ostanley/phaseprep

fMRI phase processing interfaces and workflows in nipype

phaseprep's Issues

Refactor phase preprocessing

Two steps to complete:

• motion correct before phase unwrapping by doing it on the real and imaginary images
• avoid manual scaling input in phase preprocessing by using scl_slope and scl_inter from niftis.

Phase processing without task information

@ostanley, my understanding of the current workflow is that it requires task and rest timing information- specifically from a block design.

Do you plan to support phase processing/regression in the absence of task information? I think that Curtis, Hutchison, & Menon (2014) used a band-pass filter before estimating the standard deviations of the signal and phase for resting-state data, but in similar cases like film-viewing or other naturalistic paradigms, I don't how comfortable I feel with just applying a one-size-fits-all filter. Is that your preferred approach?