Abstract
The relationship between the brain’s structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We propose a linear, hierarchical graph spectral model of brain activity at mesoscopic and macroscopic scales that accurately predicts spatial and spectral features of neural oscillations across the brain. This novel model yields an elegant closed-form solution of the structure-function problem specified by the graph Laplacian spectrum of the structural connectome with simple, universal rules of dynamics specified by few unknown parameters. This parsimony stands in contrast to conventional complex numerical simulations of coupled non-linear lumped neural mass models (NMM). The model was highly successful in reproducing empirical spatial and spectral patterns of activity measured by scalp magneto-encephalography (MEG) after source localization, in contrast to NMM. The model may represent an important step towards understanding the fundamental relationship between network topology and the macroscopic whole-brain dynamics.
