Browsing by Subject "Florbetapir"

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    Comparison of Pittsburgh compound B and florbetapir in cross-sectional and longitudinal studies
    (Elsevier, 2019-02-22) Su, Yi; Flores, Shaney; Wang, Guoqiao; Hornbeck, Russ C.; Speidel, Benjamin; Joseph-Mathurin, Nelly; Vlassenko, Andrei G.; Gordon, Brian A.; Koeppe, Robert A.; Klunk, William E.; Clifford, R. Jack, Jr.; Farlow, Martin R.; Salloway, Stephen; Snider, Barbara J.; Berman, Sarah B.; Roberson, Erik D.; Broschi, Jared; Jimenez-Velazques, Ivonne; van Dyck, Christopher H.; Galasko, Douglas; Yuan, Shauna H.; Jayadev, Suman; Honig, Lawrence S.; Gauthier, Serge; Hsiung, Ging-Yuek R.; Masellis, Mario; Brooks, William S.; Fulham, Michael; Clarnette, Roger; Masters, Colin L.; Wallon, David; Hannequin, Didier; Dubois, Bruno; Pariente, Jeremie; Sanchez-Valle, Raquel; Mummery, Catherine; Ringman, John M.; Bottlaender, Michel; Klein, Gregory; Milosavljevic-Ristic, Smiljana; McDade, Eric; Xiong, Chengjie; Morris, John C.; Bateman, Randall J.; Benzinger, Tammie L.S.; Neurology, School of Medicine
    Introduction: Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods: Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally. Results: Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion: Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.
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    Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer’s Disease
    (Karger, 2016-03) Trzepacz, Paula T.; Hochstetler, Helen; Yu, Peng; Castelluccio, Peter; Witte, Michael M.; Dell'Agnello, Grazia; Degenhardt, Elisabeth; Department of Psychiatry, IU School of Medicine
    Aims: To assess how hippocampal volume (HV) from volumetric magnetic resonance imaging (vMRI) is related to the amyloid status at different stages of Alzheimer's disease (AD) and its relevance to patient care. Methods: We evaluated the ability of HV to predict the florbetapir positron emission tomography (PET) amyloid positive/negative status by group in healthy controls (HC, n = 170) and early/late mild cognitive impairment (EMCI, n = 252; LMCI, n = 136), and AD dementia (n = 75) subjects from the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity (ADNI-GO) and ADNI2. Logistic regression analyses, including elastic net classification modeling with 10-fold cross-validation, were used with age and education as covariates. Results: HV predicted amyloid status only in LMCI using either logistic regression [area under the curve (AUC) = 0.71, p < 0.001] or elastic net classification modeling [positive predictive value (PPV) = 72.7%]. In EMCI, age (AUC = 0.70, p < 0.0001) and age and/or education (PPV = 63.1%), but not HV, predicted amyloid status. Conclusion: Using clinical neuroimaging, HV predicted amyloid status only in LMCI, suggesting that HV is not a biomarker surrogate for amyloid PET in clinical applications across the full diagnostic spectrum.