Hi Darren,
Thank you for your prompt reply! But I am still a little bit puzzeling on this issue. if the session-
mean regressors are treated as covariete regressors (attached pic1), they will be orhogonalised to
the condition regressors, in this case, it seems the effect of different session-mean can NOT be
removed out. we also tried to treat them as condition regressors (attached pic2), but I am not sure
if it is feasible becaue they will be convolved with HRF, which you recommended not to do in such
a way. Could you please have a close look at the attached pictures and explain more.
Thank you so much!
Laurence
On Fri, 11 Jan 2008 00:27:18 -0600, d gitelman <[log in to unmask]> wrote:
>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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