I'm working with raw timeseries waveform data from a resting state
dataset. There's one run per participant, so no between-run
normalization is required. With Doug's help, I have mastered extracting
the timeseries information from individual ROIs in surface space. The
timeseries data is the product of the preproc-sess script, to which I
additionally added the -inorm parameter to extract the mean timeseries
for the entire volume. One concern I had was that correlations between
timeseries from different ROIs might be artificially inflated by scanner
drift. For example, if the overall signal increases or decreases as a
function of time over the course of the run or fluctuates periodically,
then for any two regions, their signals will go up or down together as a
result. This should in turn make the timeseries from these two regions
more correlated. I have no strong evidence that scanner drift is a
particular problem in my dataset, but it seems likely enough in a 6
minute run that I want to mitigate the problem.
I can't really put these data through selxavg, as I do not have a model
to fit for resting state data, and in any case would like to work with
the timeseries itself, rather than residuals or any other by-product of
an HRF model. I was wondering if simply subtracting the global mean
signal waveform from each ROI waveform would be a reasonable strategy
for removing drift, or if there are any standalone commands that are
called by some of the super-scripts that I can use without carrying out
a first level analysis.
Thanks,
Chris