Nonparametric Dynamic Granger Causality based on Multi-Space Spectrum Fusion for Time-varying Directed Brain Network Construction

Nonparametric estimation of time-varying directed networks can unveil the intricate transient organization of directed brain communication while circumventing constraints imposed by prescribed model-driven methods. A robust time-frequency representation - the foundation of its causality inference - is critical for enhancing its reliability. This study proposed a novel method, i.e., nonparametric dynamic Granger causality based on Multi-space Spectrum Fusion (ndGCMSF), which integrates complementary spectrum information from different spaces to generate reliable spectral representations to estimate dynamic causalities across brain regions. Systematic simulations and validations demonstrate that ndGCMSF exhibits superior noise resistance and a powerful ability to capture subtle dynamic changes in directed brain networks. Particularly, ndGCMSF revealed that during instruction response movements, the laterality in the hemisphere ipsilateral to the hemiplegic limb emerges upon instruction onset and diminishes upon task accomplishment. These intrinsic variations further provide reliable features for distinguishing two types of hemiplegia (left vs. right) and assessing motor functions. The ndGCMSF offers powerful functional patterns to derive effective brain networks in dynamically changing operational settings and contributes to extensive areas involving dynamical and directed communications.