Fig. 4: Topological characterization of the Gray-White Matter Heterogeneous Fusion Network (GWM-HFN).
From: GWM-HFN, a Gray-White Matter heterogeneous fusion network for functional connectomes
A Small-world properties—sigma (σ), normalized clustering coefficient (γ), and normalized characteristic path length (λ)—of the GWM-HFN across a range of sparsity thresholds, presented as mean ± standard deviation. B Modularity coefficient (Q) of the GWM-HFN across varying sparsity thresholds, also shown as mean ± standard deviation, indicating robust modular organization. C Degree distribution of the group-averaged GWM-HFN, best fitted by an exponentially truncated power-law model, revealing the existence of highly connected brain regions. D Identification of 14 hub nodes within the group-averaged GWM-HFN, highlighting their central roles in the brain’s functional connectivity architecture. E Hub nodes identified in the benchmark group-averaged GM-GM network, allowing for a direct comparison of the central network architecture revealed by each framework. n = 572 biologically independent participants (SLIM dataset).
