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Enregistrement W2905327090 · doi:10.1113/jp277376

Interneuron NMDA receptors change the gear of motor learning in the cerebellar machine

2018· letter· en· W2905327090 sur OpenAlexafffundabout
Ádám Fekete, Lu‐Yang Wang

Notice bibliographique

RevueThe Journal of Physiology · 2018
Typeletter
Langueen
DomaineNeuroscience
ThématiqueVestibular and auditory disorders
Établissements canadiensMental Health Research CanadaHospital for Sick ChildrenUniversity of Toronto
Organismes subventionnairesCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
Mots-clésNeuroscienceCerebellumMotor learningMotor cortexPsychologyStimulationBiology

Résumé

récupéré en direct d'OpenAlex

Since the 19th century the cerebellum has been known for its function in motor control including the maintenance of equilibrium (balance, posture, eye movement), coordination of the timing and force of muscle groups, adjustment of muscle tone, learning motor skills and speech. Supported by the rich connections between the cerebellum, the cerebral cortex and the limbic system, the cerebellum is increasingly recognized for its non-motor functions such as cognition (language and social interaction), motivation and emotions and consequently for its role in autism-spectrum, obsessive–compulsive, attention-deficit hyperactivity and bipolar disorders, and schizophrenia (Schmahmann et al. 2007; Yang et al. 2018). The need for more detailed cellular and molecular analyses of the cerebellar circuitry and plasticity is pressing. As per the adaptive-filter model (Dean et al. 2010), the cerebellar cortex is a signal processing device in which the signal components are carried by the parallel fibres (PFs) directly to the Purkinje cells (PCs) as the sole output neurons, or indirectly through the molecular layer interneurons (MLIs), to de-correlate from an error signal delivered by the climbing fibres (CFs). The PF and CF signals are integrated on the PCs and MLIs. Decorrelation of the signal components and the error signal requires the constant adjustment of the relative weights of the PF–PC synapses depending on the presence or absence of the error signal. The cellular correlate of the temporal weight adjustment is the long-term modulation of synaptic strength that is expressed postsynaptically in the PF–PC synapses. Single PF stimulation evokes long-term potentiation (LTP), but the coactivation of the CFs causes long-term depression (LTD) and thus decorrelation from the error (Dean et al. 2010). While LTD and low-frequency-evoked LTP (1 Hz) are known to be essential for motor learning, their mechanism is different: LTD is NMDA receptor (NMDAR) and nitric oxide (NO) dependent, the 1 Hz stimulation-evoked LTP depends only on NO. This implies an important role of NMDARs and NO in vivo in signal integration. Different studies have debated whether NMDARs are activated on the presynaptic membrane of PFs, the axon terminal and somatodendritic region of MLIs or postsynaptically at the adult CF–PC synapses (older than 2 months). Despite detailed analyses of the cerebellar microcircuits, the precise cellular and subcellular localization of NMDARs and their distinct roles in LTD and motor learning have remained controversial. In this issue of The Journal of Physiology, Kono et al. (2019) provide compelling evidence about the role of NMDARs in cerebellar LTD in vitro and motor learning in vivo by generating cell-specific (granule cell (GC), PC, MLI/PC) conditional knock-outs (cKOs) of the obligatory GluN1 NMDAR subunit encoded by the Grin1 gene. They confirmed by patch-clamp recordings in brain slices that functional NMDARs can be deleted from GCs, MLIs and PCs. They tested LTD by paired stimulation protocols and motor learning during optokinetic response adaptation. They found that NMDARs expressed on MLIs are essential to LTD induction and motor learning while NMDARs on GCs and PCs are dispensable (Fig. 1). The acute slice experiments were performed in the presence of GABAA receptor blocker to exclude direct MLI–PC transmission, implying an interaction between the direct and indirect PF pathways. Since the LTD and motor learning are neuronal NO synthase (nNOS) dependent, the authors reasoned that the diffusible messenger NO likely mediated LTD. Indeed, the NO donor 2-(N,N-diethylamino)-diazenolate-2-oxide (DEANO) rescued LTD in MLI/PC cKOs (Fig. 1; note the experimental condition that AMPA receptor exocytosis in the PCs is blocked by botulinum neurotoxin). These results by Kono et al. (2019) represent a major step forward to cut the Gordian knot regarding the precise subcellular localization of NMDARs in the cerebellar microcircuitry and the cellular mechanism of PF–CF signal integration. Future experiments with cell-specific cKO of nNOS will help localize the cellular source of NO. Because optokinetic response adaptation was measured without the GABAA blocker that was employed for LTD in acute slices, manipulation of the activity of cerebellar MLIs specifically expressing excitatory or inhibitory channelrhodopsins in vivo (Kruse et al. 2014) will further establish the distinct roles of interneuron NMDARs in motor and non-motor functions. Exciting results using Cre–LoxP-based cKOs as exemplified by Kono et al. (2019) will prove invaluable in advancing our understanding of how long-term synaptic plasticity contributes to the cerebellar motor and non-motor functions via the engagement of interneurons to adjust the gears in the cerebellar machine (Eccles et al. 1967). No competing interests declared Both authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. L.-Y.W. received funding from the Gouvernement du Canada/Canadian Institutes of Health Research (Institutsde recherche en santé du Canada): PJT – 156439; and Gouvernement du Canada/Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada): Discovery.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,036
Tête enseignante GPT0,264
Écart entre enseignants0,228 · 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'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations1
Publié2018
Routes d'admission3
Résumé présentoui

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