Michael Cornelius Donohue, Ph.D.
USC/University of Southern California
Los Angeles, CA - United States
Both Alzheimer’s disease and Parkinson’s disease are progressive brain diseases that are thought to begin many years before clinical symptoms emerge. To help develop treatments for either disease, it is important to understand the ways each disease starts, the ways the diseases progress over time and how the brain changes associated with each disease differs from the brain changes occurring during normal aging.
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To follow the onset and long-term progression of disease in an individual would require that person to participate in multiple medical tests and brain imaging over several decades. Currently, no collection of such data is available on an individual basis; however several large databases have collected brain imaging and disease information for substantial numbers of people, each followed for a few years.
Michael Cornelius Donohue, Ph.D., and colleagues have proposed work to develop new statistical methods to analyze data from these large, existing databases in order to better understand how Alzheimer’s and Parkinson’s disease begin and progress over many decades. One goal of this work is to assess which biomarkers of disease measured in the medical clinic may be the best predictors of disease onset and progression. Another goal is to develop better ways of identifying people who are in the very early stages of disease and determining if therapeutic treatments are having a beneficial effect.