Thank you!
----- Ursprüngliche Mail ----- Von: "Douglas Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 16:46:19 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
In most neuroimaging, people use p<.01 (sig threshold > 2). It is more important to have a more stringent threshold when using gaussian random fields because the assumptions built into it. Since we use simulations directly, we don't have problems with those assumptions and so I think you can go down to p<.05 (sig>1.3). Other than that, there are not good guidelines.
On 4/13/16 8:36 AM, Clara Kühn wrote:
Hi Doug,
thanks for your reply. It made things a lot clearer. I totally understand that you're probably receiving more than one cry for help per day.
What would you say are the conventions for picking a threshold for analyses on structural data?
Cheers, Clara
----- Ursprüngliche Mail ----- Von: "Douglas N Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 00:15:41 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
sorry, I could have sworn that I answered this one
On 04/12/2016 08:57 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
- Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
No, you would use neg when you have an apriori assumption that your effect is going to be negative. Once you look at the results, it is no long apriori
- When do I use abs?
If you do not have an apriori assumption about the sign of the effect
- I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters:
1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
You are confusing the two types of thresholds. The voxel-wise threshold that you are changing defines what is and is not a cluster. As you change it clusters will change size. As you make it more liberal, you make it more likely that you see a cluster of a certain size by chance (ie, the p-value for the cluster gets worse). So as you make the threshold more liberal, there are two competing effects: (1) the cluster gets bigger, and (2) the p-value of a cluster of a given fixed size gets worse. If the cluster size does not increase enough to overcome the second effect, then the cluster p-value will get worse. It is just very complicated.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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