Dear Anderson,

Again thank you for your patience. Unfortunately I might have made a mistake in my reasoning in my last post. To clarify. Now we have all the subjects treated like in one group and 1-sample t-test is performed. But the reason I was thinking about an f-test in the beginning was because I essentially wanted to join two t-tests each performed for separate group. So again – I have two groups, and I wanted to do conjunction analysis (using easythresh_conj script). But this approach would give me an “intersection” of activations and I wanted “union” so whether group averages account overall significant effect – hence the idea of using and f-test (not because of the two-tailed effect - like you noticed). But standard approach as explained in GLM wiki cannot be used when there are two conditions per subject.

So going back to where we are now:

palm -i 4D_cond1.nii -i 4D_cond2.nii -t contrast.csv -npc -n 5000 -T -logp -o myresult

This is fine but as I understend this will test joint effect of conditions but not joint effect of groups (will give me average of all subjects in both groups and not joint t-contrats like f-test).

So I should account for the effect of group somehow. But like I said I am not interested in differences between groups or treating group as nuisance. What I would like to get is joint effect of groups – similar to joint effect of conditions.

 So final question is I think – which design should I use with above command to achieve my goal.

I was thinking about design and contrasts similar to an F-test from GLM wiki as a way to incorporate two t-tests and test for joint effect of these with an f-test specified in design:

Group       EV1          EV2
1              1              0
1              1              0
2              0              1
2              0              1

Contrasts:
EV1          EV2
C1            1              0
C2            0              1

And F-test for C1 & C2

Hopefully now I am clearer in what I am hoping to achieve.I would be very grateful for hopefully final clarification how to specify this design and achieve my goal.

best regards,

Chris

2015-11-11 9:08 GMT+01:00 Anderson M. Winkler <[log in to unmask]>:

Hi Chris,

So, you have 2 groups but the inference should to ignore group membership and treat all as a big pool of subjects. This is FMRI, so probably the most interesting are the positive contrast only, not the negative. So for this, let's drop the -twotail option (and with it the idea of an F-test, as both have the same effect at the end).

When a design matrix is omitted, it does by default a 1-sample t-test, checking both contrasts (positive and negative). But you really only want the positive, so supply a simple contrast file that can be just a .csv file containing the number 1 inside and nothing else. This goes with the option -t.

So, the final call would be:

palm -i 4D_cond1.nii -i 4D_cond2.nii -t contrast.csv -npc -n 5000 -T -logp -o myresult

This will test where cond1 and cond2 are jointly larger than zero, across both groups, and without taking group as nuisance (and without comparing groups either).

Hope this helps.

All the best,

Anderson

On 10 November 2015 at 14:53, Krzysztof Gociewicz <[log in to unmask]> wrote:

Dear Anderson,

I might have created a bit of chaos with my questions as my first question was indeed about joint test over two conditions in one group. And I think everything is clear to me to that point as you gave me very clear suggestions.

But in my follow up question I asked about situation in which I have two groups (rather then one) and two conditions – and this is exactly what I am interested in doing now. So joint test across two groups and two conditions. So it’s still somehow not clear to me whether I can just put all subjects from both groups and same condition into one cope file as in example below:

4D_cond1
subj1-group1
subj2-group1
subj3-group2
subj4-group2

4D_cond2
subj1-group1
subj2-group1
subj3-group2
subj4-group2

and use following command without specifying desing matrix and contrasts to get this joint effect across both groups and conditions:
palm -i 4D_cond1.nii -i 4D_cond2.nii –twotail(or not use twotail) -npc -n 5000 -T -logp -o myresult

Here I am not particularly interested in any effect of group. What I would like to show is one overall map of significant activation across both groups and both conditions. So the question is whether approach presented above is valid to achieve this joint effect or do I have to account somehow for the fact that I have two groups of different subjects (create design and contrast like you mentioned)?  

best regards,
Chris

2015-11-10 9:31 GMT+01:00 Anderson M. Winkler <[log in to unmask]>:

Hi Chris,

Yes, from what I understand from your description, this command line is correct, but you need to add -twotail so that you look for any difference across groups (this follows the same principle of the F-test). The -vg auto isn't needed, but as is it won't hurt either (everyone will be in the same VG).

This will do a 1-sample t-test, pooling together all subjects into a single group, that will tested with sign-flipping. It will do, as the title of the thread indicates, a joint test for the effect of two conditions in one group.

If you really don't want a two-tailed test (e.g., only want the positive or negative separately), then drop the -twotail, but be aware that doing so is the same as dropping the original F-test that you were considering.

Also, since you actually seem to have two groups, you may want to take group membership into account, if not as the variable of interest, as a nuisance. In this case, you'd need a design matrix and a set of contrasts. You'd test the intercept then if group is just a nuisance.

If this is still not what you want, please send more details.

All the best,

Anderson

On 10 November 2015 at 00:27, Krzysztof Gociewicz <[log in to unmask]> wrote:

Dear Anderson,

First of all thank you very much for patience in answering all my questions.

Unfortunately I think I wasn’t precise with them and I am still not 100% sure if I understand what is the appropriate way of using palm in my particular situation. You mentioned that there is no need of an EB file with NPC in situation when I have to groups of subjects and that I can specify some design matrix and you gave an example of 2-sample t-test. And I think I understand the reasoning so far. But like I stated earlier in this particular situation I am not interested in group differences but in overall effect across groups and conditions. That is why I asked about using F-test design matrix – like one explained in GLM wiki (F-test, inter-group differences, no repeated measures) as I couldn’t think about any other design appropriate in this situation but not because I was interested in two-tailed effects. But as I understand your last reply there is no need to specify any design matrix when I am interested in overall effect?  

To be sure if I understand correctly:

I have to 2 groups of subjects and 2 conditions, so 4D cope files would look like this:

4D_cond1
subj1-group1
subj2-group1
subj3-group2
subj4-group2

4D_cond2
subj1-group1
subj2-group1
subj3-group2
subj4-group2

Is following command correct in my situation?
palm -i 4D_cond1.nii -i 4D_cond2.nii –vg auto -npc -n 5000 -T -logp -o myresult

best regards,
Chris

2015-11-09 10:10 GMT+01:00 Anderson M. Winkler <[log in to unmask]>:

Hi Chris,

Yes, it can be done. Include the option -twotail to the call that runs NPC. For the MANOVA approach, the test is already two-tailed. In either case, there's no need to include an F-test.

All the best,

Anderson

On 8 November 2015 at 21:02, Krzysztof Gociewicz <[log in to unmask]> wrote:

Dear Anderson,

Just to make sure if I understand your indications – would it be appropriate in my situation to specify design matrix for an f-test (like explained in GLM wiki) and use NPC like you suggested in your previous message? Goal here world be to get 'union' activation map across two groups and two conditions. Do I understand your recommendations correctly?

best regards,

Chris

2015-10-27 9:09 GMT+01:00 Anderson M. Winkler <[log in to unmask]>:

Hi Chris,

My suggestion is to go with NPC. Then there is no need for an EB file. The design matrix would be the same as if there were just one observation per subject, and could be, for instance, a 2-sample t-test comparing groups, perhaps with additional nuisance variables (as in the examples in the FSL-GLM manual). The -vg option can also be dropped, but you'll need to indicate the design matrix and contrasts. It would be like this:

palm -i 4D_cond1.nii -i 4D_cond2.nii -d design.mat -t design.con -npc -n 5000 -T -logp -o myresult

All the best,

Anderson

On 27 October 2015 at 01:28, Krzysztof Gociewicz <[log in to unmask]> wrote:

Dear Anderson,

First of all thank you very much for very clear answer to my question and giving me straightforward cues how to use PALM to meet my needs.

I have one follow-up question. Would it be valid to use MANOVA or NPC in similar way you have described but on data from two groups (high and low memory capacity)? I am thinking about situation in which I have 2 groups and each group has 2 conditions (as in my first question). What I would like to achieve is also “union” activation map of these 4 simple effects.

So let’s say I have 2 groups and 2 subjects in each group.

If I understand documentation correctly, I need to specify exchangeability groups via csv file. Is following command and eb file correct for what I am trying to achieve?

palm -i 4D_cond1.nii -i 4D_cond2.nii -eb eb_file –vg auto -npc -n 5000 -T -logp -o myresult

eb file:

1
1
2
2

best regards,
Chris

2015-10-11 10:16 GMT+02:00 Anderson M. Winkler <[log in to unmask]>:

Hi Chris,

You can do this with PALM. Create one 4D file with the COPEs for condition 1, and another with the COPEs for condition 2, with the subjects in the same order in both files. Then you have two options:

1) Using a classical multivariate test (MANOVA):

palm -i 4D_cond1.nii -i 4D_cond2.nii -mv -n 5000 -T -logp -o myresult

The default will be Wilks' lambda statistic, but you can choose others if you want. See the help for a list of options.

2) Using Non-Parametric Combination (NPC):

palm -i 4D_cond1.nii -i 4D_cond2.nii -npc -n 5000 -T -logp -o myresult

The default will be Fisher's Chi^2, but given your description, I suspect the Tippett is a more adequate function, so you can make a slight modification:

palm -i 4D_cond1.nii -i 4D_cond2.nii -npc tippett -n 5000 -T -logp -o myresult

In the above, the -T is for TFCE, and -logp is to save the p-values as -log10(p), which is in general recommended.

Note it isn't necessary to supply a design matrix, and by default it will run a 1-sample t-test, with sign-flippings, in both directions, and it will also output the univariate statistics (i.e., for each condition separately). Note that the MANOVA statistic is two-tailed, whereas the NPC has a direction.

NPC is more powerful and recommended over MANOVA.

You can also use other options. The remaining of the documentation is available here.

Hope this helps.

All the best,

Anderson

On 10 October 2015 at 22:00, Krzysztof Gociewicz <[log in to unmask]> wrote:

Dear FSL-ers,

I have a question concerning the analysis I want to perform on my data and I could not find an answer appropriate in my exact situation .

My task have two conditions that were performed during one scanning run (low memory and high memory requirements) ,  and I would like to get activation map representing “union” of these two conditions on group level(in opposition to conjunction which I calculated using easy_conj_thresh script).

I was thinking about doing and F test on simple effects calculated for every condition but after checking the idea with FSL GLM wiki (F-test entry) I found that this approach is suitable for inter-group differences, and also is not suitable in case of repeated measures.

Therefore my question is whether there is a valid way of constructing such an F-test/other approach? Of course in worst case scenario I can calculate mean effect of both conditions but I am more interested in “union” of both activation maps.

Best regards,
Chris