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Thanks Cyril, would you say that it's more standard to do it this way or
the default (without time and dispersion)?

I understand that the canonical HRF response function was developed based
on the visual areas, so that maybe if looking at other areas should allow
for some changes in the function?

On Mon, Sep 21, 2015 at 3:56 PM, PERNET Cyril <[log in to unmask]> wrote:

> that's right Joelle,
>
> temporal derivatives weights allows earlier/later fit whilst dispersion
> derivative allows a bit of change in the shape (how wide) of the hrf - the
> model is  hrf + 1st derivative + 2nd derivative and the combination of the
> 3 regressors gives you the modeled data
>
> more info on how this works here
> http://journal.frontiersin.org/article/10.3389/fnins.2014.00001/full
>
> Cyril
>
> <http://journal.frontiersin.org/article/10.3389/fnins.2014.00001/full>
> <http://journal.frontiersin.org/article/10.3389/fnins.2014.00001/full>
> Frontiers | Misconceptions in the use of the General ...
> Misconceptions in the use of the General Linear Model applied to
> functional MRI: a tutorial for junior neuro-imagers
> Read more...
> <http://journal.frontiersin.org/article/10.3389/fnins.2014.00001/full>
>
>
>
>
> ------------------------------
> *From:* SPM (Statistical Parametric Mapping) <[log in to unmask]> on
> behalf of Joelle Zimmermann <[log in to unmask]>
> *Sent:* 21 September 2015 20:41
> *To:* [log in to unmask]
> *Subject:* [SPM] time and dispersion derivatives - Canonical HRF's
>
> Hi SPM'ers,
>
> Setting up a first-level model, Im faced with the decision if to include
> time and dispersion parameters under the Canonical HRF model. Based off of
> the help, as well as the manual, I understand that these parameters give
> the HRF function some leeway in it's 'preciseness' - ie that it can happen
> a few second earlier or later and has a larger spread (dispersion).
>
> Is this correct?
>
> Thanks,
> Joelle
>
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