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Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation

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Mumford, Jeanette A. and Nichols, Thomas E. (2008) Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation. NeuroImage, Vol.39 (No.1). pp. 261-268. doi:10.1016/j.neuroimage.2007.07.061 ISSN 1053-8119.

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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2007.07.061

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Abstract

When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80% power is not as simple as finding the smallest sample size capable of achieving 80% power, since smaller sample sizes require each subject to be scanned longer.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Statistical power analysis, Magnetic resonance imaging, Neurosciences -- Research -- Methodology, Experimental design, Autocorrelation (Statistics)
Journal or Publication Title: NeuroImage
Publisher: Elsevier
ISSN: 1053-8119
Official Date: 1 January 2008
Dates:
DateEvent
1 January 2008Published
Volume: Vol.39
Number: No.1
Number of Pages: 8
Page Range: pp. 261-268
DOI: 10.1016/j.neuroimage.2007.07.061
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)

Data sourced from Thomson Reuters' Web of Knowledge

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