Fig. 1.

Node-wise structure–function relationships. Local, node-wise structure–function relationships are estimated by fitting a multilinear regression model for each node separately. For a given node $i$, the response or dependent variable is the functional connectivity between node $i$ and node $j\hspace{0.17em}\ne \hspace{0.17em}i$. The predictor or independent variables are the geometric and structural relationships between $i$ and $j$, including the Euclidean distance, path length, and communicability. The “observations” are individual $i,j$ relationships. Model parameters (intercept $b_0$ and regression coefficients $b_1$, $b_2$, and $b_3$) are then estimated by ordinary least squares. Goodness of fit for each node $i$ is quantified by $R_i^2$ between observed and predicted functional connectivity.