Hello all,
I have sent this post to both SPM and FSL so I
apologize if anyone receives it twice.
I was just thinking about the accurate use of basis
functions to model fMRI time series data. They are
used to model variance in the shape of the hemodynamic
response function. However, what if the model of the
neuronal response is not accurate, for some reason it
doesn't accurately follow the stimulus paradigm. Then
the basis set fit to the data will be accounting for
the variance in the HRF AND the mismatch between the
REAL neuronal response and what we assume it to be. So
then the question is how do we tell what is HRF
variance and what is neuronal variance?
And if this is true. Then the approaches that apply
restrictions on the basis function fits (Woolrich et
al 2004, Friman et al. 2003, Calhoun et al. 2004,
Worlsey et al. 2006) would need to be less
restrictive? Or restrictive in a different way? And
how does this idea affect results based on fitting a
basis set to data and then determining the delay in
the data or amplitude variations?
I would love to hear all feedback and thoughts on this
topic.
Jason Steffener.
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