Table 1.
Topological properties of brain graphs examined in the current study.
| Topological properties | Descriptions |
|---|---|
| GLOBAL TOPOLOGICAL PROPERTIES | |
| Local efficiency | The average efficiency of information transfer over a node's direct neighbors |
| Global efficiency | The efficiency of information transfer through the entire graph |
| Clustering coefficient | The average inter-connectedness of a node's direct neighbors |
| Characteristic shortest path length | The average shortest path length between any pairs of nodes |
| Normalized clustering coefficient | The clustering coefficient compared to matched random networks |
| Normalized characteristic shortest path length | The characteristic shortest path length compared to matched random networks |
| Small-worldness | The normalized clustering coefficient divided by the normalized characteristic shortest path length, which reflect the balance of global efficiency and local efficiency |
| Assortativity | The tendency of nodes to link with those nodes with similar number of edges |
| Modularity | The extent to which a graph can be segregated into densely intraconnected but sparsely interconnected modules |
| REGIONAL TOPOLOGICAL PROPERTIES | |
| Degree centrality | The number (or sum of weights) of connections connected directly to a node |
| Nodal efficiency | The efficiency of information transfer over a node's direct neighbors |
| Nodal clustering coefficient | The inter-connectedness of a node's direct neighbors |
| Subgraph centrality | The participation of a node in all subgraphs comprised in a graph |
| Betweenness centrality | The influences of a node over information flow between other nodes |
| Eigenvector centrality | A self-referential measure of centrality – nodes have high eigenvector centrality if they connect to other nodes that have high eigenvector centrality |