Setting q=0.01 in FDR returns the p-value threshold such that 1% of the
voxels below that threshold (i.e. above .999972559 in your case) can be
expected to be false positives. FDR is strictly a voxel-based
threshold, and the clustering or spatial organization of the p-values in
your "p1" volume has no impact on the p-value threshold that is
returned. Thus, FDR and Gaussian Random Field cluster-based correction
(i.e,. FEAT) are two very different things.
cheers,
-MH
within the On Tue, 2011-12-20 at 16:37 +0000, Dar Meshi wrote:
> Hello,
>
> I conducted an fMRI experiment and performed a contrast using FEAT cluster correction for multiple comparisons, setting the z-threshold to 2.3 and the p-value to 0.05. This analysis yielded a nice result, with large clusters (around 400-600 voxels). However, when I did the exact same contrast, correcting for multiple comparisons with FDR and setting q to 0.01, the clusters are much smaller (around 20 voxels), with the peak voxels in the same location. Would you happen to know why this is? I may be making a simple calculation error, but I believe the z-threshold of 2.3 in cluster correction should the same as a q of 0.01 in the FDR analysis, right? And the FDR should be less conservative than the cluster correction in FEAT, right?
>
> A couple notes:
>
> 1. I used outlier de-weighting in my FEAT analysis
> 2. I performed the following 4 command line steps for the FDR analysis:
> ttologp -logpout logp1 varcope1 cope1 `cat dof`
> fslmaths logp1 -exp p1
> fdr -i p1 -m ../mask -q 0.01
> fslmaths p1 -mul -1 -add 1 -thr .999972559 -mas ../mask thresh_1_minus_p1
>
> You can see that the number I get from the "fdr" step is quite small. Any chance you know what's going on?
>
> Thanks so much and happy holidays!
> Dar
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