Dear Satoru/Dear List,
If I understand correctly: non-stationary RFT correction is
recommended for VBM analyses, but not for second-level fMRI analyses.
What would you in general recommend for water-activation PET?
Thank you in advance,
On Fri, Feb 22, 2008 at 1:37 AM, Satoru Hayasaka <[log in to unmask]> wrote:
> Hi Darren, (and SPMers),
> Let me cross-post this response to the SPM list. I think it might be
> helpful for others too.
>> First would non-stationary RFT methods apply primarily to cluster-size
> Yes, that's correct.
>> From your paper in 2004 non-stationary RFT methods perform well for
>> images under high degrees of freedom. For fmri I hope this would
>> to first level analyses where smoothing was double the voxel size
>> I see in the paper that smooth mean at least 4 voxel FWHM).
> We found that, in general, RFT (stationary or non-stationary) performs
> well with df~30 or larger. So this applies to the first level for sure.
> If you have enough subjects (30+), probably RFT performs fine in the
> second level too.
>> What about at the second level? My understanding is that
>> tests would lose sensitivity in this instance because of the low DF.
>> However, non-stationary RFT tests have better sensitivity that
>> tests in rough regions. This leaves me confused as to the "best'
> choice if
>> indeed there is a best choice.
> My guess is that, for the second level fMRI analysis, you probably would
> do well with the regular stationary RFT in SPM than the non-stationary
> RFT. The non-stationary RFT introduces additional uncertainty to the
> statistical test because the smoothness has to be estimated at each
> voxel; smaller the df is, less precise the smoothness estimate becomes.
> My guess would be that reduced sensitivity due to smoothness estimation
> would probably overwhelm the benefit of increased sensitivity in "rough"
> Now, the story is different in VBM-type analyses though. I have often
> observed clear and systematic structural variability in local smoothness
> in RPV (resel-per-voxel) images from VBM analyses. So, although
> non-stationary RFT reduces the sensitivity, it would be more appropriate
> in VBM analyses in order to minimize sensitivity biases in smooth areas.
>> Would one choose stationary RFT if primarily looking at voxel-level
>> and non-stationary RFT when looking at cluster level stats?
> Sure. In fact, our NS toolbox calculates the voxel-level stats exactly
> the same way as the regular RFT. The reason behind this is difficult to
> explain, but I had a discussion with Ged Ridgway in the past about this
> topic. See
>> Do you have any general guidelines about these choices?
> Well, in a nutshell, I suggest doing the regular stationary RFT for
> second level fMRI analyses, unless you have a strong reason to believe
> that the smoothness is dramatically different in different areas of the
> brain. You can see this by looking at the RPV image (RPV.img, a
> byproduct of an SPM analysis). Perhaps an FWHM image is more
> interpretable than an RPV image. You can transform RPV to FWHM quite
> easily. See
> Hope this helps!
Dr. A.A.T. Simone Reinders, MSc PhD
King's College London
Institute of Psychiatry (IoP)
Box P063, De Crespigny Park
London SE5 8AF