Hi, everyone!
I am doing a whole-brain seed-based functional connectivity analysis using pre-defined ROI. My aim is to find connectivity which is correlated with a individual difference data.
All of the proprocess and statistic steps were performd by Dpabi.
My results can survivive in GRF-correction (voxel p<0.025, cluster p<0.1, two-tailed, I got this from video course). However, previous papers usually use this method for group difference dectection (Z>2.3, cluster p<0.05).
Therefore, I have two questions here:
(1) Does it right to use GRF in correlation analysis?
(2) Does their use one-tailed way in setting (Z>2.3, cluster p<0.05). So voxel p<0.025,cluster p<0.1(two tailed)=Z>2.3, cluster p<0.05(one tailed)?
(3) The GRF correction (voxel p<0.025,cluster p<0.1,two tailed) in my VBM result need 640 voxels, however, in my GCA result, it needs only 74 voxels. Some thing is wrong here?
1. Yes.
1. Yes.
2. Yes.
So voxel p<0.0214, cluster p<0.1 (two tailed) = TWICE: Z>2.3, cluster p<0.05 (one tailed).
3. Because your VBM results are on voxels size of 1*1*1? And the smoothness is even big?
Best,
Chao-Gan
Thanks for reply!
Thanks for reply!
My VBM is also analysised by default setting in Dpabi(smoothness=8) and used whole brain gray matter group mask created by averging all the subjects gray matter volume when the software asked for a mask.
My GCA resutls is generated by GCA function in REST (3*3*3, I think) and used 90% mask created by dpabi.
Then I tried several resting state analysis in GRF, the standard never above 200. I want to know whether the number( 642 voxel )in VBM has some problems?
Best Wish
Wei Liu
1. 642 * 1*1*1 = 642 mm3.
1. 642 * 1*1*1 = 642 mm3.
74*3*3*3 = 1998 mm3.
Which cluster threshold is bigger?
2. VBM is smoothed by 8mm, your functional images are smoothed by 4mm? This makes a difference as well.
Best,
Chao-Gan