Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

brainprep.interfaces.qualcheck.network_entropy¶

brainprep.interfaces.qualcheck.network_entropy(network_files_regex, output_dir, entropy_threshold=12, dryrun=False)[source]¶

Comput enetwork entropy - “structure vs randomness” metric.

A commonly used metric to assess whether a connectivity matrix exhibits meaningful structure is the matrix entropy. Low entropy indicates a highly structured matrix, whereas high entropy suggests a random-like or unstructured pattern.

Parameters:

network_files_regexstr: A regular expression matching TSV files generated by func_vol_connectivity. These files must contain regions as index and columns with connectivity values.
output_dirDirectory: Directory where a TSV file containing the mean correlation values is created.
entropy_thresholdfloat: Quality control threshold applied on the entropy score. Default 12.
dryrunbool: If True, skip actual computation and file writing. Default False.

Returns:

entropy_fileFile: A TSV file containing the network entropy for each subject/session/run. The table includes the columns participant_id, session, run, and entropy, as well as a binary qc column indicating the quality control result.

Notes

A qc column is added to the output table. It contains a binary flag indicating whether the entropy score do not exceeds the threshold: qc = 1 if entropy < entropy_threshold, otherwise qc = 0.