TY - JOUR
T1 - Mapping Alzheimer's disease progression in 1309 MRI scans
T2 - Power estimates for different inter-scan intervals
AU - Hua, Xue
AU - Lee, Suh
AU - Hibar, Derrek P.
AU - Yanovsky, Igor
AU - Leow, Alex D.
AU - Toga, Arthur W.
AU - Jack, Clifford R.
AU - Bernstein, Matt A.
AU - Reiman, Eric M.
AU - Harvey, Danielle J.
AU - Kornak, John
AU - Schuff, Norbert
AU - Alexander, Gene E.
AU - Weiner, Michael W.
AU - Thompson, Paul M.
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Algorithm development and image analysis for this study was funded by grants to P.T. from the NIBIB (R01 EB007813, R01 EB008281, R01 EB008432), NICHD (R01 HD050735), and NIA (R01 AG020098), and National Institutes of Health through the NIH Roadmap for Medical Research, Grants U54-RR021813 (CCB) (to AWT and PT).
PY - 2010/5
Y1 - 2010/5
N2 - Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4±7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6±7.1 years), scanned at baseline, 6, 12, 18, and 24months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24months respectively, to detect a 25% reduction in average change using a two-sided test (α=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
AB - Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4±7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6±7.1 years), scanned at baseline, 6, 12, 18, and 24months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24months respectively, to detect a 25% reduction in average change using a two-sided test (α=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
UR - http://www.scopus.com/inward/record.url?scp=77950530012&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2010.01.104
DO - 10.1016/j.neuroimage.2010.01.104
M3 - Article
C2 - 20139010
AN - SCOPUS:77950530012
SN - 1053-8119
VL - 51
SP - 63
EP - 75
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -