Notice bibliographique
Résumé
“The mode of onset is the most important matter in the investigation of any case of epilepsy … it points to the part of the brain where the discharge begins.” —John Hughlings Jackson, “Case of Epileptiform Seizures Beginning in the Right Hand.”Medical Times and Gazette, December 23, 1871 The quest to define the origin of a seizure and the neural pathway of its progression began long before the practice of electroencephalography, and at least as early as Jackson's observations on the eponymous “march” of excitation along the motor strip. “I have for more than 10 years, and before the experiments of Hitzig and Ferrier were made, held that convolutions contain nervous arrangements representing the movements of convulsive discharges.” He also wondered whether it was the large or the small cells of the motor cortex that were the cause of the “explosive discharges.” While the slow passage of focal clonic movements spreading unilaterally from the thumb or a forefinger to the remainder of the body allowed these observations to be made by eye, powerful tools to map the functional microanatomy of seizures now permit exploration deep within the brain at spatial and temporal resolutions on the order of millimeters and milliseconds. These tools can separate the static brain lesion (when there is one) from the dynamic seizure circuitry, pinpoint neural pathways that mediate specific brain synchronization patterns, and allow both clinicians and neurosurgeons to select optimal therapeutic approaches. The 26th annual Merritt Putnam Symposium presented at the Annual Meeting of the American Epilepsy Society in December 2006 brought together speakers to review state of the art approaches that are paving the way to more precise definitions of which circuits discharge abnormally in individual patients, how seizure networks are mapped at the cellular level in the laboratory, and what the future holds for integrating various methods of imaging brain function in epilepsy patients. Mark Holmes, of the University of Washington, explores the analysis of seizure onset using dense array scalp electroencephalography, drawing from his work in clinical cases of absence, as well as medically refractory temporal and extratemporal localization-related epilepsies, where standard monitoring failed to reveal reliable ictal localization. He clearly explains its rationale and speculates on the application of this technique in research that may advance the understanding of generalized and partial epilepsies. Aimee Luat and Harry Chugani present a highly instructive review of positron emission tomography (PET) and diffusion tensor imaging (DTI) research performed in their clinical research laboratory at the Children's Hospital of Wayne State University. Their instructive review of childhood epilepsy cases concentrates on the mechanisms by which primary epileptic foci are established and persist; and how they may secondarily establish independent foci at a distance from the original focus. They provide convincing examples of how PET and DTI analysis can be applied to further elucidate the pathophysiology of epileptic networks in children. Dan McIntyre and Krista Gilby, of Carlton University, expertly review data from a time-proven experimental model of epilepsy, kindling, where the extended pace of epileptogenesis over a period of weeks allows a detailed and reproducible look at the progressive excitability changes that build in synaptically connected brain regions during repeated electrical stimulation. While this model has challenged basic researchers for decades, classic lesion techniques coupled with glucose metabolism, new biomarkers, and behavior confirm the importance of nonhippocampal structures to the generation of convulsive seizures and temporal lobe epilepsy. Dennis Spencer and colleagues at Yale School of Medicine have synthesized complementary neurochemical and electrophysiological data from his research on human temporal lobe epilepsy involving the application of microdialysis, MR-based metabolic imaging, and in vitro studies on resected epileptic tissue. They outline the basis for a neurometabolic hypothesis to define the energetics of epileptic circuitry, and describe how the interdependent metabolism of the elements within the “glial-neuronal unit” (GNU) may contribute to the epileptogenic state. Jean Gotman from the Montreal Neurological Institute of McGill University has contributed an excellent review clearly describing the optimal methodology for simultaneous recording of EEG and fMRI. He specifies major sources of artifact and how these may be surmounted. This marriage of in vivo electrophysiology and imaging is essential to validate and extend the information contributed by each of the methods, and may provide an approach to further optimize stereotaxic electrode placement. Whether by guiding a surgical laser, micropositioning a brain stimulating electrode, or delivering the latest designer drug to exactly the right molecular target, the challenge of achieving a cure for epilepsy in each patient—“no seizures”—resides in our ability to develop increasingly precise methods of identifying and treating only those brain networks and even individual cells that actually contribute to the seizure discharge—“no side effects.” What once seemed a distant goal now seems increasingly within reach. Even now, in the laboratory, it is possible to image gene expression to identify hyperactive neurons in brain regions by tagging mRNA transcripts with ferromagnetic markers (Liu et al., 2007), and use light to selectively and instantaneously depolarize or hyperpolarize identified neurons (Zhang et al., 2007). We hope, in this era of “personalized medicine,” that this symposium sparks a decade of intensified research into high resolution “personalized mapping” of brain network activity that will accelerate our ability to better define and safely correct seizures in all individuals with epilepsy. Conflict of interest: The author has declared no conflicts of interest.
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 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».