Structural basis of envelope and phase intrinsic coupling modes of the cerebral cortex

Abstract

<jats:title>Abstract</jats:title><jats:p>Intrinsic coupling modes (ICMs) provide a framework for describing the interactions of ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models as well as simple topological ingredients of SC were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are strongly related to SC, except when using ICM measures removing zero-lag synchronizations. Computational models are challenged to predict these ICM patterns consistently, and simple predictions from SC topological features can sometimes outperform them. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs bear a substantial relation to the underlying structural connectivity of the cerebral cortex.</jats:p>

A General Framework for Characterizing Optimal Communication in Brain Networks

“…Global Network Topology is More Influential Than Local Node DynamicsPrevious research has demonstrated that linear models of local dynamics are as effective, if not more so, as nonlinear models in capturing the macroscopic dynamics of the brain[44,[46][47][48]. This observation led us to hypothesize that optimal signal propagation in large-scale brain networks, as measured by fMRI-BOLD signal, might also be adequately represented by linear communication models.…”