Dear FSL experts,
I use dual regression to investigate connectivity with 10 functional networks. The tfce output of stage 3 gives me the p-map, corrected for multiple comparisons across voxels, but not across components. Therefore, I might use a Bonferroni correction and look at p-values < 0.005 instead of 0.05.
However, I recently read the following article where a different approach is suggested (page 214):
http://www.sciencedirect.com/science/article/pii/S1053811912012414
(Licata et al. (2013). NeuroImage)
It basically comes down to creating one new null distribution of the maxima across components, by using the 10 vectors (1 per component) of maximum tfce values that result from Randomise. A new threshold can then be obtained from this new distribution to examine the 10 raw tfce maps at p< 0.05 (corrected for multiple comparisons across voxels and components), by looking only at tfce values in the top 5% of this distribution.
I was wondering whether this correction method is accepted and look forward to your opinion.
Best,
Bernadet
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