Hi Matthieu,

Probably the simplest way to obtain the residuals is with the command fsl_glm (option --out_res).

About whether PET data is symmetric: even if symmetry cannot be rejected in a particular dataset (i.e., KS test not significant), I see no reason in your analysis to introduce this assumption, even if the point were to obtain a larger number of possible shufflings. There are 3003 possible permutations with the smallest groups in your data, which should be plenty, and there are yet far more with the other groups.

All the best,

Anderson

On 5 June 2016 at 12:32, Matthieu Vanhoutte <[log in to unmask]> wrote:

Hi Anderson,

Please see below:

Le 5 juin 2016 11:42, "Anderson M. Winkler" <[log in to unmask]> a écrit :
>
> Hi Matthieu,
>
> Please, see below:
>
>
> On 4 June 2016 at 14:01, Matthieu Vanhoutte <[log in to unmask]> wrote:
>>
>> Hi Anderson,
>>
>> Please see below:
>>
>> On 06/04/2016 12:07 PM, Anderson M. Winkler wrote:
>>>
>>> Hi Matthieu,
>>>
>>> The skewness of the residuals depends on the data and on the variables in the model, although the variables often don't remove all skewness if that is present in the original data. It's something that generally goes by assumption, i.e., it doesn't have to be tested in all and every study, as the modalities we use are generally members of a not too large set. FMRI and cortical thickness, for instance, can probably be safely assumed as symmetric, whereas VBM, FA, and surface area cannot.
>>
>>
>> Have you any papers that would confirm that FMRI and cortical thickness can probably be safely assumed as symmetric, whereas VBM, FA, and surface area cannot ? Any other papers concerning supplementary data types ?
>
>
> It's simpler to reject a hypothesis than confirm it. We know for sure that VBM is skewed, and so is FA. The distribution for area depends on the resolution: the finer, the more log-normally distributed (so, more skewed). For other data, there is always a possibility that the distribution is asymmetric (hence obviously not normal), hence permutation tests are so important and should be favoured.
>
>  
>>>
>>>
>>> At any rate, to test symmetry, a simple option is to take the residuals of the unpermuted data, make a copy with all signs flipped, and do a two-sample Kolmogorov-Smirnov test (a test that compares two distributions).
>>
>>
>> Is there a simple way of doing this 3 steps above with palm or any other software ?
>
>
> Yes, the KS test is available in Matlab with the Statistics toolbox (command kstest), and in Octave (command kolmogorov_smirnov_test). It's also available in R and in Python (w/ scipy). Interestingly, it's possible to put the test in a for-loop and do a simple permutation test here too, that is, a permutation test to see if the distribution is symmetric.

How could I get the residuals of the unpermuted data ? Sorry but I don't well understand how to see if the distribution is symmetric...

>
> It seems any of this won't be needed, though, please see below:
>  
>>>
>>>
>>> Perhaps more important is that more shufflings (permutations and/or sign-flippings) do not increase power, and in fact, using sign-flippings when the distribution is skewed causes the test to be conservative (See Figures 1 and 2, and Table 2 of the Supplementary Material of this paper).
>>>
>>> How many subjects are there in each sample? How many permutations are possible? What type of imaging data are you using?
>>
>>
>> 1) I have 4 groups with respectively 29, 5, 10 and 10 subjects.
>> 2) How many permutations are possibles are defined in the Neuroimage 2014 paper ? But practically, using -n 5000 is a good way, isn't it ?
>
>
> This is a quite large sample. The overall (all groups) number of possible unique permutations is (29+5+10+10)!/29!/5!/10!/10!, which is a big number. No need to add sign-flippings so as to increase the number of shufflings (and thus incurring the risk of violating symmetry assumptions).
>
> If you run a contrast comparing only the smallest groups (5 and 10), the number of possible permutations here is smaller, 15!/10!/5! = 3003, but still ok, and the result is exact. No need for sign-flippings here either.

Ok thanks !

>
> All the best,
>
> Anderson
>
>  
>>
>> 3) I am using FDG-PET data

Any prior knowledge to know about this kind of data ?

Best regards,
Matthieu

>>
>> Hope you could give me any advice !
>>
>> Best regards,
>> Matthieu
>>
>>>
>>> All the best,
>>>
>>> Anderson
>>>
>>>
>>> On 3 June 2016 at 21:53, Matthieu Vanhoutte <[log in to unmask]> wrote:
>>>>
>>>> Dear FSL experts,
>>>>
>>>> I'd like to compare 4 groups of patients with palm. I have looked at the paper explaining permutation inference from Neuroimage (2014), but would like a personal advice about use of -ee and -ise.
>>>>
>>>> Classically I would use the -ee option for my study, but why not -ise at the same time to increase statistical power un case of small groups ?
>>>>
>>>> How to determine that in a model the errors are symmetric ?
>>>>
>>>> Best regards,
>>>>
>>>> Matthieu
>>>
>>>
>>
>