Dear Jeffery,
This is an interesting approach for estimating globals.
I think the HPF step is a good idea. A few questions:
Are you using fully automatic procedures or are you eyeballing histograms ?
How do you set the cut-off for within-brain voxels ? (there is a cut-off
is'nt there or am I misunderstanding step 2 ?)
When you compute the mode how do you determine the resolution of the bins in
your histogram ?
Best wishes,
Will.
Jeffrey P Lorberbaum wrote:
> Dear Will and George:
>
> In my search through my image volumes which were spatially normalized tot
> he epi.img twemplate (lots of out of brain voxels), almost all subjects
> had a beta0 image volume with mean within the brain voxels above 100
> (actually in some people it was 130 and
> in others 300). Tom Nichols also noticed this problem and has a solutioon
> on his website that was helpful theoretically but I was unable to
> integrate it into spm2. Other people e-mailed me having the same problem.
> Some recommended going back to spatially normalize the images to a
> template that did not have so much out side the brain voxels. SPM
> developed their code for determining what is in and outside the brain
> (which the beta0 scaling relies on) using tightly crpped image (little
> outside the brain space)
>
> Our solution here (with the help of Jejo Koola's programming) was to first
>
> (1) high pass filter all image volumes using the same scheme as spm2
> generating a time series of the filtered images as well as a mean image
> volume of this time series.
>
> (2) Then, on the mean filtered image volume, I used a histogram of x
> (intensity) versus y (# of voxels showing particular intensity) to find
> the mode intensity of within-brain voxels
> (ie. the peak of the distribution showing within brain voxles). We did
> this in medx. Tom Nichols has a program to calculate this as well
>
> (3) We then grand mean scaled the image volumes by 100 / this
> within brain mode intensity value- that is, at each time pint, each
> voxel in the brain was multiplied by 100/ mode.
>
> This created a time series of grand mean scalesd images for a subject
> with a baseline intestiy of 100 in what we determined were within
> brain voxels.
>
> (4) Put the grand mean scaled, high pass filtered image volume series
> through fMRI stats --> design, data, estimate, etc without high pass
> filtering the data and essentially turning off the grand mean scaling step
> from within the spm_fMRI_ui.m file I think containing the fMRI design
> code.
>
> The estimation step always produced beta0 image volumes
> with a within-brain mode of 100 in all 50 subjects looked at to date.
>
> The temporal filter from step (1) appears to look accurate and is correct.
> That is, in several subjects I looked at the beta0 image volume for spm2's
> way of doing things with filtering the data during stats versus
> our added steps. The spm2 regular way produced a beta0
> image volume that was simply some multiple of the one we got (ie. beta0 in
> our approach = some constant* beta0 in spm2's regular approach. That
> is, this constant was the same for each voxel in beta 0 - however, the
> constant was different for each subhject as that was the problem with
> the spm2 scaling in the first place--> different weightings for different
> subjects.
> If the filter did not work then I would not expect this to be true.
>
> If you are doing indivudal subject analyses and not grouping subjects then
> none of this is relevant. However, if you are grouping data across
> subjects thwen this is all relevant.
>
> A possible alternative and easier way to do this scaling would be to
> determine the mode intensity of within brain voxels for a subject and
> then scale all beta image volumes by ( 100 / scaling factor intensity).
> If you are using each subjects residual variances as well then you need to
> making sure they are multiplied by a scaling factor as well (I think it is
> (100/ scaling factor) squared).
>
> We did not do things this easier way because we want to easily average
> time curves across subjects from our grand mean scaled, filtered image
> volumes and compare the time curves between groups
>
> Please let me know if anyone notices any errors in the above scheme(s).
>
> Thanks
> Jeff Lorberbaum
>
> On Fri, 19
> Dec 2003, Will Penny wrote:
>
>
>>George Tourtellot wrote:
>>
>>
>>>Hello,
>>>question 1)
>>>in the first-level analysis, if I want to divide my beta values for each voxel by the
>>>voxel's mean, is there an easy way to do this? (am I missing some existing
>>>functionality, since lot's of talk is devote to global normalization, but they don't say
>>>how they're doing it?)
>>>
>>>
>>
>>There were a few mailings on this topic quite recently. See eg.
>>
>>http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind0312&L=spm&P=R349&I=-1, ie. mail
>>
>>from Jeffery Lorberbaum and Russ Poldrack's reply.
>>
>>Essentially, you should'nt need to rescale the beta's as the data are already rescaled to have a
>>mean of 100 (over scans and over the whole volume).
>>This is implemented by first computing the global mean value, g, over all time series, excluding
>>non-brain voxels, and then scaling each time series by the factor 100/g.
>>
>>
>>
>>>question 2)
>>>is there any detailed documentation corresponding to the '-> Bayesian' button in
>>>SPM2? No options appear, so I don't even know what it expects the SPM.mat file to
>>>contain.
>>>I'm very interested in using Bayesian estimation for 2nd level analysis, so please fill
>>>me in on usage.
>>>
>>>
>>
>>The best thing to read is K. Friston and W. Penny (2003) Posterior Probability Maps and SPMs.
>>Neuroimage, 19(3), pages 1240-1249.
>>
>>Best wishes,
>>
>>
>>Will.
>>
>>
>>
>>>thanks,
>>>George
>>>
>>>
>>>
>>>
>>
>>--
>>William D. Penny
>>Wellcome Department of Imaging Neuroscience
>>University College London
>>12 Queen Square
>>London WC1N 3BG
>>
>>Tel: 020 7833 7478
>>FAX: 020 7813 1420
>>Email: [log in to unmask]
>>URL: http://www.fil.ion.ucl.ac.uk/~wpenny/
>>
>>
>
>
>
--
William D. Penny
Wellcome Department of Imaging Neuroscience
University College London
12 Queen Square
London WC1N 3BG
Tel: 020 7833 7478
FAX: 020 7813 1420
Email: [log in to unmask]
URL: http://www.fil.ion.ucl.ac.uk/~wpenny/
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