Using CoSMoMVPA multiple-comparison correction

Rationale

A previous exercise showed how to generate a whole-brain map. How do we know which features (voxels) show a significant effect?

A few approaches for whole-brain multiple-comparison methods have been proposed in the literature, but are not used in CoSMoMVPA:

  • Bonferroni correction; typically too conservative.
  • FDR: can lead to invalid inferences.
  • Parametric cluster-baed methods (SPM, AFNI, FSL): can be too liberal.
  • Fixed-treshold cluster-based approach: requires choosing an uncorrected (feature-wise) threshold.

In CoSMoMVPA we supply an implementation for Threshold-Free Cluster Enhancement ([SN09]). Optionally this can also be used with first-level null-data ([SCT13]), although that is not done in this exercise.

Exercises

Note: Since data in the present dataset is not in MNI space, we cannot do group analysis. This exercise therefore considers data from one subject using data in ten chunks corresponding to ten runs. However, the appraoch can also be used (in exactly the same way) for group analysis, where each chunk corresponds to one subject.

In this exercise, load data from ten runs from the ak6 datasets. Compute, for each chunk, the effect of stimulus presentation versus baseline. Then, using cosmo cluster neighborhood and cosmo montecarlo cluster stat, compute a TFCE zscore map corrected for multiple comparisons.

Extra exercise: do the same analysis for primates versus insects.

Template: run multiple comparison correction skl

Check your answers here: run multiple comparison correction / Matlab output: run_multiple_comparison_correction