I think you said you had an event related not block design. I usually
use 20-25 seconds. Each person has their own HDR shape estimated so
differences between groups can be detected. You can always redo the
analysis with a longer duration if the HDR shapes look like they're
not going back to baseline for one of the groups. The multidimensional
analysis that we do with FIR models might find group differences on
HDRs on one network, but usually it's only about 1/5 or 2/5 that
differ between groups.
Best,
Todd
On Sat, May 2, 2020 at 12:45 PM Jeffrey Browndyke
<[log in to unmask]> wrote:
>
> Thanks to you both for the detailed information. With FIR are there any guidelines on setting optimal onset and duration relative to block length, particularly in pathological conditions where one might anticipate a delayed HRF?
>
> Regards,
> Jeff
>
> Sent from my iPhone
>
>
>
> Sent from my iPhone
> > On May 2, 2020, at 2:22 PM, Todd S. Woodward <[log in to unmask]> wrote:
> >
> > I agree with Chris that "more flexible HRF models (gammas or FIR) will
> > operate like canonical+derivatives but with a less constrained
> > interpretation of the meaning and interpretation of the associated
> > parameters." Perhaps this paper will help make the point:
> >
> > http://www.cnoslab.com/pdfs/Functional-brain-networks-involved-in-lexical-decision.pdf
> >
> > "An additional concern with the brain imaging studies on LD reviewed
> > in the meta-analysis (Murphy et al., 2019) was the dependence on
> > statistical matches to synthetic canonical hemodynamic response (HDR)
> > shapes for each condition (expressed as “beta weights” derived from
> > regressing the blood-oxygen level dependent (BOLD) signal onto the
> > design matrix containing synthetic canonical HDR shapes). Using
> > synthetic HDR shapes in the design matrix can conflate the operations
> > involved in accessing lexical representations with additional
> > attentional/response processes. This is because all three processes
> > will partially match the synthetic HDR shape, causing the temporal
> > uniqueness of these cognitive operations to be extremely difficult to
> > retrieve. This concern was recognized in a previous LD study (Henson
> > et al., 2002), and in order to address this, the authors developed
> > temporal derivatives of the HDR shape, placing them in the design
> > matrix alongside the canonical HDR shape, with the objective of
> > capturing deviations in the latency of BOLD responses. The temporal
> > derivatives did retrieve a version of the task-positive/multiple
> > demands network; however, this network was very similar to that
> > derived from the canonical response analysis. The reason for this
> > detected “multiple demands” BOLD signal delay is (in part) due to the
> > delays in the HDRs of attentional/response processes, as well as
> > linguistic processes. In the current analysis, a finite impulse
> > response (FIR) model was used, which makes no a priori assumptions
> > concerning the shape of the HDR (Henson, Rugg, & Friston, 2001), and
> > simply indexes change in the BOLD signal that is consistent over
> > stimulus presentation trials during a specified period of time (e.g.,
> > 20 seconds). This will capture all HDR fluctuations that are
> > consistent over trials, including the canonical HDR and any delayed
> > HDRs."
> >
> >> On Thu, Apr 30, 2020 at 6:57 AM Christophe Phillips
> >> <[log in to unmask]> wrote:
> >> Hi Steve & Jeff,
> >> Adding the derivatives does not hurt the model (usually) but the question is rather what you'll be doing with the parameters of those extra-regressors.
> >> At the first level, i.e. fMRI time series of each subject, the HRF derivatives will capture the variance associated with the HRF shape (earlier/later, thinner/fatter) but since these regressors are orthogonal to the one containing canonical HRF, the amplitude of the HRF response is still solely captured by the canonical HRF regressor! The inclusion of the derivatives will thus affect stats on the canonical parameter performed at the 1st level (FFX) : the parameter estimate (the numerator in the t-test) will remain the same but potentially more variance is captured and the residual variance is reduced, so reducing the denominator in the t-test. At the 2nd level (RFX), if you only pass the beta's of the canonical HRF from each subject, it will not matter for your stat results if you included or not the derivatives at the 1st level.
> >> Now if the question you have is about the difference in amplitude *and/or shape* of the response between your 2 groups, you need to include the beta's for the canonical *and* derivates in your GLM. Then, when building contrasts be careful not to mix up the different types of beta's as it does NOT make sense to average (or differentiate) canonical with derivatives. To look for any difference (in amplitude and/or shape) between the 2 groups, you would need to use an F-test with one row per image type, i.e. one for the difference in canonical, one for the 1st derivative, and one more for the 2nd derivative.
> >> BTW more flexible HRF models (gammas or FIR) will operate like canonical+derivatives but with a less constrained interpretation of the meaning and interpretation of the associated parameters.
> >> HTH,
> >> Chris
> >> Christophe Phillips, Ir., Ph.D.
> >> FRS-FNRS Senior Research Associate
> >> & Associate Professor
> >> GIGA in silico medicine &
> >> Cyclotron Research Centre
> >> University of Liège, B30
> >> 4000 Liège, Belgium
> >> T: +3243662316
> >> F: +3243662946
> >> ________________________________
> >> From: "Jeff Browndyke" <[log in to unmask]>
> >> To: [log in to unmask]
> >> Sent: Wednesday, 29 April, 2020 23:00:09
> >> Subject: Re: [SPM] Event-related design in aging study: Temporal and dispersion derivatives?
> >> Hi, Steve.
> >> I think it is a wise idea to include the 2nd order derivatives, particularly if your older adult cohort has a history of cardiac, statin use or hypertension-related issues. Inclusion of these derivatives probably should also be guided by your task structure and event timing, but that’s a bit beyond my wheelhouse.
> >> Hope this helps,
> >> Jeff
> >> Duke University Medical Center
> >> Div. of Geriatric Behavioral Health
> >>> On Apr 29, 2020, at 2:25 PM, Steve Petersen <[log in to unmask]> wrote:
> >> Dear SPM experts,
> >> Please, could you give to me some suggestions about my previous email?.
> >> Thanks for your time.
> >> Best regards,
> >>> El mié., 22 abr. 2020 a las 17:11, Steve Petersen (<[log in to unmask]>) escribió:
> >>> Dear all,
> >>> I am conducting a fMRI study (event-related design) in old adults. As I understand, aging can induce changes in latency and amplitude of the haemodinamuc response (that´s right?).
> >>> Taking into account this, I was wondering if it is a good choice uses the Canonical HRF with time and dispersion derivatives in my first level. More generally speaking, in which cases is it advisable to include derivatives?
> >>> Thank you in advance.
> >>> Best regards,
> >>> Steve.
> >
> >
> > --
> > Todd S. Woodward, Ph.D.
> > Cognitive Neuroscience of Schizophrenia Lab (CNoS)
> > http://bcchr.ca/our-research/researchers/results/Details/todd-woodward
> > http://www.cnoslab.com/donate.html
> > 604-875-2000 x4724
> > *****
> > Professor
> > Department of Psychiatry
> > University of British Columbia
> > Vancouver, Canada
--
Todd S. Woodward, Ph.D.
Cognitive Neuroscience of Schizophrenia Lab (CNoS)
http://bcchr.ca/our-research/researchers/results/Details/todd-woodward
http://www.cnoslab.com/donate.html
604-875-2000 x4724
*****
Professor
Department of Psychiatry
University of British Columbia
Vancouver, Canada
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