Dear Felix and Sebastian,
>
>We have a simple eventrelated Design, as follows:
>
>Task:
>Every 4'th fMRI scan (TR = 3000 ms, scan duration 1650
>ms) the probant have to make a decision with respect
>to a presented stimuli.
>This was done 125 times.
>
>Control:
>The same than Task, but no decission was made.

So it sounds like your 'task' conditions and your 'control' 
conditions were run in different blocks.  Is it OK just to make a 
couple of comments about your design?

There is possibly a bit of a problem with session effects, imperfect 
spatial realignment between blocks etc.  You may already have have 
tried to minimize this by randomizing which block came first, task or 
control, across subjects, but obviously this only helps you when 
looking at the group data.  It would be far preferable to interleave 
task and control conditions.  The strength of event-related is that 
you can even randomize task and control events, but if this is not 
possible (e.g. because it would all be too slow if you had to cue the 
subject, for every task, as to whether a decision is required) then 
it may be better to have an epoch-related design with epochs of 
frequent decisions, epochs of frequent non-decisions, and probably 
also 'rest' epochs.  In this case, a cue at the start of each little 
block would tell the subject what to do.  Block designs are, after 
all, more sensitive.

Is there a psychological reason why the events had to be so far apart 
(12 seconds)?  This greatly reduces the number of events in the whole 
experiment, and from the point of view of measuring the differential 
response between two event types it would be much more efficient to 
pack in lots of events, with a SOA as short as 1-2 seconds.  With 
more than one type of event (and probably with 'null events' added), 
this is fine.  If you are staying with an event-related design you 
could even pseudo-randomize, but artificially increase the frequency 
with which runs of one or other event occurs to increase your 
sensitivity.

Incidentally, is there a reason why the scan itself only occupies 
half of your TR?  I am sure that the physicists told me that you 
should have a scan length which occupies as much of your TR as 
possible.  Could the TR be made shorter?

>In a first Analysis we want to look only on the
>task-run, just asking witch aereas are activated
>(independly from the decission made).

Note that you are looking at the total activation generated by 
everything that is time-locked to the stimulus (expectation, arousal, 
attention, sensory effects of the stimulus, decision-related 
activity, motor responses...).  You would expect to see a lot of 
areas active in such a comparison.

>We have done preprocessing:
>realign, time and slice, normalize, smoothing

I think that the usual recommendation is that you should do slice 
timing before realigning, but I know that there's been quite a bit of 
discussion about it on this list.

>and specifyed our fMRI model as:
>-> specify and estimate a model
>-> No. of sessions 1
>-> interscan interval 3
>-> trials 1
>-> SOA fixed, 4, 0
>-> parametric modulation none
>-> events
>-> hrf with time derivation
>-> filter gaus 4, hrf 32
>-> F contrast
>-> contrast 1 0 0

So presumably the first decision was made at the very time that the 
first scan started?  And the slice timing procedure used the first 
scan acquired as its reference slice?

The idea of using an F contrast would usually be to try to capture 
all of the variance which can be explained by the hrf AND its 
temporal derivative.  This is how you would give yourself the most 
latitude in terms of picking up responses of slightly different 
latencies.  If this is what you want to do then choose a contrast 1 1 
0

>When we examine the Result, we get for a uncorrected p
>Value of 0.001 very large activated areas.

Very much as you would expect; see above.  Everything related to the 
task is showing up here.  If you were using visual stimuli you would 
expect to see, at the very least, lots of visual areas, superior 
parietal cortex bilaterally extending down into the intraparietal 
sulcus, bilateral prefrontal areas in an around the region of the 
frontal eye fields, and so on.

>Now decreasing the p value at first shrinks the
>activated areas, but then, for a long range (1e-5 ...
>1e-15) the changes are very small, and then, by a step
>of 1e-17 (from 0.6e-16 to 0.5e-16) all activations
>vanihses.

So far this is not all that surprising.  The main point is that when 
p values are very small, then a large change in signal corresponds to 
a very small change in p value (because the area under the curve is 
so tiny at the extreme of the bell-shaped curve).  So your fairly 
uniform peak p values may be concealing quite a big difference in 
signal between one cluster and the next.

Also, you are looking at smoothed data.  Signal at the edge of a 
cluster of active voxels will tend to be reduced by the smoothing 
procedure (since they are being averaged with voxels in which there 
is no signal).  They will be nibbled away by your early threshold 
increases.  Then there is a group of voxels in the middle of the 
cluster whose voxels have all been averaged out by the smoothing, and 
therefore the p values for these are very similar.  Thus within an 
area you might expect all of the voxels to 'vanish' with a relatively 
small threshold change.

Even between clusters, if the noise is fairly similar, and the 
maximum BOLD signal that can be generated in these areas is fairly 
similar, then similar p values are not altogether surprising.

>This is in particular suprising because the last map
>where we get activatios has a wide range of diffrent
>grayscaled (p valued) voxels (and still quite large
>activated areas).

I wouldn't rely on 'grey-scale' to estimate the range of p values. 
On the 'glass-brain' view areas where there are lots of voxels on top 
of each other tend to look blacker, so I don't think that increasing 
blackness equates with increasing significance.

>Is this a numeric problem ? May we have to change numeric resolution 
>in matlab ?,

I don't know.  I would have to let someone more experienced answer 
that question.  But from what I have said already I don't think that 
it has to be.  But perhaps there is a sealing on how high a p value 
is recorded within the program.  I don't think that it really matters 
anyway (see below).

>and: are the small p Values realistic ?

Probably.  You are looking at the main effect of everything that is 
related to the task.  There could indeed be hugely significant 
results.  Trying to interpret them might be difficult, and clearly 
you were aiming ultimately to compare task and control, which is much 
more interesting biologically.

I am not sure why you are even interested in the exact p values when 
they are so small.  If an area shows an effect which is significant 
at less than p < 0.01 corrected, then you have an effect which you 
can report.  An increase in the significance to p < 0.0001 corrected 
might make you feel extra confident about your result.  Beyond this 
level the drop in p value doesn't really tell you anything extra.  It 
doesn't tell you how big your effect is, and you can't say one area 
is 'more active' than another.  It does, I guess, tell you that when 
you look for differential effects (between task and a well-matched 
control) then you have a reasonable chance of picking something up. 
But the difference between one incredibly small p value and another 
incredibly small p value doesn't seem very interesting.

>Thanks for help in advance !

Ultimately I am afraid that I haven't answered your question, and 
perhaps someone who knows SPM99 better than I do can do this.  But I 
hope that there is something useful for you in these comments.

Best wishes,

Richard.
-- 
from: Dr Richard Perry,
Clinical Lecturer, Wellcome Department of Cognitive Neurology, Darwin 
Building, University College London, Gower Street, London WC1E 6BT.
Tel: 0207 679 2187;  e mail: [log in to unmask]


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