Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study

Abstract Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York). In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations. We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p  The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting.