> On Dec 20, 2015, at 9:23 PM, ZUO, Nico <[email protected]> wrote: > > > Dear Dr. Greg Burgess and other HCP colleagues, > > Many thanks for your quick and detailed reply and I have benefited much. > Currently I used the S500 dataset. I have still questions about the > preprocessed data and please help me to clarify. > For both the volume and grayordinate data, you said “the level1 fsf files are > only included for reference or for modification. If it is your goal to > include nuisance confound regressors, then it may be worthwhile”, so you mean > the provided processed pre-results, including Feat level1 and level2 results, > has NOT been regressed out the head motion and white matter/CSF confound > nuisance ? Since I have checked the *.fsf configuration file in the both > level1 and level2 folder and I found the regression items have not been set, > which are as following, > <log> > …… > # Motion correction > # 0 : None > # 1 : MCFLIRT > set fmri(mc) 0 > …… > # Add motion parameters to model > # 0 : No > # 1 : Yes > set fmri(motionevs) 0 > set fmri(motionevsbeta) "" > set fmri(scriptevsbeta) "" > …… > </log>
Sorry if I was unclear. There are no additional confound regressors removed from the data (e.g., no regression of motion parameters, CSF or WM, motion spike regressors, or FIX noise regressors). As an aside, the fsf file describes processing that occurs AFTER the minimal preprocessing described in Glasser et al. 2013. Rigid-body motion correction was applied in the minimal preprocessing pipelines using a customized mcflirt. > So, if I want to get the group-average activation areas for each task, the > higher-level post-state analysis FSL/feat based on the provided level2 > results (without regressing out the cofound nuisance) is able to achieve a > reasonable results ? The task fMRI data will have artifact and noise in the timeseries. These are most problematic for task fMRI if they are confounded with task contrasts of interest (i.e., greater during one condition than another). If you decide that you are concerned about confounded sources of noise, you should investigate using some denoising procedure. > The second question is purely for the usage of the FSL/Feat about setting the > regressing cofounds. (1). In the “Stats” Tab, does the “Standard Motion > Parameters” item will call a default motion matrix file ? You should not use that option with the minimally preprocessed task fMRI data, because the rigid-body motion correction was already performed and you don’t want to do it again. You should use use the Movement_Regressors.txt file, or some portion of it, as a confound EV. > (2). Except the Motion parameters, if I want to remove the white matter/CSF > confounds, it is the right way to set it by “Add additional confound EVs” ? > (This requires me to firstly generate the regressor file by fslmaths) This is an acceptable way to do it. > Thank you so much, > Nico Zuo > >> >> -- >> >> >> -------------- >> PhD, Brainnetome Center >> NLPR, Institute of Automation >> Chinese Academy of Sciences >> >> -----Original Messages----- >> From: "Greg Burgess" <[email protected]> >> Sent Time: Saturday, December 19, 2015 >> To: "ZUO, Nico" <[email protected]> >> Cc: [email protected] >> Subject: Re: [HCP-Users] Questions for the *.fsf file for task activation >> analysis by FSL/Feat >> >> Two small comments: >> >> 1) As noted in Barch et al. 2013, the EMOTION and LANGUAGE tasks do not >> include a fixation baseline condition. Therefore, activation estimates for >> an individual condition (e.g., Fearful faces) will not be valid. The only >> valid estimates are between conditions (Fearful faces vs. neutral shapes). >> >> 2) I mis-spoke below. Feat automatically demeans the timeseries data and >> the design matrix (rather than adding a B0 intercept term to the GLM). >> Either way, you should not demean the task data before running it through >> Feat. >> >> --Greg >> >> ____________________________________________________________________ >> Greg Burgess, Ph.D. >> Staff Scientist, Human Connectome Project >> Washington University School of Medicine >> Department of Neuroscience >> Phone: 314-362-7864 >> Email: [email protected] >> >> > On Dec 18, 2015, at 11:06 AM, Greg Burgess <[email protected]> wrote: >> > >> > Hello Nico, >> > >> >> On Dec 18, 2015, at 9:39 AM, ZUO, Nico <[email protected]> wrote: >> >> >> >> >> >> Dear HCP Colleagues, >> >> >> >> Here I want to obtain an group-average areas for a task activation, for >> example for the Emotion task, and I want to get the activation areas either >> from FEAR or NEUT task by constrast to the fixation duration. The HCP >> website said that the Q1 release contained such kind of (mybe) data, >> >> >> >> >> http://www.humanconnectome.org/documentation/Q1/data-in-this-release.html#group_average >> , called HCP_Q1_GroupAvgUnrelated20.zip, however I did’nt find the entrance >> for downloading from the HCP database. I am not sure it is not available any >> more. >> > >> > >> > If at all possible, you should move to the latest releases of task fMRI >> data (500 subjects and 900subjects data releases). The later releases >> include subject-level (aka Level 2) FEAT outputs, making it unnecessary to >> run FEAT on the minimally-preprocessed images at all! >> > >> > >> >> Additionally, if I want to find these activation areas from the >> minimally processed data (the path in the S500 release (based on volume >> data) is like “…100307/MNINonLinear/Results/tfMRI_EMOTION_LR…”), but I have >> a little confusion about the adhered .fsf file, >> tfMRI_EMOTION_LR_hp200_s4_level1.fsf . The processed task fMRI volume data, >> for example, tfMRI_EMOTION_LR.nii.gz, has been detrened, but not been >> regressed out the head motion (although it contains the transformation file, >> Movement_Regressors.txt ) and white matter/CSF. And it has not been >> filtered to removing the noise. I am not sure my understanding is right ? >> However, it seems that the confiuration file, >> tfMRI_EMOTION_LR_hp200_s4_level1.fsf, is directly acting on the unpolished >> data, tfMRI_EMOTION_LR.nii.gz. I am not sure it is right for calling >> FSL/Feat ? >> > >> > >> > The level1 fsf files are only included for reference or for modification. >> If it is your goal to include nuisance confound regressors, then it may be >> worthwhile. Otherwise, stop and use the publicly available analyzed feat >> outputs. >> > >> > Additional information about the processing is also available in the >> follow two papers: >> > Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, >> B. L., Corbetta, M., et al. (2013). Function in the human connectome: >> Task-fMRI and individual differences in behavior. NeuroImage, 80, 169–189. >> http://doi.org/10.1016/j.neuroimage.2013.05.033 >> > Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., >> Fischl, B., Andersson, J. L., et al. (2013). The minimal preprocessing >> pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. >> http://doi.org/10.1016/j.neuroimage.2013.04.127 >> > >> > I will try to answer some of your questions here. The >> minimally-preprocessed image files (for example, tfMRI_EMOTION_LR.nii.gz) >> have NOT been demeaned or detrended. The minimal preprocessing for NIFTI >> volume files essentially performs distortion corrections, rigid-body motion >> correction, and registration to a group-template (see Glasser et al Figure >> 19). The CIFTI grayordinate files map voxels to surface and subcortical gray >> matter, which involves some resampling and minimal spatial smoothing >> (Glasser et al Figure 20). >> > >> > The Feat lower-level analysis subsequently performed spatial smoothing >> (4mm FWHM in volume, or several different levels of smoothing in CIFTI >> grayordinates), highpass temporal filtering (using fslmaths -bptf with 200 >> second cutoff), and prewhitening using FILM. There are no additional >> confound regressors removed from the data (e.g., no regression of motion >> parameters, CSF or WM, motion spike regressors, FIX noise regressors). Be >> forewarned that these tend to overlap with the task design regressors. More >> details are included in Barch et al. 2013. >> > >> > >> >> Another question is for processing the filtered/regressed/demean-ed >> tfMRI_EMOTION_LR.nii.gz (I assumed my above understanding is right so I >> continue to try…). The demean-ed task fMRI data is floating around 0, for >> example [-310, 320]. Unfortunately the FSL/Feat is not able to generate the >> mask during the “Prestats” step, the log file is as follows, >> > >> > >> > It is not correct to run analyses using Feat on demeaned data. Feat will >> remove the mean for you by including a B0 intercept term in the GLM. >> > >> > It is possible to run Feat analyses using the FSL Feat_Gui. However, >> there are optimizations in the process, especially the analysis of surface >> data in CIFTI format, if you use the Task fMRI HCP Pipelines available at >> > https://github.com/Washington-University/Pipelines >> > >> > >> > --Greg >> > >> > ____________________________________________________________________ >> > Greg Burgess, Ph.D. >> > Staff Scientist, Human Connectome Project >> > Washington University School of Medicine >> > Department of Neuroscience >> > Phone: 314-362-7864 >> > Email: [email protected] >> > >> > >> >> >> >> <log> >> >> >> >> …… >> >> >> >> Prestats >> >> >> >> /mnt/software/fsl5.0/bin/fslstats prefiltered_func_data -p 2 -p 98 >> >> >> >> -114.3 125.35 >> >> >> >> /mnt/software/fsl5.0/bin/fslmaths prefiltered_func_data -thr -108.2 >> -Tmin -bin mask -odt char >> >> >> >> …… >> >> >> >> </log> >> >> >> >> Certainly here the function “fslmaths” is definitely not able to >> generate a meaningful mask by the “-108.2 -Tmin” parameters since the data >> distribute on the both sides of 0. Since the HC data has already been >> stripped, so the mask.nii.gz is already there (Actually the “-Tmin” doesn’t >> make any sense here). Maybe this question should be posted to the FSL >> forum, but it is related to the HCP data so I posted it here too. >> >> >> >> Any reponse would be Appreciated. Thank you. >> >> >> >> Nico Zuo >> >> >> >> -- >> >> Institute of Automation >> >> Chinese Academy of Sciences >> >> Beijing 100190, China >> >> >> >> >> >> >> >> >> >> _______________________________________________ >> >> HCP-Users mailing list >> >> [email protected] >> >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> >> >> > >> > >> > > > > _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
