Parkinson disease (PD), progressive supranuclear palsy (PSP), and multiple-system atrophy (MSA) are known to affect dopaminergic neurons of the brain stem and striatum with different preferential involvement. Here we investigated differences in striatal subregional dopamine transporter loss in PD, PSP, and MSA and assessed the diagnostic value of 18 F-fluorinated-N-3-fluoropropyl-2-b-carboxymethoxy-3-b-(4-iodophenyl)nortropane ( 18 F-FP-CIT) PET in differentiating PSP and MSA from PD. Methods: Forty-nine patients with PD, 19 patients with PSP, 24 patients with MSA, and 21 healthy people (healthy controls) were examined with 18 F-FP-CIT PET. The PET images were spatially normalized and analyzed with 12 striatal subregional volume-of-interest (VOI) templates (bilateral ventral striatum [VS], anterior caudate [AC], posterior caudate, anterior putamen, posterior putamen [PP], and ventral putamen [VP]) and 1 occipital VOI template. The nondisplaceable binding potential (BP ND ) and intersubregional ratio (ISR; defined as the ratio of the BP ND of one striatal subregion to that of another striatal subregion) of subregional VOIs were calculated. Results: The BP ND of all VOIs in the PD, MSA, and PSP groups were significantly lower than those in the healthy controls (P , 0.05). The BP ND of AC and the AC/VS ISR in the PSP group were significantly lower than those in the PD group. The BP ND of VP was significantly lower, but the PP/VP ISR was significantly higher in the MSA group than in the PD group. At the cutoff value for the AC/VS ISR (,0.7), the sensitivity and specificity for differentiating PSP from PD were 94% and 92%, respectively. At the cutoff value for the PP/VP ISR (.0.65), the sensitivity and specificity for differentiating MSA from PD were 90% and 45%, respectively. The diagnostic accuracy of visual analysis was similar to that of quantitative analysis for differentiating PSP from PD but was significantly higher for differentiating MSA from PD. Conclusion: Compared with PD, PSP and MSA showed more prominent and earlier dopamine transporter loss in the AC and VP, respectively. These findings could be useful for suggesting PSP or MSA in parkinsonian cases without characteristic atypical features.

Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson’s Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

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Little is known of the precise relationship between the expression of disease-related metabolic patterns and nigrostriatal dopaminergic dysfunction in parkinsonism. We studied 51 subjects with Parkinson's disease (PD) (18 non-demented, 24 demented, and 9 dementia with Lewy bodies) and 127 with atypical parkinsonian syndromes (47 multiple system atrophy (MSA), 38 progressive supranuclear palsy (PSP), and 42 corticobasal syndrome (CBS)) with 18 F-fluorodeoxyglucose PET to quantify the expression of previously validated disease-related patterns for PD, MSA, PSP, and CBS and 18 F-fluoropropyl-b-CIT PET to quantify caudate and putamen dopamine transporter (DAT) binding. The patients in each group exhibited significant elevations in the expression of the corresponding disease-related pattern (p < 0.001), relative to 16 healthy subjects. With the exception of cerebellar MSA (MSA-C), all groups displayed significant reductions in putamen DAT binding relative to healthy subjects (p < 0.05). Correlations between the dopaminergic and metabolic measures were significant in PD and CBS but not in MSA and PSP. In all patient groups with the exception of MSA-C and CBS, pattern expression values and DAT binding correlated with disease duration and severity measures. The findings suggest that in these parkinsonian disorders, metabolic network expression and DAT binding provide complementary information regarding the underlying disease process.