Role of KCC2-Dependent Potassium Efflux in 4-Aminopyridine-Induced Epileptiform Synchronization
Bibliographic record
Abstract
A balance between excitation and inhibition is required to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be untrue, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the co-transporter KCC2 in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices of either sex revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). Further, we developed a biophysically realistic network model implementing complex dynamics of the ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Model predicted that interneuron stimulation triggered interneuron firing that was accompanied by an increase in intracellular chloride and a subsequent KCC2-dependent gradual accumulation of extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".