If you’re referring to what is sometimes called a “state-item” design (cf.
you should not use a canonical / assumed response shape. That’s because the
variance that is not captured by your assumed HRF can be misattributed to your
state / sustained regressor.
For these designs, your event-related effects should be modeled with a basis
set that will capture varying response shapes (e.g., FIR or FLOBS) to ensure
that you do not misattribute poorly-modeled activation to the sustained
regressor. I don’t know much about the inverse logit basis set, but you might
consider looking at it too (Lindquist et al. 2009). An advantage of the FIR
basis set is that you can easily look for interactions with “time” to test if
the response shape varies between regions or individuals.
Lindquist, M. A., Meng Loh, J., Atlas, L. Y., & Wager, T. D. (2009). Modeling
the hemodynamic response function in fMRI: efficiency, bias and mis-modeling.
NeuroImage, 45(1 Suppl), S187–98.
Greg Burgess, Ph.D.
Staff Scientist, Human Connectome Project
Washington University School of Medicine
Department of Psychiatry
> On Sep 22, 2016, at 2:05 PM, Michael Dreyfuss <mdreyfus...@gmail.com> wrote:
> Thank you both.
> This is for our task which is actually a mixed design. I'm not too concerned
> about the blocks because like you say the main goal is estimating amplitude
> there. For the jittered events, however, I would want more flexibility in the
> basis function because like you said the HRF could have quite different
> shapes in different regions and different individuals. Regardless, the
> activation patterns I'm seeing seem reasonable. I'm just wondering if the
> double gamma is also better fitted to visual cortex and so activation there
> is more detectable than in other regions, and if so maybe activity in other
> regions would be better detected using a more flexible basis function like
> FLOBS of FIR. I think your explanation about proximity to the head coil may
> be a big part of that too, though, so I'm reluctant to assume there is a
> problem with using double gamma (and there is a cost to estimating the basis
> function everywhere too).
> I will continue to look into these other options...
> Thanks again,
> On Thu, Sep 22, 2016 at 2:31 PM, Burgess, Gregory
> <burge...@psychiatry.wustl.edu> wrote:
> Hi Michael,
> A few things:
> 1) Matt’s point about the increased activation estimates in visual cortex is
> a good one. There is increased signal in occipital cortex in functional
> connectivity analyses that do not assume a response shape. In part, this may
> result from the back of the head being closer to the head coil than other
> brain regions (because participants are laying down).
> 2) To the best of my knowledge, the HCP consortium has not ventured to
> recommend a single, ideal HRF for use in task fMRI analysis. In fact, I’d
> wager that most people in the consortium expect the hemodynamic response to
> vary across brain regions and across people in such a way that there is no
> single ideal canonical HRF.
> 3) We chose the double-gamma during very early analysis of HCP pilot data.
> Using 2.5s TR data, the default double-gamma showed zstat maps with slightly
> higher statistical significance at the group-level than the default gamma HRF
> (in Feat). The double-gamma also seemed to be used more widely in the
> literature, in part due to the commonly observed undershoot at the end of the
> hemodynamic response (see Glover, 1999). We made this choice in piloting, and
> stayed with it for analysis of the Phase II HCP. We did not re-evaluate HRFs
> in the fast TR HCP data.
> 4) In HCP tfMRI, we utilized blocked designs. Blocked designs are good for
> detecting the response, but are not good for estimating the shape of the
> response function. It may follow that differences between canonical HRFs will
> matter less for blocked designs, but I don’t know if anyone has looked at
> that systematically.
> 5) If you’re referring to analysis of your own data using an event-related
> design, your best bet will likely be using a basis set. FSL has FLOBS, folks
> at Wash U tend to use FIR basis sets, but there are others out there as well.
> There are quite a few papers out there to help you choose between those basis
> sets. However, I’m not sure it would make much sense in the context of a
> blocked design.
> Hope this all helps!
> Greg Burgess, Ph.D.
> Staff Scientist, Human Connectome Project
> Washington University School of Medicine
> Department of Psychiatry
> Phone: 314-362-7864
> Email: gburg...@wustl.edu
> > On Sep 22, 2016, at 12:25 PM, Glasser, Matthew <glass...@wustl.edu> wrote:
> > BOLD fluctuations are generally stronger on the occipital cortex
> > (independent of the chosen HRF). See for example the attached functional
> > CNR map (BOLDVariance / UnstructuredNoiseVariance).
> > Peace,
> > Matt.
> > On 9/21/16, 7:29 PM, "hcp-users-boun...@humanconnectome.org on behalf of
> > Michael Dreyfuss" <hcp-users-boun...@humanconnectome.org on behalf of
> > mdreyfus...@gmail.com> wrote:
> >> Hello,
> >> What kind of basis function are you recommending for tfMRI data?I have
> >> been using double-gamma HRF but I notice that the signal is always
> >> strongest in occipital cortex, so I was wondering if this is not optimal
> >> for other regions. If so, do you have a more customized recommendation
> >> that would better fit HRF functions in other parts of the brain to detect
> >> signal there?
> >> Thank you,
> >> Michael
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