Dear Dr Polak,
Following from my previous message: having looked again at J.B.
Poline's slides from the SPM short course (which can be downloaded
from our department's web site), it looks as though the F contrast
which I quoted in the last paragraph of my previous answer is not
quite correct. I think it should be:
1 0 -1 0
0 1 0 -1
(where the ordering of covariates is new hrf, new 1st temporal
derivative, old hrf, old 1st temporal derivative).
Best wishes,
Richard.
>X-Sender: [log in to unmask]
>Date: Thu, 19 Oct 2000 16:25:56 +0100
>Subject: Re: event-related analysis
>From: Richard Perry <[log in to unmask]>
>To: Jo Polak <[log in to unmask]>
>Cc: [log in to unmask]
>X-Unsub: To leave, send text 'leave spm' to [log in to unmask]
>Reply-To: Richard Perry <[log in to unmask]>
>Sender: [log in to unmask]
>
>Dear Dr Polak,
>
>Here's an ugly solution.
>
>You presumably have prior expectations about where your significant
>voxels are going to be. In this case, you can just specify your
>events correctly for this brain slice. E.g. assuming your top slice
>was acquired first, if you expect activations in your bottom slice
>then you need to subtract 0.96 (2.76/2.88) from all of your event
>timings. Obviously it is now possible to have a negative event
>timing, e.g. an event at the onset of the first scan occurs 2.76 sec
>before the scan reaches the bottom slice, and should therefore have
>an onset of -0.96 scans.
>
>If you don't have prior expectations about where your significant
>voxels are going to be, then you could I suppose do a separate
>analysis for every level in the brain (not every single slice - that
>would be excessive - but maybe predefine 10 levels). Now you are
>strict with yourself about only looking at the analysis which
>corresponds to the slice that the voxel you are interested in falls
>closest to. Thus in theory you don't have to make any correction
>for multiple comparisons here, because you are only looking at one
>analysis for each voxel.
>
>Having done this, you could run an analysis with slice timing, and
>see how the statistics for each cluster compares. You may find more
>power left in the analysis than you expected (OK, I admit it, I
>haven't read Rik's abstract yet).
>
>By the way, you could just choose to include the temporal
>derivative. You can still do a 1 -1 't' contrast on the hrf
>covariates, to find out about the relative heights of the responses
>to new vs old words, and a 1 -1 contrast on the temporal derivative
>covariates to find out about the relative delays between the
>responses to new and old words.
>
>However, this analysis is making an assumption that the wave form of
>the real haemodynamic response is the same in both cases, with just
>a variable delay. If this assumption is wrong, and in fact the form
>of the responses to the two conditions is different, then you might
>be introducing a bias. To take an absurdly extreme example, imagine
>that the BOLD response to new words has a waveform which looks like
>the hrf, whilst the response to old word has a waveform which
>actually looks like the temporal derivative rather than the hrf
>itself. Even if the response to old words is much larger than the
>response to new words, the voxel could still show up in the
>comparison new vs old.
>
>If you are happy to identify areas which are sensitive to the
>difference between new and old words, but you don't really mind in
>which direction this difference is manifested, you could do an F
>contrast. I'm not absolutely sure how you implement it; I think
>that it would be an F contrast with 1 0 0 0, 0 1 0 0 (for the hrf
>and the deriv for new words) and 0 0 -1 0, 0 0 0 -1 (for the hrf and
>deriv of the old words). I think that this gives you voxels where
>the two 'new words' covariates explain significantly more of the
>variance than the two 'old words' covariates. It certainly doesn't
>tell you the direction of any effects (e.g. if the response to new
>words is positive or negative),
>
>Good luck,
>
>Richard.
>
>>Hi,
>>
>>I am analysing a word reading/recognition task which was run in a
>>blocked design (20 s on, 20 s rest). That is fine and simple.
>>
>>Within each 20 s on period, there are 10 words presented (1 every 2
>>seconds), half are previously presented words and half are new
>>words. I want to use an event-related analysis to find the
>>difference between new words and previously presented words. This
>>is essentially a stochastic design (since words are presented in
>>random order) with SOAmin of 2 seconds which, according to Friston
>>et al (1999, NeuroImage 10:607-619), should have high efficiency
>>for analysing the differential response.
>>
>>But I have 24 slices with TR=2.88s (120 ms per slice) so there is a
>>2.76s delay between acquisition of the first and last slice. I can
>>not use the slice timing correction in this case because the
>>frequency of BOLD changes I want to model is much higher than
>>1/{2TR} (according to the abstract of Henson et al, HBM1999,
>>NeuroImage 9(2):S125).
>>
>>If I include temporal derivatives with the canonical HRF to
>>compensate for this delay between slices, is this an appropriate
>>method? If so, how do I specify contrasts? Does anyone have any
>>other suggestions?
>>
>>
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>
>--
>from: Dr Richard Perry,
>Clinical Lecturer, Wellcome Department of Cognitive Neurology,
>Institute of Neurology, Darwin Building, University College London,
>Gower Street, London WC1E 6BT.
>Tel: 0207 679 2187; e mail: [log in to unmask]
--
from: Dr Richard Perry,
Clinical Lecturer, Wellcome Department of Cognitive Neurology,
Institute of 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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