Sample size estimates for well-powered cross-sectional cortical thickness studies

Authors

  • Heath R. Pardoe,

    1. Brain Research Institute, Florey Neuroscience Institutes, Melbourne Brain Centre, Austin Hospital, Heidelberg, Victoria, Australia
    2. Department of Medicine, The University of Melbourne, Victoria, Australia
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  • David F. Abbott,

    1. Brain Research Institute, Florey Neuroscience Institutes, Melbourne Brain Centre, Austin Hospital, Heidelberg, Victoria, Australia
    2. Department of Medicine, The University of Melbourne, Victoria, Australia
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  • Graeme D. Jackson,

    Corresponding author
    1. Brain Research Institute, Florey Neuroscience Institutes, Melbourne Brain Centre, Austin Hospital, Heidelberg, Victoria, Australia
    2. Department of Medicine, The University of Melbourne, Victoria, Australia
    3. Department of Radiology, The University of Melbourne, Victoria, Australia
    • Brain Research Institute, Florey Neuroscience Institutes, Melbourne Brain Centre, Austin Hospital, 245 Burgundy St, Heidelberg, Victoria 3084, Australia. E-mail: BRI@brain.org.au

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  • The Alzheimer's Disease Neuroimaging Initiative

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Abstract

Introduction: Cortical thickness mapping is a widely used method for the analysis of neuroanatomical differences between subject groups. We applied power analysis methods over a range of image processing parameters to derive a model that allows researchers to calculate the number of subjects required to ensure a well-powered cross-sectional cortical thickness study. Methods: 0.9-mm isotropic T1-weighted 3D MPRAGE MRI scans from 98 controls (53 females, age 29.1 ± 9.7 years) were processed using Freesurfer 5.0. Power analyses were carried out using vertex-wise variance estimates from the coregistered cortical thickness maps, systematically varying processing parameters. A genetic programming approach was used to derive a model describing the relationship between sample size and processing parameters. The model was validated on four Alzheimer's Disease Neuroimaging Initiative control datasets (mean 126.5 subjects/site, age 76.6 ± 5.0 years). Results: Approximately 50 subjects per group are required to detect a 0.25-mm thickness difference; less than 10 subjects per group are required for differences of 1 mm (two-sided test, 10 mm smoothing, α = 0.05). Sample size estimates were heterogeneous over the cortical surface. The model yielded sample size predictions within 2–6% of that determined experimentally using independent data from four other datasets. Fitting parameters of the model to data from each site reduced the estimation error to less than 2%. Conclusions: The derived model provides a simple tool for researchers to calculate how many subjects should be included in a well-powered cortical thickness analysis. Hum Brain Mapp 34:3000–3009, 2013. © 2012 Wiley Periodicals, Inc.

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