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Principal Investigator  
Principal Investigator's Name: Tobey Betthauser
Institution: University of Wisconsin-Madison
Department: Medicine
Country:
Proposed Analysis: I would like to investigate the relationship between PET neuorimaging markers of beta-amyloid and tau, and the effects the data processing pipeline on longitudinal outcomes. Some items include coregistration, reference region delineation for PET analysis, and PET denoising strategies. I would like to have access to unprocessed and processed PET data (e.g. PiB and AV1451) and corresponding structural MRI.
Additional Investigators  
Investigator's Name: Lianlian Du
Proposed Analysis: The goal of this research project is to develop and validate to the extent possible methods to characterize the temporal events that occur in Alzheimer's disease. Some events included are subtle cognitive change points, clinical dementia, amyloid onset, tau onset, and inflection points within these variables. Proposed methods include change-point models, logistic growth models, and non-parametric curve fitting methods to approximate time from various data, and relative time between events. Various methods will provide group-level and individual-level estimates for the timing of these key events.
Investigator's Name: Rebecca Koscik
Proposed Analysis: key personnel
Investigator's Name: Elena Ruiz De Chavez
Proposed Analysis: Elena will be aiding in investigation of multi-cohort longitudinal biomarker and dementia modeling studies in Dr. Betthauser's lab.
Investigator's Name: Erica Irizarry Pagan
Proposed Analysis: Erica is a graduate student in Dr. Betthauser's lab. She is working on a research project investigating the role of social determinants of health on Alzheimer's disease biomarker and dementia trajectories. This is a multi-cohort study that will use ADNI for one of the cohorts in the study.
Investigator's Name: Finnuella Carey
Proposed Analysis: This NIA-funded study (R01 AG080766, PI: Dr. Tobey Betthauser) will apply the Amyloid Clock framework to existing data from that Washington University Knight ADRC, the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Registry for Alzheimer’s Prevention, the, the Mayo Clinic Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative to identify critical factors that modify the timing of events in preclinical Alzheimer’s disease. This information has tremendous potential to (1) improve precision of dementia risk models, (2) contextualize AD risk regarding demographic, genetic, health and social factors, (3) clarify the generalizability of observational biomarker study results, (4) identify optimal treatment windows for disease intervention like anti-amyloid, anti-tau and/or combination therapies, and (4) improve clinical trial design and monitoring.
Investigator's Name: Ruvini Navaratna
Proposed Analysis: This NIA-funded study (R01 AG080766, PI: Dr. Tobey Betthauser) will apply the Amyloid Clock framework to existing data from that Washington University Knight ADRC, the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Registry for Alzheimer’s Prevention, the, the Mayo Clinic Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative to identify critical factors that modify the timing of events in preclinical Alzheimer’s disease. This information has tremendous potential to (1) improve precision of dementia risk models, (2) contextualize AD risk regarding demographic, genetic, health and social factors, (3) clarify the generalizability of observational biomarker study results, (4) identify optimal treatment windows for disease intervention like anti-amyloid, anti-tau and/or combination therapies, and (4) improve clinical trial design and monitoring.
Investigator's Name: Jordan Teague
Proposed Analysis: This NIA-funded study (R01 AG080766, PI: Dr. Tobey Betthauser) will apply the Amyloid Clock framework to existing data from that Washington University Knight ADRC, the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Registry for Alzheimer’s Prevention, the, the Mayo Clinic Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative to identify critical factors that modify the timing of events in preclinical Alzheimer’s disease. This information has tremendous potential to (1) improve precision of dementia risk models, (2) contextualize AD risk regarding demographic, genetic, health and social factors, (3) clarify the generalizability of observational biomarker study results, (4) identify optimal treatment windows for disease intervention like anti-amyloid, anti-tau and/or combination therapies, and (4) improve clinical trial design and monitoring.