Hi, Osman-
This is a pretty large topic, and I think the best place to start would be with some of the underlying theory of FMRI modeling, which we have some AFNI Academy videos about here:
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www.youtube.com]
Somewhat more focusedly, in the afni_proc.py start-to-finish videos:
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www.youtube.com]
... Rick goes through a single subject task-based FMRI analysis with afni_proc.py; resting state has some differences, but looking at the scaling description will be relevant to what is here (and subsequently):
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www.youtube.com]
Really, going through the theory and start-to-finish video would be quite useful.
Going through videos #30 and 31 here (from an AFNI Bootcamp we taught at MIT):
[
cbmm.mit.edu]
... would be useful for some resting-state specific processing issues.
As a short answer: there is *data* everywhere in the dataset field of view, both inside the brain and outside the brain. We measure 'signal' response in a general sense of brain, ventricle, skull, face, and air. Some of those areas are more useful to focus on for our scientific questions than others---but performing statistical modeling throughout the whole volume and looking at results everywhere is reeeeaaallly useful for fully understanding the acquired data and avoiding potential acquisition/processing pitfalls. Hence, we don't mask the data at this stage.
The data outside the brain is reaaallly noisy, so it can have veeery large features in it, esp in the residual time series, which is what the errts* dataset is. Trying to gauge goodness/badness from just the values in the errts is not a good road to be going down, because of scaling in particular. Looking at seedbased correlation from a seed within the brain is better---that is where the final analyses will be based, anyways. So, understanding the FMRI-based modeling, the difference between fitts and errts time series (the former being more used in task-based FMRI and the latter in resting state FMRI), and how each can be used to answer specific research/clinical questions, would be good to aims. Those are larger topics than can be adequately typed about here---going through those videos would be useful for that, and subsequent questions could be addressed here.
-pt
