Dear Anjie
I think the answer to your question will also depend strongly on the MRI modality you are using for your statistical analyses (fMRI, GM/WM segments from T1w images for VBM, or quantitative MRI images, e.g. MT or FA, for VBM-style of analyses).
In particular, VBM-style of analyses of quantitative MRI measures will strongly depend on the smoothing kernel you use (see, e.g., DK Jones 2005). For these kind of data, it is useful to apply a "small" smoothing kernel to preserve the local information of your quantitative maps (see also discussion in Mohammadi et al. 2012).
All the best
Siawoosh
--
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Dr. Siawoosh Mohammadi
Institut für Systemische Neurowissenschaften
Universitätsklinikum Hamburg-Eppendorf
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> Gesendet: Dienstag, 09. Dezember 2014 um 23:38 Uhr
> Von: "MCLAREN, Donald" <[log in to unmask]>
> An: [log in to unmask]
> Betreff: Re: [SPM] smoothing kernel size and spatial resolution
>
> I think you are over simplifying things.
>
> If you have 2 structures that are separated by 6mm, then the data is
> probably adjacent voxels (for fMRI data). I think a reviewer would have
> issues trying to say that two voxels are separate activations.
>
> Smoothness also comes from two other sources: (1) intrinsic smoothness -
> for example, the GM/WM boundary which is very clear on an actual brain is
> fuzzy on an MRI image; and (2) processing steps that reslice and
> interpolate the data lead smooth the data as well. Thus, the smoothness of
> the data is a combination of (1), (2), and your smoothing kernel.
>
> Areas very close together will likely end up being in the same cluster,
> rather than separate clusters. Additionally, because of multiple
> comparisons, you need many voxels to be significant to find effects. The
> clusters will likely be more than 6mm in radius.
>
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Research Fellow, Department of Neurology, Massachusetts General Hospital and
> Harvard Medical School
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Website: http://www.martinos.org/~mclaren
> Office: (773) 406-2464
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> On Thu, Dec 4, 2014 at 9:58 AM, Anjanette P Harris <[log in to unmask]>
> wrote:
>
> > Dear SPMers,
> >
> > If I smooth with a 6x6x6 mm FWHM Gaussian kernel, would I realistically be
> > able to detect between adjacent structures that are
> > less than 6mm in diameter? or is this over simplifying things? I know that
> > one drawback from smoothing is a loss of spatial precision.. but I am
> > not sure how to convey this in a paper discussion.
> >
> > Thanks for any advice,
> > Anjie
> >
> > The University of Edinburgh is a charitable body, registered in
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