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
