canICA uses a template, [MultiNiftiMasker(Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging), to mask the brain. It has multiple masking techniques:
The strategy used to compute the mask:
‘background’: Use this option if your images present a clear homogeneous background.
‘epi’: Use this option if your images are raw EPI images
‘whole-brain-template’: This will extract the whole-brain part of your data by resampling the MNI152 brain mask for your data’s field of view.
‘gm-template’: This will extract the gray matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.
‘wm-template’: This will extract the white matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.
- What is the difference between background and whole brain? I thought they are the same.
- Do you have a use case where a strategy is preferred over the other? More specifically I am interested in when to use the whole-brain and when to use gray matter.