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

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.