Piramal (United States)

The effects of acute sleep deprivation on β-amyloid (Aβ) clearance in the human brain have not been documented. Here we used PET and 18F-florbetaben to measure brain Aβ burden (ABB) in 20 healthy controls tested after a night of rested sleep (baseline) and after a night of sleep deprivation. We show that one night of sleep deprivation, relative to baseline, resulted in a significant increase in Aβ burden in the right hippocampus and thalamus. These increases were associated with mood worsening following sleep deprivation, but were not related to the genetic risk (APOE genotype) for Alzheimer's disease. Additionally, baseline ABB in a range of subcortical regions and the precuneus was inversely associated with reported night sleep hours. APOE genotyping was also linked to subcortical ABB, suggesting that different Alzheimer's disease risk factors might independently affect ABB in nearby brain regions. In summary, our findings show adverse effects of one-night sleep deprivation on brain ABB and expand on prior findings of higher Aβ accumulation with chronic less sleep.

IntroductionStandardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the Aβ presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA.MethodsSUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared.ResultsComposite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83–94% for histopathology, and 85–94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers.ConclusionThe accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as Aβ-positive or Aβ-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

PurposeThe Centiloid (CL) method enables quantitative values from Aβ-amyloid (Aβ) imaging to be expressed in a universal unit providing pathological, diagnostic and prognostic thresholds in clinical practice and research and allowing integration of multiple tracers and methods. The method was developed for 11C-PiB scans with zero CL set as the average in young normal subjects and 100 CL the average in subjects with mild Alzheimer’s disease (AD). The method allows derivation of equations to convert the uptake value of any tracer into the same standard CL units but first requires head-to-head comparison with 11C-PiB results. We derived the equation to express 18F-florbetaben (FBB) binding in CL units.MethodsPaired PiB and FBB PET scans were obtained in 35 subjects. including ten young normal subjects aged under 45 years (33 ± 8 years). FBB images were acquired from 90 to 110 min after injection. Spatially normalized images were analysed using the standard CL method (SPM8 coregistration of PET data to MRI data and the MNI-152 atlas) and standard CL regions (cortex and whole cerebellum downloaded from http://www.gaain.org).ResultsFBB binding was strongly correlated with PiB binding (R2 = 0.96, SUVRFBB = 0.61 × SUVRPiB + 0.39). The equation to derive CL values from FBB SUVR was CL units = 153.4 × SUVRFBB − 154.9. The CL value in the young normal subjects was −1.08 ± 6.81 for FBB scans compared to −0.32 ± 3.48 for PiB scans, giving a variance ratio of 1.96 (SDFBB CL/SDPiB CL).Conclusions18F-FBB binding is strongly correlated with PiB binding and FBB results can now be expressed in CL units.