Dear Andersson, thanks again for your answers. I will try to set
eschangability blocks per time-bin and let you know how it works out.
Kind regards,
Johan
On Mon, Jan 14, 2013 at 4:59 PM, Anderson M. Winkler
<[log in to unmask]> wrote:
> Dear Johan,
>
>
> 2013/1/14 Johan <[log in to unmask]>
>>
>> Dear Anderson, thank you very much for your answers :-) I have some
>> follow-up question though:
>>
>> Indeed I have only a single group. If I understand correctly, the SnPM
>> plugin uses sign-flippings to assess the H0 that the inputs are
>> (about) equal to 0 (so assigning +1 and -1 wouldn't make a difference
>> under H0), and doesn't do anything to the design matrix. Would the
>> workaround with randomise you suggested also do some sort of
>> sign-flipping (or rather shuffling of the design matrix)?
>
>
> I haven't been using SnPM for a while but if I remember correctly, both
> randomise and SnPM do the same in this regard, although interacting
> differently with the user. For both, if there is a single group of subjects,
> and the contrast is to test whether a regressor is zero for a column of
> ones, then there will be sign-flippings instead of permutations. The reason
> is that permuting the design matrix (or the relevant columns, or the data)
> is ineffective: there is no net effect of shuffling in these cases, and then
> no useful empirical distribution could be built. The sign-flippings make it
> possible and come with a slight change of assumptions, but it's still a
> non-parametric test and so, with all the benefits over parametric
> counterparts.
>
>
>>
>> The
>> sign-flipping in the SnPM plugin says that there are 2^(12-1) = 2048
>> permutations of assinging +1 and -1 to 12 scans. Randomise will maybe
>> say that there are 2048 permutations for each bin, so a total of
>> 2048^10 permutations... so I am not totally sure about this, but I
>> will try this in any case.
>
>
> If the design is the same, both SnPM and randomise should give the same
> total number of possible sign-flippings.
>
>
>> Would the solution of doing 10 separate 1-sample T-tests with
>> randomise be something like a Tripled Two-Group Difference ("Tripled"
>> T-Test) such as is outlined in the FEAT user guide?
>
>
> No no, not the same...
>
>
>>
>> As an additional workaround, I could also use fslmaths to TFCE my
>> con(cope) images, then unput those in the SnPM tool of SPM.
>
>
> Hmm... you could, but it would be an entirely different test, and a strange
> one... TFCE is a positive quantity, so test whether it's different than zero
> wouldn't make much sense I think. It's more informative to test whether the
> con/cope images are different than zero, and then use TFCE as the statistic
> for the inference, just as randomise does.
>
> All the best!
>
> Anderson
>
>
>
> On Fri, Jan 11, 2013 at 6:41 PM, Anderson M. Winkler
> <[log in to unmask]> wrote:
>> Dear Johan,
>>
>> Replying your first email: this would be correct in principle, and you
>> only
>> would need to add the --permuteBlocks option. Ignoring this option would
>> cause the permutations to happen within subject and so, across the bins,
>> which I understand is exactly what you don't want.
>>
>> However, it seems you have just a single group, right? In this case, the
>> permutations will never change the design matrix. These cases are normally
>> dealt with by replacing the permutations with sign-flippings. However, the
>> --permuteBlocks is a beta-option and I'm not sure whether block
>> sign-flipping has already been implemented for this case.
>>
>> As a workaround, and to ensure that the shufflings happen within bin, you
>> can keep the same design matrix and redefine your design.grp to use one
>> group per bin (so, 10 groups rather than 12) and then don't use the
>> --permuteBlocks option (which is what you suggest in your second email).
>>
>> Yet another possibility is to split the 120 volumes into 10 sets (one for
>> each bin) of 12 subjects each, and run a 1-sample t-test 10 times
>> separately
>> in randomise (perhaps using the same seed for each run), one for each bin.
>> You'll only lose the ability to do an F-test directly (there is a
>> workaround
>> for this too, but it's not as trivial).
>>
>> Hope this helps!
>>
>> All the best,
>>
>> Anderson
>>
>>
>> Am Freitag, 11. Januar 2013 schrieb Johan :
>>
>>> Dear FSL list, a few days ago I sent a mail (see below) on how to do a
>>> within-subject one-way ANOVA on 12 subjects (each with 10 "time bin"
>>> contrast images) with randomise.
>>>
>>> Basically, I would like to check if there's an effect in any of the
>>> contrast images (i.e., if any of them is > or < than 0), much like in
>>> what is described in section 30.5 of the SPM manual (FIR basis set),
>>> or what the snpm_pi_ANOVAwithinS plugin does for SnPM.
>>>
>>> My main question is if this is at all possible with randomise, and
>>> also how I should do the exchangability blocks in this case: group
>>> each subject, or group each time bin (I think the latter).
>>>
>>> Many thanks in advance and kind regards,
>>> Johan
>>>
>>>
>>>
>>> On Wed, Jan 9, 2013 at 2:07 PM, Johan <[log in to unmask]> wrote:
>>> > Dear FSL list,
>>> >
>>> > I have a question about how to go about doing a 2nd level FIR analysis
>>> > with Randomise and TCFE:
>>> >
>>> > Basically I am interested just localizing one specific (event-related)
>>> > activity in the brain in a group of 12 subjects. I use a FIR basis set
>>> > of 10 time bins at single-subject level.
>>> >
>>> > I did all the first-level analysis in SPM, and have made contrast
>>> > images, one for each of the 10 bins, so I have 120 images for
>>> > Randomise that I put into 1 4D file.
>>> >
>>> > I then made a design matrix with Glm.. The design looks like this (the
>>> > contents of 4D file to the left):
>>> > Sub1Bin1 1 0 0 ... etc, until 10 rows
>>> > Sub1Bin2 0 1 0 ...
>>> > Sub1Bin3 0 0 1 ...
>>> > Sub1Bin4 0 0 0 ...
>>> > Sub1Bin5 0 0 0 ...
>>> > Sub1Bin6 0 0 0 ...
>>> > Sub1Bin7 0 0 0 ...
>>> > Sub1Bin8 0 0 0 ...
>>> > Sub1Bin9 0 0 0 ...
>>> > Sub1Bin10 0 0 0 ...
>>> > Sub2Bin1 1 0 0 ...
>>> > Sub2Bin2 0 1 0 ...
>>> > ...
>>> > ... etc, until Sub12Bin10.
>>> >
>>> > I also make 1 T-contrast for every bin and 1 F-contrast over all bins
>>> > (and save it).
>>> >
>>> > Then I adjust the design.grp file (to adjust the exchangability
>>> > blocks) so that each subject has its own ID (so that Randomise will
>>> > swap subjects with each other and not time bins within subjects).
>>> >
>>> > Later on, i'll do the cluster and the atlasquiery commands with the
>>> > _tfce_corrp_fstat images to try to search for the significant
>>> > clusters.
>>> >
>>> > Would this be the way to go about it (or am I missing something
>>> > essential), or would you recommend a better way to go about it?
>>> >
>>> > Thanks for your time and kind regards,
>>> > Johan
>>> >
>>> > Johan van der Meer
>>> > CANLAB, Dept. of Neurology
>>> > Otto v. Guericke University
>>> > Leipziger Str. 44
>>> > D-39120 Magdeburg
>>> > Tel: +49 391 61 17 536
>>> > Fax: +49 391 61 17 531
>
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