Thats not entirely correct. You can see activations on the glass
brain, although it requires a little work. If all events are equal
duration, you can do the following. If they are different, then you
should have a different event type for each duration.
Construct a hemodynamic response function for the timing of the FIR
bins, ussually, the same as the TR using spm_hrf(TR). Use the first N
values (where N is the number of FIR bins) in a t-contrast. If you are
comparing 2 conditons, you can invert the values for the second
condition (like 1 -1 if it was just the canonical model).
Hope this helps.
Best Regards, Donald McLaren
=================
D.G. McLaren
University of Wisconsin - Madison
Neuroscience Training Program
Office: (608) 520-0586
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On Sun, Aug 8, 2010 at 2:14 PM, Jonathan Peelle <[log in to unmask]> wrote:
> Hi Vadim
>
>> 1. So, according to what you are saying if I use FIR I have no way to see
>> t-contrast based activations in a glass brain (unless they are averaged My
>> design is faces / objects, which localizes faces selective regions as
>> t-contrast faces-objects. So, I have no use of F-contrast per se. As you
>> suggested I can plot the regressors response, but this still doesn't provide
>> me with overview of the whole brain.
>
> Right. However, it might not be as bad as you think. For example, if
> you have an idea of where you expect a faces > houses difference (i.e.
> in the fusiform gyrus), and you run an F test, and you get a
> difference in a reasonable anatomical location, you can just check the
> response in that region to ensure it's in the expected direction.
>
> At the same time, it's nearly always the case that a t-test on the
> canonical HRF will be more straightforward to interpret; if this seems
> to work well for your data (and the contrast you are interested in), I
> would go with that.
>
>
>> 2. Does not HRF estimated t-contrast make some sort of average? More
>> general, is there any rule of thumb when it's preferable to use FIR instead
>> HRF? Would it be correct to say that for Event-Related design the FIR is
>> more suitable?
>
> There are several other posts on this; you might also want to take a
> look at Chapter 30 of the SPM8 manual (Face group fMRI data), which
> does some comparisons of basis functions.
>
> For block designs, the assumed shape of the HRF probably matters less,
> because most of what you measure is going to be more sustained, so I
> suspect that these considerations are more relevant for event-related
> designs.
>
> Whenever you create a first-level model, you are describing how you
> think the BOLD signal will be in response to your experimental design.
> A canonical HRF presumes a a particular shape to this response, and
> because of this is fairly easy to interpret. Of course, for brain
> areas that are NOT well described by a canonical HRF, your model will
> not be very good. Including derivatives of the canonical HRF will
> help with this, though (again, see chapter 30 of the SPM manual).
>
> An FIR model makes no assumptions about the shape of the response.
> Thus, it's much more flexible, and well-suited for capturing a wide
> variety of responses. The downside is that this flexibility makes it
> more complex to interpret.
>
> The question is really, is there reason to think that the HRF in the
> regions you are interested in differs significantly from the canonical
> HRF (or the canonical HRF + derivatives)? Most of the time these
> functions seem to do a pretty good job. However, this isn't often
> quantitatively evaluated, and may differ based on region, age,
> task...etc.
>
> Hope this helps,
> Jonathan
>
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