UNLABELLED: In a previous [18F]fluorodeoxyglucose (FDG) PET study we analyzed regional metabolic data from a combined group of Parkinson's disease (PD) patients and healthy volunteers (N), using network analysis. By this method, we identified a unique pattern of regional metabolic covariation with an expression which accurately discriminated patients from healthy volunteers. To assess the reproducibility of this pattern as a potential marker for PD, we compared the pattern's topography with that of the disease-related covariance patterns identified in three other independent populations of patients with PD and healthy individuals studied in different PET laboratories. METHODS: The following patient populations were studied: group A (original cohort: 22 PD, 20 N; resolution: 7.5 mm full width at half maximum [FWHM]); group B (18 PD, 12 N; resolution: 4.2 mm FWHM); group C (25 PD, 15 N; resolution: 8.0 mm FWHM); and group D (14 PD, 10 N; resolution: 10 mm FWHM). Region weights for the PD-related covariance pattern (PDRP) identified in the group A analysis were correlated with those for the disease-related patterns identified in the analyses of groups B, C and D. In addition, subject scores for the group A PDRP were computed prospectively for every individual in each of the study populations. PDRP scores for PD and N within each cohort were compared. RESULTS: The PDRP topography identified in group A was highly correlated with each of the corresponding topographies identified in the other populations (r2 approximately 0.60, P < 0.0001). Prospectively computed subject scores for the group A PDRP significantly discriminated PD from N in each population (P < 0.004). CONCLUSION: The PDRP topography identified previously in Group A is highly reproducible across patient populations and tomographs. Prospectively computed PDRP scores can accurately discriminate patients from controls in multiple populations studied with different tomographs. Brain network imaging with FDG PET can provide robust metabolic markers for the diagnosis of PD.
UNLABELLED: In a previous [18F]fluorodeoxyglucose (FDG) PET study we analyzed regional metabolic data from a combined group of Parkinson's disease (PD) patients and healthy volunteers (N), using network analysis. By this method, we identified a unique pattern of regional metabolic covariation with an expression which accurately discriminated patients from healthy volunteers. To assess the reproducibility of this pattern as a potential marker for PD, we compared the pattern's topography with that of the disease-related covariance patterns identified in three other independent populations of patients with PD and healthy individuals studied in different PET laboratories. METHODS: The following patient populations were studied: group A (original cohort: 22 PD, 20 N; resolution: 7.5 mm full width at half maximum [FWHM]); group B (18 PD, 12 N; resolution: 4.2 mm FWHM); group C (25 PD, 15 N; resolution: 8.0 mm FWHM); and group D (14 PD, 10 N; resolution: 10 mm FWHM). Region weights for the PD-related covariance pattern (PDRP) identified in the group A analysis were correlated with those for the disease-related patterns identified in the analyses of groups B, C and D. In addition, subject scores for the group A PDRP were computed prospectively for every individual in each of the study populations. PDRP scores for PD and N within each cohort were compared. RESULTS: The PDRP topography identified in group A was highly correlated with each of the corresponding topographies identified in the other populations (r2 approximately 0.60, P < 0.0001). Prospectively computed subject scores for the group A PDRP significantly discriminated PD from N in each population (P < 0.004). CONCLUSION: The PDRP topography identified previously in Group A is highly reproducible across patient populations and tomographs. Prospectively computed PDRP scores can accurately discriminate patients from controls in multiple populations studied with different tomographs. Brain network imaging with FDG PET can provide robust metabolic markers for the diagnosis of PD.
Authors: T Nakamura; M F Ghilardi; M Mentis; V Dhawan; M Fukuda; A Hacking; J R Moeller; C Ghez; D Eidelberg Journal: Hum Brain Mapp Date: 2001-01 Impact factor: 5.038
Authors: Jul Lea Shamy; Christian Habeck; Patrick R Hof; David G Amaral; Sania G Fong; Michael H Buonocore; Yaakov Stern; Carol A Barnes; Peter R Rapp Journal: Cereb Cortex Date: 2010-12-01 Impact factor: 5.357