Table 5.

Selected global and nodal network metric features.

	NMglobal		NMnodal
	t	Name	t	ROI	Name
1	0.20	Shortest path length	0.30	Right globus pallidus	Clustering coefficient
2	0.15	Assortivity	0.30	Left crus I of cerebellar hemisphere	Clustering coefficient
3	0.15	Lambda	0.15	Left middle temporal pole	Local efficiency
4	0.30	Small-worldness	0.25	Right cuneus	Shortest path length
5	0.25	Shortest path length	0.05	Lobule X of vermis (nodulus)	Degree
6	0.10	Clustering coefficient	0.10	Left orbital part of inferior frontal gyrus	Shortest path length
7	0.10	Shortest path length	0.25	Left middle frontal gyrus, orbital part	Shortest path length
8	0.15	Gamma	0.05	Left hippocampus	Shortest path length
9	0.20	Degree	0.10	Right superior occipital	Shortest path length
10	0.25	Gamma	0.05	Lobule X of vermis (nodulus)	Shortest path length

The metric in bold indicates that it has been identified by group comparison as well.

t is the threshold used for matrix binarization.

Degree: the number of connections linked directly to a node

Neighbour degree: the average degree of the neighbours of a node

Global efficiency: the global efficiency of information propagation in the network

Local efficiency: the efficiency of information propagation through the direct neighbours of a node

Clustering coefficient: the extent of the local density or cliquishness of the network

Shortest path length: the extent of average connectivity or overall routing efficiency of the network

Gamma: the ratio between the extent of local clustering of a network and the surrogate random networks

Lambda: the ratio between the extent of overall routing efficiency of a network and the surrogate random networks

Smallworldness: the extent of a network between randomness and order

Assortativity: a bias in favour of connections between network nodes with similar characteristics

Transivity: the fraction of triple-nodes that have their third edge filled in to complete the triangle.