G*Power is an excellent tool to estimate statistical power:
I have also attached an example output for a two-sample T-test. For medium effects you
need about 50 subjects in each group to obtain reasonable power (1-beta = 0.8).
However, this is a power analysis of a single voxel (univariate power) using the well
known formulas. The calculation might be too conservative in the case of detecting an
effect in more than one voxel (mass univariate power). I have found only a few papers
regarding this issue (Friston et al., NI 1996; Zarahn & Slifstein, NI 2001; Desmond &
Glover, J Neurosci M 2001). After reading the papers I am still unsure about the right way
to calculate power for VBM data, which should be equivalent to a second level analysis of
fMRI data. What is the right way to correct the power calculation for mass univariate
I guess this issue might be quite important for many people to estimate the
sample size needed to detect effects with effect size d using alpha level p
for mass univariate data with a given smoothness (or size of resels).
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Jahnstrasse 3, D-07743 Jena, Germany
Tel: ++49-3641-934752 Fax: ++49-3641-934755
e-mail: [log in to unmask]
On Thu, 22 Jan 2009 14:56:56 +0800, dfwang <[log in to unmask]> wrote:
>When doing VBM on two groups of subjects, how to assure the number of participants is
enough to conclusively identify brain abnormalities? How to do power calculation to
determine the required numbers?
>Thanks a lot.