Hi Matthieu,

For the design, have a look in the Example 6 of the randomise paper: https://doi.org/10.1016/j.neuroimage.2014.01.060

In the case of subjects with a different number of visits, only those with same number of visits should be permuted with each other, such that multi-level exchangeability blocks are required (these are described elsewhere, though: https://doi.org/10.1016/j.neuroimage.2015.05.092). Define a block per subject and mark these with negative indices (3rd column belo). Then one level above (2nd column), define one block for each number of visits a subject has, and keep these with positive indices. At the top level, a single block with a negative number (to prevent subjects with different number of visits from being mixed with each other). At the level of observations, unique indices per subject (and be one value per visit). Something like this:

-1,2,-1,1 (subj1, visit1)

-1,2,-1,2 (subj1, visit2)

-1,2,-2,1 (subj2, visit1)

-1,2,-2,2 (subj2, visit2)

-1,3,-3,1 (subj3, visit1)

-1,3,-3,2 (subj3, visit2)

-1,3,-3,3 (subj3, visit3)

-1,3,-4,1 (subj4, visit1)

-1,3,-4,2 (subj4, visit2)

-1,3,-4,3 (subj4, visit3)

 

Subjects 1 and 2 have two visits each; subjects 3 and 4 have three visits. Subjects 1 and 2 can be permuted with each other; subjects 3 and 4 can be permuted with each other, but subjects with two visits can't be permuted with subjects with three visits (negative indices in the 1st column). The visits cannot be permuted with each other (negative indices at the 3rd column).

Hope this helps.

All the best,

Anderson

On 11 November 2017 at 03:45, Matthieu Vanhoutte <[log in to unmask]> wrote:

Hi Anderson,

Thank you for replying. In case I want to study between subject factors, as longitudinal progression in one group vs another one, how should I define the design and contrasts with PALM ?

Missing timepoints in some of the subjects will not be problematic ?

Best,

Matthieu

Le 11 nov. 2017 3:17 AM, "Anderson M. Winkler" <[log in to unmask]> a écrit :

Hi Matthieu,

It is, but this requires either compound symmetry, or that only between-subject factors are investigated.

All the best,

Anderson

On 8 November 2017 at 09:34, Matthieu Vanhoutte <[log in to unmask]> wrote:

Hi Anderson,

I have 1 to 4 timepoints per subject, depending on the availability and quality of the processed data. In my case, is this possible to deal with PALM ?

Best regards,

Matthieu

2017-11-08 14:11 GMT+00:00 Anderson M. Winkler <[log in to unmask]>:

Hi Matthieu,

Regarding the longitudinal aspect, in PALM compound symmetry has to be assumed. If there are 2 timepoints, this is fine. With more than 2, it becomes progressively more difficult. How many timepoints do you have?

About the 2 modalities, these can be entered in the same call as two separate inputs (two "-i"), and the option "-corrmod" to correct over them, or "-npc" for a joint analysis via NPC.

All the best,

Anderson

On 8 November 2017 at 03:44, Matthieu Vanhoutte <[log in to unmask]> wrote:

Dear Anderson,

Thank you for your answer. My question concerns vertex-wise cortical surface data I have within 2 modalities over time. And I would like to correlate this two modalities across entire cortical surface longitudinally.

Could you indicate me a process or how to do it with PALM ?

Best regards,

Matthieu

2017-11-08 2:41 GMT+00:00 Anderson M. Winkler <[log in to unmask]>:

Hi Matthieu,

Just adding to Niels' answer: for other types of vertexwise data, it's possible to do in PALM (i.e., it will read surface formats such as FreeSurfer's or CIFTI).

All the best,

Anderson

On 7 November 2017 at 15:55, Niels Bergsland <[log in to unmask]> wrote:

Hi Matthieu,

There isn't a direct longitudinal FIRST pipeline, but you can do it.

What you need to do is run the concat_bvars command with all of your data. Then you run first_utils like in the guide. At this point, you need to use fslsplit on the output and then perform the subtractions with fslmaths and then merge the difference images back together with fslmerge. You probably know this already, but the order of the inputs to concat_bvars corresponds to their order in the 4D output from first_utils. So when you do the fslmaths and fslmerge, just make sure that you have things in the right order. 

Good luck!

Niels

On Tue, Nov 7, 2017 at 9:43 PM, Matthieu Vanhoutte <[log in to unmask]> wrote:

Dear FSL's experts,

Do you know if there would be a mean to compare longitudinal evolution of two vertex-wise data (i.e. kind of longitudinal correlation) with PALM or others ?

Best regards,

Matthieu

--

Niels Bergsland
Integration Director / Research Assistant Professor of Neurology
Buffalo Neuroimaging Analysis Center / University at Buffalo
100 High St. Buffalo NY 14203
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