Hi again!
Came across a follow-up question related to this.
As an example: Let's say we have a 5 min fMRI run during which we have information about a physiological process (concentration of some metabolite) from minute 2 until minute 4. Our goal is to preform a GLM using 3dDeconvolve but only for the period of time where we have this information (i.e. min 2-4).
The question arises when creating the stim-file. It's a file that in arbitrary units describe the levels of this metabolite (during min2-4). Currently we create a single column .1D file where each row matches one TR. We fill with zeroes until 2 min, then the values come until 4 minutes and zeroes until the end of the scan 4-5 min. We implement it via:
-stim_file k sname sname = filename of kth time series input stimulus
*N.B.: This option directly inserts a column into the
regression matrix; unless you are using the 'old'
method of deconvolution (cf below), you would
normally only use '-stim_file' to insert baseline
model components such as motion parameters.
We do match the number or rows to the number of TRs here but do you have to for -stim_file? Or does it stretch/fit the file data across the whole run? (hence making the zero-filling important). Then we use -nfirst and -last to specify which part we want to be run in the regression.
Is this approach fine?
Because I also saw the option:
[-input1D dname] dname = filename of single (fMRI) .1D time series
where time run downs the column.
This also seem to make sense. What is the difference between -stim_file and -input1D? Would the stimfiles look the same with the need of zero filling?
The documentation hinted that -stim_files should be use for baseline stuff like motion regressors. I currently use it to regress out physiological noise and now trying to make a regressor of interest from our metabolite measures. Perhaps -input1D is better?
Thanks!
Edited 5 time(s). Last edit at 03/29/2018 08:13AM by Robin.
