[pymvpa] Event-related designs - Is the modeling of the HRF not recommened?

[Michael Notter]

Hi,

I'm used to the univariate approach of analysing neuroimaging data and therefore always try to put an HRF through my data. The modeling of an HRF also allows to analyse (rapid) event-related designs and not just block designs. The PyMVPA tutorial shows under http://www.pymvpa.org/tutorial_eventrelated.html also very nicely how this can be done.

This leads me to my opinion that the modeling of such an HRF regressors should be the way to go, if the stimuli are presented by onself. In other words, if you have an event-related design with clearly separated stimuli, the modeling of the HRF is the way to go, correct?

But even though this is the case for many MVPA studies, most of them chose to take another approach. They either only take volumes that are expected to contain the peak of the BOLD response, average volumes around the peak to one volume or use a GLM (in which the HRF is modeled) to create beta maps on which they than apply the MVPA. Even though they would have enough volumes (ISI of 6-12s) to model the HRF correctly.

Is there a specific reason why this is the case? Is an MVPA analysis that models the HRF or also considers the time dimension less robust and not recommended? Has it been shown to be less accurate or is the usage of only peak BOLD volumes the best way to go?

On this note: Is it better to present stimuli separated by an ISI of at least 8s? What are pitfalls if I want to record an event-related design.

Thanks,
Michael 		 	   		  
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