Figure 1.

Schematic illustrating local and global thresholding methods, and how these methods impact on the modular structure of graphs constructed from a correlation matrix. Starting with a model correlation matrix, which shows the functional connectivity between a subset of 11 brain regions for one subject, the two different thresholding methods are used to construct graphs with increasing numbers of edges. On the left, applying a local threshold produces connected supersets of the k nearest neighbor graph (k-NGG), which includes edges for each node's k highest functional connections, shown here for k = 1, 2, 3. The minimum spanning tree (MST) is a connected superset of the 1-NNG, and connects all 11 nodes with the lowest possible number of edges and the highest possible functional connectivity. On the right, applying a global threshold simply includes edges between the pairs of nodes with the highest functional connectivity in order. Nodes of the same color are in the same module, as determined by the fast greedy algorithm, showing the influence of graph construction and edge density on the modular partition.