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Hi Helmut - thanks for your response. Yes, the data has already been realigned and normalized (as well as low pass filtered) within FSL. And I have the motion realignment parameters - which are stored in a .par file (which is basically a .txt file). The format looks the same (ie 6 columns = 6 regressors, 700 rows = 700 time points in the ts) between SPM and FSL. I don't plan on doing any additional preprocessing within SPM or FSL. I think that I can just input the normalized and realigned 4D functional .nii file to SPM and start the first-level analysis. Thanks, Joelle On Tue, Jun 9, 2015 at 3:30 PM, Joelle Zimmermann < [log in to unmask]> wrote: > Thank you Donald. Great - now it's clear to me about the motion > regressors. > > On Tue, Jun 9, 2015 at 3:07 PM, MCLAREN, Donald <[log in to unmask]> > wrote: > >> See below. >> >> On Tue, Jun 9, 2015 at 4:45 AM, Joelle Zimmermann < >> [log in to unmask]> wrote: >> >>> Hi Donald, >>> Thanks for your response. I actually already have motion correction done >>> as well in FSL, and have the .par motion parameter output, which I will >>> input as a regressor into SPM following the advice of some others on the >>> mailing list. >>> >> >> Yes. The par file values would be fine. >> >>> >>> SPM uses a non-linear normalization approach. The non-linear approach is >>> actually an affine transform+non-linear deformations. This is similar to >>> FNIRT in FSL. >>> So more specifically, does SPM first put functional to T1 space, and >>> then from there to standard space? That's the procedure I'm using with FSL. >>> >> >> No. Normalization takes an image and normalizes it to MNI space. This is >> the same in FSL. If you are saying you coregistered the EPI to the T1 and >> then normalized the T1 in FSL, you could do that in two steps in SPM as >> well. The programs are very similar. >> >> >>> >>> As to whether to include the motion parameters in the analysis. The >>> inclusion of the motion parameters usually reduces the noise in the model >>> and could lead to more accurate estimates; however, if you move with each >>> stimulus, then the motion would be correlated with the predicted HRF and >>> this would reduce the accuracy of the estimates. As long as you don't have >>> stimulus correlated motion, I would include them as a covariate. >>> That's an interesting point. Aren't the motion parameters included as >>> "Multiple Regressor"? I am fairly new to this, so correct me if I am wrong. >>> I mean that I would expect that the motion is REGRESSED OUT from the HRF. >>> So any patterns in the motion are being excluded from the brain activity >>> patterns.... >>> >> >> Yes. They are regressed out of the HRF task regressors. Thus, the >> patterns of motion are being excluded. The issue is that if the >> "timecourse" of motion is correlated with the HRF task regressors, then you >> will remove the brain activity effect. For example, say I rotate my head >> forward at the beginning of each block and back at the end of each block. >> The "timecourse" of motion will be correlated with my motion. If I include >> the motion "timecourse" as a multiple regressor, I might remove the block >> effects in my data - particularly if the rotation is delayed by a few >> seconds. >> >> >>> >>> >>> Thanks, >>> Joelle >>> >>> >>> >>> >>> On Mon, Jun 8, 2015 at 8:57 PM, MCLAREN, Donald < >>> [log in to unmask]> wrote: >>> >>>> If you use realignment in SPM, then you should use these images as the >>>> input into normalization (FSL or SPM). I would not repeat the realignment >>>> in FSL and then do normalization in FSL. You also need to smooth the data >>>> after normalization. >>>> >>>> I don't see why you would use SPM for some pre-processing, then switch >>>> to FSL for a single step, then switch back for smoothing. I would do all >>>> the pre-processing in FSL if you prefer the FSL normalization. >>>> >>>> SPM uses a non-linear normalization approach. The non-linear approach >>>> is actually an affine transform+non-linear deformations. This is similar to >>>> FNIRT in FSL. >>>> >>>> As to whether to include the motion parameters in the analysis. The >>>> inclusion of the motion parameters usually reduces the noise in the model >>>> and could lead to more accurate estimates; however, if you move with each >>>> stimulus, then the motion would be correlated with the predicted HRF and >>>> this would reduce the accuracy of the estimates. As long as you don't have >>>> stimulus correlated motion, I would include them as a covariate. >>>> >>>> Best Regards, Donald McLaren >>>> ================= >>>> D.G. McLaren, Ph.D. >>>> Research Fellow, Department of Neurology, Massachusetts General >>>> Hospital and >>>> Harvard Medical School >>>> Postdoctoral Research Fellow, GRECC, Bedford VA >>>> Website: http://www.martinos.org/~mclaren >>>> Office: (773) 406-2464 >>>> ===================== >>>> This e-mail contains CONFIDENTIAL INFORMATION which may contain >>>> PROTECTED >>>> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is >>>> intended only for the use of the individual or entity named above. If >>>> the >>>> reader of the e-mail is not the intended recipient or the employee or >>>> agent >>>> responsible for delivering it to the intended recipient, you are hereby >>>> notified that you are in possession of confidential and privileged >>>> information. Any unauthorized use, disclosure, copying or the taking of >>>> any >>>> action in reliance on the contents of this information is strictly >>>> prohibited and may be unlawful. If you have received this e-mail >>>> unintentionally, please immediately notify the sender via telephone at >>>> (773) >>>> 406-2464 or email. >>>> >>>> On Mon, Jun 8, 2015 at 6:59 AM, Joelle Zimmermann < >>>> [log in to unmask]> wrote: >>>> >>>>> Hi Luis, >>>>> >>>>> Thanks very much for your response. So you would suggest: >>>>> 1) doing realignment in SPM on the non-normalized images, to get the >>>>> rp.txt motion parameter file >>>>> 2) doing normalization in FSL. (question: would this be done on the >>>>> realigned images from SPM or the original?) >>>>> 3) doing a first-level analysis in SPM on the normalized (from FSL) >>>>> images, with the rp.txt file that I made in step #1 as an additional >>>>> regressor >>>>> >>>>> Is that right? >>>>> >>>>> A few more questions: >>>>> How necessary/standard procedure is it to include the realignment >>>>> parameters (the rp.txt) as additional regressors? >>>>> >>>>> In the GUI, does SPM do normalization in a linear way (ie linear >>>>> registration?). >>>>> >>>>> Thanks, >>>>> Joelle >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Mon, Jun 8, 2015 at 12:32 PM, Luis Morís < >>>>> [log in to unmask]> wrote: >>>>> >>>>>> Hi Joelle, >>>>>> >>>>>> Why not take the non normalized images and do the realignment on >>>>>> them? Then you can normalize the images after realigning them using FSL. >>>>>> >>>>>> I wouldn't expect the realignment parameters calculated this way to >>>>>> be very accurate. In fact I would expect them to be artificially close as >>>>>> when normalizing you are bringing images into a template space, and >>>>>> therefore realigning them indirectly. >>>>>> >>>>>> Cheers, >>>>>> >>>>>> Luis >>>>>> >>>>>> >>>>>> On Mon, Jun 8, 2015 at 11:39 AM, Joelle Zimmermann < >>>>>> [log in to unmask]> wrote: >>>>>> >>>>>>> Hi - I'm doing a first-level analysis of fMRI data in SPM, and want >>>>>>> to use realignment parameters (rp text file in the preprocessing folder) as >>>>>>> additional regressors (ie select the rp file as "Multiple regressor"). >>>>>>> >>>>>>> However, I've already done normalization in FSL (using nonlinear >>>>>>> registration). >>>>>>> >>>>>>> Can I input the already normalized 4D functional to SPM, and run >>>>>>> realignment on this in SPM, to get that rp text file that I can later input >>>>>>> as a regressor? >>>>>>> >>>>>>> I understand that in SPM, the procedure is usually to do realignment >>>>>>> (followed by coregistration, segmentation), and only then normalization. >>>>>>> >>>>>>> Thanks, >>>>>>> Joelle >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >