ddmra.utils.assess_significance

assess_significance(curve, null_curves, distances, intercept_distance, v2)[source]

Measure significance of smoothing curve against null curves.

Parameters

  • curve (numpy.ndarray of shape (U,)) – U is distance value.

  • null_curves (numpy.ndarray of shape (P, U)) – U is distance value, P is permutation.

  • distances (numpy.ndarray of shape (U,)) – Distances in mm.

  • intercept_distance (float)

  • v2 (float)

Returns

  • p_inter (float) – P-value for intercept. Higher intercepts with lower p-values indicate both focal and global effects of the QC metric on functional connectivity.

  • p_slope (float) – P-value for slope. Slope, in this case, is calculated as the intercept’s value minus the second location’s value, so that negative slopes go up and positive slopes go down. Higher slopes (i.e., those going down) with lower p-values indicate focal effects of the QC metric on functional connectivity (i.e., enhanced local connectivity and decrease long-distance connectivity due to the QC measure).