On 8/3/16 3:45 AM, Ajay Kurani wrote:
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?
I was not planning to. The 809 subjects that I used for thickness do not
have lGI run on them, but I think it is possible to run it on a subset
without too much trouble. Not sure when I'll get to it.
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?
Sort of. It is the Gaussian assumption that is incorrect, so there is no
one FWHM that is correct (it is not a question of it simply being too
small).
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?
Permutation uses your data and permutes (ie, randomly swaps) the class
label associated with a subject, the data with this new labeling are
analyzed and clusters computed. Under the null, the label is irrelevant,
so clusters are interpreted as false positives. After several thousand
of these swaps, one builds a list of the probability of seeing a cluster
of a certain size under the null, and this is used to generate the
p-value. Permutation will then naturally take into account all the
non-Gaussian aspects of the data. The monte carlo (MC) simulation is
similar, but it uses smoothed synthesized gaussian noise instead of the
real data and so the gaussian assumption is built into it.
Thanks,
Ajay
On Mon, Aug 1, 2016 at 11:43 PM, Ajay Kurani <dr.ajay.kur...@gmail.com
<mailto:dr.ajay.kur...@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.
1) http://www.pnas.org/content/113/28/7900.full.pdf?with-ds=yes
2) biorxiv.org/content/early/2016/07/26/065862
<http://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
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