Search

Purpose: Alpha-synuclein often co-occurs with Alzheimer’s disease (AD) pathology in Dementia with Lewy Bodies (DLB). From a dynamic [18F]flortaucipir PET scan we derived measures of both tau binding and relative cerebral blood flow (rCBF). We tested whether regional tau binding or rCBF differed between DLB patients and AD patients and controls and examined their association with clinical characteristics of DLB. Methods: Eighteen patients with probable DLB, 65 AD patients and 50 controls underwent a dynamic 130-minute [18F]flortaucipir PET scan. DLB patients with positive biomarkers for AD based on cerebrospinal fluid or amyloid PET were considered as DLB with AD pathology (DLB-AD+). Receptor parametric mapping (cerebellar gray matter reference region) was used to extract regional binding potential (BPND) and R1, reflecting (AD-specific) tau pathology and rCBF, respectively. First, we performed regional comparisons of [18F]flortaucipir BPND and R1 between diagnostic groups. In DLB patients only, we performed regression analyses between regional [18F]flortaucipir BPND, R1 and performance on ten neuropsychological tests. Results: Regional [18F]flortaucipir BPND in DLB was comparable with tau binding in controls (p > 0.05). Subtle higher tau binding was observed in DLB-AD+ compared to DLB-AD- in the medial temporal and parietal lobe (both p

An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine (SVM) and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.