Dataset Information

Cross-sectional comparison of small animal [18F]-florbetaben amyloid-PET between transgenic AD mouse models.

ABSTRACT: We aimed to compare [18F]-florbetaben PET imaging in four transgenic mouse strains modelling Alzheimer's disease (AD), with the main focus on APPswe/PS2 mice and C57Bl/6 mice serving as controls (WT). A consistent PET protocol (N = 82 PET scans) was used, with cortical standardized uptake value ratio (SUVR) relative to cerebellum as the endpoint. We correlated methoxy-X04 staining of ?-amyloid with PET results, and undertook ex vivo autoradiography for further validation of a partial volume effect correction (PVEC) of PET data. The SUVR in APPswe/PS2 increased from 0.95±0.04 at five months (N = 5) and 1.04±0.03 (p<0.05) at eight months (N = 7) to 1.07±0.04 (p<0.005) at ten months (N = 6), 1.28±0.06 (p<0.001) at 16 months (N = 6) and 1.39±0.09 (p<0.001) at 19 months (N = 6). SUVR was 0.95±0.03 in WT mice of all ages (N = 22). In APPswe/PS1G384A mice, the SUVR was 0.93/0.98 at five months (N = 2) and 1.11 at 16 months (N = 1). In APPswe/PS1dE9 mice, the SUVR declined from 0.96/0.96 at 12 months (N = 2) to 0.91/0.92 at 24 months (N = 2), due to ?-amyloid plaques in cerebellum. PVEC reduced the discrepancy between SUVR-PET and autoradiography from -22% to +2% and increased the differences between young and aged transgenic animals. SUVR and plaque load correlated highly between strains for uncorrected (R = 0.94, p<0.001) and PVE-corrected (R = 0.95, p<0.001) data. We find that APPswe/PS2 mice may be optimal for longitudinal amyloid-PET monitoring in planned interventions studies.

SUBMITTER: Brendel M

PROVIDER: S-EPMC4338066 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Cross-sectional comparison of small animal [18F]-florbetaben amyloid-PET between transgenic AD mouse models.

Brendel Matthias M Jaworska Anna A Grießinger Eric E Rötzer Christina C Burgold Steffen S Gildehaus Franz-Josef FJ Carlsen Janette J Cumming Paul P Baumann Karlheinz K Haass Christian C Steiner Harald H Bartenstein Peter P Herms Jochen J Rominger Axel A

PloS one 20150223 2

We aimed to compare [18F]-florbetaben PET imaging in four transgenic mouse strains modelling Alzheimer's disease (AD), with the main focus on APPswe/PS2 mice and C57Bl/6 mice serving as controls (WT). A consistent PET protocol (N = 82 PET scans) was used, with cortical standardized uptake value ratio (SUVR) relative to cerebellum as the endpoint. We correlated methoxy-X04 staining of β-amyloid with PET results, and undertook ex vivo autoradiography for further validation of a partial volume effe ...[more]

PMID: 25706990

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Project description:BACKGROUND:Florbetaben, a 18F-labeled stilbene derivative (Neuraceq®, formerly known as BAY-949172), is a diagnostic radiopharmaceutical developed to visualize ?-amyloid plaques in the brain. Here, we report a pilot study evaluating patients with suspected cardiac amyloidosis for systemic extent of disease. METHODS:We prospectively enrolled nine patients, 61-86 year old (mean?±?SD 69.4?±?8.6), referred from the cardiac amyloid clinic. First, dynamic imaging of the heart was acquired immediately after injection of 222-318.2 MBq (mean?±?SD 270.1?±?33.3) of 18F-florbetaben using the GE SIGNA PET/MRI. This was followed by a whole-body PET/MRI scan 60-146.4 min (mean?±?SD 98?±?33.4) after injection. Cardiac MRI sequences included ECG-triggered cine SSFP, T2-weighted, and late gadolinium-enhanced imaging. Whole-body MRI sequences included MRAC and axial T1-weighted imaging. RESULTS:High early uptake and delayed high uptake in the left ventricle correlated with amyloid deposition in five patients, while low uptake on early and delayed cardiac imaging was noted in four patients. Cardiac function measurements were successfully obtained in all participants. Areas of increased abnormal 18F-florbetaben accumulation were noted on delayed whole-body imaging in the bone marrow (seven patients), stomach (diffuse in five patients and focal in one patient), brain (five patients), salivary glands (three patients), tongue (three patients), spleen (three patients), skeletal muscles (three patients), ocular muscles (two patients), thyroid (two patients), pleura (two patients), kidneys (two patients), and lungs (two patients). CONCLUSIONS:Whole-body 18F-florbetaben PET/MRI is promising for localization of systemic amyloid deposition. This technique may provide important structural and functional information regarding the organs involved by disease, with potential to guide biopsy and evaluate response to treatment. TRIAL REGISTRATION:Clinicaltrials.gov registration: NCT03119558 .

Project description:BACKGROUND:A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [18F]flutemetamol and [18F]florbetaben studies. METHODS:Rate constants for subjects across the Alzheimer's disease spectrum (i.e., non-displaceable binding potential (BPND) in the range 0.02-0.77 and 0.02-1.04 for [18F]flutemetamol and [18F]florbetaben, respectively) were established based on clinical [18F]flutemetamol (N?=?6) and [18F]florbetaben (N?=?20) data, and used to simulate tissue time-activity curves (TACs) of 110?min using a reference tissue and plasma input model. Next, noise was added (N?=?50) and data points corresponding to different intervals were removed from the TACs, ranging from 0 (i.e., 90-90?=?full-kinetic curve) to 80 (i.e., 10-90) minutes, creating a dual-time-window. Resulting TACs were fitted using the simplified reference tissue method (SRTM) to estimate the BPND, outliers (??1.5?×?BPND max) were removed and the bias was assessed using the distribution volume ratio (DVR?=?BPND?+?1). To this end, acceptability curves, which display the fraction of data below a certain bias threshold, were generated and the area under those curves were calculated. RESULTS:[18F]Flutemetamol and [18F]florbetaben data demonstrated an increased bias in amyloid estimate for larger intervals and higher noise levels. An acceptable bias (??3.1%) in DVR could be obtained with all except the 10-90 and 20-90-min intervals. Furthermore, a reduced fraction of acceptable data and most outliers were present for these two largest intervals (maximum percentage outliers 48 and 32 for [18F]flutemetamol and [18F]florbetaben, respectively). CONCLUSIONS:The length of the interval inversely correlates with the accuracy of the BPND estimates. Consequently, a dual-time-window protocol of 0-30 and 90-110?min (=maximum of 60?min interval) allows for accurate estimation of BPND values for both tracers. [18F]flutemetamol: EudraCT 2007-000784-19, registered 8 February 2007, [18F]florbetaben: EudraCT 2006-003882-15, registered 2006.

Project description:High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depending on their structure: Inception3D, ResNet3D, and VGG3D. After training with 80% of the DAUH dataset, an appropriate model was selected, and the rest of the DAUH dataset was used for model evaluation. The generalization potential of the selected model was then validated using the external dataset. The accuracy of the model evaluation for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, and the accuracy of the external validation was 76.7%, 67.1%, and 85.3%, respectively. Inception3D and ResNet3D were retrained with the external dataset; then, the area under the curve was compared to determine the binary classification performance with a significance level of less than 0.05. When external validation was performed again after fine tuning, the performance improved to 15.3%p for Inception3D and 16.9%p for ResNet3D. In [18F]Florbetaben amyloid brain PET scan classification using CNN, the generalization potential can be seen through external validation. When there is a significant difference between the model classification performance and the external validation, changing the model structure or fine tuning the model can help improve the classification performance, and the optimal model can also be found by collaborating through a web-based open platform.

Project description:INTRODUCTION:Standardized 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 A? presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. METHODS:SUVR 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. RESULTS:Composite 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. CONCLUSION:The 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 A?-positive or A?-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

Project description:BackgroundA low amount and extent of Aβ deposition at early stages of Alzheimer's disease (AD) may limit the use of previously developed pathology-proven composite SUVR cutoffs. This study aims to characterize the population with earliest abnormal Aβ accumulation using 18F-florbetaben PET. Quantitative thresholds for the early (SUVRearly) and established (SUVRestab) Aβ deposition were developed, and the topography of early Aβ deposition was assessed. Subsequently, Aβ accumulation over time, progression from mild cognitive impairment (MCI) to AD dementia, and tau deposition were assessed in subjects with early and established Aβ deposition.MethodsThe study population consisted of 686 subjects (n = 287 (cognitively normal healthy controls), n = 166 (subjects with subjective cognitive decline (SCD)), n = 129 (subjects with MCI), and n = 101 (subjects with AD dementia)). Three categories in the Aβ-deposition continuum were defined based on the developed SUVR cutoffs: Aβ-negative subjects, subjects with early Aβ deposition ("gray zone"), and subjects with established Aβ pathology.ResultsSUVR using the whole cerebellum as the reference region and centiloid (CL) cutoffs for early and established amyloid pathology were 1.10 (13.5 CL) and 1.24 (35.7 CL), respectively. Cingulate cortices and precuneus, frontal, and inferior lateral temporal cortices were the regions showing the initial pathological tracer retention. Subjects in the "gray zone" or with established Aβ pathology accumulated more amyloid over time than Aβ-negative subjects. After a 4-year clinical follow-up, none of the Aβ-negative or the gray zone subjects progressed to AD dementia while 91% of the MCI subjects with established Aβ pathology progressed. Tau deposition was infrequent in those subjects without established Aβ pathology.ConclusionsThis study supports the utility of using two cutoffs for amyloid PET abnormality defining a "gray zone": a lower cutoff of 13.5 CL indicating emerging Aβ pathology and a higher cutoff of 35.7 CL where amyloid burden levels correspond to established neuropathology findings. These cutoffs define a subset of subjects characterized by pre-AD dementia levels of amyloid burden that precede other biomarkers such as tau deposition or clinical symptoms and accelerated amyloid accumulation. The determination of different amyloid loads, particularly low amyloid levels, is useful in determining who will eventually progress to dementia. Quantitation of amyloid provides a sensitive measure in these low-load cases and may help to identify a group of subjects most likely to benefit from intervention.Trial registrationData used in this manuscript belong to clinical trials registered in ClinicalTrials.gov ( NCT00928304 , NCT00750282 , NCT01138111 , NCT02854033 ) and EudraCT (2014-000798-38).

Project description:Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (? 2?years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-? (A?) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included A?1-42, A?1-40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ?4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., ?story recall: ??=?+0.43 [p?<?0.001] and +?0.37 [p?=?0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline A? burden only if SUVR was referenced to the subcortical white matter (e.g., ?story recall: ??=?-0.28 [p?=?0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

Dataset Information

Cross-sectional comparison of small animal [18F]-florbetaben amyloid-PET between transgenic AD mouse models.

Publications

Cross-sectional comparison of small animal [18F]-florbetaben amyloid-PET between transgenic AD mouse models.

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