Neocortical Post-Traumatic Epileptogenesis Is Associated with Loss of GABAergic Neurons
Bibliographic record
Abstract
The subtle mechanisms of post-traumatic epileptogenesis remain unknown, although the incidence of chronic epilepsy after penetrating cortical wounds is high. Here, we investigated whether the increased frequency of seizures occurring within 6 weeks following partial deafferentation of the suprasylvian gyrus in cats is accompanied with a change in the ratio between the number of excitatory and inhibitory neurons. Immuno-histochemical labeling of all neurons with neuronal-specific nuclear protein (NeuN) antibody, and of the GABAergic inhibitory neurons with either gamma-aminobutyric acid (GABA) or glutamic acid decarboxylase (GAD 65&67) antibodies, was performed on sections obtained from control and epileptic animals with chronically deafferented suprasylvian gyrus. Quantification of the labeled neurons was performed in control animals and at 2, 4, and 6 weeks following cortical deafferentation, in the suprasylvian and marginal gyri, both ipsi- and contra-lateral to the cortical trauma. In all epileptic animals, the neuronal loss was circumscribed to the deafferented suprasylvian gyrus. Inhibitory GABAergic neurons were particularly more sensitive to cortical deafferentation than excitatory ones, leading to a progressively increasing ratio between excitation and inhibition towards excitation, potentially explaining the increased propensity to seizures in chronic undercut cortex.
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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".