Hi Steve,
Sorry for the confusion, yes I agree that mean_func is what I need to scale the copes to
percent signal change units, but in a separate analysis (#2 from my original posting), I
want to compare changes in the mean BOLD signal as a function of drug level across
subjects. So for that I need a non-normalized mean functional image for each run, as
well as its variance. Sorry if that wasn't clear. Is there an easy way to get those non-
normalized mean images (and its variance) out of the FSL stream? Thanks for your help!
--Patrick
On Thu, 14 May 2009 18:30:19 +0100, Steve Smith <[log in to unmask]> wrote:
>Hi,
>
>On 14 May 2009, at 17:47, Patrick Purdon wrote:
>
>> 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?
>
>I'm confused, because mean_func IS what you want - AFTER all the
>preprocessing (including "grand-mean" intensity normalisation),
>mean_func is calculated - and, given that the COPE are also calculated
>on the basis of that same preprocessed data, mean_func has the correct
>scaling relative to the COPE and VARCOPE images. At this point
>(after all preprocessing), the original scaling of the data is
>irrelevant/arbitrary. Maybe I'm missing something?
>
>Cheers.
>
>
>> 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
>>> ---------------------------------------------------------------------------
>>
>
>
>---------------------------------------------------------------------------
>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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