Kv1.1 channels mediate network excitability and feed-forward inhibition in local amygdala circuits
Bibliographic record
Abstract
Abstract Kv1.1 containing potassium channels play crucial roles towards dampening neuronal excitability. Mice lacking Kv1.1 subunits (Kcna1−/−) display recurrent spontaneous seizures and often exhibit sudden unexpected death. Seizures in Kcna1−/− mice resemble those in well-characterized models of temporal lobe epilepsy known to involve limbic brain regions and spontaneous seizures result in enhanced cFos expression and neuronal death in the amygdala. Yet, the functional alterations leading to amygdala hyperexcitability have not been identified. In this study, we used Kcna1−/− mice to examine the contributions of Kv1.1 subunits to excitability in neuronal subtypes from basolateral (BLA) and central lateral (CeL) amygdala known to exhibit distinct firing patterns. We also analyzed synaptic transmission properties in an amygdala local circuit predicted to be involved in epilepsy-related comorbidities. Our data implicate Kv1.1 subunits in controlling spontaneous excitatory synaptic activity in BLA pyramidal neurons. In the CeL, Kv1.1 loss enhances intrinsic excitability and impairs inhibitory synaptic transmission, notably resulting in dysfunction of feed-forward inhibition, a critical mechanism for controlling spike timing. Overall, we find inhibitory control of CeL interneurons is reduced in Kcna1−/− mice suggesting that basal inhibitory network functioning is less able to prevent recurrent hyperexcitation related to seizures.
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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".