aslprep.interfaces.parcellation module
Handling functional connectvity.
- class ParcellateCBF(from_file=None, resource_monitor=None, **inputs)[source]
Bases:
SimpleInterface
Extract timeseries and compute connectivity matrices.
Write out time series using Nilearn’s NiftiLabelMasker Then write out functional correlation matrix of timeseries using numpy.
- Mandatory Inputs:
atlas (a pathlike object or string representing an existing file) – Atlas file.
atlas_labels (a pathlike object or string representing an existing file) – Atlas labels file.
in_file (a pathlike object or string representing an existing file) – File to be parcellated.
mask (a pathlike object or string representing an existing file) – Brain mask file.
- Optional Inputs:
min_coverage (a float) – Coverage threshold to apply to parcels. Any parcels with lower coverage than the threshold will be replaced with NaNs. Must be a value between zero and one. Default is 0.5. (Nipype default value:
0.0
)- Outputs:
coverage (a pathlike object or string representing an existing file) – Parcel-wise coverage file.
timeseries (a pathlike object or string representing an existing file) – Parcellated time series file.