Sample Size Estimation for Comparing Parameters using Dynamic Causal Modeling

Functional Magnetic Resonance Imaging (fMRI) has proven to be useful for analysing the effects of illness and pharmacological agents on brain activation. Many fMRI studies now incorporate effective connectivity analyses on data to assess the networks recruited during task performance. If these techniques are to be applied confidently it is useful to assess the sample size necessary for carrying out such calculations. Here we present a method of estimating the sample size required for a study to have sufficient power. Our approach uses Bayesian Model Selection to find a best fitting model and then using a bootstrapping technique to provide an estimate of the parameter variance. As illustrative examples we apply this technique to two different tasks and show that for our data approximately 20 volunteers per group is sufficient. Because of variability between task, volunteers, scanner and acquisition parameters this would need to be evaluated on individual datasets. This approach will be a useful guide for Dynamic Causal Modelling studies.