voxe-wise data analysis for resting-state fMRI

I’m trying to do the voxel-wise analysis to my data but I don’t know the steps.
I have a raw .nii file that contains my rs-fMRI data for a subject.
There are some standard preprocessing steps (Like slice timing, Head motion correction and . . . ) that should be done in general, but I have heard that in voxel-wise processing some of these preprocessing should not be done.

My first question is this:
What preprocessing steps (in general) should be done before jumping to voxel time series extraction? is there any standard procedure for this that I don’t know?

And my second question:
After doing the preprocessing steps, I would like to generate a matrix that contains the time-series for each voxel (it would be a very huge matrix, something like 30000 x 250). Is it possible to do so with afni?

Your questions are too vague to answer, and you don’t give enough information about your planned analyses to provide any advice.

What do you mean by “voxel-wise analyses”? That’s pretty much all that the standard AFNI programs do, and they do the preprocessing steps that you mention. Where did you hear “that in voxel-wise processing some of these preprocessing should not be done”?

In fact, the subject of preprocessing FMRI is not a settled area of knowledge, despite 20+ years of experience. There are significant differences between the way AFNI, SPM, and FSL preprocess time series datasets. In some cases, these differences affect the final results fairly substantially.

Thank you very much Bob Cox, I am new to fMRI data processing and Im sorry if my question was vague. I will try to explain more details.

By voxel-wise analysis I mean that every single voxel should be considered as a ROI. you can find more details in this article. As far as I know common approaches tend to find seed voxels or find the ROIs via atlases. But in this approach each single voxel is a ROI. What I need is to extract all these ROI signals(time series) for all these ROIs and put them in a matrix, so I can use hub detection or clustering algorithms to process them.

My teacher told me that some steps like smoothing should be handled carefully when each voxel is a ROI. To be honest we (me and my teacher) don’t know much about fMRI data analysis but we are excited about the big data challenges that the voxel-wise approach brings.

I would recommend you start with doing just head motion correction. The effect of head movements on FMRI data can be huge, and so removing them (as much as possible) is important. In particular, since the head moves as a unit, head motion produces highly correlated signal changes across wide regions of the brain. In FMRI processing, it is common also to linearly regress the 6 estimated movement parameters out of each voxel time series to further reduce the impact of motion.

For your purpose, you might not want to do slice timing correction – you’ll have to experiment to see how big an effect it has on your results. The same applies to spatial smoothing – which of course blurs the signals between the voxel time series.

If you are going to be combining data across groups of subjects, then you need to align (register) their brain datasets together as far as possible. If you are dealing with only 1 subject at a time, then you can avoid this step, at least at first.

If you are planning to use AFNI, and you have an FMRI dataset in the NIfTI format (e.g., from OpenFMRI), you can do the volume registration with program 3dvolreg. If you want to further project out the motion parameters, program 3dTproject can do that for you. Program 3dmaskdump can then dump out all the data time series to ASCII text (which will be HUGE) and then you can take it from there.