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Background and purpose Atypical Parkinsonian disorders ( APD) frequently overlap in clinical presentations, making the differential diagnosis challenging in the early stages. The present study aimed to evaluate the accuracy of the [18F]fluoro-deoxy-glucose positron emission tomography Statistical Parametric Mapping ( SPM) optimized procedure in supporting the early and differential diagnosis of APD. Methods Seventy patients with possible APD were retrospectively included from a large clinical cohort. The included patients underwent [18F]fluoro-deoxy-glucose positron emission tomography within 3 months of the first clinical assessment and a diagnostic follow-up. An optimized SPM voxel-wise procedure was used to produce t-maps of brain hypometabolism in single subjects, which were classified by experts blinded to any clinical information. We compared the accuracy of both the first clinical diagnosis and the SPM t-map classifications with the diagnosis at follow-up as the reference standard. Results At first diagnosis , 60% of patients were classified as possible APD (progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies, multiple system atrophy) and about 40% as APD with uncertain diagnosis, providing 52% sensitivity, 97% specificity and 86% accuracy with respect to the reference standard. SPM t-map classification showed 98% sensitivity, 99% specificity and 99% accuracy, and a significant agreement with the diagnosis at follow-up ( P < 0.001). Conclusions The SPM t-map classification at entry predicted the second diagnosis at follow-up. This indicates its significantly superior role for an early identification of APD subtypes, particularly in cases of uncertain diagnosis. The use of a metabolic biomarker at entry in the instrumental work-up of APD may shorten the diagnostic time, producing benefits for treatment options and support to the patients. [ABSTRACT FROM AUTHOR]

Background: The amnestic presentation of mild cognitive impairment (aMCI) represents the most common prodromal stage of Alzheimer's disease (AD) dementia. There is, however, some evidence of aMCI with typical amnestic syndrome but showing long-term clinical stability. The ability to predict stability or progression to dementia in the aMCI condition is important, particularly for the selection of candidates in clinical trials. We aimed to establish the role of in vivo biomarkers, as assessed by cerebrospinal fluid (CSF) measures and [ 18 F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging, in predicting prognosis in a large aMCI cohort., Methods: We conducted a retrospective study, including 142 aMCI subjects who had a long follow-up (4-19 years), baseline CSF data and [ 18 F]FDG-PET scans individually assessed by validated voxel-based procedures, classifying subjects into either limbic-predominant or AD-like hypometabolism patterns., Results: The two aMCI cohorts were clinically comparable at baseline. At follow-up, the aMCI group with a limbic-predominant [ 18 F]FDG-PET pattern showed clinical stability over a very long follow-up (8.20 ± 3.30 years), no decline in Mini-Mental State Examination score, and only 7% conversion to dementia. Conversely, the aMCI group with an AD-like [ 18 F]FDG-PET pattern had a high rate of dementia progression (86%) over a shorter follow-up (6.47 ± 2.07 years). Individual [ 18 F]FDG-PET hypometabolism patterns predicted stability or conversion with high accuracy (area under the curve = 0.89), sensitivity (0.90) and specificity (0.89). In the limbic-predominant aMCI cohort, CSF biomarkers showed large variability and no prognostic value., Conclusions: In a large series of clinically comparable subjects with aMCI at baseline, the specific [ 18 F]FDG-PET limbic-predominant hypometabolism pattern was associated with clinical stability, making progression to AD very unlikely. The identification of a biomarker-based benign course in aMCI subjects has important implications for prognosis and in planning clinical trials., (© 2020 European Academy of Neurology.)