Hi Boris,
no, that's not correct, contrasting different parts of a taylor polynomial expansion (what parametric modulation really is) does not make anyt sense, the 0th and 1st order components model different things (the constant term and the expansion by RT). Contrasting the parameter estimates for a normal and parametrically modulated regressor would therefore make as much sense as subtracting the speed of your car from the milage traveled.
You already 'clean up' your data for RT-related amplitude modulation of your HRF by simply including the parametrically modulated regressor in your GLM in the first place. This is true for all GLM statistics in general, not only SPM.
So when ' cleaning up' is what you want, use [1 0].
Cheers,
Bas
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Division of Brain Research
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Utrecht University Medical Center
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-----Oorspronkelijk bericht-----
Van: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] Namens boris suchan
Verzonden: woensdag 15 april 2009 10:16
Aan: [log in to unmask]
Onderwerp: [SPM] Regressor Covariate or parametric modulate on first level analysis
Hi all,
we would like to model out some variables (like RT) at the first level.
If I would use RT as a regressor I must have the same number of variabels like I have scans.
But I have as many variables as conditions.
If I would use additionally parametric modulation and than set up a contrast between the "normal" regressor and the parametric modulated regressor (1 -1) and analyse the con image at the 2nd level. Does that mean that I have the activation "cleaned " by my additional variable?
Or are ther anay other ways to do this?
Many thanks in advance
bs
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Priv.- Doz. Dr. Boris Suchan
Institute of Cognitive Neuroscience
Department of Neuropsychology
GAFO 05/613
Universitätsstrasse 150
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