Remodeled cortical inhibition prevents motor seizures in generalized epilepsy
Bibliographic record
Abstract
Objective Deletions of CACNA1A, encoding the α1 subunit of CaV2.1 channels, cause epilepsy with ataxia in humans. Whereas the deletion of Cacna1a in γ‐aminobutyric acidergic (GABAergic) interneurons (INs) derived from the medial ganglionic eminence (MGE) impairs cortical inhibition and causes generalized seizures in Nkx2.1Cre;Cacna1ac/c mice, the targeted deletion of Cacna1a in somatostatin‐expressing INs (SOM‐INs), a subset of MGE‐derived INs, does not result in seizures, indicating a crucial role of parvalbumin‐expressing (PV) INs. Here we identify the cellular and network consequences of Cacna1a deletion specifically in PV‐INs. Methods We generated PVCre;Cacna1ac/c mutant mice carrying a conditional Cacna1a deletion in PV neurons and evaluated the cortical cellular and network outcomes of this mutation by combining immunohistochemical assays, in vitro electrophysiology, 2‐photon imaging, and in vivo video‐electroencephalographic recordings. Results PVCre;Cacna1ac/c mice display reduced cortical perisomatic inhibition and frequent absences but only rare motor seizures. Compared to Nkx2.1Cre;Cacna1ac/c mice, PVCre;Cacna1ac/c mice have a net increase in cortical inhibition, with a gain of dendritic inhibition through sprouting of SOM‐IN axons, largely preventing motor seizures. This beneficial compensatory remodeling of cortical GABAergic innervation is mTORC1‐dependent and its inhibition with rapamycin leads to a striking increase in motor seizures. Furthermore, we show that a direct chemogenic activation of cortical SOM‐INs prevents motor seizures in a model of kainate‐induced seizures. Interpretation Our findings provide novel evidence suggesting that the remodeling of cortical inhibition, with an mTOR‐dependent gain of dendritic inhibition, determines the seizure phenotype in generalized epilepsy and that mTOR inhibition can be detrimental in epilepsies not primarily due to mTOR hyperactivation. Ann Neurol 2018;84:436–451
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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) | 0.001 | 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) | 0.002 | 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".