Table 1.

Glossary

Resting-state FC	The temporal correlation between the spontaneous fluctuations of the BOLD signal extracted from brain voxels or regions.
Resting-state neural network	A set of brain regions that show coherent spontaneous FC. The composition of the networks varies across studies. Most commonly reported networks and their functions are: Default mode network (DMN): Spontaneous, self-referential cognition Frontoparietal network (FPN): Executive control, a.k.a. central executive network (CEN). Dorsal attention network (DAN): Visuospatial attention Task-positive network (TPN): Anti-correlates with the DMN, composed of FPN and DAN Salience network (SN): Detection of salient events, “switch” between the DMN and FPN Sensorimotor network (SMN): Motor control Others: Visual, auditory, language, basal ganglia, and cerebellar networks
Independent component analysis (ICA)	A method to decompose the BOLD signal into its spatial components (i.e., networks) that are assumed to be statistically independent from each other. Once aggregated group level spatial components are estimated, these components are back-reconstructed to obtain subject-specific spatial components and their time series. ICA is also an efficient way of separating noise from the signal [4].
Graph theory	The study of graphs that are structures used to model the pairwise connections (edges) between elements (nodes). The neural networks are thought of as graphs. The brain regions and the connectivity between them that constitute these networks represent the nodes and edges, respectively [5].
Degree	Number of links (i.e., BOLD signal correlations) of a node.
Node strength	Sum of weights of links (i.e., BOLD signal correlation strengths) of a node. It indicates how strongly one node is connected to the rest of the nodes in the network.
Clustering coefficient	A measure of the extent to which immediate neighbors of a node are also connected to one another.
Local efficiency	A measure of how relevant a node is for the communication among neighbors.
Modularity	A measure of the community structure of a network. Communities are defined as groups of densely interconnected nodes that are sparsely connected with the rest of the network. The modular organization of a network is related to its segregation-integration properties and determines the network dynamics and information flow.
Functional segregation	A network’s ability for specialized processing within clusters of nodes.
Functional integration	A network’s ability to bind information efficiently from distributed regions.
Path	The shortest distance (i.e., minimum number of connections) between a node and every other node in the network.
Global efficiency	Inverse of the average shortest path length between all pairs of nodes in the network. It is a measure of functional integration across the network.
Node betweenness centrality	A measure of how central a node is to the communication among other nodes in the network. It is computed as the fraction of all shortest paths in the network that contain a given node.
Hub	Node with high values of betweenness centrality
“Small-worldness” of a network	The clustering coefficient of a small-world network is greater than that of a random network, whereas its average shortest path length is comparable to that of a random network. This allows efficient communication within the “cliques” of nodes and between remote nodes in the network. The brain is considered to have a small-world organization.
Eigenvector centrality (EC) mapping	The EC attributes a value to each voxel in the brain such that a voxel receives a large value, if it is strongly correlated with many other nodes that are themselves central within the network. EC mapping identifies hubs in an unbiased manner [6].
Dynamic causal modeling	It is used to estimate causal coupling (i.e., effective connectivity) between nodes in a network and how that coupling is influenced by experimental manipulations. Realistic models of interacting nodes are created. Using a Bayesian model comparison procedure, the best model is then identified from the observed fMRI data [7].
Dynamic (i.e., time-varying) FC	Denotes changes of network FC patterns over time. The sliding window method is the most common. The FC of the networks is computed over a time window iteratively by sliding the window by a specific step every time. This computation yields connectivity time courses representing the temporal evolution of the functional connectome. Finally, k-means clustering is used to identify the dynamic brain “states.” The amount of time spent in a particular state (dwell time), number of visits in each state, and number of switches between states are reported [8].
Support vector machine (SVM)	A data-driven machine learning model used in classification and regression of the rs-fMRI data. Based on a set of “training” data, the SVM algorithm builds a model that assigns new data in a “test” set to one class or the other. In the case of regression, SVM finds a regression model based on the training set that can correctly predict novel data (e.g., predicting behavioral performance based on fMRI data).