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Enregistrement W2115420158 · doi:10.1113/jphysiol.2007.129411

General anaesthetic action: ubiquity, complexity and relevance for neuroscience

2007· letter· en· W2115420158 sur OpenAlexaff
Gilles Plourde

Notice bibliographique

RevueThe Journal of Physiology · 2007
Typeletter
Langueen
DomaineNeuroscience
ThématiqueNeuroscience and Neuropharmacology Research
Établissements canadiensMontreal Neurological Institute and HospitalMcGill University
Organismes subventionnairesnon disponible
Mots-clésNeuroscienceAction (physics)MedicineExcitatory postsynaptic potentialPsychologySensory systemInhibitory postsynaptic potential

Résumé

récupéré en direct d'OpenAlex

General anaesthetics reversibly produce unconsciousness and block motor response to noxious stimuli. These drugs are among the most useful in modern medicine. They not only prevent pain during surgery but also permit operations of a complexity that was unimaginable 150 years ago. What is perhaps less appreciated is that these drugs play a similar role for in vivo experimentation by providing pain-free animal and ideal testing conditions for the experimenter. However, anaesthetics pose a particular problem for the neuroscientist because they influence the activity of the system under study. The nature and magnitude of this influence are rarely known with confidence. These factors depend on the structure being studied, the species of the animal as well as on the type and dose of anaesthetic agents. The main target of general anaesthetic action is the synapse. The most common direct effect of general anaesthetics is to enhance inhibitory and attenuate excitatory transmission (Rudolph & Antkowiak, 2004). Furthermore, these drugs also exert a myriad of secondary and higher order actions because of the immense level of interconnectivity in the CNS (Eckenhoff & Johansson, 1999). It is thus surprising (and fortunate) that our knowledge of brain function and sensory processing, the vast majority of which was accumulated from experiments on anaesthetized animals, applies, for the most part, to the awake, behaving condition (for example see Snodderly & Gur, 1995, for visual processing). There are, however, exceptions, and some higher order processes are affected by general anaesthesia (Lamme et al. 1998; Pack et al. 2001). It is thus puzzling that many neuroscientists working with anaesthetized animals seem to have only a modest interest for studies primarily aimed at the neurophysiological effects of general anaesthetics, even though these may have implications for their own work. Neuroscientists have so little interest for general anaesthetics that the name of the anaesthetic used is on occasions relegated to the on-line material and not even mentioned in the article (Logothetis et al. 2001; Bruno & Sakmann, 2006). This lack of interest for anaesthesia is also reflected by the very frequent omission to measure the concentration of inhaled anaesthetic, even when the process under study is known to be influenced by general anaesthetics (for example, see the very interesting work on gamma oscillations by Barth & MacDonald (1996)) Musizza et al. (2007) are to be commended for making the effects of general anaesthetics the central theme of their article in this issue of The Journal of Physiology. Using complex tools from non-linear analysis and information theory, they studied interactions between neural (EEG), respiratory and cardiac oscillations in rats anaesthetized with ketamine and xylazine or pentobarbital, agents that are commonly used in neuroscience investigations. They showed that, for both groups, respiration drives the cardiac oscillator during deep anaesthesia and that the transition from deep to light anaesthesia is accompanied by an increase in θ wave activity. With ketamine–xylazine, the cardio-respiratory interaction either reverses or becomes negligible; with pentobarbital the interactions become weaker during light anaesthesia without other observable changes. These findings thus provide a detailed characterization of the anaesthetic state. The authors also deserve praise for having included the cardiac and respiratory oscillations to obtain a wider view than that provided by exclusive attention to the EEG. Although the anaesthetic influences on cardiac and respiratory control have been recognized for a long time (Guedel, 1937; Biscoe & Millar, 1966) and reflect of the ubiquity of anaesthetic action, most studies attempting to understand how general anaesthetics impair brain function limit their scope to one modality. Here are suggestions for future studies. For animal experiments in line with the very interesting work of Musizza et al. (2007), I would suggest the study of chronically instrumented animals to obtain awake baseline and recovery data. Consideration should also be given to precise control of the concentration of the anaesthetic agent. The control of concentration makes it possible to adjust the level of anaesthesia, to keep it constant, to control its duration and to include different levels within each experimental session. The control of concentration is easily done for inhaled drugs with the combination of airtight recording chambers and commercially available anaesthetic gas analysers that provide on-line measure of anaesthetic concentration. For parenteral drugs, such as those used by Musizza et al. (2007) the concentration can be adjusted by the combination of intravenous administration via chronic intravenous catheters and computer-controlled infusions based on pharma-cokinetic data (Shafer & Gregg, 1992). For investigations with patients, I would suggest including measurements of heart rate and respiration because these are readily provided by the clinical monitors. Wilder Penfield once wrote ‘The problem of neurology is to understand man himself.’ I will conclude by paraphrasing him: ‘Investigations of the neurophysiological effects of general anaesthetics aim to understand the nervous system itself.’

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)0,002
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: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,009
Communication savante0,0050,010
Science ouverte0,0010,002
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,217
Tête enseignante GPT0,418
Écart entre enseignants0,201 · 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

Citations4
Publié2007
Routes d'admission1
Résumé présentoui

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