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Hi Darren and SPMers, In order to eliminate the effect of different session means when concatenate the multi-session data, we created the block-type regressors. My questioin is that these regressors should be entered into the design matrix either as "condition" (in this case, they will be convolved in the estimation ) or as covariate regressors, as the attached picture showed (ses2-ses6)(in this case,they will be orthogonal to the conditions). Could you please tell me which option is appropriate during processing the concatenated data? Thanks in advance Lawrence On Sun, 17 Jun 2007 18:36:57 -0500, d gitelman <d- [log in to unmask]> wrote: >Luke > >> -----Original Message----- >> From: SPM (Statistical Parametric Mapping) >> [mailto:[log in to unmask]] On Behalf Of Luke Stoeckel >> Sent: Sunday, June 17, 2007 12:26 PM >> To: [log in to unmask] >> Subject: [SPM] DCM with multiple sessions per subject >> >> For some reason, the original message was not included with my reply. >> Please see the issue below. Thanks. >> >> DCM mavens: >> >> We have collected 6 runs (not repetitions) of block-design >> fMRI data for each subject. I want to test a model using DCM >> including the data from all 6 runs. In a PPI analysis, this >> was simple...I would extract the time series for a given VOI >> (using the same seed voxel and sphere dimensions) for each >> run separately and create a model including 6 sessions. >> However, it does not appear to be that simple using DCM in >> SPM5. I have read through the postings about this issue and >> one solution I have found suggests concatenating the data >> from the 6 sessions and including 2 regressors, one for the >> session number (i.e., 1..2..3..4..5..6) and one for the >> transition period (specifying the last time point in a >> session and the first time point in the following session). >> Is it necessary and/or appropriate to do this? > >yes and no > >- do concatenate the sessions. >- create additional block-type regressors for the number of runs - 1 > >you can make the regressors easily with the kron function. So if you had >runs = 6 >scans = 100 (number of scans per run) > >r = kron(eye(runs-1),ones(scans,1)); > > >>It seems more >> appropriate and straightforward to take the mean of the time >> series from each session for each of my VOIs to include in >> the analysis in a way similar to the PPI approach. However, >> this was not easy to implement within the DCM architecture >> within SPM5. > >with PPI the only reason to concatenate the runs is to setup the entire PPI >at one go. otherwise you can do what you did and run the ppi on each run >separately and put them all into a design later. you cannot do it this way >with dcm (i guess you could analyze each run separately, but if each run has >a different trial mix or too few trials you might not get an appropriate >result). > > >darren >=========================================================================