Rafael
to add regressors to your matrix it is usually done via the parametric
modulation, i.e. each event or block of a condition get multiplied by
the values in your regressor as well as orthogonalized - if you add
regressors in the same way you do for the motion param there is no
orthogonalization (nor convolution) (then i *think* the whole matrix is
filtered)
as for the order well you have to decide ; see spm_orth this is like the
Gram-Schnidt so order definitely matters ; one way to go is starting by
the stuff you don't want! i.e. if you have an effect on the stuff you
want, that's great because everything else has been accounted for (so
your effect cannot be explained by the other factors since they took as
much as possible of the variance, cf orthogonalization)
good luck
cyril
> Dear SPM users
>
> In SPM, regressors (e.g. movement parameters) are not treated the same way
> as conditions (with onsets), e.g they wont be convolved. But will they be
> hp-filtered, and will they get orthogonalized as the conditions will? For
> confounds as motion parameters, this is not imortant. But I use additional
> regressors not as confounds but rather as regressors which explained BOLD
> variance is of main interest. (How could I check this by myself?)
>
> I previously read a lot about orthogonalization in the list archives. A more
> specific question: Is the spm orthogonalization a Gram-Schmidt
> decorrelation? And since the orthogonalization works on declining order
> between regressors in the design matrix, do you define these conditions of a
> task first, that are of most interest?
>
> Thanks a lot for any useful comment
>
> Best, Rafael
>
>
>
--
Dr Cyril Pernet,
fMRI Lead Researcher SINAPSE
SFC Brain Imaging Research Center
Division of Clinical Neurosciences
University of Edinburgh
Western General Hospital
Crewe Road
Edinburgh
EH4 2XU
Scotland, UK
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tel: +44(0)1315373661
http://www.sbirc.ed.ac.uk/cyril
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