I have a fast event-related task for which I would like to conduct a whole brain searchlight MVPA analysis. From what I’ve read, it seems like I should use single-trial estimates, however, some papers seem to use the raw beta parameter estimates, and others, a la Misaki et al. (2010), use t-statistics.
At this point, I’ve generated a 4D beta series using the LS-S procedure (Mumford et al., 2012) for each participant, but I want to make sure the searchlight I run will have the highest chance for success possible - I’ll be using a linear SVM classifier.
What I would like to know is how necessary is it to transform my beta series estimates to t-statistics, and if I need to do so, what the best way to perform that transformation might be. For example, I know you divide the beta estimates by the std. error to get the t-statistic, but I’m not sure as to how to calculate the std. error. (i.e. trial-wise? voxel-wise?). It might be worth noting that I still plan to detrend and normalize (zscore - this IS different than using the t-statistic, right?) the data prior to conducting the searchlight, and that I’m using Python/PyMVPA to do so.
Any specific help or general advice is appreciated. I really love how supportive the community has been, so far - it’s been an awesome help - and I’ll take whatever I can get! This is my first foray into MVPA, and the amount of “experimenter degrees of freedom” is pretty overwhelming!!
As a bonus question: should I have used the method from Turner et al. (2012) to generate my parameter estimates? Reading other papers has provided mixed advice regarding which method to choose. If so, I’m a little unclear on what the model looks like i.e. for the LSS method I used there’s a regressor for 1) the current trial, 2) all other trials of that type, 3) all other trials of the other type, 4) and nuisance variables such as motion parameters. I did the LS-S modeling in SPM, if that makes a difference.
Thanks again, everybody!