Neuronal Synchronization and Thalamocortical Rhythms during Sleep, Wake, and Epilepsy

Timofeev, Igor; Bazhenov, Maxim; Seigneur, Josée; Sejnowski, Terrence J.

doi:10.1093/med/9780199746545.003.0013

Cited by 70 publications

(71 citation statements)

References 172 publications

(151 reference statements)

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“…1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4 Previous excellent reviews have thoroughly described cellular and network bases of spindle generation [3,6,7]. He...…”

Section: Highlightsmentioning

confidence: 99%

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Manipulating sleep spindles – expanding views on sleep, memory, and disease

Astori

Wimmer²,

Lüthi³

2013

Trends in Neurosciences

127

113

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Abstract:Sleep spindles are distinctive electroencephalographic (EEG) oscillations emerging during non-rapid-eye-movement sleep (NREMS) that have been implicated in multiple brain functions, including sleep quality, sensory gating, learning and memory. Despite considerable knowledge about the mechanisms underlying these neuronal rhythms, their function remains poorly understood and current views are largely based on correlational evidence. Here, we review recent studies in humans and rodents that have begun to broaden our understanding of the role of spindles in the normal and disordered brain. We show that newly identified molecular substrates of spindle oscillations, in combination with evolving technological progress, offer novel targets and tools to selectively manipulate spindles and dissect their role in sleep-dependent processes. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationAstori et al. Highlights Newly recognized ion channel subtypes generating spindle rhythms are described. The contribution of spindles to arousal threshold and sleep quality is discussed. The proposed role of spindles in memory consolidation is examined. A function of thalamic spindles in neural development is suggested. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4 Previous excellent reviews have thoroughly described cellular and network bases of spindle generation [3,6,7]. Here, we first review recent work on genetic models that has expanded the mechanistic understanding of this EEG rhythm through the modification of novel molecular substrates. We then discuss the current views about the functional aspects of spindles in brain physiology and pathology, as obtained from studies in naturally sleeping animals and humans. We highlight studies indicating that spindles are accessible for selective interventions, and that, with emerging technologies, will open avenues to decipher spindle function. Novel molecular aspects of spindle generationSleep spindles emerge from a limited set of cellular participants: the resonating core of these waxing-and-waning oscillations resides in the nRt-TC loop, which sustains repetitive burst discharges of its cellular components under the control of cortical inputs (Figure 1 Another important factor in the generation and maintenance of reticular intrinsic oscillations is the aforementioned SK2 or Kcnn2 channel. The vigorous Ca 2+ influx in nRt dendrites originating from Ca V 3.3 channels gates SK2 channels, thereby creating a burstafterhyperpolarization (bAHP) [12,19]. As T channels recover partially from inactivation duri...

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Section: Highlightsmentioning

confidence: 99%

“…Previous excellent reviews have thoroughly described cellular and network bases of spindle generation [3,6,7]. Here, we first review recent work on genetic models that has expanded the mechanistic understanding of this EEG rhythm through the modification of novel molecular substrates.…”

Section: Nucleus Reticularis Thalami (Nrt) Cells and Thalamocortical mentioning

confidence: 99%

Manipulating sleep spindles – expanding views on sleep, memory, and disease

Astori

Wimmer²,

Lüthi³

2013

Trends in Neurosciences

127

113

View full text Add to dashboard Cite

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“…This result is compatible with the decreasing SWA in NREM sleep during recovery [16], which in our study resulted in increased complexity values potentially due to the reduced SWA toward the end of the period and sleep compensation process. Therefore, this was reflected in PLZC analysis as increased complexity possibly due to reduced neuronal synchronisation [62,63]. In other periods, non-significant changes in PLZC values were observed both in wakefulness and REM sleep ( Figure 3A, right panel).…”

Section: Discussionmentioning

confidence: 94%

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Tosun

Abásolo

Stenson

et al. 2017

Entropy

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Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG). Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS), i.e., wakefulness, rapid-eye movement (REM) and non-rapid-eye movement (NREM) sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel-Ziv complexity (PLZC), a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD). The results obtained with PLZC were contrasted with a related non-linear method, Lempel-Ziv complexity (LZC). Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

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“…We selected gain coefficients to control the RCLSJ system (6) and synchronize with the Hodgkin-Huxley model (1). The initial values of the system variables RCLSJ controller shown in (6) are (0.5, 0.6, 0.9) [4], [11].…”

Section: Numerical Simulation and Resultsmentioning

confidence: 99%

“…Neuronal synchronization is defined as correlated appearance in time of two or more events associated with various aspects of neuronal activity at different levels, from single cell to the whole brain. A common scenario involves adjustment or phase locking of rhythms of two or more neurons leading to a stable phase difference of membrane voltage oscillations (periodic or not), e.g., coincidence of action potentials [1].…”

Section: Introductionmentioning

confidence: 99%

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

Téquita

Naranjo

2016

ing.cienc.

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We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purpose of the manuscript is show a method to syncronize a RCLshunted Josephson junction to a neuronal dynamics represented by the HH model. Thus the RCLSJ circuit is able to mimics the behavior of the HH neuron. We controlated the RCLSJ circuit, using and improved adaptative track scheme, that with the improved Lyapunov functions and the two controllable gain coefficients allowing synchronization of two neuronal models. Results will provide the path to follow forward the understanding neuronal networks synchronization about, generating the intrinsic brain behavior. Sincronización de la actividad eléctrica neuronal, utilizando el modelo de Hodgkin-Huxley y el circuito RCLSJ ResumenSimulamos la actividad eléctrica neuronal mediante el modelo de HodgkinHuxley (HH) y un circuito superconductor, que contiene uniones Josephson. El modelo HH simulan las características principales de la dinámica neuronal tales como potenciales de acción, umbrales de disparo y el perío-dos refractarios. El propósito del manuscrito es mostrar un método para sincronizar un circuito con union Josephson RCLSJ a una dinámica neuronal representado por el modelo HH. Así, el circuito RCLSJ es capaz de imitar el comportamiento de la neurona HH. Controlamos el circuito RCLSJ, utilizando un esquema de control adaptativo, que con funciones de Lyapunov y dos coeficientes de ganancia controlables nos permiten la sincronización de los dos modelos neuronales. Los resultados proporcionan una ruta a seguir adelante en el entendimiento de la sincronización de redes neuronales, generadas por el comportamiento intrinseco del cerebro.Palabras clave: método modelo de Hodgkin-Huxley; uniones Josephson; funciones de Lyapunov; analisis númerico.

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Neuronal Synchronization and Thalamocortical Rhythms during Sleep, Wake, and Epilepsy

Cited by 70 publications

References 172 publications

Manipulating sleep spindles – expanding views on sleep, memory, and disease

Manipulating sleep spindles – expanding views on sleep, memory, and disease

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

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