Hi Doug, Thank you very much for your update regarding this issue.
1)Just curious, will LGI be included in this report as this is another analysis of interest?
2)As for the cortical thickness I originally used 15mm in the analysis so based on your email I think using 5-10mm may be more prudent in order to minimize FPR. From your email, I understand that mris_surf2surf (command I use to convert individual subject to fsaverage or template and smooth to 10-15mm) assumes an ACF estimation of smoothness which DOES NOT take into account the long tail distribution. Does this mean that when using mri_mcsim on my own template, the cluster extents for a given smoothness will be undersampled due to the fact that the "true" smoothness is more than what is estimated in the simulation, correct? For instance, when I select 15mm in qdec, it would point to the 21mm folder (fwhm.dat=20.8mm estimate), and I would select a given cluster extent for p=0.05. However, in this case, 15mm may translate to a larger FWHM than the estimated 21mm, correct?
3)You mentioned that I can use mri_glmfit-sim which is permutation testing based. I am struggling a bit in understanding how this differs from the simulation ran with mri_mcsim/qdec? Does qdec monte carlo simulation option run mri_glmfit-sim in the background to estimate the smoothness which looks up the cluster extent within the mri_mcsim based on the estimated FWHM? If so, is this estimate incorrect due to the fact that the long tails are not taken into account?
Thanks, Ajay
On Mon, Aug 1, 2016 at 11:43 PM, Ajay Kurani dr.ajay.kurani@gmail.com wrote:
Hello Freesurfer Experts, Recently there were two article published regarding clusterwise simulations for volumetric fmri analyses and potential errors for underestimating clusterwise extent thresholds.
- http://www.pnas.org/content/113/28/7900.full.pdf?with-ds=yes
- biorxiv.org/content/early/2016/07/26/065862
One issue pointed out from these articles seems software specific, however the second issue is determining the proper clustersize. The heavy-tail nature of spatial smoothness seems to be ignored and a gaussian shape is generally assumed, leading to an underestimation of the spatial smoothness which can affect cluster size calculations. The issues are highlighted in the second article above.
I created my own monte carlo simulation in Freesurfer for a specific brain template and I wanted to find out if these concerns also apply to my surface based simulations? I am not sure if it does since the monte carlo tool is a GRF simulation as opposed to an analytic equation, however given that these articles were highlighted very recently, I wanted to ensure I am running things appropriately for surface based cortical thickness/dti analyses.
Thanks, Ajay