i meant hrf.
covariate. you don't want them convolved with the hrf.
darren
----------
> -----Original Message-----
> From: <Laurence> <Wang> [mailto:[log in to unmask]]
> Sent: Thursday, January 10, 2008 11:10 PM
> To: [log in to unmask]; Darren G
> Cc: <Laurence> <Wang>
> Subject: regressors for concatenation
>
> 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
> >=============================================================
> ==========
> >==
>
>
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