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Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amy...

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Autores principales: Su, Yi, Blazey, Tyler M., Owen, Christopher J., Christensen, Jon J., Friedrichsen, Karl, Joseph-Mathurin, Nelly, Wang, Qing, Hornbeck, Russ C., Ances, Beau M., Snyder, Abraham Z., Cash, Lisa A., Koeppe, Robert A., Klunk, William E., Galasko, Douglas, Brickman, Adam M., McDade, Eric, Ringman, John M., Thompson, Paul M., Saykin, Andrew J., Ghetti, Bernardino, Sperling, Reisa A., Johnson, Keith A., Salloway, Stephen P., Schofield, Peter R., Masters, Colin L., Villemagne, Victor L., Fox, Nick C., Förster, Stefan, Chen, Kewei, Reiman, Eric M., Xiong, Chengjie, Marcus, Daniel S., Weiner, Michael W., Morris, John C., Bateman, Randall J., Benzinger, Tammie L. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807073/
https://www.ncbi.nlm.nih.gov/pubmed/27010959
http://dx.doi.org/10.1371/journal.pone.0152082
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author Su, Yi
Blazey, Tyler M.
Owen, Christopher J.
Christensen, Jon J.
Friedrichsen, Karl
Joseph-Mathurin, Nelly
Wang, Qing
Hornbeck, Russ C.
Ances, Beau M.
Snyder, Abraham Z.
Cash, Lisa A.
Koeppe, Robert A.
Klunk, William E.
Galasko, Douglas
Brickman, Adam M.
McDade, Eric
Ringman, John M.
Thompson, Paul M.
Saykin, Andrew J.
Ghetti, Bernardino
Sperling, Reisa A.
Johnson, Keith A.
Salloway, Stephen P.
Schofield, Peter R.
Masters, Colin L.
Villemagne, Victor L.
Fox, Nick C.
Förster, Stefan
Chen, Kewei
Reiman, Eric M.
Xiong, Chengjie
Marcus, Daniel S.
Weiner, Michael W.
Morris, John C.
Bateman, Randall J.
Benzinger, Tammie L. S.
author_facet Su, Yi
Blazey, Tyler M.
Owen, Christopher J.
Christensen, Jon J.
Friedrichsen, Karl
Joseph-Mathurin, Nelly
Wang, Qing
Hornbeck, Russ C.
Ances, Beau M.
Snyder, Abraham Z.
Cash, Lisa A.
Koeppe, Robert A.
Klunk, William E.
Galasko, Douglas
Brickman, Adam M.
McDade, Eric
Ringman, John M.
Thompson, Paul M.
Saykin, Andrew J.
Ghetti, Bernardino
Sperling, Reisa A.
Johnson, Keith A.
Salloway, Stephen P.
Schofield, Peter R.
Masters, Colin L.
Villemagne, Victor L.
Fox, Nick C.
Förster, Stefan
Chen, Kewei
Reiman, Eric M.
Xiong, Chengjie
Marcus, Daniel S.
Weiner, Michael W.
Morris, John C.
Bateman, Randall J.
Benzinger, Tammie L. S.
author_sort Su, Yi
collection PubMed
description Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer’s Network (DIAN), an autosomal dominant Alzheimer’s disease population. Cross-sectional and longitudinal [(11)C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer’s disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.
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spelling pubmed-48070732016-03-25 Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group Su, Yi Blazey, Tyler M. Owen, Christopher J. Christensen, Jon J. Friedrichsen, Karl Joseph-Mathurin, Nelly Wang, Qing Hornbeck, Russ C. Ances, Beau M. Snyder, Abraham Z. Cash, Lisa A. Koeppe, Robert A. Klunk, William E. Galasko, Douglas Brickman, Adam M. McDade, Eric Ringman, John M. Thompson, Paul M. Saykin, Andrew J. Ghetti, Bernardino Sperling, Reisa A. Johnson, Keith A. Salloway, Stephen P. Schofield, Peter R. Masters, Colin L. Villemagne, Victor L. Fox, Nick C. Förster, Stefan Chen, Kewei Reiman, Eric M. Xiong, Chengjie Marcus, Daniel S. Weiner, Michael W. Morris, John C. Bateman, Randall J. Benzinger, Tammie L. S. PLoS One Research Article Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer’s Network (DIAN), an autosomal dominant Alzheimer’s disease population. Cross-sectional and longitudinal [(11)C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer’s disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted. Public Library of Science 2016-03-24 /pmc/articles/PMC4807073/ /pubmed/27010959 http://dx.doi.org/10.1371/journal.pone.0152082 Text en © 2016 Su et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Su, Yi
Blazey, Tyler M.
Owen, Christopher J.
Christensen, Jon J.
Friedrichsen, Karl
Joseph-Mathurin, Nelly
Wang, Qing
Hornbeck, Russ C.
Ances, Beau M.
Snyder, Abraham Z.
Cash, Lisa A.
Koeppe, Robert A.
Klunk, William E.
Galasko, Douglas
Brickman, Adam M.
McDade, Eric
Ringman, John M.
Thompson, Paul M.
Saykin, Andrew J.
Ghetti, Bernardino
Sperling, Reisa A.
Johnson, Keith A.
Salloway, Stephen P.
Schofield, Peter R.
Masters, Colin L.
Villemagne, Victor L.
Fox, Nick C.
Förster, Stefan
Chen, Kewei
Reiman, Eric M.
Xiong, Chengjie
Marcus, Daniel S.
Weiner, Michael W.
Morris, John C.
Bateman, Randall J.
Benzinger, Tammie L. S.
Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title_full Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title_fullStr Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title_full_unstemmed Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title_short Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group
title_sort quantitative amyloid imaging in autosomal dominant alzheimer’s disease: results from the dian study group
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807073/
https://www.ncbi.nlm.nih.gov/pubmed/27010959
http://dx.doi.org/10.1371/journal.pone.0152082
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