A network is defined by a collection of nodes (vertices), and links (edges) between pairs of nodes. Nodes in large scale brain networks usually represent brain regions, while links represent anatomical, functional, or effective connections, depending on the dataset. Networks may be represented by their connectivity (adjacency) matrices. Rows and columns in these matrices denote nodes, while matrix entries denote links. In addition to the type of connectivity (anatomical, functional or effective), links are also differentiated on the basis of their weight and directionality. These functions threshold connectivity matrices by absolute weight, or by proportion of strongest weights. All remaining weights (if present) are retained.*Thresholding:*
threshold_absolute.m, threshold_proportional.m (BU, BD, WU, WD networks). Contributor: MR.
This function may binarize a weighted connection matrix, normalize a weighted connection matrix, convert a weighted connection matrix to a weighted connection-length matrix, or fix common connection-weight problems.*Weight conversion:*
weight_conversion.m (WU, WD networks). Contributor: MR.
Figure from: Rubinov M, Sporns O (2010) NeuroImage 52:1059-69. |