Wednesday, February 26, 2020

Udall Parkinson’s Disease Research Update - Presented by F. DuBois Bowman, PhD, Dean, School of Public Health and Professor of Biostatistics - Wednesday, February 26th, 2020

12:30 PM to 1:30 PM

Undergraduate Science Building, Room 4130, 204 Washtenaw Ave, Ann Arbor, MI 48109

Precision Discovery of Neuroimaging Biomarkers for Parkinson's Disease

Abstract: In Parkinson's disease (PD), there is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, and the onset of hallmark motor symptoms, creating an urgent need to develop biomarkers that may yield early PD detection. Neuroimaging offers a non-invasive approach to examine the utility of a vast number of functional and structural brain characteristics as biomarkers. We present a statistical framework for analyzing neuroimaging data from multiple modalities to determine features that reliably distinguish PD patients from healthy control subjects. This pursuit involves precision discovery from ultra-high dimensional data. Our approach builds on the statistical learning procedure elastic net, performing regularization and variable selection, while introducing additional criteria centering on parsimony and reproducibility. We apply our methods to data from two studies of PD. We demonstrate high accuracy, assessed via cross-validation, and identify brain regions in the basal ganglia and outer cortex that are implicated in the neurodegenerative PD process.

Dr. F. DuBois Bowman

Dr. F. DuBois Bowman

Dean F. DuBois Bowman, PhD, School of Public Health
Dr. F. DuBois Bowman, Professor of Biostatistics, University of Michigan

A renowned expert in the statistical analysis of brain imaging data, F. DuBois Bowman is dean of the University of Michigan School of Public Health and Professor of Biostatistics. Dr. Bowman’s work mines massive data sets and has important implications for mental and neurological disorders such as Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, and substance addiction. His research has helped reveal brain patterns that reflect disruption from psychiatric diseases, detect biomarkers for neurological diseases, and determine more individualized therapeutic treatments. Additionally, his work seeks to determine threats to brain health from environmental exposures and optimize brain health in aging populations.