Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

Abstract

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 brain regions, whose structural connectivity is obtained from tractography data. The activity of each brain area is governed by a Jansen neural mass model and we normalize the total input received by each node so it amounts the same across all brain areas. This assumption allows for the existence of an homogeneous invariant manifold, i.e., a set of different stationary and oscillatory states in which all nodes behave identically. Stability analysis of these homogeneous solutions unveils a transverse instability of the synchronized state, which gives rise to different types of spatiotemporal dynamics, such as chaotic alpha activity. Additionally, we illustrate the ubiquity of this route towards complex spatiotemporal activity in a network of next-generation neural mass models. Altogehter, our results unveil the bifurcation landscape that underlies the emergence of function from structure in the brain.

Bifurcations and synchrony in a ring of delayed Wilson–Cowan oscillators

“…Nonetheless, the improved understanding of how dynamic network instabilities arise in simplified systems acquired from this study provides a solid foundation for the future analysis of the dynamic behaviour of these more complicated networked systems. Note that a recent study by Clusella et al [39] discovered outcomes comparable to those demonstrated in this work for large-scale brain models without delay. In particular, the authors in [39] proposed that transverse instabilities in the synchronization manifold provide a possible mechanism underlying experimentally observed spatio-temporal neural activity.…”

“…Note that a recent study by Clusella et al [39] discovered outcomes comparable to those demonstrated in this work for large-scale brain models without delay. In particular, the authors in [39] proposed that transverse instabilities in the synchronization manifold provide a possible mechanism underlying experimentally observed spatio-temporal neural activity. Given the results presented here, it would be fascinating to investigate how the presence of delays affects dynamic brain network models.…”

Local Spatial Flows and Propagative Attractors: A Novel “Flownectome” Framework for Analyzing Bold Fmri Dynamics

“…While there have been explorations of clearly characterized aerial-view spatial phenomena such as, e.g. traveling waves, in BOLD fMRI [33][34][35][36][37][38][39][40][41][42][43][44][45][46], more granular, data-driven analyses of local directional flows and their larger propagative implications have not been reported in the fMRI literature. The methods we present below are a first step toward addressing a potentially important and long-neglected aspect of the BOLD signal.…”

Variability in neural representations of fear: insights from computational modeling

“…For example, several studies have used data-driven approaches to identify and characterize whole-brain states that occur in a variety of task environments from more traditional "stimulus-response" type paradigms to passive viewing of lengthy movie clips (Nastase et al, 2021;Saarimäki, 2021;Simony & Chang, 2020). While it is now well-accepted in the EEG literature that the brain appears to traverse through several states during a single trial (Clusella et al, 2023;Kringelbach & Deco, 2020), research in functional connectomics, too, has also modeled "resting" fMRI data as dynamic, whole-brain connectomic states (Allen et al, 2014;Ciric et al, 2017) in part because the distribution of these states may carry clinically-relevant diagnostic information (Damaraju et al, 2014;Reinen et al, 2018).…”