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Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.

The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-β AD conversion–related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG–based AD conversion–related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion:18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.

The value of 18F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: 18F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18F-FDG PET.

Abnormal covariance pattern of regional metabolism associated with Parkinson disease (PD) is modulated by dopaminergic pharmacotherapy. Using high-resolution 18F-FDG PET and network analysis, we previously derived and validated a parkinsonism-related metabolic pattern (PRP) in nonhuman primate models of PD. It is currently not known whether this network is modulated by experimental therapeutics. In this study, we examined changes in network activity by striatal implantation of human levodopa-producing retinal pigment epithelial (hRPE) cells in parkinsonian macaques and evaluated the reproducibility of network activity in a small test–retest study. Methods:18F-FDG PET scans were acquired in 8 healthy macaques and 8 macaques with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)–induced bilateral nigrostriatal dopaminergic lesions after unilateral putaminal implantation of hRPE cells or sham surgery. PRP activity was measured prospectively in all animals and in a subset of test–retest animals using a network quantification approach. Network activity and regional metabolic values were compared on a hemispheric basis between animal groups and treatment conditions. Results: All individual macaques showed clinical improvement after hRPE cell implantation compared with the sham surgery. PRP activity was elevated in the untreated MPTP hemispheres relative to those of the normal controls (P

Two multidisciplinary teams of investigators have reported results from double-blind, placebo-controlled trials of human embryonic dopaminergic tissue transplantation for advanced Parkinson’s disease (PD). In the first study, 39 PD patients were randomized to either bilateral tissue implantation in the putamen or sham surgery without immunosuppressive therapy and followed for 1 y with blinded clinical and 18F-fluorodopa (18F-FDOPA) PET evaluations (1). We found that transplantation significantly improved standardized motor ratings, although substantial clinical benefit was limited to younger (age ≤ 60 y) subjects. Nonetheless, putamen uptake of 18F-FDOPA, an index of graft viability, increased significantly in the transplanted cohort, regardless of age (2). In the second double-blind study, 34 PD patients underwent bilateral fetal dopaminergic cell transplantation in the putamen, with 1–4 donors per side, or placebo surgery along with pre- and postoperative immunosuppressive therapy over 6 mo (3). That study showed clinical benefit in less severely affected subjects and significant increases in putamen 18F-FDOPA uptake in all the transplant recipients at the 2-y follow-up. The results of these blinded studies agreed with reports from small open-label cohorts of PD patients showing persisting increases in 18F-FDOPA uptake at the site of implantation (4–6), histopathologic evidence of engraftment (7,8), and associations between 18F-FDOPA PET indices of graft viability and clinical outcome (9,10). To date, reports of the long-term outcome of fetal cell transplantation for advanced PD have been limited to small groups of subjects (4,6). Both double-blind clinical trials also reported the development of graft-induced dyskinesia (GID) in some of the transplant recipients when these recipients were assessed after dopaminergic medications had been stopped (1,3). It remains to be determined whether the clinical response to transplantation is offset by the troubling side effect of GID. In the present study, we report long-term clinical and 18F-FDOPA PET findings from our blinded transplantation trial (1,2). After the 1-y blinded phase of that study, 14 of the 20 placebo-operated patients went on to receive bilateral putamen grafts, resulting in a total of 33 transplanted patients. We assessed postoperative changes in motor ratings and striatal 18F-FDOPA uptake in these subjects at 1 and 2 y after implantation. Of these subjects, 15 were followed for an additional 2 y. In addition to correlating the clinical and PET changes over time, we determined whether these indices were influenced by subject age or sex. We also assessed the impact on the data of the 5 transplant recipients in whom GID developed (11). Last, we correlated clinical outcome with preoperative 18F-FDOPA PET to identify predictive imaging features (12) that might be used for patient selection in future trials of cell-based interventions for PD and related disorders.