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A number of intrinsic functional brain networks can be identified in individuals at rest, that correspond to networks found in task-based functional MRI studies.

The edges of brain networks can be weighted or unweighted, directed or undirected.

By incorporating axonal cell death models [30], damage to functional brain networks can be computed.

Brain networks can be divided into two categories: structural and functional networks.

By this method, brain networks can be investigated in (denoised) dynamic data obtained with any PET tracer.

The methods to construct structural brain networks can be divided into two categories: cerebral cortex correlation and white fiber tracking.

The brain network can be defined by a connection matrix (a graph theory concept), also called a connectome [26].

Overall, current research on tensor imaging analysis presents two directions: (1) supervised: for a particular brain disorder, a classifier can be trained by modeling the relationship between a set of neuroimages and their associated labels (disease status or clinical response); (2) unsupervised: regardless of brain disorders, a brain network can be discovered from a given neuroimage.

And one fMRI study, by employing different parcellation schemes to construct the functional networks, suggested that the topological organization of the brain network can be affected by the brain parcellation atlases [72].

A structural brain network can be abstracted from human MRI data by compiling a matrix of correlations from morphological measurements (cortical thickness, RGMV, and surface area) between all pairs of regions in some parcellation scheme and then applying a threshold to create a graph representing strong (suprathreshold) correlations to connect regions [26], [27], [28].

In the presence of severe brain injury, the structure of the brain network can be crucially affected.