Symmetry breaking organizes the brain's resting state manifold

Abstract

Spontaneously fluctuating brain activity patterns emerge at rest and relate to brain functional networks involved in task conditions. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a complete mechanistic description in terms of the constituent entities and the productive relation of their causal activities leading to the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major empirical data features including spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding.

Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms

“…Fluidity captures the recurring patterns of co-activation across time and, more specifically, the switches between highly correlated and non-correlated patterns. Even though direct evidence is sparse due to challenges in probing human brain activity’s manifold, fluidity is believed to reflect the complexity of the energy landscape and the number of attractors(13, 28). The distribution of fluidity in the model’s parameter space (Fig 2c top-left) shows a peak near the working point value, reflecting recurring cascades during simulation, and decreases monotonously as the regime is drawn away from it.…”

“…Yet these coactivations patterns are not purely stochastic (27). They are often repetitive, revisiting the same neighborhood in state space (28).…”

State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer’s Disease

“…Another question is what the mechanistic origin could be of the observed spatio-temporal complexity of dFC (and of its alterations). Previous studies have shown that structured dFC may emerge as an effect of global brain network dynamics to be tuned at a slightly subcritical working point (Arbabyazd et al, 2020; Glomb et al, 2017; Hansen et al, 2015), or as a consequence of cascades of neuronal activations (Rabuffo et al, 2021) that occur due to the flow on the manifold created by the symmetry breaking of the connectome (Fousek et al, 2022). However, these studies did not use very precise criteria when referring to their capacity to render dFC.…”

Probabilistic Inference on Virtual Brain Models of Disorders

“…By coupling the brain regions via an additive current in the average membrane potential equations, the dynamics of the whole-brain network can be described as follows (Rabuffo et al, 2021; Fousek et al, 2022): where G is the network scaling parameter that modulates the overall impact of brain connectivity on the state dynamics (see Supplementary, Fig S1), SC i,j denotes the symmetrical connection weight between i th and j th nodes with i, j ∈ { 1, 2, …, N } , and the dynamical noise ξ( t ) ∼ 𝒩 (0, σ 2 ) follows a Gaussian distribution with mean zero and variance σ 2 = 0.03. The brain network model was implemented in TVB software (Sanz Leon et al, 2013; Rabuffo et al, 2021) and equipped with BOLD forward solution comprising the Balloon-Windkessel model (Stephan et al, 2007) applied to the firing rate.…”

“…The input current I stim represents the stimulation to selected brain regions, which increase the basin of attraction of the up-state in comparison to the down-state, while the fixed points move farther apart (Rabuffo et al, 2021). By coupling the brain regions via an additive current in the average membrane potential equations, the dynamics of the whole-brain network can be described as follows (Rabuffo et al, 2021;Fousek et al, 2022):…”