Neuronal Synchrony during Anesthesia: A Thalamocortical Model

Abstract

There is growing evidence in favor of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in particular, that theta (3.5-7.5 Hz) and delta (0.5 < 3.5 Hz) oscillations facilitate information coding. The theta- and delta-rhythms are shown to be involved in various sleep stages, and during anesthesia, they undergo changes with the depth of anesthesia. We introduce a thalamocortical model of interacting neuronal ensembles to describe phase relationships between theta- and delta-oscillations, especially during deep and light anesthesia. Asymmetric and long-range interactions among the thalamocortical neuronal oscillators are taken into account. The model results are compared with experimental observations. The delta- and theta-activities are found to be separately generated and are governed by the thalamus and cortex, respectively. Changes in the degree of intraensemble and interensemble synchrony imply that the neuronal ensembles inhibit information coding during deep anesthesia and facilitate it during light anesthesia.

Event-Related Desynchronization in Diffusively Coupled Oscillator Models

“…Dramatic decrease in delta band (1)(2)(3)(4) intensity and an increase in theta (4)(5)(6)(7)(8) intensity is reported to be a signature of transition from deep to light anesthesia [3] (see Fig. 1).…”

“…The desynchronized group of neurons contribute to the emergence of theta band oscillations with relatively lesser intensity than those of the delta band oscillations. A model to simulate this experimental observation concluded that some neuronal oscillators desynchronize from the thalamus population (delta band) and synchronize with the cortical population (theta band) for this transition to occur [4]. Both ERS/ERD occur due to incoming signals that represent the awaiting task, which may be a motor function, memory tasking or signals from auditory and/or visual cortex that need recognition.…”

Mean-field and mean-ensemble frequencies of a system of coupled oscillators

“…In the infinite limit, (6) is introduced into (4). By taking the time derivative and applying the substitution (5), the evolution of the complex order parameter is obtaineḋ…”

Analysis of Synchronization in a Neural Population by a Population Density Approach

“…These signals reveal significant but minor fractions of the whole brain (or brain regions) electrical activity and most of the information stream dynamics which are yet essential to a global understanding of its intrinsic properties remain out of sight. Among these network-scale properties, the synchronization mechanisms in large neuronal assemblies is one of the most widely investigated by the neuroscientists since it is known to play key roles in physiological as well as in pathological processes (Dejean et al, 2008;Sheeba et al, 2008). A widespread method to deal with this problem, and more generally to investigate complex biological structures and having a full access to all their components, consists in building their most reliable possible computational model.…”