2 Into the gray zone: assessing residual cognitive function after serious brain injury
Notice bibliographique
Résumé
Adrian M. Owen OBE, PhD is currently a Professor of Cognitive Neuroscience and Imaging in the Depts of Physiology & Pharmacology and Psychology at the University of Western Ontario, Canada. He also directs the Azrieli program in Brain, Mind, and Consciousness funded by the Canadian Institute for Advanced Research (CIFAR) and is on the Executive Committee of the CFREF funded initiative BrainsCAN at the University of Western Ontario. Dr. Owen was previously the Assistant Director of the Medical Research Council Cognition and Brain Sciences Unit at Cambridge University & the Canada Excellence Research Chair in Cognitive Neuroscience & Imaging at Western University. His research combines structural and functional neuroimaging with neuropsychological studies of brain-injured patients and has been published in many of the world’s leading scientific journals. He is best known for showing that functional neuroimaging can reveal conscious awareness in some patients who appear to be entirely vegetative and can even allow some of these individuals to communicate their thoughts and wishes to the outside world. These findings have attracted widespread media attention on TV, radio, in print and online and have been the subject of many TV and radio documentaries. He has published over 300 peer-reviewed articles and chapters and a best-selling popular science book ‘Into the Gray Zone: A Neuroscientist Explores the Border Between Life and Death. Dr. Owen was awarded an OBE) in the Queen’s Honors List, 2019, for services to scientific research. Abstract The thought of being ‘locked in’ following a brain injury or aware during general anaesthesia troubles us all because it awakens the old terror of being buried alive. But what does it mean to be awake, but entirely unable to respond and what can this tell us about consciousness itself? In recent years, rapid technological developments in the field of neuroimaging have provided a number of new methods for revealing thoughts, actions and intentions based solely on the pattern of activity that is observed in the brain. I will describe how we are using some of these methods, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), to detect covert conscious awareness in patients who are behaviourally entirely non-responsive (e.g. vegetative, comatose) and even to allow some of these individuals to communicate their wishes and thoughts. From this perspective, I will contrast those circumstances in which imaging data can be used to infer awareness in the absence of a reliable behavioural response, with those circumstances in which it cannot. This distinction is fundamental for understanding and interpreting patterns of brain activity in various states of consciousness (including vegetative state, coma, anaesthesia and sleep), and has profound implications for clinical care, diagnosis, prognosis, ethics and medical-legal decision-making after severe brain injury. It also sheds light on more basic scientific questions about how consciousness is measured and the neural representation of our own thoughts and intentions.
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 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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 ».