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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] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%