Cellular and neurochemical basis of sleep stages in the thalamocortical network

Krishnan, Giri P.; Chauvette, Sylvain; Shamie, Isaac; Soltani, Sara; Timofeev, Igor; Cash, Sydney S.; Halgren, Eric; Bazhenov, Maxim

doi:10.7554/elife.18607

Cited by 78 publications

(142 citation statements)

References 93 publications

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“…On the other hand, one potential mechanism for SO reduction post-HR burst may be via NTS projections to basal forebrain and locus coeruleus. Stimulation of these areas by the NTS increases acetylcholine and norepinephrine release in cortex, which is known to reduce slow oscillations 41 . Taken together, this suggests that the increase of SO prior to HR burst could mediate an increase of HR through combination of sympathetic and parasympathetic pathways and that the reduction of SO following HR burst may arise from increased activity of NTS through parasympathetic output via the vagal nerve.…”

Section: Discussionmentioning

confidence: 99%

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

Naji

McDevitt

Bazhenov

et al. 2017

Preprint

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While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 seconds and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4Hz) and sigma (12-15Hz) power and an elevated density of slow oscillations (0.5-1Hz) about 5 secs prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

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Section: Discussionmentioning

confidence: 99%

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

Naji

McDevitt

Bazhenov

et al. 2017

Preprint

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“…We consider the model we present here a prototype which can be expanded to analyze and shape hypotheses related to a number of open questions, for example: 1) the influence of hippocampal reactivation on cortical reactivation can be studied in this model when connecting it to thalamo-cortical models of sleep rhythmic activity (Bazhenov, Timofeev et al 2002, Krishnan, Chauvette et al 2016), together with the role of cortical input in hippocampal SWR replay; 2) which synaptic changes are performed in the hippocampus when learning competing (interfering) memories, and how they can affect sleep-dependent consolidation of interfering memories (Mednick, Cai et al 2011) 3) the model can be expanded to introduce plasticity to interneurons, and physiological awake spiking activity in the whole CA3-CA1 network (characterized by theta-gamma rhythmic interaction during active exploration (Kopell, Börgers et al 2010)) to study the relationship between the theta phase spiking of a sequence and its reactivation during sleep SWR (Mizuseki, Royer et al 2012, Grosmark andBuzsaki 2016).…”

Section: Resultsmentioning

confidence: 99%

Learning-induced sequence reactivation during sharp-wave ripples: a computational study

Malerba

Tsimring

Bazhenov

2017

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During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns -replay -during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp wave -ripples (SWR), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location ('place cells') show that recently learned trajectories are reactivated during SWR in the following sleep SWR. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.We developed a model of SWR activity, to study the effects of learning-induced synaptic changes on spontaneous sequence reactivation during SWR. The model implemented a paradigm including three epochs: Pre-sleep, learning and Post-sleep activity. We first tested the effects of learning on the hippocampal network activity through changes in a minimal number of synapses connecting selected pyramidal cells. We then introduced an explicit trajectory-learning task to the model, to obtain behaviorinduced synaptic changes. The model revealed that the recently learned trajectory reactivates during sleep more often than other trajectories in the training field. The study predicts that the gain of reactivation rate during sleep following vs sleep preceding learning for a trained sequence of pyramidal cells depends on Pre-sleep activation of the same sequence, and on the amount of trajectory repetitions included in the training phase.

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“…In a manner similar to our previous studies, Bonjean et al, 2011;Krishnan et al, 2016), the state of the network was set to be stage 2 sleep state by modifying the intrinsic and synaptic currents to mimic the level of low acetylcholine, norepinephrine and histamine. In this state, the network spontaneously generated electrical activity consisting of multiple randomly occurring spindle events involving thalamic and cortical neuronal populations.…”

Section: Resultsmentioning

confidence: 99%

“…In this report we combine neural and biophysical models to generate M/EEG sleep spindles. The neural model is based on our previous computational modeling including the thalamic and cortical local and distant circuits involved in spindles, including matrix and core Bonjean et al, 2012;Krishnan et al, 2018bKrishnan et al, , 2016. All relevant thalamic ligand-and voltage-gated currents are included.…”

Section: Introductionmentioning

confidence: 99%

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

Rosen

Krishnan

Šanda

et al. 2017

Preprint

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BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. MethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. ResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. Comparison with Existing MethodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales simultaneously. ConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

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Cellular and neurochemical basis of sleep stages in the thalamocortical network

Cited by 78 publications

References 93 publications

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

Learning-induced sequence reactivation during sharp-wave ripples: a computational study

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

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