>I still do not quite understand why, using the above FEAT settings, I
>get different results if I use slice time correction: this should not
>make any difference if Convolution = None?

I don't personally use slice timing corrections for any paradigms because I
don't agree that one can "correct" for data that simply wasn't acquired so
I'm perhaps not the best person to answer this. But my understanding is
that slice timing works by changing the data (rather than the model),so I'm
not surprised you find different results.

>However we already have data where we vary the Stimulus-> EPI latency by
>jittering the stimulus presentation time (TR is kept constant at 10
>seconds to preserve equilibrium), and thus we collect more time samples
>of the HDR curve than just one. Then I believe that the slice timing
>correction becomes essential. Let's assume that we have three time
>samples at 3.2/4.4/5.6 seconds (from stimulus onset to EPI onset). Could
>you tell me what would be the answers to the same questions I posted in
>the previous mail in this situation?

Well, you still don't need slice timing corrections for this but in this
case, you do need to model the time series explicitly since you are
sampling the HRF at different latencies.

>Also we have data with TR=2.5 seconds (EPI full volume acquisition
>duration = 1.2 seconds again). This does not allow enough time for BOLD
>response to acoustical scanner noise to return to baseline between
>stimuli. Currently we present a single stimulus between each full volume
>acquisition, but keep the Stimulus-> EPI latency fixed; this is thus an
>event-related design that requires deconvolution of the HDR waveforms
>(that we sample once every 2.5 seconds), and slice timing is essential.
>In this case, what would be the answers to the questions about FEAT
>slice timing correction / stimulus paradigm file timing correction setup?

This is a clustered or interleaved acquisition and you're right, this also
requires modeling the HRF. But this is a tricky design because it can lead
to a strongly biased sampling if there is no jitter to produce oversampling
of the HRF.

In both of the last two cases, you're initial posting was the right way to
go. Namely, put in the actual TR value (rather than 1) and convolve the
stimulus onsets with a basis function (typically the HRF and maybe its
temporal derivative). Then you need to adjust the timings in your EV files
to reflect the fact that FEAT assumes they occur half-way into each
TR. It's a bit of a pain but it's not hard to do. As I said, you're
earlier posting looked good on those details.

Good luck.

Joe