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Record W2162004591 · doi:10.1113/jphysiol.2014.271304

Cholinergic involvement in control of REM sleep paralysis

2014· letter· en· W2162004591 on OpenAlexaff
Zoltan A. Torontali, Kevin P. Grace, Richard L. Horner, John Peever

Bibliographic record

VenueThe Journal of Physiology · 2014
Typeletter
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeuroscienceSleep paralysisNon-rapid eye movement sleepSleep (system call)CholinergicSlow-wave sleepRapid eye movement sleepSleep spindleREM sleep behavior disorderPsychologyParalysisK-complexBrainstemEye movementMedicineNeurologyPolysomnographyElectroencephalographyCataplexyNarcolepsyPsychiatry

Abstract

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REM sleep, also known as dreaming sleep, is marked by intense cortical activation and absence of skeletal muscle tone, so-called REM sleep paralysis (atonia). It is commonly believed that REM sleep paralysis functions to prevent movement during vivid dreams. Indeed, REM sleep behaviour disorder – a neurological condition marked by violent dream enactment – results from loss of REM sleep paralysis. For the last 50 years, biologists have focused on the identification of brain mechanisms responsible for REM sleep. A majority of evidence suggests that a brainstem region known as the sublaterodorsal nucleus (SLD), also called the subcoeruleus, is important for REM sleep generation (Jouvet 1962). However, there is uncertainty concerning the chemical mechanisms by which the SLD triggers REM sleep phenomena. For example, some data suggest that cholinergic modulation of SLD cells underlies REM sleep generation, whereas, other data suggest that GABAergic disinhibition and glutamatergic excitation of SLD cells are critical for REM sleep control (Boissard et al. 2002; Lu et al. 2006). The recent study by Weng et al. (2014) provides a potentially new framework for understanding REM sleep control by showing that both cholinergic and glutamatergic processes operating within the SLD could be important for triggering REM sleep paralysis. The SLD contains cells that are crucial for generating REM sleep paralysis. Pharmacological and electrical activation of SLD neurons produces a REM-like sleep state that is characterized by muscle paralysis and cortical activation. In contrast, SLD lesions produce REM sleep without atonia in animals, and neurodegeneration of the SLD region is associated with REM sleep behaviour disorder in humans. REM sleep-active SLD neurons are glutamatergic and are thought to induce REM paralysis by activating GABA and glycine-containing neurons in the ventromedial medulla and spinal cord, which in turn trigger motor atonia by inhibiting skeletal motoneurons (Boissard et al. 2002). During REM sleep, acetylcholine is thought to participate in the activation of these descending atonia pathways. For example, application of cholinergic agonists into the SLD can induce long-lasting periods of cortical activation and muscle paralysis (Steriade & McCarley, 2005). Nevertheless, not all experimental interventions produce this same effect. In fact, cholinergic stimulation of the SLD can also induce prolonged bouts of wakefulness (George et al. 1964). Taking these contradictions to heart, Weng and co-workers set out to understand better how cholinergic mechanisms regulate REM sleep paralysis at the level of the SLD. Because the SLD contains a heterogeneous pool of neurons that mediate a range of behaviours, Weng and co-workers developed a new approach for studying how cholinergic mechanisms affect the function of spinally projecting SLD neurons specifically. They did this by retrogradely labelling SLD neurons from the spinal cord and then used in vitro electrophysiology (i.e. patch clamp recordings) to characterize how cholinergic drugs (e.g. carbachol) influence their activity. This approach provides a powerful tool for determining how (or if) cholinergic mechanisms contribute to the control of REM sleep paralysis. The authors made several important observations that will probably affect the way sleep biologists understand REM sleep control. First, they found that spinally projecting SLD cells are excited by carbachol in vitro (a cholinergic receptor agonist) (Fig. 1). This observation is important because it indicates that acetylcholine could trigger REM paralysis by directly activating SLD neurons in vivo. Next, they found that carbachol increases the frequency of glutamatergic EPSCs on to SLD neurons. Although they did not identify the source of these glutamatergic inputs, this finding is none the less important because it suggests cholinergic mechanisms regulate REM sleep paralysis by controlling the glutamatergic drive at the SLD (Fig. 1). Lastly, Weng et al. (2014) demonstrate that both the pre- and postsynaptic effects of carbachol are mediated by co-activation of M1 and M3 muscarinic receptors. Together, these results are important because they suggest that REM sleep paralysis may be driven by a cholinergic mechanism that acts at both the level of SLD neurons themselves, and at the glutamatergic inputs terminating on SLD cells (Fig. 1). Pathways mediating excitation of SLD neurons by exogenous cholinergic stimulation as identified by Weng et al. Glu, glutamate; MAch, muscarinic acetylcholine receptor; SLD, sublaterodorsal nucleus. In summary, the study by Weng et al. (2014) establishes proof-of-principle of a cholinergic mechanism mediating sleep paralysis whereby acetylcholine activates spinally projecting SLD neurons directly as well as indirectly through amplification of glutamatergic drives. Validation of this mechanism as well as determination of the necessary involvement of acetylcholine in other aspects of REM sleep generation will require in vivo studies. Existing in vivo evidence neither confirms nor refutes the involvement of SLD cholinergic neurotransmission in producing REM sleep or its component phenomena. Future studies should aim to block cholinergic neurotransmission in the SLD and/or selectively inactivate REM sleep-active cholinergic inputs to the SLD (e.g. pedunculopontine and laterodorsal tegmental nuclei). Furthermore, combining these interventions with simultaneous modulation of other neurotransmitter systems could be used to reveal neuromodulatory interactions that are important for REM sleep control. Ultimately, developing an inclusive theory of REM sleep generation requires that sleep biologists identify how complex interactions between multiple neurotransmitter systems, including GABAergic, glutamatergic and cholinergic circuitry give rise to REM sleep. Therefore, by characterizing the stimulatory effects of acetylcholine on neural activity and glutamatergic neurotransmission in the SLD, the study by Weng et al. (2014) stands as a significant contribution to the field of sleep neurobiology. None declared.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.299
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2014
Admission routes1
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