Unfortunately, it does not help much. Much was made of the bug that Eklund, et al, found in the alphasim program (the AFNI mc simulator), but even after fixing it, the volume-based analyses still had very high false positive rates. This is because the problem is that the smoothness in the data is not Gaussian, so any method that assumes Gaussianity will be inaccurate.
On 5/15/17 10:34 PM, Kevin Aquino wrote:
For the tables hat we distribute I ran it to 10,000, but there will probably not be much difference with 1000. If you look in the cluster summary file, it will actually give the 95% confidence intervals on the cluster p-values. If the worst is ok, then you don't really need to run it more.Hi all,
First of all, Freesurfer V6 is working like a dream, my 0.7 mm segmentations are running really well and in comparisons to hi-resrecon in 5.3 and early beta, I'm having to do fewer manual corrections!
Now for my questions,
1. I'm running mri_mcsim in order to correct for multiple comparisons via the FS-FAST stream. I'm wondering how many iterations are advised, and how can one check for convergence in an automatic fashion.
I've run the simulations with 1000 and 10,000 iterations on a 1mm segmentation with the FWHM simulations at 8mm. (i.e. using mri_mcsim --o . --base mc-z --save-iter --surf subject lh/rh --nreps 10000 --fwhm 8) and I can't see many differences between the two when correcting for multiple comparisons (i.e. using cluster-sess -analysis myanalysis -thresh 3 -cwp .05 -s SESSION -sign pos).
You can check out "Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data" by Don Hagler
2. I'm trying to find some references that detail the simulations and form the corrections, does anyone have advice which list I can read/start off with, as well as some key papers that use it (esp on a single subject level).
Unfortunately, it does not help much. Much was made of the bug that Eklund, et al, found in the alphasim program (the AFNI mc simulator), but even after fixing it, the volume-based analyses still had very high false positive rates. This is because the problem is that the smoothness in the data is not Gaussian, so any method that assumes Gaussianity will be inaccurate.I really like this approach and It does look to circumvent (I think...) a lot of the problems of cluster-wise corrections described with Eklund et al. (Cluster failure paper).
doug
Cheers,
Dr Kevin Aquino
Research fellow,Sir Peter Mansfield Magnetic Resonance Center, The University of Nottingham.
Honorary Research Fellow
School of Physics, Faculty of Science, University of Sydney
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