Righto - so is there still an outstanding query here? Is FLAME giving
sensible (and slightly different) results for you now?
Cheers.
On 13 May 2009, at 22:38, Patrick Purdon wrote:
> Sorry for the flurry of posts, but I solved one of the issues listed
> below. For the percent
> signal change calculations, I placed the re-scaled copes/varcopes in
> the first level
> directory subjectN.feat/reg_standard/pctsig/ for each subject and
> used the copefiles there
> as inputs. However, I noticed from the log file that when flame12
> runs, it grabs the
> lower level copes from subjectN.feat/reg_standard/stats/ which
> explains the identical
> results. Kind of a tough bug to catch since I listed the files
> explicitly as coming from my
> "pctsig" directory.
>
>
> On Wed, 13 May 2009 18:12:12 +0100, Patrick Purdon
> <[log in to unmask]> wrote:
>
>> Hi David,
>>
>> Thanks for sending along that link, I think I understand all that,
>> I'm trying to answer
>> slightly different questions that aren't addressed by the standard
>> 4D global
> normalization.
>> In particular, 1) I'd like to compare activity changes between drug
>> levels in terms of
>> percent signal changes (where the percentage is computed relative
>> to the local voxel
>> mean for each run), and 2) I would also like to use flame12 to
>> detect any region-
> specific
>> changes in mean BOLD signal.
>>
>> I tried a work around for (1) where I scaled the first level copes
>> by 100/(mean_func)
> and
>> varcopes by (100/mean_func)^2, but for some reason that gave
>> identical results to the
>> unscaled case (is there some scaling/normalization that happens
>> when copes are passed
>> up to 2nd level?), and for (2) I'm wondering if there's an easy way
>> to get a mean BOLD
>> image and its variance from the standard FSL output (I know how I'd
>> do this from
> scratch,
>> but am hoping there's a way to get it for free from what FSL
>> already computes).
>>
>> Any ideas FSL experts? Thanks for your help!
>>
>> --P
>>
>> PS-- In case it isn't clear, when I scaled the first level copes to
>> "convert" them to
> percent
>> signal change units, what I did was:
>>
>> scaled_cope(x,y,z) = 100*cope(x,y,z)/mean_func(x,y,z);
>>
>> (the reason I did this is so that the resulting 2nd level pe's and
>> copes would be in terms
>> of percentage signal changes)
>>
>>
>> On Tue, 12 May 2009 21:22:50 -0400, David V. Smith <[log in to unmask]
>> >
>> wrote:
>>
>>> Hi Patrick,
>>>
>>> Somebody will probably have to correct me on this, but I don't
>>> think you
>>> really need to do any normalization here for two reasons. In fact,
>>> depending
>>> on how you do it, normalizing may create spurious deactivations in
>>> your data
>>> (see Laurienti, 2004, JoCN).
>>>
>>> 1) I think only relative changes matter, so the mean intensity
>>> shouldn't
>>> affect your stats
>>>
>>> 2) All of the data are scaled to a preset mean. You can see some
>>> of the FSL
>>> course slides for more info on this.
>>> http://www.fmrib.ox.ac.uk/fslcourse/lectures/feat1_part1.pdf
>>>
>>> Hopefully I'm not steering you in the wrong direction. I defer to
>>> the
>>> experts on this one...
>>>
>>> Cheers,
>>> David
>>>
>>>
>>> -----Original Message-----
>>> From: FSL - FMRIB's Software Library [mailto:[log in to unmask]]
>>> On Behalf
>>> Of Patrick Purdon
>>> Sent: Tuesday, May 12, 2009 5:52 PM
>>> To: [log in to unmask]
>>> Subject: [FSL] Adjusting Global Intensity Normalization? Group
>>> Analysis on
>>> Mean BOLD signal?
>>>
>>> Hi FSL'ers,
>>>
>>> I'm analyzing data from a drug study, where the drug is likely to
>>> change the
>>> mean BOLD signal in a region-specific manner, in addition to
>>> altering
>>> functional responses to stimulation. To account for any possible
>>> region-specific mean BOLD signal changes as a function of drug
>>> level, I
>>> would like to:
>>>
>>> 1. Normalize each data set (or cope image) by its temporal mean
>>> (like
>>> "mean_func.nii.gz"), essentially creating a "percent signal change
>>> image."
>>> This would allow me to compare drug-level effects in my group
>>> analysis in
>>> terms of percent signal changes.
>>> --I tried doing this on some simulated data by scaling the
>>> first-level
>>> "cope1" by 100/mean_func and varcope1 by (100/mean_func)^2, but
>>> the flame12
>>> 2nd-level pe's and copes ended up being identical to the unscaled
>>> case
>>> (????). What could be happening here? Is there a straightforward
>>> way to
>>> accomplish this?
>>>
>>> 2. Run a flame12 analysis on the mean BOLD signal as a function of
>>> drug
>>> level.
>>> --Is there an easy way to get an unscaled temporal mean and
>>> variance
>>> from the first-level analysis that can be passed up to flame12?
>>>
>>> Any suggestions on how to do this? Thanks a lot for your help!
>>>
>>> --Patrick
>
---------------------------------------------------------------------------
Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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