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Enregistrement W2981701352 · doi:10.1093/schbul/sbz088

GABA and Negative Affect—Catatonia as Model of RDoC-Based Investigation in Psychiatry

2019· letter· en· W2981701352 sur OpenAlexaff
Dušan Hirjak, Robert Christian Wolf, Georg Northoff

Notice bibliographique

RevueSchizophrenia Bulletin · 2019
Typeletter
Langueen
DomainePsychology
ThématiqueMental Health Research Topics
Établissements canadiensRoyal Ottawa Mental Health CentreUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésCatatoniaAffect (linguistics)PsychologyPsychiatryPsychotherapistMedicineClinical psychologySchizophrenia (object-oriented programming)

Résumé

récupéré en direct d'OpenAlex

We were very pleased to read the excellent article by Taylor and colleagues,1 which has highlighted the central role of the GABAergic system in determining stress vulnerability and modulation of negative affect (NA) in schizophrenia. We fully agree that the relevance of GABAergic system goes far beyond schizophrenia, because GABAergic dysfunction might be responsible for a number of affective, behavioral, and cognitive symptoms through neurodevelopmental disturbance of emotional regulation across several mental disorders.1 Here, we propose that a first step toward better understanding of the transdiagnostic contributions of aberrant GABAergic system would be through investigating catatonia in the literal sense of the terms “psycho” and “motor.” Catatonia is one of the oldest syndromes described in clinical psychiatry which occurs in 9%–17% of acute mental disorders and is characterized by a triad of affective, motor, and behavioral symptoms.2 Catatonia can be classified as “catatonic schizophrenia” in ICD-10 and as “catatonia not otherwise specified,” that is, as residual category in DSM-5.3 In DSM-5, unlike in ICD-10, catatonia is also linked to other mental disorders or specific medical conditions. Still, there is intensive effort to recognize catatonia as an independent diagnostic entity in ICD-11.4 For about a decade, the RDoC initiative provides a platform for neuroscientific research of mental disorders based on dimensions of observable behavior and neurobiological measures.5–7 In line with this framework, there are two main reasons to support catatonia as a paradigmatic model for RDoC-based investigation of GABAergic system: (1) Clinical syndrome and its pathophysiological basis: The precise clinical description of former psychiatrists could achieve a good differentiation of catatonia as a psychomotor syndrome from other psychiatric disorders including both affective and schizophrenic psychoses. More recent researchers showed that catatonia characterized by its three symptom dimensions (motor, affective, and behavioral) is based on dysfunction of GABAergic cortical circuits.8–12 Targeting the GABAergic system in frontoparietal regions with lorazepam (positive allosteric modulation at the GABAA receptor)13,14 or GABAergic mediated electroconvulsive therapy (ECT)15 leads to an improvement of motor, affective and behavioral symptoms not only in schizophrenia, but also in autism and affective disorders.16 This is in line with Taylor and colleagues1 and their emphasis on the relation of GABA and NA as catatonic patients often experience/show extreme uncontrollable fear and anxiety from which they can be relieved by GABAergic drugs. Hence, the case of catatonia strongly extend the dimensional as well as the syndromal nature of GABA and NA beyond schizophrenia as emphasized by Taylor and colleagues.1 (2) Future directions: We expect that the different levels of the relation of GABA and NA can be extended even more in the future in the case of catatonia. There is a mechanistic animal model of catatonia, which will help us to understand genes, molecules, and cells of the GABAergic system in mice and men.17 Catatonic symptoms can be easily measured using instrumental assessments for detecting sensorimotor dysfunction and multimodal MRI.18 That’s what makes the investigation of aberrant circuits, physiology, and behavior associated with GABAergic dysfunction so convenient. Studying the dysfunction of the GABAergic system (dysbalance between GABAA and GABAB) in animal models and human beings will help to reduce the risk of failure in clinical trials (which we are still lacking).19 Multimodal MRI research on catatonia will provide important clues to the complex interplay between dysfunctions and dynamics of neural circuitry20 underlying sensorimotor function, behavior, affective processing, and cognition.7 In particular, we will better delineate the interaction between basal ganglia, cerebellar, and cortico-motor circuits, which are not solely responsible for sensorimotor function/dysfunction.7 Not to be forgotten are also first-person reports or citations of patients’ statements to investigate the structure of patients’ subjective (a priori) experience (eg, following a phenomenological approach)21 before and after development of catatonic symptoms.22 Therefore, we strongly endorse the notion that mental disorders must be understood as a dysfunction of individual neurotransmitter systems with a focus on specifically GABA and associated brain circuits (and not as rigid categories) to develop neurobiologically plausible therapies. Finally, catatonia can be defined as primarily “psycho” and “motor” disorder that is based on dysbalance between GABAergic and serotonergic as well as dopaminergic neurotransmission that essentially modulates both affective and motor systems, as well as their cortico-subcortical functional interplay.23,24 Pathophysiology- and dimension-based research framework on catatonia as proposed by RDoC initiative does not only open the door for developing more proper treatment of this devastating condition but also into the psycho-motor, for example, affective-motor and cognitive-motor mechanisms and functions of the healthy brain.25 The authors have declared that there are no conflicts of interest in relation to the subject of this commentary.

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: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,014

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
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,041
Tête enseignante GPT0,339
Écart entre enseignants0,298 · 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'étudeThéorique ou conceptuel
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

Citations16
Publié2019
Routes d'admission1
Résumé présentnon

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