Wednesday, June 6, 2012

temporal compression for different image acquisition schemes

A question was posted on the mvpa-toolbox mailing list about how do temporal compression: which volumes should you pick to correspond to an event, particularly if the timing of the events is jittered. What follows is a version of my reply.

The case when stimulus onset is time-locked to image acquisition is the easiest. In this case I generally guess which images (acquired volumes) should correspond to peak HRF and average those. This is straightforward if the TR is short compared to the time period you want to temporally compress (e.g. a twenty-second event and two-second TR) but can get quite dodgy if the events and TR are close in time (e.g. events that last a second). In these cases I generally think of analyzing single timepoints or generating PEIs.

If stimulus onset is jittered in relation to image acquisition I follow a similar logic: if the jitter is minimal compared to the TR (e.g. events start either half or three-quarters of the way through a 1.5 second TR) or to the number of volumes being averaged (e.g. a block design and 12 volumes are being averaged each time) I'll probably just ignore the jitter. But if the jitter is large (e.g. 4 sec TR and completely randomized stimulus onset) I'll think of PEIs again.

By PEIs I mean "parameter estimate images" - fitting a linear model assuming the standard HRF and doing MVPA with the beta weights. I described some of this and presented a comparison of doing averaging and PEIs on the same datasets in "The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines".

As a general strategy I look at the TR, stimulus timing, and event duration for each particular experiment and question then think about in which volumes the BOLD response we're looking for probably falls. If it's a clear answer, I pick those volumes. If not, I design PEIs or reformulate the question. None of this is a substitute for proper experimental design and randomization, of course, and fitting PEIs is not a cure-all.

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