Dear SPMers,

I am working on a gPPI analysis but am stuck on a few specific issues
about adapting the example wrapper and input structure. I already
searched the SPMlist and couldn't find the answers to these questions
so I wondered if someone out there might be able to advise me?

To give the background, my original (event-related) model has 10 event
types, I ultimately want to look at the PPI for at least two contrasts
from this model, and using seed regions based on the mean across a
predefined ROI. I am ultimately looking at individual differences so
it is particularly important that the VOIs for each subject are
equivalent (this was a problem I ran into when trying to make the VOIs
using the 'common method' of VOI extraction as discussed in the email
exchange forwarded below).  From what I read on the SPM list, gPPI
seems to be a more suitable than the standard PPI toolbox in SPM for
achieving these aims.  I have been following the instructions
regarding editing the ppi_wrapper and templatemaster from here:

http://brainmap.wisc.edu/PPI

My questions are:

1. Am I right in understanding that P.Tasks in the template_master
file refers to the event types in my model? If so, should I list only
the events that I am interested in looking at the contrasts for, or
all events? For example, if I'm only interested in the third and fifth
event in the model, called 'event3' and event5', would I set P.Tasks =
{1 'event3' 'event5'}

2. P.Weights: I am assuming if I have listed only two events in P.
Tasks I only need to weight these? So, if for example P.Tasks is set
as above, and I want to look at a contrast of event3-event5, would
P.Weights = [1 -1]? Or should I not be weighting this?

3. When I try to run the ppi_wrapper script, with the template set as
above, it seems to get stuck on P. Contrasts, with error messages
saying that P.Contrasts.left and P.Contrasts.right are not formatted
correctly, and that P.Contrasts cannot be defined automatically. I am
unsure as to what I should be entering into this variable?

4. Am I right in assuming that, if I enter a .nii file as my ROI, this
mask will be used to create individual VOI time-series for each
subject which will then be used as the seed region for the PPI? I
couldn't find where this initial part happens in the script so just
wanted to check I am understanding the process correctly.

5. More minor point but I am a little confused about how the wrapper
loops through seed regions. If anyone has an example of the
ppi_wrapper script that they edited and got working that they wouldn't
mind forwarding to me I would find that very helpful.

Any advice would be greatly appreciated!

Many thanks and best wishes,

Sophie



---------- Forwarded message ----------
From: MCLAREN, Donald <[log in to unmask]>
Date: 23 February 2012 15:09
Subject: Re: [SPM] VOI extraction for PPI analysis
To: [log in to unmask]


There are a number of ways to define VOIs. I've commented on your
methods and described my own below.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Thu, Feb 23, 2012 at 9:25 AM, Matthias Schmidt <[log in to unmask]> wrote:
>
> Dear colleagues,
>
> I have two questions about VOI extraction from a source region for PPI analyses:
>
> 1) A common procedure is to identify a peak voxel of a contrast of interest and define a sphere around this voxel (chapter 33.3 and 33.4 ).


The sphere is drawn based on the single subject peak and only includes
significant voxels.

>
> However, I have a second approach, which I want to discuss with you:
>
> E.g. you are interest in a PPI analyses with the seed voxel in the amygdala.
> First, you create an amygdala mask for each subject individually containing only significant voxels in the amygdala (derived from a contrast of interest). After that, you extract the VOI data of these voxels and not of an arbitrary sphere. What do you think of this approach?


I like the approach of using a mask and looking at things inside the
mask; however, as you point out in #2, finding significant areas might
not yield overlapping areas. The larger the mask, the more likely you
could get different areas. In your case, you might have some subjects
with activity in the ventral amygdala and some with activity in the
dorsal amygdala. If you have two groups and the groups are split by
the location of activity, then you could bias the connectivity
results. I generally just use a single mask and grab all voxels in the
mask for all subjects. I also compute the mean, rather than the
eigenvariate as not to skew the contributions of different voxels in
different subjects.

>
> 2)If you use the ‘common’ procedure for extracting the VOIs (define a sphere around a voxel), you define a p-value adjustment (e.g. FWE .05) as can bee seen in chapter 33.4 (Point 1). However, this voxel is below this threshold in some subjects and if you press “eigenvariate” SPM will take the next neighboured voxel, reaching the threshold. However, sometimes this voxel is really far from the originally designated voxel. How do you handle this problem (e.g. decrease the threshold)?


This is a big problem, with no easy solution -- see comments above.

The gPPI toolbox allows various region definitions including the ones
that you have mentioned here (http://www.nitrc.org/projects/gppi).


>
>
> Best,
> Mat
>
>
> --
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