Ligand-gated mechanisms leading to ictogenesis in focal epileptic disorders
Bibliographic record
Abstract
We review here the neuronal mechanisms that cause seizures in focal epileptic disorders and, specifically, those involving limbic structures that are known to be implicated in human mesial temporal lobe epilepsy. In both epileptic patients and animal models, the initiation of focal seizures - which are most often characterized by a low-voltage fast onset EEG pattern - is presumably dependent on the synchronous firing of GABA-releasing interneurons that, by activating post-synaptic GABAA receptors, cause large increases in extracellular [K+] through the activation of the co-transporter KCC2. A similar mechanism may contribute to seizure maintenance; accordingly, inhibiting KCC2 activity transforms seizure activity into a continuous pattern of short-lasting epileptiform discharges. It has also been found that interactions between different areas of the limbic system modulate seizure occurrence by controlling extracellular [K+] homeostasis. In line with this view, low-frequency electrical or optogenetic activation of limbic networks restrain seizure generation, an effect that may also involve the activation of GABAB receptors and activity-dependent changes in epileptiform synchronization. Overall, these findings highlight the paradoxical role of GABAA signaling in both focal seizure generation and maintenance, emphasize the efficacy of low-frequency activation in abating seizures, and provide experimental evidence explaining the poor efficacy of antiepileptic drugs designed to augment GABAergic function in controlling seizures in focal epileptic disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".