Things to consider¶
Why conduct a power analysis?¶
- To determine your sample size
- To determine whether it is even possible to find an effect with the given means (technical settings, available budget for subjects)
- To optimize your pre-processing software given your scanner settings
How to define an effect size?¶
- Test statistic
- Cluster extent
- Relate one contrast of interest A to the strength of another contrast B → see Matan Mazor and David Mehler
- Length of recording → number of trials
Tools for univariate power analysis¶
- GPower, Pangea
- Suited if clearly defined ROIs/ localizers are available
- How to clearly define ROIs? How to define ROIs on a subject-specific level?
- G*Power is suitable for very particular questions/ analyses, but require many decisions a-priori → defining ROIs, defining summary statistics on ROIs
- Can even increase power (e.g. for very small volumes) because you avoid correction for multiple comparisons
Tools for 3-dimentional power analysis¶
- Simulation: take available data set (resting-state) with a realistic noise profile, add an effect of a certain extent and strength, see if your (preprocessing + analysis) finds it
- What are the parameters to vary?
- Sample size
- Effect strength/ extend
- Noise profile
- Using nuisance regressors or not