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Hi all, I have quite a few of questions regarding slice timing and stimulus paradigm file timing in sparse sampling (ie clustered volume acquisition). I was able to figure out the basics from the FSL archives but I did not fully understand the details. I am new to to FEAT and thus (?) some of the questions are rather simple and/or their logic might fall, but I'd rather ask a stupid question than reach a wrong conclusion by myself. Sorry for any inconvenience this might cause :) I hope the answers will benefit all of us who use sparse sampling designs. *** Background *** In a fMRI auditory experiment, to avoid the confounding effect of the acoustical scanner noise, we collect just one full volume EPI (24 slices) every 10 seconds. It takes 1.2 seconds to collect all the 24 slices, which is then followed by a pause of 8.8 seconds during which no EPI data is collected. A single auditory stimulus (duration 0.3 seconds) is presented 4 seconds before each EPI onset. Thus, there is just 1 stimulus for each EPI. The order of collecting the EPI slices is from bottom of the stack (basal) towards the top of the head (1-24). We use a 3T Siemens Trio with a birdcage head coil. For describing the stimulus timings, we use the 3-column paradigm file format for "Basic shape". For several reasons we would rather stick to the 3-column format rather than the 1-column format. We have 9 different stimulus categories (plus REST) presented in a random order, thus we have the corresponding 9 paradigm files and define REST implicitly (ie REST epochs are where none of the 9 paradigm files suggests there would have been a stimulus). Thus, the sequence of events (in real time) is something like this: EPI0 (0 sec) --- Stim1 (6 sec) --- EPI1 (10 sec) --- Stim2 (16 sec) --- EPI2 (20 sec) ... Despite the short stimulus duration, the above paradigm seems to work extremely well - the data we have collected so far have shown very strong activations in auditory areas. Thus all of the questions below mainly serve to tune the analysis correctly to get the best out of the data, and to reduce artifacts that arise from suboptimal analysis settings. Hopefully understanding better what happens at TR=ISI=10 will also allow us to improve our paradigms towards a more rapid even-related design. ********** QUESTIONS: ********** *** Filtering and prewhitening *** For the above values TR = 10 sec / EPI length = 1.2 sec / stimulus duration = 0.3 sec, what values would you recommed for (i) Data/High pass filter cutoff [sec], (ii) Pre-sats High pass filter [ON/OFF], and (iii) Stats/Use FILM prewhitening [ON/OFF]? *** Slice timing and the correct use of slice timing correction in different Convolution models (None or Gamma) *** If I understood previous comments correctly, then without slice-timing correction, FEAT would assume that the 24 slices (that form one full volume) were taken with timing of each slice spread out evenly during the 10 seconds time period. Here I assume that time 0 = onset of first slice of first full volume EPI. Thus, as far as FEAT is concerned, slice 1 = 0 seconds, slice 2 = 0.42 seconds, slice 3 = 0.83 seconds , ..., slice 23 = 9.17 seconds, and slice 24 = 9.58 seconds. Thus for the top slice (number 24) there is a discrepancy of 9.58 (FEAT value) - 1.15 (reality) = 8.43 seconds. Ok so far? Therefore, any Convolution model that would try to take into account the timing between the stimulus and EPI (gamma function etc) would go increasingly wrong towards the top slices. Is this correct? If I would like to use a gamma function, would the "Add temporal derivative" (alone, without slice timing correction) be expected to correct for such a large timing discrepancy correctly? Then, to analyze these kind of data without slice-timing-correction, I guess I should only use Convolution models that do not take the timing between the stimulus and EPI into account at all (is Convolution=None my only sensible possibility) ? In this case, I guess I should also turn off the "Add temporal derivative"? A second point of confusion was what happens when I apply slice-timing correction in the above situation. If I understood correctly, with slice-timing correction, FEAT assumes that all 24 slices were taken instantenously at a time point halfway through the acquisition. However it was not entirely clear to me whether this halfway, in this example, would be at 0.6 seconds (1.2 seconds/2) or at 5.0 seconds (10.0 seconds/2). I guess 5 seconds is correct? A comment (I hope I understood this correctly): In using slice-timing correction, I attempt to fix the slice timing in a way that it would seem that all 24 slices were taken instantenously at midway of the EPI; thus the first and last slices would still be 0.6 seconds off the _actual_ slice collection time, but in the current application this is accurate enough. (Steve told me that there is also a way to get the slice timings corrected to the _really_ exact values, but for now this is not necessary). *** 3-column paradigm file timing corrections when using slice timing correction *** If the answer to the previous question is 5 seconds, then if I do apply slice-timing correction, I should adjust my stimulus timings that are listed in the 3-column paradigm files accordingly. If I understood Steve correctly, then if in REAL time (0=onset of first EPI) the first two lines of my 3-column paradigm file (for eg stimulus category 1) would be, e.g., 6 0.3 1 ; in reality, first stimulus occurs at 6 seconds after onset of EPI0 ( = 4 seconds before EPI1) 16 0.3 0 ; second stimulus occurs at 16 seconds after onset of EPI0 ( = 4 seconds before EPI2), but it is not a category 1 event ... then to correct for the FEAT assumption that the first EPI started at 5 seconds (real time) = 0 seconds (FEAT time), I should shift the paradigm file by 5 seconds, and thus list instead 1 0.3 1 ; first stimulus is listed to start at 1 seconds (6 seconds real time minus 5 seconds FEAT time = 1 second), and FEAT thinks that EPI1 occurs at 4.0 seconds 11 0.3 0 ; etc. ... Would this be correct? *** How are the above adjustments correctly taken into account when setting gamma function values? *** If i would be analyzing these sparse sampling data with a gamma function, using both (i) the slice timing correction and (ii) the correction for the timing in the 3-column paradigm file, then should one adjust the "mean lag" value of the gamma function to reflect the time corrections? For example, if I would expect that the peak of the HDR to the auditory stimulus is reached at 4.0 seconds after the onset of the stimulus, should I set the gamma function mean lag = 4.0 seconds (time from stimulus onset to expected HDR peak), or should I add 5 seconds to this value to compensate for the -5 seconds time correction in the paradigm file, and thus set mean lag = 9.0 seconds? If I use slice timing correction in a sparse sampling experiment and analyze that by using a gamma function, would it be equally good practice to set the "phase" of the gamma function to -5 seconds (or +5 s???) as compared with shifting the latencies in the 3-column paradigm files by -5 seconds? Would the method of subtraction (from paradigm file or from gamma phase value) affect the correct choice of mean lag value? *** Analysis of these data with Convolution = None, and 1-column paradigm file format *** I would expect the above slice timing correction and 3-column format paradigm file timing corrections would not be needed at all for Convolution model = None, but only become meaningful if one would like to use a model that does take the timing between the stimulus and EPI into account (e.g., a gamma function)? When I have tried to analyze these data with a 1-column paradigm file format (ie no accurate stimulus timings are listed), and used Convolution = None (ie stimulus-to-EPI latency should not be modelled?), I would expect that the Slice timing correction and Add temporal derivatives should not affect the result at all. However, in reality, both Slice timing correction and Add temporal derivatives, apparently independently of each other, affect the analysis of the result somewhat. Slice timing correction had a larger effect in the top slices (removed some activations that appeared to be artefacts), which in some way makes sense to me because this is where the timing discrepancy is largest - however the finding that these parameters, in this specific 1-column paradign format analysis, without Convolution, had ANY effect, was unexpected. Any suggestions as to why this happens? Or is my reasoning that these should not have any effect unfounded? **************** Phew. Your advice will be highly appreciated! Thanks in advance, Tommi -- Tommi Raij, M.D., Ph.D. Research Fellow MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging Building 149, 13th Street, Mailcode 149-2301 Charlestown, MA 02129 U.S.A. [log in to unmask] FAX 1-617-726-7422