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Enregistrement W3043684996 · doi:10.1113/jp280388

What happens in vagus, no longer stays in vagus

2020· letter· en· W3043684996 sur OpenAlexaff
Jordan B. Lee, Lucas J. Omazic, Muhammad M. Kathia

Notice bibliographique

RevueThe Journal of Physiology · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueHeart Rate Variability and Autonomic Control
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésVagal toneVagus nerveMedicineHeart rateBaroreflexMicroneurographyBaroreceptorAutonomic nervous systemAnesthesiaCarotid sinusValsalva maneuverReflexCardiologyInternal medicineBlood pressureStimulation

Résumé

récupéré en direct d'OpenAlex

The extensive network of fibres arising from the vagus nerve relays afferent feedback from numerous organs in the neck, trunk and abdomen. Vagal afferents can be sensitive to chemical, mechanical, thermal and nociceptive stimuli. Efferent activity of the vagus nerve forms a significant portion of the parasympathetic nervous system, and has significant effects on cardiovascular, respiratory and gastrointestinal function. Alterations in parasympathetic activity modulate cardiac chronotropy, with increased cardiovagal activity leading to a decrease in heart rate (and vice versa). Cardiovagal activity is modulated by respiratory phase, known as respiratory sinus arrhythmia (RSA), which is dependent on pulmonary vagal afferent feedback. RSA increases the heart rate during inspiration via vagal withdrawal, whereas the heart rate decreases during expiration with increased vagal activity. The arterial baroreceptors in the aortic arch and carotid arteries can also modulate heart rate via increasing or decreasing parasympathetic activity during blood pressure rises or drops, respectively (Chapleau & Sabharwal, 2011). Unfortunately, our understanding of vagus nerve axonal discharge is based on single-unit recordings in animals, with data in humans relegated to indirect measures of tonic or reflex cardiac vagal modulation (e.g. heart rate variability, respiratory sinus arrhythmia, cardiac baroreflex sensitivity) (van Bilsen et al. 2017). In an article in the Journal of Physiology, Ottaviani et al. (2020) report the impressive strides made with respect to bridging the gap between animal and human work on vagus nerve neurophysiology. Ultrasound-guided microneurography was used to record from the cervical branch of the right and left vagus nerves in three human participants. Successful multi-unit recordings were obtained from all participants on both sides, whereas four single units were found in two participants on the left side. At the same time, measures of blood pressure, heart rate, respiratory rate and depth, and gastric activity were recorded in an effort to discern the physiological correlate of each recorded axon. Single-unit activity was quantified as spike frequency, as well as a moving average, and was compared visually with the physiological variables measured. Interestingly, the four single units and one reported multi-unit displayed heterogenous relationships with other physiological signals. One single unit was related to the cardiac cycle, where it fired more frequently during the prolongation of the RR interval. This unit was classified as a cardioinhibitory axon. Another recording site with multi-unit activity was also related to the cardiovascular outcomes, with greater activity in the period preceding the electrocardiogram R spike. This was suggested to be a cardiac sensory nerve detecting atrial wall tension. Two single units related to the respiratory phase were recorded, with one unit displaying greater activity during expiration, whereas the other was active during both inspiration and expiration. These were thought to be motor axons of the laryngeal adductors and the cricothyroid, respectively. Finally, one single unit was reported to fire tonically but had no relationship with any of the cardiovascular, respiratory, or gastric markers. It was speculated that this unit could have been a sensory or motor unit of the gastrointestinal system, although with activity unrelated to the electrogastrography. Over the last 50 years, microneurography has provided valuable neurophysiological information, although it also remains technically challenging and does not always result in successful data collection if suitable recording sites are not found. In recording from superficial peripheral nerves that are typically accessed in humans (e.g. common fibular nerve), it is possible to identify afferent, sudomotor or vasomotor activity, the relevance of which will be dictated by the question being investigated. As is evident from the recent work by Ottaviani et al. (2020), the possibility of recording afferent or efferent single units innervating distinct tissues is increased many fold in the vagus nerve. As such, further research using this technique might require multiple testable hypotheses in a single study based on the availability and properties of the axons found in a recording session. Furthermore, another consideration requiring investigation is the classification of firing patterns and rates in the human vagus nerve. Unlike recordings of muscle sympathetic nerve activity (MSNA), which have been well characterized in humans, there are no prior data, beyond those of the study by Ottaviani et al. (2020), providing insight into the typical behaviour of the vagus nerve in humans. This consideration will require further investigation to allow for better repeatability and standardization of vagal recordings. A key challenge will be devising manoeuvres or tests that microneurographers can use in real time to determine the destination of vagal traffic, similar to the use of a loud clap or end-expiratory apnoea to distinguish between postganglionic sympathetic activity directed towards vasculature in the skin or muscle in peripheral motor nerves. Assuming the technical aspect can be accounted for, direct vagal recordings could elucidate many questions regarding the regulation of heart rate that until now have only indirectly assessed parasympathetic activity with measures such as RSA and heart rate variability. Because heart rate is regulated by both sympathetic and parasympathetic influences, pharmacological blockade of sympathetic and vagal outflow is often used to evaluate their contributions to changes in heart rate across various conditions, such as exercise. Unfortunately, autonomic blockade disrupts the normal integrative balance of cardiovascular control that could alter the responsiveness of the cardiovascular and autonomic measures of interest. Direct assessment of cardiac parasympathetic activity, such as with the proposed cardio-inhibitory unit, would provide valuable insight into the regulation of heart rate at rest and during exercise without requiring the use of pharmacological autonomic blockade. The required recording stability of microneurography would entail that exercise conditions be limited to small muscle mass, such as handgrip. Even though Ottaviani et al. (2020) found a cardio-inhibitory unit affecting the sinoatrial node in the left vagus nerve, it is important to note that the majority of efferent parasympathetic innervation to the sinoatrial and atrioventricular node comes from the right and left vagus nerve, respectively (Chapleau & Sabharwal, 2011). Hence, further characterization of such cardio-inhibitory units in both the right and left vagus nerve will enhance our understanding of the cardiac parasympathetic regulation. Future studies could isolate and characterize the different neurons responsible for either decreasing heart rate or the force of contraction in healthy and diseased populations. The results of the study by Ottaviani et al. (2020) also offer the possibility to record the heterogenous populations of baroreceptive and chemoreceptive vagal afferents located throughout the atria, ventricular, coronary and pulmonary vasculature. For example, the application of lower body positive pressure, a stimulus that results in multi-unit MSNA sympathoinhibition, was found to activate a subpopulation of MSNA single units, comprising a response that was accentuated in patients with heart failure with reduced ejection fraction (Millar et al. 2015). One hypothesis for these findings was the parallel stimulation of cardiopulmonary afferents that can elicit opposing effects on peripheral sympathetic outflow. Other work has postulated the existence of a positive feedback sympathetic reflex originating from pulmonary baroreceptors (Simpson et al. 2020). To date, human work has relied on assessing reflex responses to a variety of cardiopulmonary stimuli with limited capacity to quantify afferent activity. The capacity to record from vagal afferents now offers the potential to elucidate specific mechanistic pathways regulating neural outflow. Vagus nerve stimulation (VNS) is often used in the treatment of epilepsy and has been proposed for use in other affective, cognitive and cardiovascular conditions. Although VNS is effective in reducing seizures, a notable side effect is an increased incidence of sleep-disordered breathing (Parhizgar et al. 2011). One proposed mechanism is that VNS activates motor activity of the laryngeal adductors, constricting the airway and causing episodes of hypopnoea or apnoea during sleep. Different stimulation parameters can selectively affect the physiological outcomes; for example, prolonging stimulus duration can cause both bradycardia and laryngeal activation, whereas shorter duration, higher amplitude stimulation selectively causes only bradycardia (Yoo et al. 2016). Although it would not be possible to concurrently record vagal activity when performing vagal stimulation, apnoeas can occur both during and after application of VNS (Parhizgar et al. 2011). It is unclear whether residual effects of stimulation also cause laryngeal adduction, or if there are additional apneic mechanisms. Laryngeal vagal efferent recordings could help to identify the optimal therapeutic settings of VNS for treating epilepsy at the same time as minimizing deleterious side effects of sleep disordered breathing by identifying the mechanism associated with apnoea during the off setting of VNS. These recordings might also be of benefit in exploring disease mechanisms in other respiratory conditions such as obstructive sleep apnoea, where laryngeal adduction has also been investigated as a potential cause of apnoea (Insalaco et al. 1993). A final clinical consideration is that, unlike the situation with epilepsy, VNS has shown little effectiveness in treating cardiac dysfunction in heart failure, despite positively affecting subjective measures of health and improved function in animal models (van Bilsen et al. 2017). One critique has been that VNS might only be effective in those with reduced cardiac vagal activity, although direct measures of cardiac vagal activity such as acetylcholine levels are not feasible as a result of its instability in plasma. Direct assessments of cardiac vagal outflow with microneurography could instead help to identify responders and non-responders to VNS in heart failure. In summary, the findings of the work by Ottaviani et al. (2020) offer the potential to study many questions in neuro-cardiovascular regulation that up to now have only been indirectly assessed. Much work is needed to further understand and characterize the nature of the recordings obtained via vagus nerve microneurography and to establish guidelines towards making safe, valid and reproducible measurements of this nerve. Provided that these requirements are established, future studies should look towards investigations geared at characterizing both afferent and efferent vagal activity in healthy populations and clinical conditions involving disturbances in cardiac vagal activity, such as heart failure and sleep apnoea. None. All authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of this work. All persons designated as authors qualify for authorship and all those who qualify for authorship are listed. None. We thank Dr Philip Millar for providing insight and discussion regarding our manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,036

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,008
Communication savante0,0050,010
Science ouverte0,0010,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0110,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,263
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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Même revueThe Journal of PhysiologyMême sujetHeart Rate Variability and Autonomic ControlTravaux en français237 207