Hi Jeff,
In situations where the HRF is a bad fit, it may be a good idea to use
derivatives. But this does come at a cost, because you have three times
as many regressors that, in general, are not orthogonal. There is
variance that is explained by the HRF, and variance explained by the
derivatives, but there is also variance that could be attributed to both.
In the case of a consistent mismatch of the HRF, it may be better to
- look for a different HRF (ths may sound a bit drastic)
- use AR modelling to get rid of autocorrelations in the noise
The second option is available in SPM. Kalina Christoff made a tool to
do the first option
(http://www-psych.stanford.edu/~kalina/SPM99/Tools/eHRF.html). It's a
very simple tool, but it does the job.
Best,
Alle Meije
Jeff Browndyke, Ph.D. wrote:
> A few questions about the use of time and dispersion derivatives:
>
> If one uses time and dispersion derivatives in a model, does this mean that
> error variance associated with the canonical HRF is "soaked up" in the
> model? If this is the case, then why wouldn't these be included as a matter
> of routine when employing the standard HRF? I understand there are
> waveforms that are devoid of some of the canonical HRF constraints, but
> wouldn't using one of these mean that you are just incorporating into your
> model what would otherwise be error variance in the canonical HRF situation?
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