Extracellular Potassium and Seizures: Excitation, Inhibition and the Role of Ih
Bibliographic record
Abstract
Seizure activity leads to increases in extracellular potassium concentration ([K[Formula: see text]]o), which can result in changes in neuronal passive and active membrane properties as well as in population activities. In this study, we examined how extracellular potassium modulates seizure activities using an acute 4-AP induced seizure model in the neocortex, both in vivo and in vitro. Moderately elevated [K[Formula: see text]]o up to 9[Formula: see text]mM prolonged seizure durations and shortened interictal intervals as well as depolarized the neuronal resting membrane potential (RMP). However, when [K[Formula: see text]]o reached higher than 9[Formula: see text]mM, seizure like events (SLEs) were blocked and neurons went into a depolarization-blocked state. Spreading depression was never observed as the blockade of ictal events could be reversed within 1-2[Formula: see text]min after the raised [K[Formula: see text]]o was changed back to control levels. This concentration-dependent dual effect of [K[Formula: see text]]o was observed using in vivo and in vitro mouse brain preparations as well as in human neocortical tissue resected during epilepsy surgery. Blocking the Ih current, mediated by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, modulated the elevated [K[Formula: see text]]o influence on SLEs by promoting the high [K[Formula: see text]]o inhibitory actions. These results demonstrate biphasic actions of raised [K[Formula: see text]]o on neuronal excitability and seizure activity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".