Dear Narender and other SPMers,

    Thank you very much for your response.  I quite agree that modeling the
8 trial types separately is probably most appropriate for this design.  In
doing so, I've been thinking that it might be a good idea to specify two
onsets each time a trial occurs: one for the centrally presented cue and one
for the lateralized target that occurs 1 TR (1.5 s) later. Modelling each
event type with 2 onsets - one for each stimulus - might better capture
event-related activity than modeling an event type with just one onset
(e.g., an onset for the cue).  Each trial type could be modelled by a
"summed" HRF that is actually the sum of two canonical HRFs, which occur 1
TR apart.  Thus, the beta for each trial type would reflect how much the
"summed" HRF needed to be scaled in order to fit the combined response of
the cue and target.

    Importantly, the goal here would not be to distinguish between cue and
target responses within the 8 individual event types.  Rather, it would be
to come up with Beta coefficients for each event type that could be used
later to make distinctions among the different types of targets.  For
example, as Narender pointed out, a main effect of Visual Field of Target
(LVF, RVF) could be determined by contrasting the average beta for the 4
types of LVF trials to the average beta for the 4 types of RVF trials.
Since exactly the same centrally presented cue stimuli precede targets in
LVF and RVF trials, differences between the LVF and RVF beta coefficients
should reflect only the fact that targets occur in the LVF vs. the RVF.

    One issue that I've thought might be problematic is the fact that a
given voxel might be very differently activated by the cue and target
stimuli within each event type.  For example, neurons in visual cortex that
represent the fovea will be activated by the cue but not the target. To the
extent that the responses for cues and targets differ, I've wondered whether
a "summed" HRF is adequate for modeling these types of compound trials.  In
the extreme case, just 1 of the 2 stimuli (e.g., the cue) might activate a
voxel.

    After playing with SPM's canonical HRFs in the MATLAB window, it seems
as if the "summed" HRF might be adequate so long as the cue and target
stimuli occur within 1 TR of each other.  If only one stimulus (e.g., the
cue) produces a response in a given voxel, then the "summed" HRF needs to be
multiplied by a factor of about 0.5 in order to scale it to the height of a
single response.  The "summed HRF" also needs to be shifted approximately 1
TR back in time if only the cue produced a response and approximately 1 TR
forward in time if only the target produced a response.  I believe (but
correct me if I'm wrong) that the temporal derivative only allows shifts of
up to 1 TR in either direction.  Therefore, using a "summed HRF" when only 1
of 2 stimuli produces a response might work best if the cue and target are
presented within 1 TR of each other.  Finally, I noticed that the width of
the response produced by two stimuli in quick sucession is greater when both
stimuli produce responses than when only 1 does.  It seems as if a
dispersion derivative could handle this, though.

    So, after all of this, I'm wondering whether the "summed" HRF approach
above might be a good way to model event-related trials in which 2 stimuli
occur, so long as (1) the goal is not to distinguish between the responses
to these 2 stimuli within a trial and (2) the two stimuli are presented
within 1 TR of each other.  In practice, I'd just set up 8 regressors (one
for each event type in my study) and then specify both the cue and target
onset times within each regressor.

    I'd be curious to hear what people think about the validity of this
approach.


Thanks in advance (as usual!)

:> Daniel





> Dear Daniel,
>
> I thought I might have a go at answering this one...
>
> At 13:10 23/10/2001 -0400, you wrote:
> >Dear SPMers
> >
> >I have a question about how to best model a particular design.  In this
> >design,
> >  there are 8 trial types.  Each trial type has 2 stimuli  -- one cue
> >and one target, which are separated by 1 TR (1.5 seconds).  This is a
cued
> >attention task, in which there are two types of cues (task 1 cue and task
> >2 cue)
> >and eight types of targets, which are defined by crossing Task (task 1,
> >task 2), Visual
> >Field (LVF, RVF) and Congruency (congruent, incongruent).  I've
> >listed the eight types of randomized events below.
> >
> >1.  Task1 Cue + LVF congruent Task 1 target
> >2.  Task1 Cue + LVF incongruent Task1 target
> >3.  Task1 Cue + RVF congruent Task1 target
> >4.  Task1 Cue + RVF incongruent Task1 target
> >5.  Task2 Cue + LVF congruent Task2 target
> >6.  Task2 Cue + LVF incongruent Task2 target
> >7.  Task2 Cue + RVF congruent Task2 target
> >8.  Task2 Cue + RVF incongruent Task2 target
> >
> >
> >     My question concerns the best way to model these events.  One way is
to
> >assign one regressor to all Task1 cues, a second regressor to all Task2
> >cues, and then eight more regressors, one for each type of target.  I'm
> >concerned, though, that since there are no cue-only trials (i.e., trials
in
> >which only a cue appears) it might be difficult for the multiple
regression
> >technique to distinguish between responses to cue and target stimuli.
> >Moreover, the SOA between cue and target stimuli is fixed (i.e., not
> >jitterred).
> >Would the fact that a given cue can be followed by 4 different types of
> >targets
> >help with distinguishing cue and target responses if the data were
modeled
> >in this way?
>
> I agree with the point that you will not be able to reliably disambiguate
> the cue and target, and would go further to suggest that this is the case
> whatever modeling method you choose. However, you have a balanced 2 x 2 x
2
> factorial design. Given that you have a sufficient number of trials and a
> rapid enough TR, you will be able to test for the main effects (and the
> interaction if this is relevant to your study). If you were to model the 8
> event types individually, then you will be able to test for the main
> effects of visual field laterality using the t-contrast [-1 -1 1 1 -1 -1 1
> 1].  This will localise voxels in which the mean activity for
presentations
> in the left visual field is greater than the mean activity for
> presentations in the right, over and above all of the other factors in
your
> design. Obviously, you could also test for the main effect of congruence
> over and above all other components using [1 -1 1 -1 1 -1 1 -1]. In answer
> to your question, this is possible because the contrast is balanced across
> both levels of cue, and thus the activity from each level of cue
> contributes equally to each type of target.
>
> >     A second modeling option is to model each of the 8 event-related
> > trial types listed above
> >separately.  This could be done by specifying an onset at the time of cue
> >presentation and then using temporal and dispersion derivatives to catch
the
> >target response.  However, I'm concerned that this might not capture
enough
> >of the target response.  It could also be done by specifying an onset at
> >the time of target presentation.
> >In this case, however, it seems like each target regressor would probably
> >model variance that was due to the target
> >as well as the immediately preceding cue.  Is that correct?
>
> That is absolutely correct. There would be little point in using a the
> temporal derivative to capture target-related activity. You might, for
> example, end up modeling cue-related activity that occurred late (remember
> that the forms of haemodynamic responses are heterogeneous), and this
would
> be mis-attributed to the target. There is also no point in modeling the
> targets separately for the reasons you mention.
>
> With best wishes,
>
> Narender
>
>
> ********************************************************************
> Dr Narender Ramnani
>
> Sensorimotor Control Group
> Department of Physiology
> University of Oxford
> Parks Road
> Oxford OX1 3TP
> UK
>
> Oxford University Centre for
> Functional Magnetic Resonance Imaging of the Brain,
> John Radcliffe Hospital,
> Headington,
> Oxford OX3 9DU
> UK
>
> Tel. 01865 222704 (Direct)
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>
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>