Open AccessComparison of Pittsburgh compound B and florbetapir in cross‐sectional and longitudinal studies
Author(s) -
Su Yi,
Flores Shaney,
Wang Guoqiao,
Hornbeck Russ C.,
Speidel Benjamin,
JosephMathurin Nelly,
Vlassenko Andrei G.,
Gordon Brian A.,
Koeppe Robert A.,
Klunk William E.,
Jack Clifford R.,
Farlow Martin R.,
Salloway Stephen,
Snider Barbara J.,
Berman Sarah B.,
Roberson Erik D.,
Brosch Jared,
JimenezVelazques Ivonne,
Dyck Christopher H.,
Galasko Douglas,
Yuan Shauna H.,
Jayadev Suman,
Honig Lawrence S.,
Gauthier Serge,
Hsiung GingYuek R.,
Masellis Mario,
Brooks William S.,
Fulham Michael,
Clarnette Roger,
Masters Colin L.,
Wallon David,
Hannequin Didier,
Dubois Bruno,
Pariente Jeremie,
SanchezValle Raquel,
Mummery Catherine,
Ringman John M.,
Bottlaender Michel,
Klein Gregory,
MilosavljevicRistic Smiljana,
McDade Eric,
Xiong Chengjie,
Morris John C.,
Bateman Randall J.,
Benzinger Tammie L.S.
Publication year - 2019
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1016/j.dadm.2018.12.008
Subject(s) - pittsburgh compound b , amyloid (mycology) , multivariate statistics , nuclear medicine , white matter , neuroimaging , linear regression , medicine , psychology , alzheimer's disease , pathology , mathematics , statistics , magnetic resonance imaging , neuroscience , disease , radiology
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.