The Epileptology of Aicardi–Goutières Syndrome: Electroclinical Phenotype and Clinical–Radiological Correlation
Notice bibliographique
Résumé
Objectives: Aicardi–Goutières syndrome (AGS) is a monogenic interferonopathy. Most individuals affected by AGS show various degrees of neurological symptoms. Although at least a quarter of patients with AGS experiences epileptic seizures, their phenotypic and electrophysiological characterizations remain elusive. To encompass this lack of knowledge, we collected patients with established diagnosis of typical AGS and detailed electroclinical data with the aim to characterize epilepsy phenotypes and electroencephalographic patterns and identify possible correlations between them and clinical, genetic, and neuroradiologic features. Content: Methods: This is a multicenter retrospective study. We enrolled 27 patients with established diagnosis of typical AGS, adequate EEG study, and contemporary neuroimaging. Medical records, EEGs, and MRI/CT findings were reviewed for each patient. In particular, 63 EEGs were independently and blindly reviewed by three board‐certified pediatric epileptologists. Results: Epilepsy is present in 37% (10 out of 27 patients), mean age at seizure onset was 9.5 (range: 1–36) months. Semiology was mainly represented by epileptic spasms and focal seizures. Significant correlation was found between presence of epilepsy and calcification severity ( p = 0.016) and presence of startle reactions ( p = 0.05). Concerning EEG pattern, background activity was disrupted in 85%. Organization was rated as disorganized or markedly disorganized in 73% of cases. Both background activity and EEG organization disruption appeared to be statistically related to high abnormality of MRI signal intensity ( p = 0.028 and 0.022). Disrupted EEG organization was also significantly related to microcephaly ( p < 0.001). Physiological organization was found to be more adequate in sleep (87%) than in wakefulness (38%). Focal slow activity was recorded in more than one third of cases. Fast activity was found more frequently in waking (78%), rather than in sleep (50%), either generalized or with frontal localization. Interictal epileptiform discharges (IEDs) were present in 33% of awake and in 45% of sleep recordings. Significant correlation was found between presence of IEDs during sleep and epileptic seizures ( p = 0.008). Conclusion: This is the first study providing detailed epileptological description and its correlation with clinical and neuroradiological data. Hallmarks of EEG pattern in our sample are represented by organization disruption, focal slow and fast activity, presence of IEDs both in patients with and without epilepsy. Epilepsy and EEG pattern are at least partly related to neuroradiologic findings, but their complex anatomo-electro-clinical basis still needs to be fully elucidated. Publication History Article published online: 16 March 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0,000 | 0,002 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».