Because there is no widely available test to confirm the clinical diagnosis of Parkinson's disease (PD), other parkinsonian condifions are frequently misdiagnosed as PD. This poses problems for clinical trials and pafient care. Using FDG PET and spatial covariance mapping, we have idenfified patterns of brain metabolism that disfinguish PD, mulfiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the two most common atypical parkinsonian syndromes. Ufilizing a novel automated differenfial diagnosis algorithm, image-based classificafions can provide excellent diagnostic accuracy for idiopathic PD, even eariy in the disease course. Metabolic imaging with automated pattern analysis is thus a promising approach for accurate early diagnosis of PD vs. MSA or PSP. In this project, we will validate this approach in stricfiy defined patient populations, establish its false positive and false negative rates, and determine how early the metabolic abnormalifies are detectable by studying a populafion at risk for PD (individuals with REM behavior disorder, or RBD). Collaborations with Stanford University and the Udall Center at the University of Pennsylvania will broaden our patient base and enable us to test the feasibility of our approach across different movement disorder clinics and imaging centers.
In Specific Aim 1 we will test the sensitivity and specificity of our image-based automated classification in patients with unequivocal clinical diagnoses of PD, MSA, or PSP.
Specific Aim 2 will assess the diagnosfic accuracy of FDG PET in a real world context by testing our method in early parkinsonian subjects with an uncertain diagnosis.
Specific Aim 3 moves toward determining whether we can develop a biomarker to detect prodromal PD: we will determine whether there are presymptomatic changes in brain metabolism and dopaminergic funcfion in subjects with REM behavior disorder (RBD), a significant percentage of whom go on to develop PD. The development of a reliable technique for the early, objective, and accurate diagnosis of PD will improve clinical management, and will help to optimize the conduct of PD clinical research.
This project will validate a widely available method (FDG PET) for use in the differential diagnosis of parkinsonism, and will assess this method's usefulness in a real world populafion of patients with early parkinsonism. In addifion, the study will ufilize PET imaging to examine abnormalities in brain funcfion in pafients at risk for developing Parkinson's disease (PD) who do not yet show signs of the disease (i.e. patients with REM Behavior Disorder). The development of a technology for the early, objective, and accurate diagnosis of PD will improve clinical management, and will help to opfimize the conduct of PD clinical research.
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