Figure 7.

The degree and betweenness distributions of the human cortical network. (a) Log-log plot of the cumulative node degree distribution; (b) log-log plot of the cumulative node-betweenness distribution; (c) log-log plot of the cumulative edge-betweenness distribution. The plus sign represents observed data, the solid line is the fit of the exponentially truncated power-law (p(x) ∼ x^α−1exp(x/x_c)), the dashed line is an exponential (p(x) ∼ exp(x/x_c)), and the dotted line is a power-law (p(x) ∼ x^α−1). R² was calculated to assess the goodness-of-fit (a larger value indicates a better fitting; R_etp, R² for exponentially truncated power-law fit; R_e, R² for exponential fit; R_p, R² for power-law fit). The exponentially truncated power-law is the best fitting for all the 3 distributions (a, estimated exponent α = 1.66 and cutoff degree k_c^node = 3.65; b, estimated exponent α = 0.96 and cutoff betweenness B_c^node = 76.93; c, estimated exponent α = 1.10 and cutoff betweenness B_c^edge = 18.95).