These results look reasonable given what each method is doing. The FDR 
blob is there because it is very bright (significant). It is lost in the 
cluster-wise correction because it is small, and the cluster-wise 
correction does not care how significant something is as long as it 
meets threshold. The RMT blob is there in the cluster-wise correction 
because it is big; it is lost in FDR because it is not significant. As 
you mention, these are very different methods, and it is incorrect to 
say that you use a p=0.05 in both methods. For the simulation, you set 
the voxel-wise threshold to .05. For FDR, you set the false discovery 
rate to .05; this is not a p-value and it is not a voxel-wise threshold.

A p-value is a False Positive Rate (FPR) which is interpreted 
differently than an FDR. For your FDR blob, the interpretation is that 
5% of the voxels that are remaining are false positives (and so 95% are 
true positives); but you don't know which ones. If voxels remain active 
in FDR after correction, then you draw the conclusion that there is 
activation SOMEWHERE IN THE SEARCH SPACE. In this case, you can say that 
there is an effect somewhere in the left hemisphere. Where exactly? In 
your case, it is a little easier since you have a single blob. So you 
have to ask yourself if you remove any 5% of the active voxels whether 
you would change your conclusion. In this case, probably not because you 
only have one blob. If you had another smaller blob that was 5% of the 
total, you probably would not want to declare an effect for the smaller 
one. Note that this does not address the FPR (ie, the probability that 
that blob itself is there by chance in the first place).

For the cluster-wise correction, the interpretation is that a cluster of 
the given size in the RMT would occur by chance only 5% of the time.

It's a good question as to which method, if either or both,  you should 
use to draw your conclusions. I'm not sure, I'd have to think about it 
more. Maybe some of the statisticians would like to weigh in.

doug

Stefan Brauns wrote:
> Hi there,
>
> we are struggling with one question and haven't yet heard back. We 
> would be very grateful for any advice or recommendation.
>
> We were running an analysis (95 subjects) examining a group effect 
> (two groups, controlling for the effects of a covariate and two 
> cofactors, DODS model) on cortical thickness with mri_glmfit. We then 
> corrected our results for multiple comparisons using two methods, FDR 
> and Monte Carlo simulation with 4000 repeats. The threshold was set at 
> p=0.05 respectively. Although examining the same population we got 
> results in very different regions.
>
> command for the simulation:
>
> mri_glmfit-sim --glmdir xxxx.glmdir --sim mc-z 4000 1.301 mc-z 
> abs.1.301 --sim-sign abs
>
> the fdr results were obtained setting the fdr-threshold at p=0.05 with 
> the tksurfer script command (sclv_set current_threshold_using_fdr 0.05 0)
>
> With the fdr method we found a spot in the left supramarginal gyrus 
> and with the clusterwise correction we got a cluster in the right 
> middle temporal lobe (see pictures of corrected and uncorrected 
> results attached). No other clusters/findings survived one of the 
> correction methods.
>
> We are well aware of the fact that FDR and Monte Carlo simulation are 
> very different statistical methods and that FDR is more conservative. 
> Does that explain the discrepant results? Would you expect a highly 
> robust FDR finding to not show up at all when using Monte Carlo? What 
> additional information could be used to decide which method to use for 
> the final models?  
>  
> Thank you,
>
> Stefan
>
>
>
>
>
> ------------------------------------------------------------------------
>
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>
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-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358 
Fax: 617-726-7422

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