Journal article

Sample Size Estimates for Well-Powered Cross-Sectional Cortical Thickness Studies

Heath R Pardoe, David F Abbott, Graeme D Jackson

HUMAN BRAIN MAPPING | WILEY | Published : 2013

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..

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Grants

Awarded by National Institutes of Health


Awarded by Victorian Life Sciences Computation Initiative (VLSCI; Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government)


Awarded by NHMRC program


Awarded by Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health)


Awarded by NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES


Awarded by NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE


Awarded by NATIONAL INSTITUTE ON AGING


Funding Acknowledgements

Contract grant sponsor: National Institutes of Health; contract grant number: NIH-NINDS R37-31146; Contract grant sponsor: Victorian Life Sciences Computation Initiative (VLSCI; Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government); Contract grant number: VR0056; Contract grant sponsor: Victorian Government's Operational Infrastructure Support Program; Contract grant sponsor: Scobie nd McKinnon Trust; Contract grant sponsor: NHMRC program; Contract grant number: 628952; Contract grant sponsor: Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health); Contract grant number: U01 AG024904.