Dear FSL experts,
I am using randomise to extract areas of FA differences between two groups (~ 60 subjects per group), controlling for the usual covariates (age, gender, education, handedness). Using TFCE, I find a small cluster of significance at p < 0.05 (139 voxels). A few questions:
1) How do I get the minimum FWE-corrected cluster size, or is this not a relevant number for TFCE?
2) I would also like to report clusters seen at a lower threshold. The (yes, clearly subjective) reason for doing this is a clear bilateral pattern in the data which I can see when looking at the unthresheld data, e.g. if I look at p < 0.3, there are clearly two areas of significance, one centered around the original (p < 0.05) cluster and the other one on the contralateral side. The anatomical symmetry of the signal gives me some confidence that these are real, but I don't have enough statistical power to detect the contralateral signal at p < 0.05. So, do you have a recommendation as to how to report these results? I could simply present the unthresheld maps which show this symmetry, but I'm wondering if there is an objective way to quantify the bilateral nature of the results. Can I report clusters at a lower statistical threshold, and if so, what would be a reasonable threshold to use?
3) If I can do what I'm proposing above, namely report clusters at some lower threshold, should all clusters be reported? When I do this, e.g. for p < 0.3, I get some very small clusters (< 10 voxels). Should I be reporting even these tiny clusters, or is there a way to objectively set a cluster size threshold and not report those below this threshold?
4) A related question - I would like to look at the significance (or non-significance) of differences in other DTI parameters within these clusters (e.g. RD, as well as NODDI-based parameters). So, do you think it is valid to choose a lower statistical threshold where I can detect bilateral clusters from which to extract these other parameter values across my subject groups?
Thanks for any advice you can provide,
Mark Wagshul