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Thanks Ken! It turns out my problem was all hinging on my understanding of the hrf--I've seen the "canonical" hrf presented many times, and it always looked like the attached (cut without permission from from Daniel A. Handwerker, John M. Ollinger, Mark D'Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, NeuroImageVolume 21, Issue 4, , April 2004, Pages 1639-1651...). But I'd never looked closely at the SPM one, and I didn't realize there were several different shapes in use in the literature. Thanks for your help!! Jess -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Ken Roberts Sent: Monday, June 23, 2008 10:42 AM To: [log in to unmask] Subject: Re: [SPM] question re: using and intepreting spm_hrf.m Hi Jessica, > I'm unclear on how to interpret the output of spm_hrf: When I call it > with a TR of 1, I get a vector of 32 entries, and when I plot them, they > look like the canonical HRF; but the units are neither time or TRs > (which should be equivalent since TR = 1 s). The units are in TRs. Give the following code a try: % this plots hrf with TR=1 plot(0:length(spm_hrf(1))-1, spm_hrf(1)); hold on; % this plots hrf with TR=2 plot(0:2:2*(length(spm_hrf(2))-1), spm_hrf(2)/2, 'r') % plots baseline plot(0:30, zeros(31, 1), 'g'); One thing to remember is that the first element in an array is element 1, but the first element returned by SPM represents a TR starting at time 0, or exactly synchronous with the onset of the stimulus. The extra code here adjusts the x-axis so that it corresponds to seconds after stimulus presentation. It also normalizes the heights of the two hrfs wrt to each other. Does this look better? I see the hrf's peaking at 5s, and returning to baseline at 12s. In my experiments, the peak seems pretty accurate, but we generally see a return to baseline a little faster. If you model with the three canonical basis functions (hrf + time_deriv + dispersion_deriv) your data should be fit pretty well, even if your hrf in your experiments has a different peak latency or width. > When I set up a stick function of onsets, say [1 0 0 0 0 0 1 0 0 0 0 0], > and convolve that with the output of spm_hrf(2), ... That should work, and give you the signal in units of TR. Ken ---------------------------------------------------- Ken Roberts Woldorff Laboratory Center for Cognitive Neuroscience, Duke University (919) 668-1334 ----------------------------------------------------