Dear Russ

> Have you tried analyzing any rapid-presentation event-related fmri
> data, a la Dale and Buckner, using the SPM event-related approach?  I
> am about to collect some such data and I'm curious about what you may
> have found.

There are two components to this question. (i) have we tried
rapid-presentation event-related fmri? (i.e. an experimental design
issue) and (ii) have we implemented selective averaging using our
event-related approach (an analysis issue)?

(i) Yes we use rapid-presentation and stochastic designs and consider
them to be very useful and efficient.  There are a number of people
working on the efficiency of stochastic designs with small SOAs
including Eric Zarahn and Anders Dale.  The conclusion is that smaller
SOAs lead to more efficient designs.  We usually adopt a lower limit of
about 1 second (to avoid nonlinear saturation effects).  Anders and his
colleagues have shown that a 500ms SOA is viable in visual studies.


(ii) We do not use 'selective averaging' and will not.  The reason is as
follows:

Our general approach is to use temporal basis functions, that are
convolved with a stimulus function to give explanatory variables in the
design matrix.  The stimulus functions can be a collection of 'stick'
functions (event-related) or box cars (epoch-related).  Temporal basis
functions are central in that they allow for a graceful transition from
FIR models to fixed-form response estimates.  They avoid the problems
of biased sampling associated with FIR motivated analyses (see below),
yet retain their flexibility in modeling voxel-specific response forms.

Selective averaging is the same as using the general linear model to
estimate the finite impulse response (FIR) associated with each trial
type.  This is in turn equivalent to using temporal basis functions
that comprise a series of delta functions at each TR following stimulus
onset.  The fundamental problem with this approach is that the data
have to be acquired at these discrete time points, engendering a biased
sampling of the peristimulus interval.  Not only is there a biased
sampling but the nature of this bias changes from slice to slice.  The
importance of temporal basis functions is that one can sample the
interstimulus interval in a uniform and unbiased way with minimal loss
of flexibility (by desynchronizing stimulus presentation and data
acquisition).

Clearly this argument becomes more potent at long TRs.  Much of the
published work using selective averaging has used short TRs to look at
small brain volumes and should not be criticised along these lines.

In short it is easy to do selective averging in SPM but we would never
design an experiment where it could be used, so we have little
experience with it.

Very best wishes - Karl


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