NIH funds research to identify Parkinson's biomarkers

The National Institute of Neurological Disorders and Stroke (NINDS), part of the National Institutes of Health (NIH), awarded a three-year, $900,000 grant to the Center for Biomedical Imaging Statistics at Emory's Rollins School of Public Health. The grant will fund the center's biomarker research in Parkinson's disease to identify non-invasive imaging measures that can detect changes in brain function and biochemistry.

Led by F. DuBois Bowman, PhD, associate professor and director of the Center for Biomedical Imaging Statistics, the team is one of nine research groups funded by NINDS that supports efforts to develop new technologies and tools for biomarker discovery, and data and sharing across the Parkinson's community. The idea is to create a larger study pool to accurately identify biomarkers (changes in the body or brain that can be used to predict, diagnose) in Parkinson's disease.

Bowman's team will develop new statistical tools that identify multiple biomarkers in the brain by observing differences in neural activity or abnormal alterations in brain function and structure.

Our primary goal is to achieve a better prognosis for patients by identifying neuro-degeneration earlier, says Bowman. In doing this, we prompt the development of new treatments, accurately identify who is likely to progress to develop Parkinson's, and develop findings that can be used to set up future clinical trials. There are currently no proven biomarkers for this disease.

Parkinson's disease is a movement disorder that affects nearly 1 million people in the United States. The lack of biomarkers for Parkinson's disease has been a major challenge for developing better treatments. Classic signs of the disease include tremors, stiffness and changes in speech and gait. Inside the brain, there is a progressive loss of cells in a motor-controlled region called the substantia nigra and an accumulation of protein-filled structures called Lewy bodies.

According to Bowman, there are no diagnostic tests that confirm Parkinson's disease. Instead, physicians base Parkinson's diagnoses on a combination of medical history, symptoms, neurological and physical examinations, and response to certain medications. Bowman and his team will develop statistical algorithms that will filter through millions of different possible brain measurements to detect changes that indicate neuro-degeneration related to Parkinson's. This will help accurately distinguish groups that are more likely to develop Parkinson's, thus changing what is known about the disease, enabling diagnosis before the classic motor symptoms occur and potentially altering treatments.

The research project has two aims:

1. Imaging-based Biomarkers: Using imaging technology, the team will capture changes in brain activity, structure and pathology related to Parkinson's. A novel aspect of the research will be combining different types of imaging data as well as other biologic information to extract multidimensional Parkinson's disease biomarkers.