Most excellent. Thanks!
Jeff
-----Original Message-----
From: Vince Calhoun [mailto:[log in to unmask]]
Sent: Tuesday, October 24, 2006 7:54 PM
To: 'Jeff Browndyke, Ph.D.'; [log in to unmask]
Subject: RE: [SPM] Time & Dispersion Derivatives
Take a look at:
Calhoun VD, Stevens M, Pearlson GD, Kiehl KA. 2004. FMRI Analysis With the
General Linear Model: Removal of Latency-Induced Amplitude Bias by
Incorporation of Hemodynamic Derivative Terms. NeuroImage 22:252-257.
Also, Keith Worsely has developed a signed F-test:
Worsley KJ, Taylor JE. Related Articles, Detecting fMRI activation allowing
for unknown latency of the hemodynamic response. Neuroimage. 2006 Jan
15;29(2):649-54.
VDC
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping)
> [mailto:[log in to unmask]] On Behalf Of Jeff Browndyke, Ph.D.
> Sent: Tuesday, October 24, 2006 5:17 PM
> To: [log in to unmask]
> Subject: Re: [SPM] Time & Dispersion Derivatives
>
> Thank you for the response, Rik.
>
> This is very helpful and my approach knowing this is to
> likely take the HRF
> and time derivative to the 2nd level analyses. However, now
> that the 2nd
> level analyses are constrained to a F-contrast,
> directionality is lost. Is
> there a way around this with a F-contrast with multiple basis
> functions?
>
> I thank others on this thread, as well, for their input. It's clear I
> touched off a debate that apparently is still hotly contested.
>
> Regards,
> Jeff
>
>
>
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping)
> [mailto:[log in to unmask]] On
> Behalf Of Rik Henson
> Sent: Tuesday, October 24, 2006 4:39 AM
> To: [log in to unmask]
> Subject: Re: [SPM] Time & Dispersion Derivatives
>
> Just to add to Alle's comments:
>
> >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.
>
> Actually, the canonical HRF and its first-order partial derivatives
> functions
> are nearly orthogonal by definition, and in fact all basis
> functions are
> explicitly orthogonalised to be sure (see spm_get_bf).
> However, Alle is
> correct
> that correlation can be induced into the resulting regressors
> (ie, after
> convolution) owing to 1) non-random ordering of different
> event-types (e.g,
> for
> two event-types close together in time, the *difference* in
> their canonical
> HRFs
> will be correlated with the temporal derivative for either
> one), and 2)
> undersampling by the TR.
>
> However, such correlation is not always a problem. Yes, it
> will affect
> T-contrasts on one basis function alone (e.g, the canonical HRF). But
> generally
> when one uses multiple basis functions, one should perform F-
> rather than
> T-contrasts over all basis functions, for which
> intercorrelation does not
> matter
> (ie., shared variance will be captured).
>
> In the absence of correlation, the inclusion of extra basis
> functions can
> reduce
> the residual error in 1st-level models, and hence improve
> T-contrasts on one
>
> basis function alone (eg the canonical HRF). However, the
> inclusion of such
> extra basis functions will not affect 2nd-level analyses on
> only one basis
> function in SPM99, or only minimally so in SPM2/5, because this
> orthogonality
> means that the parameter estimates are not affected under
> OLS, and only
> minimally under WLS. Thus it is a common misapprehension that
> including, eg
> the
> temporal derivative of the canonical HRF in 1st-level models
> somehow allows
> for
> latency differences in 2nd-level analyses on the canonical
> HRF alone. It
> does
> not. The answer is to take (contrasts of) all basis functions
> to 2nd-level
> analyses, and again perform F-contrasts.
>
>
> >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.
>
> Using an empirically-defined, subject-specific HRF may help
> if you want to
> use
> only one basis function. However, it is not a perfect
> solution, since 1)
> there
> may be different-shaped HRFs in other brain regions within
> the same subject,
> 2)
> there will always be estimation error associated with the
> empricially-defined
> HRF. The use of multiple basis functions is a more elegant
> solution, IMHO.
>
> Rik
>
>
> 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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