Thanks for the reply.
That is basically what I was worried about; altering the autocorrelations. I am not sure the confound regressor route is what I want either though, as I am familiar with using that when I expect some signal to be explained by my confound. In this case, for a hypothetical 10-volume fMRI run, I want something like this:
confound1=[0 1 1 0 1 0 1 0 0 1 ]
So that where there are 0's, the data for those timepoints are effectively ignored in fitting the model parameters / calculating residuals, etc. I would then be able to run a second analysis with:
confound2=[1 0 0 1 0 1 0 1 1 0 ]
To independently analyze the other half of the data. This would allow you to use the un-altered 4D fMRI file and design files that preserve auto-correlations.
It is not clear to me that the confound regressors in feat would accomplish this?
-Tynan
HI - I'm not sure it makes sense to do "split-half" analysis on FMRI timeseries modelling in this way - hard to avoid messing up the temporal autocorrelation in the data/model. It is possible to tell FEAT to "ignore" specific volumes just by adding in appropriate confound regressors - but you will end up with a vast model if you want to add a separate regressor for every time point ignored.
Cheers.
On 16 Sep 2013, at 17:34, Tynan Stevens <[log in to unmask]> wrote:
Hello,
I am trying to figure out what the best way to handle volume deletion for a FEAT analysis is.
My goal is to be able to do a split-halves analysis, by randomly assigning volumes in my original dataset to 1 of two subsets, and then analyzing these. I know I could do this by removing volumes from my time-series manually, and likewise from my response model, but I feel like this isn't very sophisticated, as it would alter the signal dynamics.
In AFNIs 3dDeconvolve I know there is an option to provide a list of 1s/0s to include/exclude volumes from the model fit. Is something like this possible in FSL?
-Tynan
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