Hyperexcitability of intact neurons underlies acute development of trauma‐related electrographic seizures in cats <i>in vivo</i>
Bibliographic record
Abstract
Cortical trauma can lead to development of electrographic paroxysmal activities. Current views of trauma-induced epileptogenesis suggest that chronic neuronal hyperexcitability and extensive morphological reorganization of the traumatized cortex are required for the generation of electrographic seizures. However, the mechanisms responsible for the initiation of electrographic seizures shortly after cortical injury are poorly understood. Here we show that, in the experimental model of partially deafferented (undercut) cortex, an increase in intrinsic and synaptic excitability of neurons in areas adjacent to the undercut cortex is sufficient for the generation of electrographic paroxysmal activity within few hours after partial cortical deafferentation. Locally increased and spatially restricted neuronal excitability arose from the increased incidence of intrinsically bursting neurons, enhanced intrinsic and synaptic neuronal responsiveness, and slight disinhibition. These mechanisms only operate in neurons located in the vicinity of partially deafferented sites because, after the cortical injury, partially deafferented neurons are mostly silent and hypoexcitable. Our results suggest that trauma-induced electrographic seizures first arise in cortical fields that are closest to the site of injury and such seizures do not require long-term neuronal reorganization.
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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 | 0.001 |
| 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".