Hi
Did you use the --des_norm option in the second call to fsl_glm?
cheers
Christian
On 4 Mar 2012, at 18:11, Tobias wrote:
> Hi,
>
> i was doing some data analysis using dual regression, the script of which is calling fsl_glm twice to compute the regression parameters (time courses/spatial maps respectively). Since the second stage outputs the Z statistics as well, I just tried to check the output by computing the residual noise by myself and scaling the voxel intensities by the estimates for the noise standard deviation.
>
> Actually this should be straightforward starting from res = (data - time_courses*spatial_maps) (as the unbiased procedure was also stated in Woolrich et al./Beckmann et al.), but I got the Z scores only qualitatively correct up to a scaling factor, when I compared my Z_maps with the corresponding ones from fsl_glm. I checked the (voxel-wise) scaling factor and it turned out it is appr. constant (for a sequence of 150 images (one subject) and 20 components in the order of e.g ~7) for each component map, but differs slightly among different component maps. Then I double-checked this scaling again between my Z-maps and the raw spatial modes from fsl and - as it should be - the mean voxel-wise scaling (mean standard deviation) was constant across components.
>
> Where is my flaw? How does fsl_glm handle the estimation of the noise standard deviations/Z_maps? Is there any noise autocorrelation matrix involved to allow for AR characteristics? Although it would make no sense to have this different for each component to explain the variations...
>
> Cheers,
> Tobias
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