Hi,

I have noticed that when I set P.ConcatR=1 to concatenate across all runs when I do a gPPI analysis, that for the regressors that I don't
include in my contrasts, the corresponding PPI regressor has 0 variance/no "events"/"not unique" when reviewing the design matrix. I don't have this problem when P.ConcatR=0. I've attached the SPM.mat for a random subject for which I specified a minimum of 9 events for the contrast. Below is how I specified the contrasts.

Perhaps I missed something in the gPPI config? Thanks in advance!
Gina


conditions = {'conCinsTcnfrem0' 'conCinsTcnfrem1'...
'conCinsDcnfrem0' 'conCinsDcnfrem1' 'conIinsTcnfrem0' 'conIinsTcnfrem1'...
'conIinsDcnfrem0' 'conIinsDcnfrem1'};

P.ConcatR = 1; % use concatenated approach to deal with low # trials (<10 events)/event type
P.wb = 0;         % 0=slice-by-slice computations rather than whole brain -- this is already set by default

P.CompContrasts = 1; % estimate contrasts

for c=1:length(conditions)
     conname = conditions{c};
     P.Contrasts(c).name = conname; % gppi auto prepends con_PPI to name
     P.Contrasts(c).left = {conname};
     P.Contrasts(c).right = {''};
     P.Contrasts(c).STAT = 'T';
     P.Contrasts(c).MinEvents = 9;
end