Sorry for the newbie question-
I’ve executed fmriprep for the first time.
How do I manually extract timeserie(s) for a certain (x,y,z) MNI position(s)?
(i.e.- how to I manually slice, or- how do I transform between MNI outputs of fmriprep and the nifti’s indexes)
Thank you, though I’ve asked now someone from my lab, and he told me that the (x,y,z) mni usually refers to mm, so I’m a bit puzzled…
There is a broken link in the page-
“The requested URL /pipermail/nipy-devel/2013-October/009515.html was not found on this server.”
Can you please add another word about this-
So- the “sub-001_task-mytask_bold_space-MNI152NLin2009cAsym_preproc.nii” output of fmriprep is in 1 over 1 over 1 mm space?
So, in order to slice to the (i,j,k) mm- I only need to do something similar to:
template = load_mni152_template()
mmnmi=nilearn.image.resample_to_img(my nii image, template)
and then I may slice the NMI (i,j,k) directly from mmnmi?
BTW- if we are already talking about spatial interpolation-
what is the best practice for temporal upsampling of bold signals?
Does fmriprep offers such functionality, or some other python-neuro package?
Well, it would have been quite problematic to call these without the nifti’s
I meant- why is it crucial to add the confounds file if fmriprep already “cleaned them” (or- cleaned some of them?)
And another question-
The tutorial is great- thanks (I’ll also read the paper soon), though I’m still not sure why using the nilearn instead of upsampling in fmriprep makes a difference- the difference is that fmriprep does “hard parcellation” and in nilearn offers more options?