Impaired Tuning of Afferent Excitatory Synapses onto Hippocampal Fast-Spiking Interneurons by Acute Early Life Seizures
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".