Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms

Abstract

The human alpha (8 -12 Hz) rhythm is one of the most prominent, robust, and widely studied attributes of ongoing cortical activity. Contrary to the prevalent notion that it simply "waxes and wanes," spontaneous alpha activity bursts erratically between two distinct modes of activity. We now establish a mechanism for this multistable phenomenon in resting-state cortical recordings by characterizing the complex dynamics of a biophysical model of macroscopic corticothalamic activity. This is achieved by studying the predicted activity of cortical and thalamic neuronal populations in this model as a function of its dynamic stability and the role of nonspecific synaptic noise. We hence find that fluctuating noisy inputs into thalamic neurons elicit spontaneous bursts between low-and high-amplitude alpha oscillations when the system is near a particular type of dynamical instability, namely a subcritical Hopf bifurcation. When the postsynaptic potentials associated with these noisy inputs are modulated by cortical feedback, the SD of power within each of these modes scale in proportion to their mean, showing remarkable concordance with empirical data. Our state-dependent corticothalamic model hence exhibits multistability and scale-invariant fluctuations-key features of resting-state cortical activity and indeed, of human perception, cognition, and behavior-thus providing a unified account of these apparently divergent phenomena.

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

“…Each resulting waveform is classified according to several criteria that discern specific dynamical regimes. This could be, for example, the dynamic behavior of the system's state variables such as fixed points or limit cycles-switching between the two has been shown, for example, for the posterior alpha-rhythm (Freyer et al, , 2011(Freyer et al, , 2012. Another feature could be the expression of certain spatial patterns such as resting-state networks (RSNs) known from fMRI (Greicius et al, 2003;Gusnard and Raichle, 2001;Mantini et al, 2007;Raichle et al, 2001) or characteristic spectral distributions (brain chords).…”

“…Signals and other data can often be naturally described as points and flows on a manifold; that is, they are manifold-valued (Perdikis et al, 2011). Several physiologically motivated models of neuronal activity indicate that neuronal activity patterns have an underlying structure that is inherently lower dimensional and contained on the surface (or tightly around the surface) of a low-dimensional manifold that is embedded within the higher-dimensional data space (Deco et al, 2010;Freyer et al, , 2011Freyer et al, , 2012. Therefore, we view manifolds as geometric representations of meaningful system dynamics.…”

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

“…The time courses of functional connectivity are not constant -signifying the formation and dissolution of these resting-state networks-and typically show fractal scaling or 1/f properties (Bullmore et al, 2001;He, 2011;Maxim et al, 2005). Computational models show that resting-state dynamics reflect the slow modulations of long-range synchronization of fast local neuronal activity (Breakspear et al, 2004;Deco et al, 2009;Freyer et al, 2011;Ghosh et al, 2008;Honey et al, 2007). Recent studies on resting-state dynamics have hence focused on resting-state electrophysiology-with its high temporal resolution-to capture the synchronization dynamics of these faster time scales (Britz et al, 2010;Brookes et al, 2011;He et al, 2008;Hipp et al, 2012;Mantini et al, 2007;Mehrkanoon et al, 2014;Siegel et al, 2012).…”

Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors

“…Some of these regions are also part of the Default Mode Network (Greicius et al, 2003), which identifies regions showing the greatest deactivation during externally imposed cognitive challenges. Interestingly multistable attractors (more accurately, bistable limit cycles) have been also postulated to be involved in the generation of the alpha rhythm (Freyer et al, 2011), which is a prominent mostly posterior rhythm of 8 -12 Hz in the human. Alpha activity arises in absence of any stimulation or task context, in particular with eyes closed.…”

“…Alpha activity arises in absence of any stimulation or task context, in particular with eyes closed. Under conditions of noise, the corticothalamic model of Freyer et al (2011) operates close to a subcritical Hopf bifurcation and captures various detailed temporal characteristics of alpha activity, including the bimodal distribution of the power spectrum and scale-invariant fluctuations. In its spirit, this modeling approach is closer to Deco et al (2009) since it places the ongoing cortical oscillations beyond the threshold onto selfsustained oscillatory attractors (limit cycles).…”