Hi Carmen,
You have two main options:
1. Don't concatenate runs (sessions). Extract per-session ROIs and have per-session DCM models. Then if you use Bayesian Model Selection to compare models, you can tell it to include each run's model for each subject. Do this if you have a reasonable amount of trials for each condition in each session.
2. Build a new SPM design matrix with concatenated runs. To achieve this, concatenate all your onsets / durations before making the design matrix, then add in nuisance (unconvolved) regressors for all but the last run (so a column for session 1, session 2, up to 5 in your case). You do this using the same part of the batch editor as where you put your motion regressors.
Be very careful when concatenating to correctly adjust the onset times in each session to follow the previous session, to remove dummy scans and account for any spare volumes acquired at the end of each session.
Best,
Peter.
Peter Zeidman, PhD Student
Wellcome Trust Centre for Neuroimaging
12 Queen Square
London WC1N 3BG
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> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
> On Behalf Of Carmen Morawetz
> Sent: 21 November 2012 16:37
> To: [log in to unmask]
> Subject: [SPM] Multiple runs
>
> Hi,
>
> I am new to DCM analysis and currently struggling with a basic problem.
> My experimental design included 6 runs with 4 different conditions.
> What is the correct way to extract the time-series? Do I have to
> concatenate accross all runs or do I have to pool the eigenvariates
> from each run seperately? And how do I concatenate accross runs? How
> does it work?
>
> Thanks in advance for help.
>
> Best,
> Carmen
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