Hello everybody again and thanks for answering.

My purpose on creating such a design would be to covary out RTs as
better as possible. For the moment parametric modulations are used to
covary out the signal related to RTs for each conditions separately.
But those papers we discussed say it is better to have variable epochs
to detect RTs.

Now I thought to have a sort of parametric modulator, not centered on
the amplitude changes for different RTs (which is what pmods do
normally) but on duration changes. That could regress out much more of
RT signal as pmods, right?

Related to Micheal answer, I thought different durations change the
length of the HRF function, thus the only difference with 0 duration
should be how lond does the peak last. Am I missing something?

Thank you.
Dorian.

2009/3/26 Jason Steffener <[log in to unmask]>:
> In response to question 1)
>
> See Jack Grinband's paper: Figure 4. There he clearly demonstrates what
> happens when epochs of varying length are convolved with the HRF.
>
> Jason
>
> On Wed, Mar 25, 2009 at 2:34 AM, MCLAREN, Donald <[log in to unmask]>
> wrote:
>>
>> In response to question 2. The duration of each event within a condition
>> can vary and is set in the condition array. The condition array has the
>> onset times and durations specified. In this way, some events can have a 0s
>> duration and other can have the RT as the the duration.
>>
>> On Wed, Mar 25, 2009 at 12:07 AM, Michael T Rubens <[log in to unmask]>
>> wrote:
>>>
>>> Hi Dorian,
>>>
>>>
>>> On Tue, Mar 24, 2009 at 3:28 PM, Dorian P. <[log in to unmask]> wrote:
>>>>
>>>> Dear all,
>>>>
>>>> As I didn't receive an answer for this topic and it interest me quite
>>>> a lot I am repeating the question again.
>>>>
>>>> Given that:
>>>> 1. The only change between 0 duration and X duration is a simple
>>>> longer HRF for longer duartion values
>>>
>>> This is not true, the difference between 0 dur vs. dur>0 is the function
>>> convolved with your HRF. A stick function for events, resulting in a delta
>>> function vs. a boxcar. The difference between durations > 0 is in magnitude
>>> (amplitude).
>>>>
>>>> 2. The reaction times are shown to be better catched by variable
>>>> durations.
>>>>
>>>> Is it plausible to manually convolve only the regressor of RTs with
>>>> custom durations, while all other durations for events of interests
>>>> are 0 (ie event related design)???
>>>
>>> This should be possible. The only problem is that spm (as far as I know,
>>> but please correct me if wrong) only allows different durations for
>>> different covariates. To have different durations for some onsets within a
>>> covariate would require hacking the spm code. Perhaps the folks at columbia
>>> (grinband or wager) could provide the code they used.
>>>
>>>>
>>>> Would this manipulation of HRF convolve for a single regressor affect
>>>> the other regressors some way?
>>>
>>> the effect on other covariates should be minimal.
>>>>
>>>>
>>>> Thanks for any possible answer.
>>>>
>>>> Dorian.
>>>
>>> This method is quite different from adding time/dispersion derivatives to
>>> the hrf, because in my understanding, those derivatives regress out the
>>> temporal and shape differences in the irf. By manipulating the duration by
>>> rt you are essentially saying that magnitude of the irf is modulated
>>> linearly as a function of rt, which seems to be a way to normalize responses
>>> within a subject. What is your purpose for pursuing this technique?
>>>
>>> Cheers,
>>> Michael
>>>
>>>
>>>
>>> --
>>> Research Associate
>>> Gazzaley Lab
>>> Department of Neurology
>>> University of California, San Francisco
>>>>
>>>> 2009/3/18 Dorian P. <[log in to unmask]>:
>>>> > Hi all,
>>>> >
>>>> > Sorry but couldn't understand the difference between neural and
>>>> > haemodynamic variations.
>>>> >
>>>> > Probably I should read more on the topic, because I thought dispersion
>>>> > derivative was also trial specific. But I can imagine a model with
>>>> > mixed properties, so that normal regressors are convolved with impulse
>>>> > HRF functions (dur = 0), while RT regressors convolved with variable
>>>> > duration HRFs (dur = RT). At the end shouldn't be difficult for SPM to
>>>> > asses both regressors. They just get e beta value who tells how well
>>>> > the HRF for that regressor explains variability. Am I correct on this?
>>>> >
>>>> > Dorian.
>>>> >
>>>> > 2009/3/18 Jason Steffener <[log in to unmask]>:
>>>> >> Yes, you have it right.
>>>> >>
>>>> >> If you currently have events modeled their durations are 0. With the
>>>> >> variable epoch model the durations become the trial specific RTs.
>>>> >> Just make
>>>> >> sure you are consistent between whether you are specifying time in
>>>> >> TRs or
>>>> >> seconds.
>>>> >>
>>>> >> Jason
>>>> >>
>>>> >> On Wed, Mar 18, 2009 at 12:29 PM, Esther Fujiwara
>>>> >> <[log in to unmask]>
>>>> >> wrote:
>>>> >>>
>>>> >>> For my understanding, in SPM would a variable epoch model be
>>>> >>> implemented
>>>> >>> by using the respective RTs as durations for single events, instead
>>>> >>> of 0s?
>>>> >>> Or is there more to it?
>>>> >>>
>>>> >>> Esther
>>>> >>>
>>>> >>> Jason Steffener wrote:
>>>> >>>>
>>>> >>>> The variable epoch model uses the RT from each trial; therefore, it
>>>> >>>> is
>>>> >>>> able to capture trial specific variance. The impulse with HRF +
>>>> >>>> derivatives
>>>> >>>> may capture some of the variance due to RTs but it essentially
>>>> >>>> takes the
>>>> >>>> average RT over all trials for this condition. And as Chris points
>>>> >>>> out there
>>>> >>>> may be some RTs where the impulse model can in no way accuratly
>>>> >>>> account for.
>>>> >>>>
>>>> >>>> I also feel that the HRF + derivatives should be used to capture
>>>> >>>> hemodynamic variations and not neural variations. Otherwise you
>>>> >>>> make it very
>>>> >>>> difficult to tease about which is which.
>>>> >>>>
>>>> >>>> Jason.
>>>> >>>>
>>>> >>>> On Tue, Mar 17, 2009 at 6:19 PM, Chris Watson
>>>> >>>> <[log in to unmask]
>>>> >>>> <mailto:[log in to unmask]>> wrote:
>>>> >>>>
>>>> >>>>    I think it would depend on the shape of your HRF. The variable
>>>> >>>> epoch
>>>> >>>>    model has boxcars that are as long as the RT,. If you used an
>>>> >>>>    impulse model, convolved with the canonical hemodynamic
>>>> >>>> response,
>>>> >>>>    even adding the dispersion derivative might not capture the
>>>> >>>> signal
>>>> >>>>    for long RT's (as the shape of the HRF in the variable epoch
>>>> >>>> model
>>>> >>>>    will be quite different from the canonical). E.g. in one of our
>>>> >>>>    tasks, we see RT's of up to 7000ms. I don't think an impulse
>>>> >>>> model
>>>> >>>>    even with both derivatives would do nearly as well as an epoch
>>>> >>>> model.
>>>> >>>>
>>>> >>>>
>>>> >>>>    Dorian P. wrote:
>>>> >>>>
>>>> >>>>        Dear all,
>>>> >>>>
>>>> >>>>        Thinking about a previous discussion on the list, we said
>>>> >>>> that
>>>> >>>>        reaction time effects are better captured by a variable
>>>> >>>> epoch
>>>> >>>>        durations, which adapts to reaction time length.
>>>> >>>>        In a couple of papers was shown that a variable epoch
>>>> >>>> aproach is
>>>> >>>>        better than parametric modulations.
>>>> >>>>
>>>> >>>>
>>>> >>>>
>>>> >>>>  http://www.sciencedirect.com/science/article/B6WNP-4T77G33-4/2/cc5ef4a8e9fbff5b4a99bd5f05663bf9
>>>> >>>>
>>>> >>>>
>>>> >>>>  http://www.columbia.edu/cu/psychology/tor/Posters/grinband_HBM06.pdf
>>>> >>>>
>>>> >>>>        But isn't this the same as adding a dispersion derivative,
>>>> >>>> which
>>>> >>>>        would
>>>> >>>>        convolve a longer HRF automatically for RTs and capture that
>>>> >>>> signal
>>>> >>>>        the same way as a variable epoch approach?
>>>> >>>>
>>>> >>>>        Best regards.
>>>> >>>>        Dorian.
>>>> >>>>
>>>> >>>>
>>>> >>>>
>>>> >>
>>>> >>
>>>> >
>>>
>>>
>>>
>>
>>
>>
>> --
>> Best Regards, Donald McLaren
>> =====================
>> D.G. McLaren
>> University of Wisconsin - Madison
>> Neuroscience Training Program
>> Office: (608) 265-9672
>> Lab: (608) 256-1901 ext 12914
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