Whole-brain computational modeling has become increasingly important for understanding the systems-level mechanisms governing the dynamics of large-scale brain activity. Brain dynamics, characterized by synchronization and criticality of neuronal oscillations, are fundamental to cognitive functions. The remarkable inter-individual variability in observables neuronal synchronization dynamics in vivo has been attributed to both individual operating point locations in the space of control parameters such as individual structural variability such as the connectivity between brain regions. We present here a new Hierarchical Kuramoto model for studying the mechanistic basis of in-vivo-like synchronization dynamics in complex brain networks.
In its simplest two-layer form, the model consists of a network of nodes, each containing a large number of coupled oscillators. Even with only two hierarchical levels, the model enables explicit representation of local and inter-areal coupling, allowing for the observation of intra- and inter-areal synchronization and the emergence of critical-like dynamics simultaneously at nodal and whole-network levels.
We first assessed the structure-function relationships in model dynamics by comparing structural connectivity with functional observables such as —inter- and intra-areal phase synchrony, amplitude correlations, and long-range temporal correlations as a function of control parameters. While nearly linear structure-phase synchronization dominates the subcritical region it decays at criticality. However, the SC-FC coupling of amplitude correlations peaked at criticality, indicating fundamentally distinct phenomenology for these forms of FC. Next, we compared simulated data with human MEG recordings and found that the highest correlation is observed when the system operates on the subcritical side of an extended critical regime. Our results support the hypothesis that the human brain operates in a slightly critical regime and the modelling approach allows to incorporate structural and functional heterogeneity opening new avenues for mechanistic understanding of experimental observations.
Contact: Vladislav Myrov, vladislav.myrov@aalto.fi
Aalto University, Helsinki
Additional Authors:
Alina Suleimanova, Aalto University, alina.suleimanova@aalto.fi; Samanta Knapic, Aalto University & University of Helsinki, samanta.knapic@helsinki.fi; Paula Partanen, University of Helsinki, paula.rj.partanen@helsinki.fi; Maria Vesterinen, University of Helsinki, annamaria.vesterinen@helsinki.fi; Wenya Liu, Aalto University & University of Helsinki, wenya.liu@helsinki.fi; Satu Palva, University of Helsinki & University of Glasgow, satu.palva@helsinki.fi; J. Matias Palva, University of Aalto & University of Helsinki & University of Glasgow, matias.palva@aalto.fi