Funded Studies
Objective/Rationale:
Magnetic resonance imaging (MRI) is a common tool used to diagnose or evaluate brain diseases such as Parkinson’s disease. Typically, MRI images are read by a radiologist. However, imaging techniques often provide large amounts of information to sort through, such as how certain areas of the brain are connected, that are difficult for a human viewer to visualize. This is the case with diffusion tensor imaging (DTI), a kind of MRI. The purpose of this project is to optimize DTI to improve our abilities to identify Parkinson’s disease and to follow progression of disease.
Project Description:
Our laboratory is using high-performance computing (such as supercomputers) to highlight differences so that they can be more easily visualized by a human viewer. We are also using the computer analysis to evaluate probability. The computer may be used to evaluate the chance, for example, that a particular brain would be seen in an individual with Parkinson’s disease. We are combining the computer predictions with other markers of disease (in this case spinal fluid proteins such as alpha-synuclein) to see how well we can predict presence of disease and how accurate we might be in measuring and ultimately predicting progression of disease.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
One serious problem in developing new treatments for Parkinson’s disease is that we don’t have very good measures of brain change over time. We propose that DTI, which gives us a lot of complex, interconnected information about brain structure and function, will be able to provide these sorts of measures, particularly as we begin to combine imaging with other markers of disease such as spinal fluid disease markers.
Anticipated Outcome:
This project will show us if DTI has the potential be used as a biomarker tool for detection of Parkinson’s disease. The project will additionally show us if DTI has the potential to be used to track disease progression in Parkinson’s disease. We will also evaluate the potential for imaging biomarkers to be combined with other biomarkers of disease such as blood and spinal fluid markers.