@article{604901a194f543c68df5fae97a954567,
title = "Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features",
abstract = "We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75. AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard {"}statistical ROI{"} approach applied to the same ventricular surfaces requires 165 MCI or 94. AD subjects. At 2. years, the best LDA measure needs only 67 MCI and 52. AD subjects, versus 119 MCI and 80. AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.",
keywords = "ADNI, Alzheimer's disease, Biomarker, Drug trial, Lateral ventricles, Linear Discriminant Analysis, Machine learning, Mild cognitive impairment, Shape analysis",
author = "Gutman, {Boris A.} and Xue Hua and Priya Rajagopalan and Chou, {Yi Yu} and Yalin Wang and Igor Yanovsky and Toga, {Arthur W.} and Jack, {Clifford R.} 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: BG and PT wrote the manuscript; BG, XH, PR, YC, AT, and PT performed image analyses; BG, YC, YW, IY, and PT developed algorithms used in the analyses. PT and IY made substantial comments on the manuscript; CJ 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 = apr,
day = "15",
doi = "10.1016/j.neuroimage.2012.12.052",
language = "English",
volume = "70",
pages = "386--401",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}