netneurotools.metrics.rich_feeder_peripheral

netneurotools.metrics.rich_feeder_peripheral(x, sc, stat='median')[source]

Calculate connectivity values in rich, feeder, and peripheral edges.

Parameters:

  • x ((N, N) numpy.ndarray) – Symmetric correlation or connectivity matrix

  • sc ((N, N) numpy.ndarray) – Binary structural connectivity matrix

  • stat ({'mean', 'median'}, optional) – Statistic to use over rich/feeder/peripheral links. Default: ‘median’

Returns:

  • rfp ((3, k) numpy.ndarray) – Array of median rich (0), feeder (1), and peripheral (2) values, defined by x. k is the maximum degree defined on sc.

  • pvals ((3, k) numpy.ndarray) – p-value for each link, computed using Welch’s t-test. Rich links are compared against non-rich links. Feeder links are compared against peripheral links. Peripheral links are compared against feeder links. T-test is one-sided.

Notes

This code was written by Justine Hansen who promises to fix and even optimize the code should any issues arise, provided you let her know.