Hi,
you are not really "de-"convolving anything in a GLM-type of analysis. You do convolve you EV, e.g. with the gamma HRF, to optain your model and then fit you data to it.
However, you may extract time-courses within a mask from your filtered_func_data using fslmeants and collapse this across epochs and subjects. Alternatively, you may use the PEs of a FLOBS model to "reconstruct" the associated fit or go for MELODIC.
Hope that helps, cheers-
Andreas
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Von: FSL - FMRIB's Software Library [[log in to unmask]] im Auftrag von Regina [[log in to unmask]]
Gesendet: Dienstag, 21. April 2009 07:21
An: [log in to unmask]
Betreff: [FSL] Plotting time series prior to deconvolution (verification of sensitivity of BOLD modeling)
Dear all,
I am interested in verifying the degree to which I am effectively modeling
the hemodynamic response to a certain event (original EV). In other words,
given it is a new paradigm, I would like to verify whether or not I might be
over or under-capturing the lenght of an event of interest.
The data I am currently analyzing were originally deconvolved using a Gamma
function. Upon running group level analyzes, I found a cluster that reached
significance. What I would like to do, as a means to see the original shape
of the typical response in an area that in principle is sensitive to this
original EV, is to plot the averaged time series for this cluster (group
average), prior to it being deconvolved with the canonical HRF (Gamma). I
believe that by doing so I would be able to get a sense of whether the Gamma
function in association with the event duration I am currently modeling are
a good option to most effectively model these responses (or whether I should
try something different, e.g., different function and/or event duration).
The way that I am currently planning on doing this is by using Featquery:
1)extracting the average time series within the cluster (binary) mask
mentioned above from my filtered_func_data for each participant (lower level
copes); 2) "manually" extracting the epochs of interest from these averaged
time series (i.e., when the event occurs); 3)averaging across participants
and epochs of interest to get the averaged time series in this area, prior
to deconvolution.
My question is: is there a more practical way of achieving this? Or is what
I describe above a good way to go about it?
Thank you very much in advance for any feedback!
Regina
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