Metrics evaluating component TE-dependence or -independence.


calculate_betas(data, mixing)

Calculate unstandardized parameter estimates between data and mixing matrix.

calculate_dependence_metrics(F_T2_maps, ...)

Calculate Kappa and Rho metrics from F-statistic maps.

calculate_f_maps(data_cat, Z_maps, mixing, ...)

Calculate pseudo-F-statistic maps for TE-dependence and -independence models.

calculate_psc(data_optcom, optcom_betas)

Calculate percent signal change maps for components against optimally-combined data.


Calculate unnormalized(?) variance explained from unstandardized parameter estimate maps.


Calculate normalized variance explained from standardized parameter estimate maps.

calculate_weights(data_optcom, mixing)

Calculate standardized parameter estimates between data and mixing matrix.

calculate_z_maps(weights[, z_max])

Calculate component-wise z-statistic maps.

compute_countnoise(stat_maps, stat_cl_maps)

Count the number of significant voxels from non-significant clusters.


Count the number of significant voxels in a set of cluster-extent thresholded maps.

compute_dice(clmaps1, clmaps2[, axis])

Compute the Dice similarity index between two thresholded and binarized maps.

compute_signal_minus_noise_t(Z_maps, ...[, ...])

Compare signal and noise t-statistic distributions with a two-sample t-test.

compute_signal_minus_noise_z(Z_maps, ...[, ...])

Compare signal and noise z-statistic distributions with a two-sample t-test.

generate_decision_table_score(kappa, ...)

Generate a five-metric decision table.

threshold_map(maps, mask, ref_img, threshold)

Perform cluster-extent thresholding.

threshold_to_match(maps, n_sig_voxels, mask, ...)

Cluster-extent threshold a map to target number of significant voxels.