Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment

International audiencePURPOSE: Positron emission tomography (PET) imaging of brain amyloid load has been suggested as a core biomarker for Alzheimer's disease (AD). The aim of this study was to test the feasibility of using PET imaging with (18)F-AV-45 (florbetapir) in a routine clinical environment to differentiate between patients with mild to moderate AD and mild cognitive impairment (MCI) from normal healthy controls (HC). METHODS: In this study, 46 subjects (20 men and 26 women, mean age of 69.0 ± 7.6 years), including 13 with AD, 12 with MCI and 21 HC subjects, were enrolled from three academic memory clinics. PET images were acquired over a 10-min period 50 min after injection of florbetapir (mean ± SD of radioactivity injected, 259 ± 57 MBq). PET images were assessed visually by two individuals blinded to any clinical information and quantitatively via the standard uptake value ratio (SUVr) in the specific regions of interest, which were defined in relation to the cerebellum as the reference region. RESULTS: The mean values of SUVr were higher in AD patients (median 1.20, Q1-Q3 1.16-1.30) than in HC subjects (median 1.05, Q1-Q3 1.04-1.08; p = 0.0001) in the overall cortex and all cortical regions (precuneus, anterior and posterior cingulate, and frontal median, temporal, parietal and occipital cortex). The MCI subjects also showed a higher uptake of florbetapir in the posterior cingulate cortex (median 1.06, Q1-Q3 0.97-1.28) compared with HC subjects (median 0.95, Q1-Q3 0.82-1.02; p = 0.03). Qualitative visual assessment of the PET scans showed a sensitivity of 84.6% (95% CI 0.55-0.98) and a specificity of 38.1% (95% CI 0.18-0.62) for discriminating AD patients from HC subjects; however, the quantitative assessment of the global cortex SUVr showed a sensitivity of 92.3% and specificity of 90.5% with a cut-off value of 1.122 (area under the curve 0.894). CONCLUSION: These preliminary results suggest that PET with florbetapir is a safe and suitable biomarker for AD that can be used routinely in a clinical environment. However, the low specificity of the visual PET scan assessment could be improved by the use of specific training and automatic or semiautomatic quantification tools

Neuropsychology and neuroimaging profiles of amyloid-positive versus amyloid-negative amnestic mild cognitive impairment patients.

Introduction: Patients with amnestic mild cognitive impairment (aMCI) are heterogeneous as regard to their amyloid status. The present study aimed at highlighting the neuropsychological, brain atrophy, and hypometabolism profiles of amyloid-positive (Aβpos) versus amyloid-negative (Aβneg) aMCI patients. Methods: Forty-four aMCI patients and 24 Aβneg healthy controls underwent neuropsychological, structural magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomography scans. Data were compared between groups in specific regions of interest and voxelwise with statistical parametric mapping. Results: When directly comparing Aβpos to Aβneg aMCI, the former had lower performances in episodic memory tests (P = .02 to P < .001) while the latter had worse scores in working memory (P = .01) and language (P < .005). Compared to Aβneg healthy controls, both aMCI subgroups showed similar profiles of atrophy and hypometabolism, with no difference between both aMCI subgroups. Conclusion: In a sample of aMCI patients recruited and scanned in the same center, the main difference at baseline between Aβpos and Aβneg aMCI concerned the neuropsychological profile, but not the structural magnetic resonance imaging or 18F-fluorodeoxyglucose positron emission tomography profiles of brain alterations

De novo induction of resistance against voriconazole in Aspergillus fumigatus.

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Cognitive and Brain Profiles Associated with Current Neuroimaging Biomarkers of Preclinical Alzheimer's Disease.

LDM TEP COLLInternational audienc

Age-related changes in fast spindle clustering during non-rapid eye movement sleep and their relevance for memory consolidation.

Sleep plays a crucial role in memory consolidation. Recent data in rodents and young adults revealed that fast spindle band power fluctuates at a 0.02-Hz infraslow scale during non-rapid eye movement (NREM) sleep. These fluctuations result from a periodic temporal clustering of spindles and may modulate sleep maintenance and memory consolidation. With age, sleep undergoes substantial changes but age-related changes in spindle clustering have never been investigated. Polysomnography data were collected in 147 older (mean age ± SD: 69.3 ± 4.1 years) and 32 young-middle aged (34.5 ± 10.9 years) adults. Sleep-dependent memory consolidation was assessed in a subsample of 57 older adults using a visuospatial memory task. We analyzed power fluctuations in fast spindle frequency band, detected fast spindles, and quantified their clustering during the night separating encoding and retrieval. Fast spindle band power fluctuated at a 0.02-Hz infraslow scale in young-middle aged and older adults. However, the proportion of clustered fast spindles decreased non-linearly with age (p < .001). This effect was not mediated by NREM sleep fragmentation. The clustering level of fast spindles modulated their characteristics (p < .001). Finally, the mean size of spindle clusters was positively associated with memory consolidation (p = .036) and negatively with NREM sleep micro-arousal density (p = .033). These results suggest that clusters of fast spindles may constitute stable sleep periods promoting off-line processes such as memory consolidation. We emphasize the relevance of considering spindle dynamics, obviously impaired during aging, to understand the impact of age-related sleep changes on memory. Clinical Trial Information: Name: Study in Cognitively Intact Seniors Aiming to Assess the Effects of Meditation Training (Age-Well). URL: https://clinicaltrials.gov/ct2/show/NCT02977819?term=Age-Well&draw=2&rank=1. See STROBE_statement_AGEWELL.doc in supplementary material. Registration: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819