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

RESEARCH ARTICLE Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group Yi Su1*, Tyler M. Blazey1, Christopher J. Owen1, Jon J. Christensen1, Karl Friedrichsen1, Nelly Joseph-Mathurin1, Qing Wang1, Russ C. Hornbeck1, Beau M. Ances2, Abraham Z. Snyder1,2, Lisa A. Cash1, Robert A. Koeppe3, William E. Klunk4, Douglas Galasko5, Adam M. Brickman6, Eric McDade2, John M. Ringman7, Paul M. Thompson8, Andrew J. Saykin9, Bernardino Ghetti9, Reisa A. Sperling10, Keith A. Johnson10, Stephen P. Salloway11, Peter R. Schofield12, Colin L. Masters13, Victor L. Villemagne13, Nick C. Fox14, Stefan Förster15, Kewei Chen16, Eric M. Reiman16, Chengjie Xiong17, Daniel S. Marcus1, Michael W. Weiner18, John C. Morris2, Randall J. Bateman2, Tammie L. S. Benzinger1, Dominantly Inherited Alzheimer Network¶ OPEN ACCESS Citation: Su Y, Blazey TM, Owen CJ, Christensen JJ, Friedrichsen K, Joseph-Mathurin N, et al. (2016) Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group. PLoS ONE 11(3): e0152082. doi:10.1371/ journal.pone.0152082 Editor: Karl Herholz, University of Manchester, UNITED KINGDOM Received: January 4, 2016 Accepted: March 8, 2016 Published: March 24, 2016 Copyright: © 2016 Su et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: In accordance with policies put in place by the DIAN Steering Committee, patient and imaging data are available upon request. The full policy regarding data access as well as instructions for submitting requests for data access are available here: http://www.dian-info.org/ resourcedb/default.htm. Funding: YS received funds from the Knight Alzheimer's Disease Research Center (http:// knightadrc.wustl.edu/) pilot award, Washington University Institute of Clinical and Translational Sciences (http://icts.wustl.edu/) Pilot Grant supported 1 Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America, 2 Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America, 3 Department of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America, 4 University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America, 5 University of California San Diego, La Jolla, California, United States of America, 6 Columbia University, New York, New York, United States of America, 7 Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 8 Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 9 Department of Radiology, Indiana University, Indianapolis, Indiana, United States of America, 10 Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 11 Butler Hospital and Brown University, Providence, Rhode Island, United States of America, 12 Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia, 13 The Florey Institute and the University of Melbourne, Parkville, Victoria, Australia, 14 Dememtia Research Centre, Institute of Neurology, London, Great Britain, 15 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) München/Tübingen and Dept. of Nuclear Medicine, Technische Universität München, München, Germany, 16 Banner Alzheimer’s Institute, Banner Health, 901 E. Willetta Street, Phoenix, Arizona, United States of America, 17 Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, United States of America, 18 Department of Radiology, University of California San Francisco, San Francisco, California, United States of America ¶ Complete information about the Dominantly Inherited Alzheimer Network can be found in the Acknowledgments. * Abstract 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 [11C]-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 PLOS ONE | DOI:10.1371/journal.pone.0152082 March 24, 2016 1 / 14 Quantitative Amyloid PET Imaging by the Clinical and Translational Science Award (CTSA) program (https://www.ctsacentral.org/) of National Institute of Health (http://www.nih.gov/): UL1TR000448. AZS and DSM received funds from National Institute of Neurological Disorders and Stroke (http://www.ninds.nih.gov/): P30NS048056; JCM and TLSB received funds from National Institute of Ageing (http://www.nia.nih.gov/): P01AG026276, U19AG032438, P50AG005681, P01AG003991. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. 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. Introduction Alzheimer’s disease (AD) is the most common form of dementia [1] with its prevalence expected to dramatically increase in the next 50 y (...truncated)