Dear Christopher,
Thanks for the quick reply.
I did some digging after posting and think that it is likely what I want is
parametric modulation with order=0 (or is it one?).
The analysis I want to perform can be summerised by this:
1. Using a mask, get the average value for an ROI for each time point
2. Put this value in as a condition in first level analysis.
I think you and I have the same idea: Do a parametric modulation, i.e.:
SPM.Session(s).U(i).P= struct('name', 'weight', 'P', <weights array>, 'h',
'0', or on the SPM UI: Name, values and Polynomial Expansion respectively. I
am using order 0 polynomial because we only have a weight, i.e., a constant.
Is this the way to approach it?
Many thanks in advance and best regards
Cinly
On 26 July 2011 21:48, Watson, Christopher <
[log in to unmask]> wrote:
> Are you talking about doing parametric modulations? What's your task?
> ________________________________________
> From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf
> Of Cinly Ooi [[log in to unmask]]
> Sent: Tuesday, July 26, 2011 3:28 PM
> To: [log in to unmask]
> Subject: [SPM] Varying the weight for 1st level design matrix
>
> Dear All,
>
> Someone asked me to run a first level analysis where instead of a standard
> boxcar or event design, I have a weighting value associated with each TR. I
> believe what I have to do is to set the onset time and duration as usual,
> then assign a weight to each onset time.i.e., instead of accepting the
> standard weight of '1,1,1,1', I want to specify them as '2,1,1,2' for
> example.
>
> Since each session will have its own unique weights, for ease of data
> entry, I will be reading the weights from files and will be processing the
> data using Rhodri's AA.
>
> A bit of digging into AA shows that I have to setup this up using
> SPM.Session(s), and I believe SPM.Session(s).U is the candidate to accept
> the weights. My problem is, after reading spm_FMRI_design.m, I still have no
> clue how to set the weight.
>
> How do I go about setting the weights? These weights are expected to be
> convoluted with HRF later.
>
> In case it helps, it is one weight per volume. So since I have 224 volumes
> in the fMRI time series, I will have 224 weights to put in.
>
> Many thanks in advance and hope to hear from you soon.
>
> Best Regards,
> Cinly
>
>
--
Best Regards,
Cinly
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--
Best Regards,
Cinly
*****
Don't bother with footer please. I don't read them and will not be bounded
by them.
It cannot be enforced legally anyway. If it can, then remember this: This
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