Stochastic synchronization of dynamics on the human connectome

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2021-04, Vol.229, p.117738-117738, Article 117738
• Main Authors: Pang, James C., Gollo, Leonardo L., Roberts, James A.
• Format: Article
• Language: English
• Subjects: Brain; Brain network model; Connectome; Functional networks; Hierarchy; Kuramoto model; Medical imaging; Neural networks; Noise; Oscillators; Phase transitions; Stochasticity; Synchronization
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.117738

•We investigate synchronization of dynamics on the human connectome.•We analyze a phase oscillator brain network model with hierarchical timescales.•We find a counterintuitive effect where the addition of disorder (noise) yields a more ordered (synchronized) state.•The connectome is particularly conducive to generating noise-enhanced synchronization versus other randomized networks.•The effect replicates on other human connectome data. Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network’s topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome’s subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.