Cortical hyperpolarization-activated depolarizing current takes part in the generation of focal paroxysmal activities
Bibliographic record
Abstract
During paroxysmal neocortical oscillations, sudden depolarization leading to the next cycle occurs when the majority of cortical neurons are hyperpolarized. Both the Ca(2+)-dependent K(+) currents (I(K(Ca))) and disfacilitation play critical roles in the generation of hyperpolarizing potentials. In vivo experiments and computational models are used here to investigate whether the hyperpolarization-activated depolarizing current (I(h)) in cortical neurons also contributes to the generation of paroxysmal onsets. Hyperpolarizing current pulses revealed a depolarizing sag in approximately 20% of cortical neurons. Intracellular recordings from glial cells indirectly indicated an increase in extracellular potassium concentration ([K(+)](o)) during paroxysmal activities, leading to a positive shift in the reversal potential of K(+)-mediated currents, including I(h). In the paroxysmal neocortex, approximately 20% of neurons show repolarizing potentials originating from hyperpolarizations associated with depth-electroencephalogram positive waves of spike-wave complexes. The onset of these repolarizing potentials corresponds to maximal [K(+)](o) as estimated from dual simultaneous impalements from neurons and glial cells. Computational models showed how, after the increased [K(+)](o), the interplay between I(h), I(K(Ca)), and a persistent Na(+) current, I(Na(P)), could organize paroxysmal oscillations at a frequency of 2-3 Hz.
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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) | 0.001 | 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".