@article{e5d51aacf24248f6895635c587a5e26a,
title = "Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials",
abstract = "Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24. months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39. AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.",
keywords = "ADNI, Aging, Alzheimer's disease, Drug trial, Mild cognitive impairment, Tensor-based morphometry",
author = "Xue Hua and Hibar, {Derrek P.} and Ching, {Christopher R.K.} and Boyle, {Christina P.} and Priya Rajagopalan and Gutman, {Boris A.} and Leow, {Alex D.} and Toga, {Arthur W.} and Jack, {Clifford R.} and Danielle Harvey and Weiner, {Michael W.} and Thompson, {Paul M.}",
note = "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; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions 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 and K01 AG030514 . Algorithm development for this study was also funded by the NIA , NIBIB , the National Library of Medicine , and the National Center for Research Resources ( AG016570 , EB01651 , LM05639 , RR019771 to PT). Author contributions were as follows: XH, DH, CC, CB, PR, BG, AT, and PT performed the image analyses; AL, BG, and PT developed algorithms used in the analyses; CJ, DH, and MW contributed substantially to the image and data acquisition, study design, quality control, calibration and pre-processing, databasing and image analysis.",
year = "2013",
month = feb,
day = "1",
doi = "10.1016/j.neuroimage.2012.10.086",
language = "English (US)",
volume = "66",
pages = "648--661",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}