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

[Hanson, Gavin Keith]

Check out Misaki, Kim, Bandettini, & Kriegeskorte, 2010 (Comparison of multivariate classifiers and response normalizations for pattern information fMRI. NeuroImage). They mention that for block or slow ER designs, beta estimates and raw data (averaged across images in a block for a block design, or the single time point for slow ER design) basically amount to the same thing (p. 105). They ultimately recommend calculating t-statistics by dividing beta estimates by std. err estimates, because this tends to de-weight noisy voxels and improved classification in their analysis (this has been my experience as well).
In the case of a slow-ER design, there is not yet any clear consensus as to the necessity of a HRF model in the analysis. In my opinion it is recommended, because it can really help model out noise – most method papers I’ve read perform a GLM to model the HRF as a standard preprocessing step. It is certainly not less robust, and has been found to increase classification performance in at least the one paper. But there are a large number of ways to handle slow ER data (concatenate spatiotemporal data together into a single feature set, average raw data across the ISI, boxcar), and they each have advantages and disadvantages. It’s not bad to just take raw data. For example, if you have a large number of stimuli, then individually modeling the HRF to each stimulus can be computationally intensive, and is unnecessary because the large number of exemplars will serve to render the occasional noisy voxel irrelevant.
Also, the use of beta maps from a GLM (often performed outside PyMVPA, in SPM, FSL or AFNI to take advantage of better HR functions and all that) is identical to using the output of the GLM step outlined in the PyMVPA tutorial, I believe.
As for ER design, the larger the ISI, the clearer the patterns of activity would be, as they are less influenced by pervious cortical activity. You rarely see a fast-ER design in MVPA for just that reason. 8-10 seconds is certainly common, and anything too much shorter would absolutely require a HRF modeling/GLM step do help separate temporally overlapping HR activity, as is mentioned in the event-related PyMVPA tutorial.
Hope this helps, and I’ll be interested to see what other people weigh in with,
- Gavin
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Gavin Hanson, B.S.
Research Assistant
Department of Psychology
University of Kansas
1415 Jayhawk Blvd., 534 Fraser Hall
Lawrence, KS 66045

On Nov 13, 2014, at 5:40 PM, Michael Notter <michaelnotter at hotmail.com<mailto:michaelnotter at hotmail.com>> wrote:

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 underhttp://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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