Elizabeth / Bas
> I would simply measure your response times and put the real stimulus durations in your model, instead
> of letting a Taylor series approximations such as temporal/dispersion derivatives estimate your varying
> response. I think series expansions are only useful when you don't now the variability in HRF
> duration/timing, and want to catch it. Since you do know the real stimulus durations (by recording
> reactions) and hence the expected changes in HRF shape, you could use that a priory knowledge to
> create your GLM instead of adding derivatives. Or perhaps I am missing the point here?
Unfortunately, I don't think it is this simple.
The derivatives of the canonical HRF are primarily to capture *haemodynamic*
variability (eg, across the brain), rather than *neural* variability (though in
principle, of course, we cannot tease these two sources apart using BOLD only).
The reason that the HRF derivatives will not help much for durations ~0-4s
is that the dominant effect of increasing the duration is to increase the *height*
of the response; it's shape changes little. In other words, the parameter estimates
for the temporal and dispersion derivatives will change little compared to the
change in the parameter estimate for the canonical HRF. This is because the
HRF effectively integrates the total neural activity over periods of seconds.
There is nothing *wrong* with modelling trials with different durations (rather
than using "events", all with duration 0). It can give quite different results
(e.g, if your trial durations vary considerably within a condition). It does
however dramatically change the interpretation of the parameter estimate:
For trials modelled by their duration, the parameter estimate reflects the response
*per unit time*.
For trials modelled as events (or by the same duration), the parameter estimate
reflects the response *per trial*.
To see the difference, imagine that stimulus is a visual stimulus presented for 200ms,
and the RT of its motor response varies from 1000-2000ms across trials:
For an area like V1, the duration of neural activity, and hence size of the BOLD
response, is likely to be constant across trials (assuming it only cares about the
duration of the visual flash). In this case, the "constant-duration" (eg "event")
model will fit better.
For an area like M1, the duration of neural activity may vary with the RT (perhaps),
hence size of the BOLD response will vary across trials. In this case, the
"varying-duration" model will fit better.
So which is a better model depends on how you think the neural processing is
affected by trial duration - *and this may differ for different regions/processes!*
For the latter reason, I prefer to model trial durations (provided they are
less than ~4s) as a parametric modulation. That way, you can get the best of
both worlds, identifying regions like V1 in the main effect of the trial, and
regions like M1 in the main effect and parametric modulation. (Though if
you want to allow for different mean RTs *across*, rather than *within*
conditions, then using a parametric modulation, rather than differing durations,
can get a bit more complicated: see my previous SPMlist emails on this topic)
Finally, you can of course model varying duration *and* convolve with the
canonical HRF and its derivatives.
Rik
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DR RICHARD HENSON
MRC Cognition & Brain Sciences Unit
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-----Oorspronkelijk bericht-----
Van: SPM (Statistical Parametric Mapping)
[mailto:[log in to unmask]]Namens Elizabeth Chua
Verzonden: vrijdag 1 april 2005 22:56
Aan: [log in to unmask]
Onderwerp: [SPM] variable durations and dispersion derivatives
Hello SPMers-
I'm using a self-paced event-related design where the stimulus offsets are controlled by the
subjects responses. There are differences in reaction times between different conditions which
are typically on the order of 1-2 seconds, but occasionally as large as 3 seconds. There is also
varability with a condition for the stimulus duration, typically on the order of 1 second.
My understanding is that modeling the durations models different "heights" for the hemodynamic
response. Is this correct? If so, does this work within a condition, between conditions, or both? Is
this recommended?
Also, my reading of the dispersion derivative is that this accounts for differences in the width/
length of the hemodynamic response. Is this something I should use with my reaction time
differences? or will it remove differences between conditions that might be meaningful?
Thank you in advance,
Elizabeth
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