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Hum Brain Mapp. 2012 Nov;33(11):2586-602. doi: 10.1002/hbm.21386. Epub 2011 Aug 9.

Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI.

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  • 1Department of Neurosciences, University of California, San Diego, La Jolla, California 92037, USA. dominic.holland@gmail.com

Abstract

Structural changes in neuroanatomical subregions can be measured using serial magnetic resonance imaging scans, and provide powerful biomarkers for detecting and monitoring Alzheimer's disease. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has made a large database of longitudinal scans available, with one of its primary goals being to explore the utility of structural change measures for assessing treatment effects in clinical trials of putative disease-modifying therapies. Several ADNI-funded research laboratories have calculated such measures from the ADNI database and made their results publicly available. Here, using sample size estimates, we present a comparative analysis of the overall results that come from the application of each laboratory's extensive processing stream to the ADNI database. Obtaining accurate measures of change requires correcting for potential bias due to the measurement methods themselves; and obtaining realistic sample size estimates for treatment response, based on longitudinal imaging measures from natural history studies such as ADNI, requires calibrating measured change in patient cohorts with respect to longitudinal anatomical changes inherent to normal aging. We present results showing that significant longitudinal change is present in healthy control subjects who test negative for amyloid-β pathology. Therefore, sample size estimates as commonly reported from power calculations based on total structural change in patients, rather than change in patients relative to change in healthy controls, are likely to be unrealistically low for treatments targeting amyloid-related pathology. Of all the measures publicly available in ADNI, thinning of the entorhinal cortex quantified with the Quarc methodology provides the most powerful change biomarker.

Copyright © 2011 Wiley Periodicals, Inc.

PMID:
21830259
[PubMed - indexed for MEDLINE]
PMCID:
PMC3782292
Free PMC Article

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