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You should probably use as the hpf double the max length of time between the same stimuli. If that happens to be 200sec, then I guess an hpf of 400 is correct. I don't remember if you can specify an hpf for each session, so take the max across sessions. However there will be a lot of low-frequency noise still included. In the future if you want to do such a design, you should use ASL. On 08/22/2012 04:35 PM, Jessica Wojtalik wrote: > thank you, they are treated separately. Find the level1 design attached > > On Wed, Aug 22, 2012 at 3:28 PM, Chris Watson > <[log in to unmask] > <mailto:[log in to unmask]>> wrote: > > Can you attach a picture of your design matrix? When you create > the matrix, are you treating each session separately, or are you > concatenating them all together? You should treat them separately. > > > On 08/22/2012 04:27 PM, Jessica Wojtalik wrote: >> I apologize, my original message to the spm list serve was >> not included. I attached those files to represent the differences >> between the 128 and 400 filter. What I attached was the "explore >> design" for the 400 filter for the 3rd scanning session high >> painful stimulus regressor, which is why you see the regressor >> onset 10 minutes in. The first stimulus occurs 50 seconds into >> the scan and lasts for 40 seconds, alternating with rest. >> The participant experiences four scanning sessions (A, B, C, D) >> with each session having 6 blocks of intermixed 3 high and 3 low >> painful stimuli with 40 seconds of rest between painful stimuli. >> My initial concern was that the default filter of 128 was >> removing much of the variance (see Design attachments). My >> concern now is if I am interpreting the explore design right for >> the 128 filter, and if adjusted as suggested with the "doubled >> longest mean interval difference" (e.g., 400seconds) am I doing >> it right? What is the right way to handle if the default >> high-pass filter in SPM is removing variance as the SPM8 manual >> states (p.69): "The frequency domain graph is useful for checking >> that experimental variance is not removed by high-pass filtering. >> The grayed out section of the frequency plot shows >> those frequencies which are removed" >> >> Here was my original question with a description of the design. >> Dear SPM, >> This is my first go at an fMRI analysis and I need your expert >> help interpreting whether or not the high-pass filter >> default(128s) in SPM8 is removing too much variance from the >> active regressors? It appears the frequencies are spiking in the >> gray shaded area (or below the .008 hz threshold), which indicate >> frequencies removed by the high pass filter. I have attached for >> you 3 examples that have been consistent across subjects. Is >> it recommended to adjust the high-pass filter at this point? If, >> so what is the correct and most appropriate way of adjusting this >> high-pass filter? >> >> This is a block design fMRI study. It encompasses 4 sessions of 6 >> blocks of stimulus. Blocks are intermixed low painful and high >> painful stimulus. The block design is 40 seconds active and >> 40 seconds rest. The participants are only receiving these two >> different painful physical stimuli. They are not performing any >> type of task. I have attached the explore design output for >> session 1 high and low active regressors and a second session >> high active regressor for one participant from the level 1 >> model specification. >> >> Much appreciation! >> Jess >> >> >> On Wed, Aug 22, 2012 at 2:57 PM, Chris Watson >> <[log in to unmask] >> <mailto:[log in to unmask]>> wrote: >> >> The first stimulus for that participant was almost 10 minutes >> into the scan? >> Also, you mention that your blocks are 40s on - 40s off; I >> don't understand why you would need an hpf of 400 in that >> case. Can you explain the design in more detail? >> >> On 08/22/2012 03:44 PM, Jessica Wojtalik wrote: >>> Hi Gabor, >>> I apologize for the delayed response, wanted to make sure I >>> had understood your response which I greatly appreciate. >>> Just to followup, the longest mean interval between >>> subsequent onsets is 200. The onsets for this high stimulus >>> regressor for this particular participant are 587.5 seconds, >>> 827.5 seconds, 987.5 seconds (240+160)/2=200. Therefore, a >>> new high-pass filter of 400 is appropriate then? How >>> acceptable is it in the field to adjust the high-pass filter >>> in SPM for block designs with blocks with longer task times >>> (e.g., 40 seconds on and 40 off in my case)? Should an >>> adjusted high-pass filter become a concern for publications? >>> Are there publications out there providing support to adjust >>> the high-pass filter for longer block periods as you have >>> suggested? >>> >>> Thank you so much again for your helpful response, >>> Jess >>> >>> On Tue, Aug 14, 2012 at 2:21 PM, Gabor Oederland >>> <[log in to unmask] <mailto:[log in to unmask]>> wrote: >>> >>> Hello Jess, >>> >>> >>> this is to the Nyquist sampling theorem, see >>> http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem >>> . http://spm.martinpyka.de/?p=51 might also give you an >>> idea what happens if the high-pass filter is "too short". >>> >>> >>> You can determine an appropriate high-pass filter by >>> calculating the mean interval between subsequent onsets >>> of one regressor. For "HighA" in Design_1_high-1 the >>> onsets seem to be approximately 40 seconds, 160 seconds, >>> and 240 seconds (in case your TR = 2s). The mean >>> difference of subsequent trials is then (120 + 80)/2 = >>> 100 seconds. For "LowA", the onsets seem to correspond >>> to 100 seconds, 300 seconds and 360 seconds, so you get >>> (200 + 60)/2 = 120 seconds. For your high-pass filter, >>> enter a value that is at least double the size of the >>> mean difference, that is 240 seconds in this case, or >>> maybe e.g. three times the size which would be 360 >>> seconds then. The high-pass filter should be the same >>> for different subjects, so if there are different onsets >>> for different subjects calculate the mean intervals for >>> all of them and take the "longest" . >>> >>> >>> You might also want to take a filter which is twice as >>> large as the longest interval between trials of the same >>> condition. This would be 400 seconds in your case. At >>> least I have already read both options in papers. Also >>> have a look at an older thread, which is a collection of >>> various postings >>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind06&L=spm&D=0&1=spm&9=A&J=on&K=4&X=35D0701792D24A3126&Y=oederland%40gmx.ch&d=No+Match%3BMatch%3BMatches&z=4&P=5990336 >>> <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind06&L=spm&D=0&1=spm&9=A&J=on&K=4&X=35D0701792D24A3126&Y=oederland%40gmx.ch&d=No+Match%3BMatch%3BMatches&z=4&P=5990336> >>> >>> >>> One problem might be that you pick up noise depending on >>> whether you suffer from scanner drifts or not. >>> >>> >>> Hope this helps, >>> >>> Gabor >>> >>> >> >