Notice bibliographique
Résumé
Brain stimulation therapies have a long history, beginning with the development of electroconvulsive (also known as electroshock) therapy in the first half of the previous century, progressing with the development of other, more refined, methods to induce electrical currents in the living brain. Those methods are used to treat various psychiatric diseases, ranging from depression and bipolar disorder to posttraumatic stress disorder and schizophrenia. Obviously, a better understanding of the mechanism of action of these treatments will allow optimization of stimulation parameters to enhance and optimize the therapeutic effects. Such understanding would also be an important milestone in development of new versions of stimulation tools, to improve and adapt existing protocols individually to each patient, and to develop objective, quantifiable measures of therapeutic outcome. An appealing possibility to explain the effects of electricity-based therapies is their ability to change the brain network activity over time; that is, to enhance or suppress global neuronal activity or specifically to encourage the creation and strengthening of existing connections.The 2 papers presented in this issue by Dr Zafiris J Daskalakis' group (see Voineskos et al1 and Rajji et al2) draw a comprehensive and consistent theory on the relation between schizophrenia, brain plasticity, and effectiveness of brain stimulation therapies. In this sense, schizophrenia is an interesting touchstone to examine the issue of brain stimulation plasticity-based treatments because it encompasses significant deficits in higher cognitive functions, including learning-related plastic changes.The first article1 is a comprehensive review demonstrating neuroplastic impairments in schizophrenia as reflected in transcranial magnetic stimulation (TMS) studies that include measurements of neural excitability and inhibition levels by various paradigms of TMS. Dr Daskalakis and colleagues' nicely introduce the concept of brain plasticity impairments in schizophrenia, and suggest that cognitive deficits accompanying this disease, especially in executive attention and working memory, are caused by reduced ability of the brain to perform adaptation of its activity in response to experience and training. Patients with schizophrenia, compared with healthy subjects, do not show adaptation of their response to motor cortex magnetic stimulation following a motor training protocol. They do not show acquisition and strengthening of association between peripheral motor stimulation and motor responses induced by TMS in a paired associative stimulation (PAS) paradigm. In these studies, patients with schizophrenia, unlike healthy subjects, show no increase or decrease in activity as a result of repetitive stimulation of the motor cortex or adjacent areas. The authors' suggest alterations in glutamate (via N-methyl-D-aspartate receptors), gammaaminobutyric acid, and dopamine neurotransmission for explaining these neuroplastic impairments as these systems had been linked with long-term plasticity processes in basic electrophysiological studies and with morbidity of schizophrenia in neuropharmacological studies.The second article, by Dr Tarek K Rajji and colleagues,2 describes current knowledge on various brain stimulation (TMS and direct electrical) approaches used in the treatment of schizophrenia. Search engines were scanned for relevant articles, and the results, concerning repetitive TMS, thetaburst stimulation, PAS, and transcranial direct current stimulation, are summarized comprehensively. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».