Impaired Tuning of Afferent Excitatory Synapses onto Hippocampal Fast-Spiking Interneurons by Acute Early Life Seizures
Notice bibliographique
Résumé
During the first year of life, newborn infants' brains have a developmentally elevated ratio of excitation to inhibition and this critical period have the highest incidence rates for ELS.Patients with early-life seizures (ELS) are often refractory to anti-epileptic drugs (AED).The current knowledge gap on the effects of seizure on the immature brain has motivated us to find novel cellular targets of ELS in the immature brain.Using a combination of electrophysiology, voltage sensitive dye imaging and immunohistochemistry in an ELS mouse model, I completed a comprehensive investigation on the effects of ELS on hippocampal interneurons in the developing brain.I demonstrated for the first time that ELS impaired the excitatory synaptic inputs into a specific subtype of interneurons, the FS interneurons.FS interneurons play an important role in mediating the excitation/inhibition balance in the neural network by providing strong and precise perisomatic inhibition to various brain regions.ELS significantly altered the probability of release and the readily releasable pools at the presynaptic membrane of the excitatory inputs into FS interneurons.This changed the short-term plasticity at these excitatory inputs and FS interneurons during repetitive excitatory activity.Additionally, ELS significantly depressed asynchronous neurotransmitter release at the excitatory inputs, resulting in impaired postsynaptic spike timing and temporal fidelity of the FS interneurons.These findings highlight ELS-induced target specific modulations in FS interneurons and provided a novel cellular mechanism by which ELS alter the immature brain and disrupt the excitation/inhibition balance.I would like to express my deepest gratitude to the many incredible individuals that I have had the pleasure of working with and learning from, over duration of my Master's degree at Carleton University.The completion of the projects for my thesis required enormous amounts of time, effort, training, patience, critical thinking and planning to complete and I could not have done it on my own.I first would like to acknowledge two lifelong friends I have made from my Master's program who were instrumental in providing me with support, laughter and guidance throughout my studies, Chaya Kandegedara and Alysia Ross.Additionally, Alysia Ross helped us a lot with our animal colonies and ensured we never ran out of lab supplies.Additionally, I have to give tremendous thanks to Teresa Fortin, from Dr. Shawn Hayley's lab for all the help and guidance she kindly provided us when we were just starting our lab at Carleton.Dr. Shawn Hayley and his lab were incredibly generous with sharing their equipment and resources with us and have also made our lab feel at home at Carleton.I would also like to thank Zachery Dwayer, an extremely talented PhD student from Dr. Hayley's lab, who also provided me a lot of academic guidance and friendship throughout these two years.Next, I want to express my deepest gratitude to my
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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 | 0,000 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».