Elsevier

Neuroscience

Volume 137, Issue 4, 2006, Pages 1087-1106
Neuroscience

Review
Grouping of brain rhythms in corticothalamic systems

https://doi.org/10.1016/j.neuroscience.2005.10.029Get rights and content

Abstract

Different brain rhythms, with both low-frequency and fast-frequency, are grouped within complex wave-sequences. Instead of dissecting various frequency bands of the major oscillations that characterize the brain electrical activity during states of vigilance, it is conceptually more rewarding to analyze their coalescence, which is due to neuronal interactions in corticothalamic systems. This concept of unified brain rhythms does not only include low-frequency sleep oscillations but also fast (beta and gamma) activities that are not exclusively confined to brain-activated states, since they also occur during slow-wave sleep. The major factor behind this coalescence is the cortically generated slow oscillation that, through corticocortical and corticothalamic drives, is effective in grouping other brain rhythms. The experimental evidence for unified oscillations derived from simultaneous intracellular recordings of cortical and thalamic neurons in vivo, while recent studies in humans using global methods provided congruent results of grouping different types of slow and fast oscillatory activities. Far from being epiphenomena, spontaneous brain rhythms have an important role in synaptic plasticity. The role of slow-wave sleep oscillation in consolidating memory traces acquired during wakefulness is being explored in both experimental animals and human subjects. Highly synchronized sleep oscillations may develop into seizures that are generated intracortically and lead to inhibition of thalamocortical neurons, via activation of thalamic reticular neurons, which may explain the obliteration of signals from the external world and unconsciousness during some paroxysmal states.

Section snippets

Neuronal circuitry in the corticothalamic system

I use the term corticothalamic, instead of TC, because axons in the descending pathway are much more numerous than in the ascending projection (Jones 1985, White 1989). Besides, the slow sleep oscillation, which is the main factor in the coalescence of brain rhythms, is generated intracortically, even in the absence of the thalamus (Steriade et al 1993e, Sanchez-Vives and McCormick 2000). Moreover, even though sleep spindles are generated within the thalamus, their near-simultaneous occurrence

Grouping of brain rhythms: evidence from intracellular recordings in animals and EEG studies in humans

Three rhythms (spindles, 7–15Hz; delta, 1–4Hz; slow oscillation, 0.5–1Hz) define slow-wave sleep, and two rhythms (beta, 20–30Hz; gamma, 30–60Hz) occur in a sustained manner during the brain-active states of waking and REM sleep, though these fast oscillations are also episodically present during slow-wave sleep when they possibly underlie dreaming mentation during this disconnected behavioral state (see below).

The importance of the slow oscillation resides in the fact that it groups other

Slow oscillation and spindles: the K-complex

The thalamic generation of sleep spindles and the crucial role of RE GABAergic neurons are discussed in detail elsewhere (Steriade, 2003). Recent experimental (Fuentealba et al., 2004) and modeling (Traub et al., 2005) studies support the notion that RE neurons are pacemakers of spindles. During the depolarizing phase of the slow oscillation, the synchronous firing of neocortical neurons impinges upon thalamic RE pacemaking neurons, thus creating conditions for formation of spindles, which are

Slow oscillation and delta waves

There are two components of delta waves. The cortical one survives thalamectomy (Villablanca 1974, Steriade et al 1993e). The thalamic component is generated through the interplay between two intrinsic currents of TC neurons, a hyperpolarization-activated cation current, IH (Leresche et al 1990, Leresche et al 1991, McCormick and Pape 1990), and a low-threshold transient Ca2+ current, IT (Llinás 1988, Huguenard 1996). Although arising from intrinsic properties of single TC neurons, the thalamic

Slow oscillation and fast (beta/gamma) and ultra-fast rhythms

The unexpected association between a slow sleep rhythm and fast oscillations that are conventionally regarded as defining the electrical activity of brain-active states is explained by the voltage-dependency of fast oscillations. Indeed, long-axon and local-circuit cortical neurons generate beta and gamma rhythmicity at relatively depolarized values of the membrane potential (Llinás et al 1991, Nuñez et al 1992, Gray and McCormick 1996, Steriade et al 1996a). Thus, fast rhythms are sustained

Traveling slow oscillation in humans and actions on distant subcortical structures

The slow oscillation (generally 0.5–1Hz) was demonstrated during natural sleep of humans using EEG (Achermann and Borbély 1997, Amzica and Steriade 1997, Mölle et al 2002, Marshall et al 2003) and MEG (Simon et al., 2000) recordings.

The intracortical propagation of the slow sleep oscillation was studied in humans, using high-density (180) EEG leads (Massimini et al., 2004). The detection of slow oscillation on the multichannel EEG is depicted in Fig. 6. The slow oscillation originates in

Synaptic plasticity during and following brain rhythms

The question whether spontaneously occurring brain waves are epiphenomena with little or no functional significance may especially apply to the state of sleep that was considered to be associated with widespread inhibition throughout the cortex and subcortical structures (Pavlov, 1923), which would lead to abolition of cognitive and conscious events. However, the rich spontaneous firing of neocortical neurons, revealed by intracellular recordings during natural slow-wave sleep (Steriade et al.,

Experimental studies on animals

The experimental model of sleep spindles is the sequence of augmenting (or incremental) responses, defined as thalamically evoked cortical potentials that grow in size during the first stimuli at a frequency of 5–15Hz, which mimics the initially waxing pattern of spindle waves. Similar incremental responses can be evoked in the thalamus by stimulating the cortex within the frequency of spindle waves. The cellular mechanisms of augmenting responses have been studied in slices maintained in vitro

Human studies on the role of sleep in memory, learning, and dreaming mentation

The above experimental data and ideas that low-frequency oscillations (spindles and slow oscillation) are associated with synaptic plasticity are supported by human studies demonstrating that the overnight improvement of discrimination tasks requires some steps, including those in early slow-wave sleep stages (Stickgold et al 2000a, Stickgold et al 2000b). Also, procedural memory formation may be associated with oscillations during early sleep stages (Gais et al., 2000). After training on a

Transition from cortical sleep rhythms to electrical seizures

The synaptic plasticity that follows rhythmic brain stimulation within the frequency range of low-frequency sleep oscillations may take paroxysmal forms. This is especially seen with cortical FRB neurons that display a peculiar enhancement of rhythmic responses, with progressively grown depolarization and dramatically increased number of action potentials, which have an epileptiform aspect (Fig. 8). The changes in responsiveness of neocortical neurons, which lead to self-sustained oscillations

Conclusions

The living brain, with intact connections between neocortex, thalamus, and various modulatory systems, displays low-frequency and fast rhythms grouped within complex wave-sequences. Some of these oscillations can be generated by interplay between intrinsic neuronal properties, but the coalescence of various rhythms and their synchronization is due to network operations in corticothalamic systems. The tendency to analyze distinct, precise frequency bands of EEG activities, in isolation from

Acknowledgments

Personal studies reported in this article were supported by the Medical Research Council of Canada (now Canadian Institutes of Health Science), Human Frontier Science Program, and National Institutes of Health of the USA. I would like to thank my previous Ph.D. students and postdoctoral fellows who collaborated in our studies mentioned here, especially D. Contreras, F. Amzica and I. Timofeev. I also thank some of my recent Ph.D. students and postdoctoral fellows, in particular F. Grenier, P.

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