Tom,
Forgot to ask.
Can having a low n per group cause the NaN problems? And do the NaN problems
originate from the individual subject data or does this problem originate
when you are calculating group data?
-Carlos
On Thu, 9 Oct 2008 16:06:31 +0100, Thomas Nichols <[log in to unmask]>
wrote:
>Carlos,
>
>The problem points are the super-high intensity points in the RPV image.
>Those super-rough regions are artificially shrinking your FWHM estimate
>which is sending your RESEL count through the roof (can you report what your
>FWHM & Resel counts are?)
>
>The solution is to mask out (better yet, understand what's going on with)
>those super-rough voxels. Worst case, take the mask image created by SPM,
>and then mask out the voxels with super-high RPV values (with some ImCalc
>arithmatic) and create a new mask that you can uses as an 'explicit mask' in
>a new analysis.
>
>Hope this helps!
>
>-Tom
>
>On Wed, Oct 8, 2008 at 8:43 PM, Carlos Faraco <[log in to unmask]> wrote:
>
>> On Mon, 6 Oct 2008 21:51:26 +0100, Thomas Nichols <[log in to unmask]>
>> wrote:
>>
>>
>>
>>
>> >Are the problems related to smoothness estimation?
>>
>> Do you mean at the individual level or 2nd level? How can I see values for
>> this?
>>
>> >I.e. is the smoothness FWHM or Resels NaN as well? Or, are their strange
>> values for the
>> >"Expected voxels per cluster" or "Expected number of clusters"? Is the
>> number of the
>> >voxels in the analysis what you expect? (Mask not way too large or too
>> >small?)
>>
>> On the within group data I receive the following output:
>>
>> For Stroop task, using default values on results:
>> 1. Using no correction: some clusters, Expected voxels per cluster (EVC) =
>> .003, Expected # of clusters (E#C) = 64075.98, Expected FDR = .66
>> 2. Using FDR: No suprathreshold clusters, EVC = NaN, E#C = NaN, EFDR = .66
>> 3. Using FWE: 2 clusters, 1 large one with 199336 voxels, EVC = 221, E#C =
>> .05, FDR = .90
>>
>> For OSPAN task:
>> 1. No correction: Showed scattered, random looking activation and gave the
>> following warning many times
>> Warning: Returning NaN for out of range arguments
>> > In spm_Pcdf at 88
>> In spm_P_RF at 106
>> In spm_P at 48
>> In spm_list at 487
>>
>> I pressed Ctrl+C to stop the repition of these warnings and the volume
>> table
>> had a 25 page list of clusters all with NaNs for Pcorrected.
>>
>> 2.Using FDR: same as in Stroop, except EFDR = .10
>>
>> 3. Using FWE: no clusters, EVC=0.000, E#C = 393619779.31, EFDR=.10
>>
>> The # of voxels for the analyses were around 205,000 voxels.
>>
>> >If there are NaN's in the smoothness estimate, look at the FWHM image
>> >(compute from the RPV image with ImCalc as FWHM = RPV.^(1/3)). Viewing
>> this
>> >image with CheckReg with other images (e.g. the mask, or T image, ResMS
>> (or
>> >its square root), etc). If the is *really* irregularly shaped (i.e. no
>> >contiguous voxels) it is possible to have almost all NaN's in the RPV
>> image.
>>
>> The RPV images for the group data have some NaN values outside of the
>> brain,
>> which as you suggested may be normal. However, the images are of a very low
>> intensity, from about .002 - 3.0, expect for a few high intensity points.
>> The FWHM from this doesn't show much of course.
>>
>> I hope this helps to describe the problem. Also, some of the activation on
>> the single subject data looks really bad, just big huge blobs.
>>
>> Thanks,
>>
>> Carlos
>> >
>> >-Tom
>> >
>> >
>> >On Mon, Oct 6, 2008 at 9:08 PM, Carlos Faraco <[log in to unmask]> wrote:
>> >
>> >> Dear SPMers,
>> >>
>> >> Today I read through many of the posts regarding NaNs, however I am
>> still
>> >> confused as to what would be the best remedy for my situation.
>> >>
>> >> The problem is I keep on receiving NaNs for the p-vals on the second
>> level
>> >> analysis. I tried changing the NaNs to 0s in the con images (which seem
>> to
>> >> appear only or mostly outside the brain; and becasue I read not to do
>> this
>> >> for the beta images) using ImCalc but that still doesn't work. I
>> therefore
>> >> checked some files from some other studies we had previously done, and
>> it
>> >> appears that setting voxels outside the brain to NaN is normal SPM
>> >> behavior.
>> >>
>> >> Is the fix for this then something that has to be modified within the
>> SPM
>> >> code?
>> >>
>> >> Thanks,
>> >>
>> >> Carlos Faraco
>> >>
>> >>
>> >
>> >
>> >--
>> >____________________________________________
>> >Thomas Nichols, PhD
>> >Director, Modelling & Genetics
>> >GlaxoSmithKline Clinical Imaging Centre
>> >
>> >Senior Research Fellow
>> >Oxford University FMRIB Centre
>> >
>>
>>
>>
>>
>
>
>--
>____________________________________________
>Thomas Nichols, PhD
>Director, Modelling & Genetics
>GlaxoSmithKline Clinical Imaging Centre
>
>Senior Research Fellow
>Oxford University FMRIB Centre
>
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