Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus, but by the nature of the brain receiving the stimulus, at that instant of time. Therapeutic brain stimulation, over the past decades, typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neuro-technological advancements enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instant of time) with sufficient temporal precision in the range of milliseconds, using feedforward algorithms applied to EEG time series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies showed that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here we survey the evidence that this is also possible at the systems level of human cortex, using EEG-informed transcranial magnetic stimulation (EEG-TMS). We discuss critically opportunities and difficulties to develop brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than achievable with conventional fixed protocols. The same real-time EEG-TMS technology will allow closing the loop by recording the effects of stimulation. This information may serve for stimulation protocol adaptation to maximize the treatment response. This way, brain states control brain stimulation, introducing a paradigm-shift from open-loop to closed-loop stimulation. In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus, but by the nature of the brain receiving the stimulus, at that instant of time. Therapeutic brain stimulation, over the past decades, typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neuro-technological advancements enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instant of time) with sufficient temporal precision in the range of milliseconds, using feedforward algorithms applied to EEG time series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies showed that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here we survey the evidence that this is also possible at the systems level of human cortex, using EEG-informed transcranial magnetic stimulation (EEG-TMS). We discuss critically opportunities and difficulties to develop brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than achievable with conventional fixed protocols. The same real-time EEG-TMS technology will allow closing the loop by recording the effects of stimulation. This information may serve for stimulation protocol adaptation to maximize the treatment response. This way, brain states control brain stimulation, introducing a paradigm-shift from open-loop to closed-loop stimulation.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,003 |
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 découle