Table 2.

Definition of specific outcome measures in the reviewed papers.

Explanation	Outcome measure	Outcome measure definition
Diffusion-weighted imaging parameters
In diffusion tensor imaging, we can model water molecule diffusion by tensors. The three main eigenvalues and eigenvectors of tensor representation can give information about the main diffusion direction. In the following definitions, consider λ1 as the maximum eigenvalue (main diffusion direction) and λ2 and λ3 as the two shortest eigenvalues (perpendicular to the main diffusion direction).	Axial diffusivity (AD)	AD = λ1 It is only sensitive to diffusion in the longest eigenvalue direction. Highly organized structures like white matter pathways and large open cavities like ventricles have generally high levels of diffusion and are sensitive to this measure.
	Radial diffusivity (RD)	RD = (λ2 + λ3)/2 It represents the two shortest eigenvalues and shows low values in highly organized and dense structures like white matter pathways, intermediate values in gray matter, and high values in regions with cerebral spinal fluid (CSF)
	Mean diffusivity (MD)	MD = (λ1 + λ2 + λ3)/3. It is specifically sensitive to CSF, which has high values of average diffusion.
	Fractional anisotropy	It provides the relative difference between the largest eigenvalue as compared to the others; it quantifies the fraction of diffusion that is anisotropic. This leads to selective high values in white matter, but not gray matter or CSF.
Diffusion kurtosis imaging parameters
Diffusion kurtosis imaging is a diffusion technique based on the non-Gaussian diffusion of water. It characterizes non-Gaussian diffusion by estimating the excess kurtosis of the displacement distribution and gives an idea of the underlying tissue complexity. We can perform a kurtosis tensor estimation of the water molecule diffusion data like the diffusion tensor estimation technique.	Axial kurtosis	The kurtosis value along the largest eigenvalue direction of the diffusion ellipsoid.
	Mean kurtosis tensor	It quantifies the degree of deviation from Gaussian diffusion and is based only on the trace of the kurtosis tensor.
Graph analysis parameters
If we represent a network as a graph, different mathematical features can be extracted from the graph which represents the topological characteristics of the network.	Bilateral thalamic resting-state network (RSN) degree of symmetry	The percentage of the total voxel number of bilateral thalamic RSNs that correlated with not only the left but also the right thalamus.
	Local efficiency	The local efficiency of a node characterizes how well information is exchanged by its neighbors when this node is removed.
	Modularity	The strength of division of a network into modules.
	Relative betweenness centrality	Betweenness centrality is a measure of the influence of a vertex over the flow of information between every pair of vertices under the assumption that information primarily flows over the shortest paths between them. The betweenness centrality increases with the number of vertices in the network, so a normalized version is often considered with the centrality values scaled between 0 and 1.
	Clustering coefficient	It is a measure of the degree to which nodes in a graph tend to cluster together.
	Minimum spanning tree (MST)	It is a subgraph containing all vertices of the main graph without any loop and with minimum total weights. The total weight of the MST of a sample graph shows the minimal broadcast cost of the main graph.
	Average shortest path	It is also called characteristic path length, is a measure of global connectivity, and shows the efficiency of information exchange in a network. The lesser values show a higher efficiency.
ERP components
Every ERP signal is characterized by peaks and troughs after stimulation time, which are called the ERP components. Although some components have specific names, others are named based on their polarity (P for positive peaks and N for negative ones) and the time of their appearance after stimulation (in millisecond). Sometimes, instead of their appearance time, their appearance order is stated	P300	A positive peak being observed around 300 ms after stimulus time and is mainly related to the consciously controlled attention.
	N350	A negative peak being observed around 350 ms after stimulus onset and is mainly related to cognitive processing during sleep onset period.
	N1	A negative peak being observed around 150–200 ms after the stimulus and is mainly related to auditory attention.
	N2b	A subcomponent of N2 occurring around 200–350 ms post-stimulus and is related to pre-attentive processing in intentional conscious attention.
	Mismatch negativity	A negative peak around 150–250 ms post-stimulus which is related to pre-attentive processing independent of conscious attention.