Hi @Sebastian,
no biggie!
Yes, exactly. The option register_to_mean=True will result in a two pass procedure:
–> initially SPM realigns each session to each other by aligning the first volume from each session to
to the first volume of the first session and subsequently all volumes within each session are aligned to the first volume of that session
–> after that the volumes from the first realignment step are used to create a mean image and than all volumes
are aligned to that mean image
Using register_to_mean=False will “just” do the initial realignment.
In the example above the resulting mean image is registered to the t1 weighted image, yep.
I’m not sure if I completely understand your second question. Do you mean if it’s okay to align images across runs? If that’s the case:
Puh, that’s one hell of a question (at least for me, I hope others with more expertise will drop in as well).
If you have multiple runs of the same task/conditions and plan to analyze your data in a mass univariate way (GLM) across runs, than time series should correspond to “roughly” the same location/voxel within and between runs. Otherwise, chances are that the signal of a given voxel contains signal from two (or more) different voxels or even types of tissues, up to signal loss in voxels near the borders of the images (e.g. in frontal areas).
If you meant something else: sorry, could you maybe elaborate on that?
HTH, best, Peer