Determining Multimodal Imaging Biomarkers for Parkinson’s Disease

Despite considerable progress in our understanding of Parkinson’s disease, reliable biomarkers are still lacking. The combination of noninvasive neuroimaging data reflecting both functional and structural characteristics of the brain, clinical information and other biologic measures provides an unprecedented opportunity for cross-cutting investigations that may yield deeper insights into Parkinson’s disease.

In this lecture, F. DuBois Bowman will discuss how he and his colleagues are working to identify functional or anatomical properties of the brain that reliably distinguish individuals with Parkinson’s disease from healthy controls. He will describe the Bayesian statistical modeling framework that incorporates imaging data from different modalities and yields classifications for study participants, as either those with Parkinson’s disease or healthy controls. The model accounts for spatial correlations between different brain locations, defined hierarchically to capture correlations globally between brain regions, between subregions of each region, and between voxels within each subregion. Bowman will then explain how the ability to isolate neural characteristics that reflect accurate signatures of Parkinson’s disease may serve as useful early-stage Parkinson’s disease biomarkers.