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. |
| 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. |
| Laplacian matrix of subject s. | |
| Eigenvector matrix of the graph Laplacian of subject s. | |
| 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. |
| Diffusion kernel at scale γi for subject s. | |
| m | Number of scales |
| Collection of all m diffusion kernels of a subject s. | |
| πin×n | Interregional co-activations corresponding to scale γi. |
| Π mn×n | Interregional co-activations collectively represented at all scales. |
| X pn×mn | |
| Y pn×n | |
| Predicted FC | |
| Functional connectivity FC when reaction only happens at k0τ. |