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. 2018 Feb 19;8:3265. doi: 10.1038/s41598-018-21456-0

Table 1.

Notations used in the models and optimization formulation.

Object Description
n Number of ROIs or the number of nodes in the brain graph.
p Number of training subjects.
SC Structural connectivity matrix.
SCs SC matrix for subject s.
Ds Degree matrix for subject s; sum of edge weights for every region.
FC Functional connectivity matrix.
FCs FC matrix for subject s.
[f1s,,fns]
W n×n Weighted adjacency matrix of a graph.
D n×n Degree matrix of a graph, computed by taking the sum of all weights on every node and diagonalizing the vector.
Ln×ns Laplacian matrix of subject s.
Ψn×ns Eigenvector matrix of the graph Laplacian of subject s.
Λn×ns Eigenvalue matrix, diagonal matrix with increasing order of eigenvalues, of the graph Laplacian of subject s.
γ i A scale at which diffusion kernel is defined.
Hin×ns Diffusion kernel at scale γi for subject s.
m Number of scales
Hn×mns Collection of all m diffusion kernels of a subject s. [H1n×nsHmn×ns]
πin×n Interregional co-activations corresponding to scale γi.
Π mn×n Interregional co-activations collectively represented at all scales. [π1n×nπmn×n]=[Π1mn×1Πnmn×1]
X pn×mn [H1Hp]
Y pn×n [FC1n×nFCpn×n]=[f11fn1f1pfnp]=[Y1pn×1Ynpn×1]
Cfs Predicted FC i=1mHisπi
Cf|k0 Functional connectivity FC when reaction only happens at k0τ.