Marcus T. Wilson

The sleep cycle modelled as a cortical phase transition.

We present a mean-field model of the cortex that attempts to describe the gross changes in brain electrical activity for the cycles of natural sleep. We incorporate within the model two major sleep modulatory effects: slow changes in both synaptic efficiency and in neuron resting voltage caused by the ∼90-min cycling in acetylcholine, together with even slower changes in resting voltage caused by gradual elimination during sleep of somnogens (fatigue agents) such as adenosine. We argue that the change from slow-wave sleep (SWS) to rapid-eye-movement (REM) sleep can be understood as a first-order phase transition from a low-firing, coherent state to a high-firing, desychronized cortical state. We show that the model predictions for changes in EEG power, spectral distribution, and correlation time at the SWS-to-REM transition are consistent not only with those observed in clinical recordings of a sleeping human subject, but also with the on-cortex EEG patterns recently reported by Destexhe et al. [J. Neurosci.19(11), (1999) 4595–4608] for the sleeping cat.

General Anesthetic-induced Seizures Can Be Explained by a Mean-field Model of Cortical Dynamics

GENERAL anesthesia is a state in which cerebral activity is usually profoundly suppressed. It is paradoxical that some general anesthetic agents—drugs whose primary action is to decrease central nervous system activity and that have been widely used to treat seizures—can also provoke cortical seizures when the patient is deeply anesthetized. Traditionally, the explanation for this phenomenon has been sought at a molecular or synaptic level of description, by finding differences between general anesthetic drugs that commonly precipitate seizure activity and those drugs that do not cause seizures. It has been suggested that proconvulsant drugs (such as enflurane) may (1) act to decrease the amplitude of miniature inhibitory postsynaptic currents or (2) elicit greater calcium-induced presynaptic mobilization of excitatory neurotransmitters than anticonvulsant drugs (such as isoflurane or thiopentone). These descriptions are qualitative. Are these observations of true causative mechanisms of seizure genesis, or are they simply observations of epiphenomena? It is not clear exactly how these observations at synaptic and molecular scales would quantitatively result in repetitive synchronous widespread burst firing of cortical neurons—the signature of seizure activity. In this article, we describe a mathematical model of cerebral cortical function (the so-called mean-field model). In our model, we are able to input the known molecular-scale effects of anesthetic drugs and see how they alter the output (a “pseudoencephalogram”) of the computer-simulated “pseudocortex.” We incorporate the values of inhibitory postsynaptic potential (IPSP) amplitude and duration that have been previously published and studied in detail for isoflurane and enflurane, and we compare the output from simulations run on our theoretical mathematical model with various experimental and clinical observations that have been previously reported in the scientific literature. We find that subtle changes in the shape of the IPSP—induced by enflurane—are sufficient to cause the model of the cerebral cortex to undergo a sudden change in behavior from a general anesthetic state (in which neuronal firing is suppressed) into a seizure-like state—manifest as oscillation between neuronal silence and supramaximal neuronal firing. We use the example of enflurane-induced seizures as a dramatic demonstration of the application of mean-field models of cortical dynamics to link molecular and macroscopic descriptions of nervous system phenomena.

The K-complex and slow oscillation in terms of a mean-field cortical model

We use a mean-field macrocolumn model of the cerebral cortex to offer an interpretation of the K-complex of the electroencephalogram to complement those of more detailed neuron-by-neuron models. We interpret the K-complex as a momentary excursion of the cortex from a stable low-firing state to an unstable high-firing state, and hypothesize that the related slow oscillation can be considered as the periodic oscillation between two meta-stable solutions of the mean-field model. By incorporating a Hebbian-style learning rule that links the growth in synapse strength to fluctuations in soma potential, we demonstrate a self-organization behaviour that draws the modelled cortex close to the edge of stability of the low-firing state. Furthermore, a very slow oscillation can occur in the excitability of the cortex that has similarities with the infra-slow oscillation of sleep.

Complementarity of Spike- and Rate-Based Dynamics of Neural Systems

Relationships between spiking-neuron and rate-based approaches to the dynamics of neural assemblies are explored by analyzing a model system that can be treated by both methods, with the rate-based method further averaged over multiple neurons to give a neural-field approach. The system consists of a chain of neurons, each with simple spiking dynamics that has a known rate-based equivalent. The neurons are linked by propagating activity that is described in terms of a spatial interaction strength with temporal delays that reflect distances between neurons; feedback via a separate delay loop is also included because such loops also exist in real brains. These interactions are described using a spatiotemporal coupling function that can carry either spikes or rates to provide coupling between neurons. Numerical simulation of corresponding spike- and rate-based methods with these compatible couplings then allows direct comparison between the dynamics arising from these approaches. The rate-based dynamics can reproduce two different forms of oscillation that are present in the spike-based model: spiking rates of individual neurons and network-induced modulations of spiking rate that occur if network interactions are sufficiently strong. Depending on conditions either mode of oscillation can dominate the spike-based dynamics and in some situations, particularly when the ratio of the frequencies of these two modes is integer or half-integer, the two can both be present and interact with each other.

A Mechanism for Ultra-Slow Oscillations in the Cortical Default Network

When the brain is in its noncognitive “idling” state, functional MRI measurements reveal the activation of default cortical networks whose activity is suppressed during cognitive processing. This default or background mode is characterized by ultra-slow BOLD oscillations (∼0.05 Hz), signaling extremely slow cycling in cortical metabolic demand across distinct cortical regions. Here we describe a model of the cortex which predicts that slow cycling of cortical activity can arise naturally as a result of nonlinear interactions between temporal (Hopf) and spatial (Turing) instabilities. The Hopf instability is triggered by delays in the inhibitory postsynaptic response, while the Turing instability is precipitated by increases in the strength of the gap-junction coupling between interneurons. We comment on possible implications for slow dendritic computation and information processing.

Numerical modelling of plasticity induced by transcranial magnetic stimulation

We use neural field theory and spike-timing dependent plasticity to make a simple but biophysically reasonable model of long-term plasticity changes in the cortex due to transcranial magnetic stimulation (TMS). We show how common TMS protocols can be captured and studied within existing neural field theory. Specifically, we look at repetitive TMS protocols such as theta burst stimulation and paired-pulse protocols. Continuous repetitive protocols result mostly in depression, but intermittent repetitive protocols in potentiation. A paired pulse protocol results in depression at short ( < ∼ 10 ms) and long ( > ∼ 100 ms) interstimulus intervals, but potentiation for mid-range intervals. The model is sensitive to the choice of neural populations that are driven by the TMS pulses, and to the parameters that describe plasticity, which may aid interpretation of the high variability in existing experimental results. Driving excitatory populations results in greater plasticity changes than driving inhibitory populations. Modelling also shows the merit in optimizing a TMS protocol based on an individual’s electroencephalogram. Moreover, the model can be used to make predictions about protocols that may lead to improvements in repetitive TMS outcomes.

An analysis of the transitions between down and up states of the cortical slow oscillation under urethane anaesthesia

We study the dynamics of the transition between the low- and high-firing states of the cortical slow oscillation by using intracellular recordings of the membrane potential from cortical neurons of rats. We investigate the evidence for a bistability in assemblies of cortical neurons playing a major role in the maintenance of this oscillation. We show that the trajectory of a typical transition takes an approximately exponential form, equivalent to the response of a resistor–capacitor circuit to a step-change in input. The time constant for the transition is negatively correlated with the membrane potential of the low-firing state, and values are broadly equivalent to neural time constants measured elsewhere. Overall, the results do not strongly support the hypothesis of a bistability in cortical neurons; rather, they suggest the cortical manifestation of the oscillation is a result of a step-change in input to the cortical neurons. Since there is evidence from previous work that a phase transition exists, we speculate that the step-change may be a result of a bistability within other brain areas, such as the thalamus, or a bistability among only a small subset of cortical neurons, or as a result of more complicated brain dynamics.

Noise-Induced Precursors of State Transitions in the Stochastic Wilson–Cowan Model

The Wilson–Cowan neural field equations describe the dynamical behavior of a 1-D continuum of excitatory and inhibitory cortical neural aggregates, using a pair of coupled integro-differential equations. Here we use bifurcation theory and small-noise linear stochastics to study the range of a phase transitions—sudden qualitative changes in the state of a dynamical system emerging from a bifurcation—accessible to the Wilson–Cowan network. Specifically, we examine saddle-node, Hopf, Turing, and Turing–Hopf instabilities. We introduce stochasticity by adding small-amplitude spatio-temporal white noise, and analyze the resulting subthreshold fluctuations using an Ornstein–Uhlenbeck linearization. This analysis predicts divergent changes in correlation and spectral characteristics of neural activity during close approach to bifurcation from below. We validate these theoretical predictions using numerical simulations. The results demonstrate the role of noise in the emergence of critically slowed precursors in both space and time, and suggest that these early-warning signals are a universal feature of a neural system close to bifurcation. In particular, these precursor signals are likely to have neurobiological significance as early warnings of impending state change in the cortex. We support this claim with an analysis of the in vitro local field potentials recorded from slices of mouse-brain tissue. We show that in the period leading up to emergence of spontaneous seizure-like events, the mouse field potentials show a characteristic spectral focusing toward lower frequencies concomitant with a growth in fluctuation variance, consistent with critical slowing near a bifurcation point. This observation of biological criticality has clear implications regarding the feasibility of seizure prediction.

Progress in Modeling EEG Effects of General Anesthesia: Biphasic Response and Hysteresis

It is a well established clinical observation that, at low concentrations, most anesthetic agents produce a surge in brain activity that occurs around the time of loss of consciousness. At higher concentrations, brain activity slows, and eventually tends towards electrical silence. A second surge in EEG power occurs during the return to consciousness. These induction and recovery biphasic power surges were first explained in terms of a first-order switching transition between distinct active and quiescent neural states, but subsequent modeling by other researchers has demonstrated that biphasic surges can also be generated by a smooth, graduated transition between normal and suppressed levels of cortical activity. In this chapter we examine the contrasting predictions of the switching model versus the continuous model for anesthetic induction. If the continuous non-switching picture is correct, then the return path to recovery will retrace the trajectory for induction, so the biphasic peaks should occur at identical drug concentrations. In contrast, the switching model predicts that there must be a hysteresis separation between the entry and recovery EEG power maxima, and that the patient will awaken at a lower drug concentration than that required to put her to sleep.

Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model

The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.