Thanks for your lights Anderson.
Best regards,
Matthieu
2016-05-31 9:54 GMT+02:00 Anderson M. Winkler <[log in to unmask]>:
> Hi Matthieu,
>
> Please, see below:
>
>
> On 30 May 2016 at 10:29, Matthieu Vanhoutte <[log in to unmask]>
> wrote:
>
>> Hi Anderson,
>>
>> Please see below :
>>
>> 2016-05-28 10:16 GMT+02:00 Anderson M. Winkler <[log in to unmask]>:
>>
>>> Hi Matthieu,
>>>
>>> Please, see below:
>>>
>>>
>>> On 27 May 2016 at 10:30, Matthieu Vanhoutte <[log in to unmask]
>>> > wrote:
>>>
>>>> Hi Anderson,
>>>>
>>>> Thanks for you clear answers !
>>>>
>>>> Please see below :
>>>>
>>>>
>>>> 2016-05-27 11:03 GMT+02:00 Anderson M. Winkler <[log in to unmask]>
>>>> :
>>>>
>>>>> Hi Matthieu,
>>>>>
>>>>> Please, see below:
>>>>>
>>>>>
>>>>> On 27 May 2016 at 09:58, Matthieu Vanhoutte <
>>>>> [log in to unmask]> wrote:
>>>>>
>>>>>> Dear FSL experts,
>>>>>>
>>>>>> I am planning to use PALM for statistical analysis at the surface level. Could you please explain me your choice of "palm" different parameters (below) and anwser to me about some questions ? :
>>>>>>
>>>>>> For completeness, here is my PALM setup: I used palm version palm-alpha95version
>>>>>>
>>>>>> Here are my commandline parameters:
>>>>>>
>>>>>> -i All_inputs.mgh
>>>>>>
>>>>>> -s fsaverage/surf/lh.white
>>>>>> -n 10000
>>>>>> -m mask.mgh (freeSurfer's mask to exclude subcortical surface)
>>>>>> -Cstat extent
>>>>>> -C 2.3
>>>>>> -d Xg.csv
>>>>>> -t contrast/mycontrast.csv
>>>>>>
>>>>>> 1) How do you compute the z-threshold (-C parameter) and which kind
>>>>>> of values are valuable ?
>>>>>>
>>>>>
>>>>> There isn't a strict rule for the cluster-forming threshold. Higher is
>>>>> probably better, and a recommendation maybe is to use something as 3.1.
>>>>> Please see this paper for an interesting discussion:
>>>>>
>>>>> Woo C-W, Krishnan A, Wager TD. Cluster-extent based thresholding in
>>>>> fMRI analyses: pitfalls and recommendations. Neuroimage. 2014 May
>>>>> 1;91:412-9.
>>>>>
>>>>
>>>> *OK, I will look at this paper. In the pvalues FWER-corrected within
>>>> contrast 1 file ("palm.clustere_tstat_fwep_c1.mgz"), I got corrected
>>>> clusters but some of them have pvalue > 0.05. Is this necessary in this
>>>> corrected file to threshold the resulting clusters at a pvalue <= 0.05 ?*
>>>>
>>>
>>>
>>> The file with the p-values shows all clusters that are formed after the
>>> cluster-forming threshold (2.3 in your case). Some of these might be
>>> considered significant (fwep < 0.05) or not. So yes, need to threshold this
>>> image to keep only the smaller p-values.
>>>
>>> Alternatively, consider using the option -logp, such that you threshold
>>> the p-values map at -log(0.05)=1.301. It makes things easier, and helps
>>> when generating figures too.
>>>
>>
>> *Ok thank you for the tips concerning figures. What the "pmethodp" and
>> "pmethodr" parameters concretely correspond to ?*
>>
>
> These refer to the method used for partitioning of the model into effects
> of interest and nuisance effects. The partitioning occurs for each
> contrast, and can use one of three methods (Guttman, Beckmann, or Ridgway),
> all described in the randomise paper
> <http://www.sciencedirect.com/science/article/pii/S1053811914000913>.
>
> This partitioning can happens in two different places: (1) to define the
> set of permutations and (2) to actually do the regression, i.e., the model
> fit. To choose the method for (1) use -pmethodp, whereas for (2) use the
> -pmethodr. These settings, however, rarely need to be changed, except in
> very special cases.
>
>
>
>>
>> *Another point : once permutation and uncorrected results computed, is it
>> possible to launch different multiple comparisons corrections without
>> re-computing all the permutations ?*
>>
>
> Unfortunately not. The correction uses the distribution produced with all
> permutations, and these are thrown away internally at each iteration. While
> it's possible to save them (-saveperms), using them would require a custom
> script. It's probably simpler to run again.
>
> All the best,
>
> Anderson
>
>
>
>>
>> *Best regards,*
>> *Matthieu*
>>
>>
>>> All the best,
>>>
>>> Anderson
>>>
>>>
>>>
>>>>
>>>>
>>>>>
>>>>>>
>>>>>> 2) Is this necessary to manually mean-centered covariates in
>>>>>> statistical model with two or more groups and is this design below coherent
>>>>>> (for example 3 groups with 2 patients and 2 mean-centered covariates) ? :
>>>>>>
>>>>>> EV1 EV2 EV3 EV4 EV5
>>>>>> 1 0 0 6.2346573213 -10.578125
>>>>>> 1 0 0 -3.8105172167 -3.578125
>>>>>> 0 1 0 -6.9508320696 3.421875
>>>>>> 0 1 0 6.8835279578 1.421875
>>>>>> 0 0 1 -1.4942954508 3.421875
>>>>>> 0 0 1 -0.1171565049 4.421875
>>>>>>
>>>>>
>>>>> It depends on the contrast. If testing the last two covariates, not
>>>>> necessary, but if testing either of the first three alone (not their
>>>>> differences) then yes.
>>>>>
>>>>> See Jeanette Mumford guide on this matter:
>>>>> http://mumford.fmripower.org/mean_centering/
>>>>>
>>>>
>>>>> All the best,
>>>>>
>>>>> Anderson
>>>>>
>>>>
>>>> Best regards,
>>>> Matthieu
>>>>
>>>>
>>>
>>
>
