ddmra.analysis.qcrsfc_analysis

qcrsfc_analysis(mean_qcs, z_corr_mats, run_covariates=None)[source]

Perform quality-control resting-state functional connectivity analysis.

Parameters:

  • mean_qcs (numpy.ndarray of shape (n_subjects,)) – QC measure (typically mean framewise displacement) across participants.

  • z_corr_mats (numpy.ndarray of shape (n_subjects, n_edges)) – Z-transformed correlation coefficients for ROI-ROI pairs. n_edges is the unique ROI-to-ROI edges, not including self-self edges. These coefficients must be sorted according to ascending distance along the second axis.

  • run_covariates (None or numpy.ndarray of shape (n_subjects, n_covariates), optional) – Run-level covariates to adjust for before correlating QC and FC.

Returns:

qcrsfc_zs (numpy.ndarray of shape (n_edges,)) – Z-transformed correlation coefficients for ROI-ROI pairs.

Notes

The basic process for the QC:RSFC analysis is:

  1. Average QC values within each participant.

  2. If run-level covariates are provided, regress them out of the mean QC values and z-transformed correlation coefficients.

  3. Correlate the mean QC values with z-transformed correlation coefficients across participants, for each ROI-ROI pair.

  4. Z-transform the edge-wise correlation coefficients.