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Subject:

spm_est_smoothness on synthetic data

From:

Nick Oosterhof <[log in to unmask]>

Reply-To:

Nick Oosterhof <[log in to unmask]>

Date:

Fri, 8 Jun 2012 19:22:36 +0200

Content-Type:

text/plain

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text/plain (55 lines)

Dear all,

Although I have some experience with fMRI, I'm very new to SPM so
apologies if this is a silly question.

I want to estimate the smoothness of some images but get weird results
for the estimates. When using spm_smooth to smooth single random
gaussian images with different kernel sizes, subsequent estimates give
much larger values than expected.

Please see this code snippet:

fn='__tmp.hdr';

V=spm_vol(fn);
[Y,XYZ]=spm_read_vols(V);
sz=size(Y);

fwhms=[];
for blur=1:12
    Y=randn(sz);
    Ysmooth=NaN(sz);
    spm_smooth(Y,Ysmooth,[1 1 1]*blur);

    newfn=sprintf('%s/__smth_%02d.img',d,blur);
    mskfn=[d '__msk.img'];

    V.fname=newfn; % volume
    spm_write_vol(V,Ysmooth);

    M=V; % mask
    M.fname=mskfn;
    spm_write_vol(M,ones(sz));

    W=spm_est_smoothness(V,M);
    fwhms(end+1)=mean(W);
end

Running this script gives values up to 6*10^5 for a smoothness of 12.
Ok, somewhere in the documentation it says it estimates the variance,
so taking the square root seemed like a good idea; this gives indeed a
more or less linear relationship, but the scale is off:

>> sqrt(fwhms)./(1:12)
 56.0264   51.1458   52.0927   53.7166   56.7196   58.8658   61.0850
61.0351   64.4306   66.0386   68.0434   64.4542

This is of by a factor of 60 or so (or 20, if we consider the voxel
size, which is 3mm isotropic). Any idea what is causing this? What is
the proper way to estimate this smoothness of such individual,
synthetic volumes?

Many thanks,

Nick

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