Kewei Chen1, Auttawut Roontiva2, Pradeep Thiyyagura2, Wendy Lee2, Xiaofen Liu2, Napatkamon Ayutyanont2, Hillary Protas2, Ji Luo Luo2, Robert Bauer2, Cole Reschke2, Daniel Bandy2, Robert A Koeppe3, Adam S Fleisher4, Richard J Caselli5, Susan Landau6, William J Jagust6, Michael W Weiner7, Eric M Reiman8. 1. Banner Alzheimer's Institute, Phoenix, Arizona Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona Department of Neurology, College of Medicine, University of Arizona, Phoenix, Arizona Arizona Alzheimer's Consortium, Phoenix, Arizona kewei.chen@bannerhealth.com. 2. Banner Alzheimer's Institute, Phoenix, Arizona Arizona Alzheimer's Consortium, Phoenix, Arizona. 3. Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan. 4. Arizona Alzheimer's Consortium, Phoenix, Arizona Eli Lilly and Company, Indianapolis, Indiana Department of Neuroscience, University of California-San Diego, San Diego, California. 5. Arizona Alzheimer's Consortium, Phoenix, Arizona Mayo Clinic, Scottsdale, Arizona. 6. School of Public Health and Helen Wills Neuroscience Institute, University of California-Berkeley, Berkeley, California. 7. Department of Radiology, University of California-San Francisco, San Francisco, California Department of Medicine, University of California-San Francisco, San Francisco, California Department of Psychiatry, University of California-San Francisco, San Francisco, California. 8. Banner Alzheimer's Institute, Phoenix, Arizona Arizona Alzheimer's Consortium, Phoenix, Arizona Division of Neurogenomics, Translational Genomics Research Institute, Phoenix, Arizona; and Department of Psychiatry, University of Arizona, Tucson, Arizona.
Abstract
UNLABELLED: In this article, we describe an image analysis strategy with improved power for tracking longitudinal amyloid-β (Aβ) PET changes and evaluating Aβ-modifying treatments. METHODS: Our aims were to compare the power of template-based cerebellar, pontine, and cerebral white matter reference regions to track 24-mo florbetapir standardized uptake value (SUV) ratio (SUVR) changes; to relate those changes to 24-mo clinical declines; and to evaluate Aβ-modifying treatments in Aβ-positive (Aβ+) and Aβ-negative (Aβ-) patients with probable Alzheimer dementia (pAD), in patients with mild cognitive impairment (MCI), in cognitively normal controls (NCs), and in cognitively normal apolipoprotein E4 (APOE4) carriers and noncarriers. We used baseline and follow-up (∼24 mo) florbetapir PET scans from 332 Aβ+ and Aβ- subjects participating in the multicenter Alzheimer's Disease Neuroimaging Initiative. Each of the proposed analyses included 31 pAD patients, 187 MCI patients, and 114 NCs. Cerebral-to-white matter, cerebellar, and pontine SUVRs were characterized in terms of their longitudinal variability; their power to track longitudinal fibrillar Aβ increases in Aβ+ and Aβ- subgroups and cognitively normal APOE4 carriers and noncarriers; the sample sizes needed to detect attenuated accumulation of or clearance of fibrillar Aβ accumulation in randomized clinical trials; and their ability to relate 24-mo fibrillar Aβ increases to clinical declines. RESULTS: As predicted, cerebral-to-white matter SUVR changes were significantly less variable and had significantly greater power to detect 24-mo fibrillar Aβ increases and evaluate Aβ-modifying treatment effects in Aβ+ pAD, MCI, and NC subjects and cognitively normal APOE4 carriers. They were also distinguished by the ability to detect significant associations between 24-mo Aβ increases and clinical declines. CONCLUSION: A cerebral white matter reference region may improve the power to track longitudinal fibrillar Aβ increases, to characterize their relationship to longitudinal clinical declines, and to evaluate Aβ-modifying treatments in randomized clinical trials.
UNLABELLED: In this article, we describe an image analysis strategy with improved power for tracking longitudinal amyloid-β (Aβ) PET changes and evaluating Aβ-modifying treatments. METHODS: Our aims were to compare the power of template-based cerebellar, pontine, and cerebral white matter reference regions to track 24-mo florbetapir standardized uptake value (SUV) ratio (SUVR) changes; to relate those changes to 24-mo clinical declines; and to evaluate Aβ-modifying treatments in Aβ-positive (Aβ+) and Aβ-negative (Aβ-) patients with probable Alzheimer dementia (pAD), in patients with mild cognitive impairment (MCI), in cognitively normal controls (NCs), and in cognitively normal apolipoprotein E4 (APOE4) carriers and noncarriers. We used baseline and follow-up (∼24 mo) florbetapir PET scans from 332 Aβ+ and Aβ- subjects participating in the multicenter Alzheimer's Disease Neuroimaging Initiative. Each of the proposed analyses included 31 pAD patients, 187 MCI patients, and 114 NCs. Cerebral-to-white matter, cerebellar, and pontine SUVRs were characterized in terms of their longitudinal variability; their power to track longitudinal fibrillar Aβ increases in Aβ+ and Aβ- subgroups and cognitively normal APOE4 carriers and noncarriers; the sample sizes needed to detect attenuated accumulation of or clearance of fibrillar Aβ accumulation in randomized clinical trials; and their ability to relate 24-mo fibrillar Aβ increases to clinical declines. RESULTS: As predicted, cerebral-to-white matter SUVR changes were significantly less variable and had significantly greater power to detect 24-mo fibrillar Aβ increases and evaluate Aβ-modifying treatment effects in Aβ+ pAD, MCI, and NC subjects and cognitively normal APOE4 carriers. They were also distinguished by the ability to detect significant associations between 24-mo Aβ increases and clinical declines. CONCLUSION: A cerebral white matter reference region may improve the power to track longitudinal fibrillar Aβ increases, to characterize their relationship to longitudinal clinical declines, and to evaluate Aβ-modifying treatments in randomized clinical trials.
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