- According to the nipype documentation:
"The fmri_spm.py integrates several interfaces to perform a first and second level analysis on a two-subject data set. "
Really- only two-subject data?
May I open a documentation defect in nipype?
What are the [important] spm functionalities which aren’t supported via the spm interface of nipype?
I have some sparse functional connectivity matrices created with nilearn.
What is the best way to calculate contrasts between conditions for FC in python? (+statistical tests support?)
Even better - you can send a pull request with improvements to the documentation (although I’m not sure how adding more subjects to the example dataset would make the tutorial more informative).
Check out https://nipype.readthedocs.io/en/latest/interfaces/generated/interfaces.spm.html to see what’s supported. The only major piece I can think of that it’s not there would be DCM.
It is not advised to perform statistical tests on sparse covariance models. You should consider using the ‘tangent’ method in Nilearn to perform further statistical tests on covariance models.
I can only mark one “solution”, but both of you (@bthirion and @ChrisGorgolewski) helped me a lot!