On Jan 20, 2014, at 10:55 AM, David Watson wrote: <<<< As it happens I have a block design so perhaps I could get away with just offsetting the timeseries, although convolving an HRF might still be preferable. But if I ever wanted to do an event-related design where measuring timings precisely is more important then I'm not sure if simply offsetting the timeseries would still be considered acceptable. Also, are there any other commonly used methods of accounting for the lag that I have missed? >>>>
Your block design will allow you a lot of flexibility with your analysis; I'm doing a rapid event-related design, and so using a TR lag proved non-viable, since regardless of how you lag the volumes from a rapid event-related experiment each volume is polluted with the hemo response from a bunch of different event presentations. Then I stumbled onto this paper [1] by Turner et al (2012). The idea is basically to do a univariate-style regression using an HRF convolved with your event schedule, except each event gets its own regressor and the other event types get their own nuisance regressor. You might take this idea and implement it a number of ways; I've done it a ridiculously computationally intensive way, in which each event in the experiment gets its own design matrix. For instance, if the experiment consisted of 10 presentations each of stimuli from four stimulus classes (say, distinguishing between pix of faces, houses, chickens, and pizzas) then there would be 10*4 design matrices, each encompassing the totality of stimulus presentations. One of the design matrices for a "face" event would have the following five regressors (not counting motion / drift / etc.): current_face, other_faces, houses, chickens, pizzas the idea is to soak up the variance in the HR owing to the event presentations at each of the trials, so that the variance left over is parcelled into the 'current' event regressor. I use a volume of these regression coefficients as input to the classifier. That's a super terse and crappy description, sorry. The paper has better details, although the paper itself is not as comprehensible as might be wished for. The idea in the paper is based on this [2] somewhat more comprehensible Mumford et al (2012) paper, which is a similar idea, except it jams _all_ the other nuisance events into a single regressor; so you'd only have two regressors in each design matrix: current_face, other_stimulus_presentations AFNI provides some support for doing a variation of this techniqe (called "least squares separate") but it does not imo do it the right way, which is to build a whole design matrix for each event presentation, as described above. The bad thing is that it takes _forever_ to run; luckily, I have access to a beast of a 16-core Xeon server I can destroy with this analysis, or it wouldn't be feasible to use. Shane [1] Turner, B. O., Mumford, J. A., Poldrack, R. A., & Ashby, F. G. (2012). Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. NeuroImage, 62(3), 1429–1438. doi:10.1016/j.neuroimage.2012.05.057 [2] Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59(3), 2636–2643. doi:10.1016/j.neuroimage.2011.08.076
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