Dear Marko, Matt and Christian,
thank you for your helpful suggestions.
The reason I was thinking that a FIR model may be better than a hrf model in
this case is somewhat intuitive, so please disregard my message if what I say
is not sound!
I am using a TR of 2 s with sufficient jitter and null events, but I am not so
much interested in teasing apart, say early from late words occurring within
sentences. In my experiment, I have a condition A in which a critical word
occurs just before the main verb, and a condition B in which the critical
word does not occur. My interest is in comparing A vs B.
The point is that it is not so much clear from a psycholinguistic point of
view (and this is the general problem with auditory sentences that you were
also mentioning) how and at which point in time the critical word gets
integrated in the syntactic and semantic computation of the entire sentence.
Therefore, locking the hemodynamic response to a specific time-point within
the sentence may be inefficient.
I thought that a FIR model - just as it is suitable for detecting
non-canonical response shapes that peak later or earlier than the canonical
response - may be suitable for detecting "canonical" responses whose onset in
time is unpredictabe within an interval t+dt (t being the onset of the
sentence and dt the sentence duration).
Given that the duration of my sentences approximates my TR, I could use a time
bin equal to 1 or 1/2 TR to model the sentences with mini-boxcars.
Christian's suggestion is also very interesting and I will definetely try it.
Does this make sense?
Marco
On Wednesday 10 May 2006 17:21, FIEBACH, CHRISTIAN J wrote:
> Hi Marco,
>
> Given jitter as suggested by Matt, and given that there is a sufficiently
> long temporal interval between the critical word and the sentence
> onset, a pragmatic approach might be to model the sentence onset
> (or more generally portions of the sentence you are not interested in)
> and the critical word with separate events. I think this could on the one
> hand ensure that your critical word does not 'drown' in the average
> signal elicited by the sentence (when modeling the whole sentence
> as an epoch) and on the other hand also make sure that
> signal related to the sentence onset is not modeled as the
> implicit baseline.
>
> Not absolutely sure about that, but maybe someone else can
> comment on this.
>
> Best,
>
> Christian
>
>
> On Wed, 10 May 2006 15:49:55 +0100
>
> Matt Davis <[log in to unmask]> wrote:
> > Hi Marko and Marco,
> >
> > I would agree with Marko that the problem is with HRF measurements rather
> > than knowing when words were presented to participants. However, I would
> > suggest that the slow TR of typical fMRI studies provides a more serious
> > limitation on the temporal resolution that can be achieved when trying to
> > identify the timing of events within sentences.
> >
> > Assuming that the rise time of the evoked HRF is consistent for different
> > events then any model that is time-locked to your events of interest
> > would seem to be suitable (FIR, or hrf + derivatives would be ok, but I'm
> > not sure that a Fourier set is suitable). However, if you are trying to
> > distinguish events that occur 1 second apart, and your TR is 3 seconds,
> > then it seems unlikely to me that you'll have be able to make reliable
> > inferences about timing differences.
> >
> > Might this be a situation in which the nyquist sampling theory applies?
> > That is, to detect events that are n seconds apart requires a TR < n/2.
> > We have a recent paper (Schwarzbauer et al, 2006, NeuroImage) that
> > detected events three seconds apart within sentences using a semi-sparse
> > sequence with a TR of 1 second - consistent with the nyquist limit.
> > However, I am also aware of papers on BOLD latency measurement (e.g.
> > Bellgowan, PNAS, 2003; Henson et al., 2002; NeuroImage), that seem to do
> > better than this - perhaps because they reduce the effective TR by
> > ensuring that the SOA is not an integer multiple of the TR - ie. adding
> > some jitter as Marko also suggested.
> >
> > hope this is helpful,
> >
> > matt
> >
> > At 14:36 10/05/2006, Marko Wilke wrote:
> >>Hi Marco,
> >>
> >>>I am also facing a similar problem, as I have tried to model time-locked
> >>> events occurring within auditorily presented sentences (of more or less
> >>> constant duration) in an event-related design with continuous fMRI
> >>> sampling. Up to now, I have tried modelling the appearance of the verb
> >>> with an informed set of hemodynamic basis functions (hrf + 2
> >>> derivatives) with very little success.
> >>
> >>This is just an uninformed destructive opinion, but I would think that
> >> the principal problem is that your excellent timing on the input side
> >> (knowing exactly when which word appears) is somewhat destroyed on the
> >> output side (the brain) by the smearing occurring by the real
> >> hemodynamic response.
> >>
> >>However, we have recently investigated the effect of removing certain key
> >> words from a sentence and replacing them with a 750ms sinus tone (Wilke
> >> et al, NeuroReport 2005). While there was a difference between a simple
> >> block design analysis and an event-related analysis, it was not huge,
> >> and our stimulus was certainly more discrete than a simple verb within a
> >> sentence.
> >>
> >>>I was now considering to use a FIR basis set time-locked to the
> >>> beginning of each sentence, in order to try to capture responses
> >>> associated to the processing of the entire sentence and/or to early or
> >>> late sentence components.
> >>
> >>I would think it's the brain that's the problem (not the basis
> >> functions), but perhaps you can overcome some of the issues by
> >> additional jittering.
> >>
> >>>Does anybody have an idea of whether a FIR model would be appropriate
> >>> for such a purpose? Would a Fourier basis set be better/worse?
> >>
> >>No idea.
> >>Best,
> >>Marko
> >>--
> >>=====================================================================
> >>Marko Wilke (Dr.med./M.D.)
> >> [log in to unmask]
> >>
> >>Universitäts-Kinderklinik University Children's Hospital
> >>Abt. III (Neuropädiatrie) Dept. III (Pediatric neurology)
> >> Hoppe-Seyler-Str. 1, D - 72076 Tübingen
> >>Tel.: (+49) 07071 29-83416 Fax: (+49) 07071 29-5473
> >>=====================================================================
> >
> > ****************************************************
> > Dr Matt Davis
> > MRC Cognition and Brain Sciences Unit
> > 15 Chaucer Road, Cambridge, CB2 2EF
> >
> > email: [log in to unmask]
> > tel: 01223 273 637 (direct line)
> > tel: 01223 355 294 (reception)
> > fax: 01223 359 062
> >
> > ****************************************************
--
Marco Tettamanti, Ph.D.
San Raffaele Scientific Institute
Department of Neuroscience
c/o L.I.T.A. - room 25/5
Via Fratelli Cervi 93
I-20090 Segrate (MI)
Italy
Tel. ++39-02-21717552
Fax ++39-02-21717558
email: [log in to unmask]
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