Code: file:///tmp/buildd/nipype-0.5.3/nipype/workflows/rsfmri/fsl/resting.py#L40
Realign a time series to the middle volume using spline interpolation
Uses MCFLIRT to realign the time series and ApplyWarp to apply the rigid body transformations using spline interpolation (unknown order).
>>> wf = create_realign_flow()
>>> wf.inputs.inputspec.func = 'f3.nii'
>>> wf.run()
Code: file:///tmp/buildd/nipype-0.5.3/nipype/workflows/rsfmri/fsl/resting.py#L81
Create a “resting” time series preprocessing workflow
The noise removal is based on Behzadi et al. (2007)
name : name of workflow (default: restpreproc)
Inputs:
inputspec.func : functional run (filename or list of filenames)
Outputs:
outputspec.noise_mask_file : voxels used for PCA to derive noise components
outputspec.filtered_file : bandpass filtered and noise-reduced time series
>>> TR = 3.0
>>> wf = create_resting_preproc()
>>> wf.inputs.inputspec.func = 'f3.nii'
>>> wf.inputs.inputspec.num_noise_components = 6
>>> wf.inputs.inputspec.highpass_sigma = 100/(2*TR)
>>> wf.inputs.inputspec.lowpass_sigma = 12.5/(2*TR)
>>> wf.run()
Code: file:///tmp/buildd/nipype-0.5.3/nipype/workflows/rsfmri/fsl/resting.py#L9
Derive components most reflective of physiological noise
Code: file:///tmp/buildd/nipype-0.5.3/nipype/workflows/rsfmri/fsl/resting.py#L27
Return the middle index of a file