Hi Kenny -
On 10 May 2010 18:13, Kevin Denny <[log in to unmask]> wrote:
> Hi FSL users,
>
> I have a question about the orthogonalise option for FEAT as there has been some confusion in my lab as to what it's doing and when to use it.
>
> I'm doing a resting-state functional connectivity analysis with 3 EVs - an ROI timecourse (1) and two regressors: average of ventricles (2) and whole brain mean timecourse (3). The way I understand it is that if I orthogonalise (1) wrt to (2) and (3) that is moving any similar components from (1) to (2) and (3) thereby allowing me to see only the ROI. First of all, am I correct here?
Partly. Orthogonalising 1 wrt 2 and 3 removes variance explainable by
2 and 3 from 1, but it will have no effect on the fit of or statistics
associated with EV 1, because in the fitting of this regressor's
parameter estimate and estimate of this parameter's variance, the GLM
algorithm in effect ignores the component of signal that can be
explained by other regressors.
> Secondly, is it necessary to orthogonalise (2) wrt to (3) and/or (3) wrt to (2)? I am under the assumption that this is unnecessary as it does not effect my ROI but others have said something about the shared variance between my two regressors having some ill effect if I do not orthogonalise them to each other.
No. The GLM deals sensibly with the shared variance.
>
> And overall, is it even necessary to orthogonalise any of my EVs? The FSL documentation points out that most designs are already in this form but since this is strictly a resting-state analysis, is it necessary to do any or all of the above mentioned?
>
No, the documentation holds true here. Orthogonalisation effectively
forces the model to assign the shared variance to one EV (the one not
orthogonalised), which is usually not a good thing, as the fit of this
regressor becomes difficult to interpret, and it's variance estimate
will probably not be realistic.
Cheers,
Eugene
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