>
> However, I would also add that Eric's two suggestions are only
> appropriate
> if the canonical HRF is a good model of the BOLD response (as he
> says). If
> this is not the case, the first-order Taylor expansion that
> underlies both
> approaches (a)+(b) breaks down. Thus in the more general case (eg
> with a set
> of gamma functions, or Fourier or FIR in SPM), one does not
> regard basis
> functions as "nuisance" variables - they all carry equal weight,
> and are
> best tested via F-tests.
Yes, of course I agree with Rik. When there is no prior knowledge
about which basis function is theoretically relevant, then proper
inference is on the set of basis functions as a whole.
>
> Rik
>
>
>
> >-----Original Message-----
> >From: SPM (Statistical Parametric Mapping)
> >[mailto:[log in to unmask]] On Behalf Of Eric Zarahn
> >Sent: 16 March 2006 23:37
> >To: [log in to unmask]
> >Subject: Re: [SPM] time derivative for block design fMRI???
> >
> >
> >Hi Matthew and Yanmei,
> >
> >
> >
> >Just to add, the last comment in that cited post:
> >
> >
> >
> >"Having said all this. It is still *BETTER* to use an F-test
> >whenever you
> >are encoding a condition using more than one basis function (the
> reason
> >for explained above). And since we can now (as of proper
> variance
> >component estimation) bring all the parameter estimates (or
> rather
> >contrasts thereof) to the second level, it is *always* the
> recommended
> >thing to do."
> >
> >
> >
> >is not correct. You can dilute your statistical effect size
> >(regression sums
> >of squares) across the multiple dimensions of an F-contrast.
> >It is better to
> >use a one-dimensional contrast when you know what you are
> looking for
> >(matched filter idea). So, for time-series statistical
> >inference I would
> >suggest using either (a) a one-dimensional contrast estimating
> > the original
> >effect in the presence of orthogonalized nuisance terms such
> >as derivatives,
> >as Jesper mentioned in the model (Zarahn, 2002), or even better
> (b) an
> >estimate of amplitude based on both the original and derivative
> terms
> >(Calhoun et al, 2004). As an aside, approaches like (a) don't
> >matter to 2nd
> >level analyses, while (b) will affect 2nd level estimators
> >(beneficially)
> >
> >
> >
> >Eric.
> >
> >
> >
> >
> >
> >Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004.
> fMRI
> >analysis with the general linear model: removal of
> >latency-induced amplitude
> >bias by incorporation of hemodynamic derivative terms.
> >Neuroimage 22, 252-7.
> >
> >
> >
> >Zarahn, E., 2002. Using larger dimensional signal-subspaces to
> >increase
> >sensitivity in fMRI time series analyses. Human Brain Mapping
> >17, 13-16.
> >
> >
> >
> >
> >
> >----- Original Message -----
> >From: "Matthew Brett" <[log in to unmask]>
> >To: <[log in to unmask]>
> >Sent: Thursday, March 16, 2006 5:11 PM
> >Subject: Re: [SPM] time derivative for block design fMRI???
> >
> >
> >Hi,
> >
> >> I have a very naive question about inclusion of time
> derivative in
> >> analysing block design fMRI data (TR = 2s, block duration =
> >20s). Is it
> >> suggested to include time derivative for block design or it
> >is only used
> >> for event-related?
> >
> >I think the answer to that would be that it is basically only
> used for
> >event-related. There is a characteristically clear explanation
> from
> >Jesper Andersson here:
> >
> >http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind04&L=SPM&P=R35
> 1987&I=-3
>
> Best,
>
> Matthew
>
>
>
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