you can't really do permutation at the first level because the temporal autocorrelation and non-orthogonal design matrix violate the permutation assumptions.

On 5/15/17 11:23 PM, Kevin Aquino wrote:

Actually,

would would be preferable would be running a permutations test something that you describe in:

http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg49846.html

is there an easy to run mri_glmfit-sim's permutation simulations on a single subject? I've run through the FS-FAST pipeline

mkanalysis-sess -fsd bold -surface self rh -fwhm 4.5 -event-related -paradigm attn.par -nconditions 4 -spmhrf 0 -TR 1.9 -analysis myanalysis -per-session -refeventdur 450 -force -nuisreg moco.txt -1 -polyfit 0 mkcontrast-sess -analysis myanalysis -contrast attn -c 1 -a 2 -a 3 -c 4 selxavg3-sess -s SESSION -analysis self_attn_sm_reg.rh -no-preproc -fwhm

But mri_glmfit-sim is expecting the output from mri_glmfit

mri_glmfit-sim --glmdir . --perm 1000 3 pos --log ./selxavg3.log

...

...

ERROR: cannot find ./mri_glmfit.log

should I just run mri_glmfit with the appropriate flags, or is there a mri_glmfit-sim version for data that has passed through selxavg3 ?

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

*E* Kevin.aquino@nottingham.ac.uk mailto:Kevin.aquino@nottingham.ac.uk, aquino@physics.usyd.edu.au mailto:aquino@physics.usyd.edu.au | *W* *MailScanner has detected a possible fraud attempt from "www.physics.usyd.edu.au" claiming to be* https://kevinaquino.github.io http://www.physics.usyd.edu.au/%7Eaquino/

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On Tue, May 16, 2017 at 12:34 PM, Kevin Aquino <aquino@physics.usyd.edu.au mailto:aquino@physics.usyd.edu.au> wrote:

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).

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). 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).

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

*E* Kevin.aquino@nottingham.ac.uk
<mailto:Kevin.aquino@nottingham.ac.uk>, aquino@physics.usyd.edu.au
<mailto:aquino@physics.usyd.edu.au> | *W* *MailScanner has
detected a possible fraud attempt from "www.physics.usyd.edu.au"
claiming to be* https://kevinaquino.github.io
<http://www.physics.usyd.edu.au/%7Eaquino/>

----------------------------------------------

The brain is a wonderful organ. It starts working the moment you
get up and does not stop until you get into the office.
-
Robert Frost

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Hi Doug,

Thanks for that! with regards to your last point

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.

From what I read, the MC simulations on the surface uses this Gaussian

assumption. Do you have any ideas (or something in the works) on how to handle these issues on a single subject level? - since the permutation test won't be applicable.

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

*E* Kevin.aquino@nottingham.ac.uk, aquino@physics.usyd.edu.au | *W* https://kevinaquino.github.io http://www.physics.usyd.edu.au/~aquino/

----------------------------------------------

The brain is a wonderful organ. It starts working the moment you get up and does not stop until you get into the office. - Robert Frost

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On Wed, May 17, 2017 at 1:18 AM, Douglas Greve greve@nmr.mgh.harvard.edu wrote:

On 5/15/17 10:34 PM, Kevin Aquino wrote:

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).

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.

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).

You can check out "Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data" by Don Hagler

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).

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. 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

*E* Kevin.aquino@nottingham.ac.uk, aquino@physics.usyd.edu.au | *W* *MailScanner has detected a possible fraud attempt from "www.physics.usyd.edu.au" claiming to be* https://kevinaquino.github.io http://www.physics.usyd.edu.au/%7Eaquino/

---

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