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Dear Eveline,

>I write to you regarding the issue of clustered-temporal acquisition 
>analysis in spm5 and I would very much appreciate if you could take 
>a few minutes to read this mail.
>
>In auditory research clustered-temporal acquisition this is a big 
>issue and but we are still unsure about the correct way of analysis.
>(Clustered-temporal acquisition would e.g. mean to present the 
>stimulus every 10 seconds in silence for 3.5 seconds and acquire 
>three volumes (TR 1) approximately 5 seconds pSO)
>In earlier studies the box-car function convolved with the hrf was 
>used to analyse each clustered temporal acquisition as an epoch.
>
>In spm5 we assumed that the FIR-function would be the most 
>appropriate to use. We then entered 10 seconds as interscan 
>interval, the FIR function as basis function with window length 3 
>(three volumes, TR 1) and first order modulation
>To correct for different T1 between acquisitions we included to 
>covariates in the model.
>
>However-  when using the hrf with time derivatives the results look 
>approximately the same, even better, even though I can no longer 
>specify the duration of the acquisition and spm would assume that 
>one volume acquisition lasts 10 seconds.
>
>Here are my questions:
>1) assuming that spm works with a TR of 10 seconds and models the 
>hrf as in the above mentioned case. Would you believe that this way 
>of modelling is appropriate, or is that almost like improving the 
>data artificially? I assume the shorter the inter-stimulus-interval 
>the more suitable the hrf would be. Is that correct?

Yes; that is correct. The form of the HRF becomes a more useful 
constraint when the TR is
small in relation to the temporal extent of the HRF.  With a TR of 10 
seconds the decimated
regressors are not really affected by the shape of the HRF and there 
is little point in using it.

If I understand your design properly, you have a difficult problem 
because the TR is not constant:
I.e., you acquire three volumes with a short TR every 10 
seconds.  This means the three volumes
sample the response over short periods of time. The most 
comprehensive model would be
one which uses a parameter for each of these observations (i.e., your 
FIR model). Note that
you should get the same result for any TR here because you are simply 
modelling the response in terms
of the a signal on three occasions after a stimulus. It may be that a 
better model for these three
responses is their average and their slope - this is effectively the 
model you are getting with an
HRF and its time derivative. The final model would be just the 
average (you would specify this as
a covariate of interest - without convolution, with three '1's for 
the three scans after each event type).

In short, the convolution in SPM is really irrelevant for you because 
SPM thinks your TR is ten seconds.
This means you might want to specify your regressors directly as 
covariates of interest that can be
one, two or three separate columns for each event type, depending on 
whether you want a simple or
more complicated model (with a mean, mean and slope or mean, slope 
and curvature).

>2) Is the FIR function as the "most general" function in spm5 
>comparable to a box-car function or would it at least be able to 
>catch modulations of a box-car kind?

FIR and box-cars are models of the underlying stimulus function or 
neuronal response. The HRF maps
the neuronal response to a hemodynamic response. In your case the sampling is
sparse and non-stationary; so the concept of a convolution kernel 
(i.e., HRF is not really relevent

>3) Is there possibly an alternative way of doing the individual 
>model, which we haven't thought of?

Try using covariates of interest as described above.

>4) does the specified microtime resolution affect my clustered 
>temporal acquisition analysis

Mathematically, it does; but because you have sparse sampling the 
shape of the HRF is largely
irrelevant and therefore it does not really matter what the micro 
time resolution of the underlying
stimulus function is.

I hop this helps - Karl