Multiscale Complexity as a Basis for Functional Brain Network Construction

Functional brain networks are conventionally constructed using measures of direct temporal synchrony between neural signals, implicitly restricting connectivity to scale-specific interactions. Here, we introduce an alternative framework in which interregional similarity is defined through correlations between multiscale entropy (MSE) profiles, enabling network construction based on scale-dependent dynamical structure rather than instantaneous alignment. Using resting-state fMRI data from the Human Connectome Project (N = 1003), we systematically compare MSE-based networks with conventional time-series-based networks across conventional/spectral graph-theoretical, and information-theoretic measures. We show that MSE-based networks exhibit stronger modular organization, enhanced local segregation, and distinct global integration patterns, reflecting a reorganization of functional architecture when multiscale dynamics are taken into account. Importantly, MSE-based networks demonstrate substantially greater sensitivity to biologically meaningful variability, revealing robust and reproducible sex differences across multiple network measures, in contrast to the limited and inconsistent effects observed in conventional networks. These findings suggest that multiscale representations provide a more informative and biologically grounded basis for functional brain network construction, capturing aspects of neural organization that are not accessible through direct synchrony alone.