Network mechanisms underlying the regional diversity of variance and time scales of the brain's spontaneous activity fluctuations.

Abstract

The brain's activity fluctuations have different temporal scales across the brain regions, with associative regions displaying slower timescales than sensory areas. This so-called hierarchy of timescales has been shown to correlate with both structural brain connectivity and intrinsic regional properties. Here, using publicly available human resting-state fMRI and dMRI data it was found that, while more structurally connected brain regions presented activity fluctuations with longer timescales, their activity fluctuations presented lower variance. The opposite relationships between the structural connectivity and the variance and temporal scales of resting-state fluctuations, respectively, were not trivially explained by simple network propagation principles. To understand these structure-function relationships, two commonly used whole-brain models were studied, namely the Hopf and Wilson-Cowan models. These models use the brain's connectome to coupled local nodes (representing brain regions) displaying noise-driven oscillations. The models show that the variance and temporal scales of activity fluctuations can oppositely relate to connectivity within specific model's parameter regions, even when all nodes have the same intrinsic dynamics -but also when intrinsic dynamics are constrained by the myelinization-related macroscopic gradient. These results show that, setting aside intrinsic regional differences, connectivity and network state are sufficient to explain the regional differences in fluctuations' scales. State-dependence supports the vision that structure-function relationships can serve as biomarkers of altered brain states. Finally, the results indicate that the hierarchies of timescales and variances reflect a balance between stability and responsivity, with greater and faster responsiveness at the network periphery, while the network core ensures overall system robustness.Significance Statement Brain regions exhibit activity fluctuations at different temporal scales, with associative areas displaying slower timescales than sensory areas. This hierarchical organization is shaped by both large-scale connectivity and local properties. The present study demonstrates that the variance of fluctuations is also hierarchically organized but, in contrast to timescales, it decreases as a function of structural connectivity. Whole-brain models show that the hierarchies of timescales and variances jointly emerge within specific parameter regions, indicating a state-dependence that could serve as a biomarker for brain states and disorders. Furthermore, these hierarchies link to the responsivity of different network parts, with greater and faster responsiveness at the network periphery and more stable dynamics at the core, achieving a balance between stability and responsiveness.