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Running Dual Regression

Explanation of outputs

Multiple-comparison correction across all RSNs

The need for correction, and correction via Bonferroni

Warning: The corrected p-values output by the final randomise (*corrp*) are fully corrected for multiple comparisons across voxels, but only for each RSN in its own right, and only doing one-tailed testing (for t-contrasts specified in design.con). This means that if you test (with randomise) all components found by the initial group-ICA, and you do not have a prior reason for only considering one of them, you should correct your corrected p-values by a further factor. For example, let's say that your group-ICA found 30 components, and you decided to ignore 18 of them as being artefact. You therefore only considered 12 RSNs as being of potential interest, and looked at the outputs of randomise for these 12, with your model being a two-group test (controls and patients). However, you didn't know whether you were looking for increases or decreases in RSN connectivity, and so you ran the two-group contrast both ways for each RSN. In this case, instead of your corrected p-values needing to be <0.05 for full significance, they really need to be < 0.05 / (12 * 2) = 0.002 !

2012-09-05 11:30