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.