Hi Steve,
Yeah, one remaining query: Is there an easy way to pull out each run's (unscaled) mean
image (like mean_func.nii.gz but not scaled by the global normalizing factor), as well as
its variance? Maybe the global normalizing constant is stored somewhere, and I could
use it to re-scale mean_func.nii.gz, and then calculate its variance using
sigmasquareds.nii.gz and my knowledge of the first-level design matrix? If the global
normalizing constant is not stored anywhere, what command could I run to quickly
calculate it?
So, on simulated data, the percent signal change 2nd-level flame12 calculation worked
great. As one would expect, the T and Z values were essentially identical to the non-
scaled case, but the pe/cope values were in "percent signal change" units. :)
--Patrick
On Thu, 14 May 2009 17:34:59 +0100, Steve Smith <[log in to unmask]> wrote:
>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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