Dear Dr. McLaren (and others),

I am running a multi-regional gPPI analysis and am wondering what the most efficient way to use the gPPI toolbox would be. Ultimately, I would like to create a n-region-by-n-region PPI connectivity matrix for each subject, which I will then do second-level statistics on.

At the moment I have calculated my contrast of interest for several ROIs in one subject and the results look sensible. I can see how I could potentially build a connectivity matrix in this fashion (i.e. by extracting the values from the gPPI results I can build a 'row' of my connectivity matrix one analysis at a time). However this is extremely time-consuming for the number of ROIs I plan to use (~20) and the number of subjects I plan to analyze. I imagine there is a much better way to utilize the toolbox than what I am currently doing. It seems to me that I am doing a lot of unneeded calculations outside of the regions I am interested in (and while that is an interesting question in itself, it is not what I want to do at the moment). Is there a way to limit my analysis just to the regions I want to investigate, therefore (hopefully) speeding up the analysis?

I hope this is clear. Apologies if this has been covered before.

Best,
Luke

There may be a work around for this issue that is already built into the code.

Can you try the following steps:
(1) Create a new 4D file that only contains your 20 ROIs as 20 "voxels";
(2) Estimate the SPM model using the new data;
(3) Add the following to your parameter structure:
P.FSFAST=1;
P.FSFAST_source=[SPM.mat filename and path to original data];
(4) Run the PPPI commands

This will extract the seed values from the original dataset and then compute the PPI models on the reduced dataset.

NOTE: I have not tested the order of operations to see if averaging --> estimating is different than estimating --> averaging are different.

Let me know if you have any questions.

Originally posted by Donald McLaren:
Hi Donald,

Thank you for the reply. I'll let you know how it goes.