Dear All,
I'm just checking to see if my understanding is correct:
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!
Does this mean that:
if after the initial group ICA stage, I got 25 components, and I'm only interested in 4 components, but I did not remove the other 21 components, and instead test all components in the dual reg step....
And when I look at the results of the 4 components, the corrected p-value should be further corrected?
Thanks,
Catherine