Could EEG be a translational biomarker in neurodevelopmental conditions? - Studies in mouse models of idiopathic and syndromic autism
Notice bibliographique
Résumé
Background: Autism is predominantly idiopathic, while many syndromic forms have also been identified. Fragile X Syndrome (FXS) is the most common syndromic form of autism and intellectual disability. No effective approaches currently exist for reducing the negative impact of autism or FXS symptoms, and drug development has suffered many failures in clinical trials, which were based on promising preclinical findings. Thus, effective translational biomarkers that bridge animal and human studies are urgently needed. Both autism and FXS are developmentally dynamic and sexually dimorphic regarding prevalence, clinical presentations and neural mechanisms. Recently, electroencephalography (EEG) has been proposed as a low-cost translational biomarker in neurodevelopmental conditions. However, there is still a dearth of EEG research in autism, especially in research using animal models. Hypothesis: EEG profiles in mouse models of idiopathic and syndromic autism vary depending on sex, age, and genetic background. Methods: We compared the BTBR model of idiopathic autism with the control B6 mice, as well as the fmr1 knockout model of FXS and syndromic autism with the control wildtype mice. A custom-made stand-alone Open-Source Electrophysiology Recording system for Rodents (OSERR) was used for EEG recording. Results: We found absolute EEG power in the beta and gamma frequency bands was increased in juvenile male BTBR mice, while relative theta power was increased and relative alpha power decreased. In the FXS model on a FVB genetic background, we confirmed previous findings of an increase in the absolute gamma power in the male mice. Detailed analysis in the females indicated that in both juvenile and adult animals, increases in the alpha and beta power were present besides an increase in the gamma power, compared with age-matched wildtype controls. Additionally, both wildtype and FXS model had increased absolute and relative gamma power in adult females compared with juvenile females. Furthermore, we analyzed phase-amplitude cross frequency coupling between gamma frequency (30–100 Hz) and theta-alpha frequency (4–12 Hz), to quantify the modulation of gamma oscillation amplitude by the phase of the theta-alpha frequency. Our results indicated that this phase-amplitude coupling was increased in the female FXS model, similar to the findings in the FXS patients. Additionally, both wildtype and FXS model had increased coupling in adult females compared with juvenile females. We also recorded EEG in the female FXS model on a B6 genetic background and observed that relative EEG power was deceased in alpha frequency but increased in gamma frequency, while phase-amplitude coupling was similar, compared with wildtype controls. Lastly, we observed that these EEG phenotypes were stable when recorded in different arenas, including home cage, open field arena, and light-dark arena, for all female groups tested. Discussion and Conclusion: Together, our findings revealed a consistent and robust increase in the gamma power in mouse models of both idiopathic and syndromic autism, and this includes female FXS models. In addition, we identified changes in the EEG signal that depended on sex, developmental stage, and genetic background. Collectively, our findings support further investigation of EEG signal as translational biomarkers in neurodevelopmental conditions, while factors including sex, developmental stage, and genetic background should be incorporated into the design and interpretation of such studies. University of Calgary Veterinary Medicine, Alberta Children's Hospital Research Foundation, FRAXA Research Foundation, NSERC of Canada, CIHR of Canada, Alberta Student Aid This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».