogeneity in the time between accumulation of these biomarkers and cognitive decline that can be explained by accumulation of other pathologies, such as amyloid accumulation or cerebrovascular disease. I plan to develop a mathematical model for how the region-specific and overall accumulation and spread of tau affects cognition and estimate time lags between tau accumulation and cognitive decline, based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) cohort of individuals aged 55-90 years at enrollment. Scientific outcomes of this project include: better understanding of variability in tau accumulation and its precise, region-specific relation to cognitive decline, evidence that is needed to determine when and for whom tau targeting treatments would be beneficial. This grant will also provide training in biological mechanisms of AD, multimodal dementia biomarkers, and the process of drug development. This project will prepare me for an independent academic research career applying mathematical modeling to 1) advance understanding of the biological processes underlying AD and 2) accelerate research on new pharmacotherapies.
Additional Investigators  
Investigator's Name: Kaitlin Swinnerton
Proposed Analysis: We will examine the added value of regional tau burden in models with or without information on prior cognitive measures in predicting cognitive trajectory, adjusting for demographic characteristics (age, sex, marital status, and education). Analyses will use a cross-validation approach with out-of-sample predictions split by participant between training and test sets. We will first use ridge regression in the training data to create an optimized transformation of tau burden for predicting future cognition. We will then used a time-series regression model to evaluate the added value of the optimized tau measure in models adjusting for zero to three past cognitive assessments following tau-PET in the testing data set. The bootstrap by individual will used to obtained p-values and 95% confidence intervals.