All-optical interrogation of excitability during seizure propagation reveals high local inhibition amidst baseline excitability
Bibliographic record
Abstract
Seizures are classically described as an epiphenomenon of hyperexcitability and hypersynchronicity across brain regions. However, this view is insufficient to explain the complex, dynamic evolution of focal-onset seizures in the brain. Recent studies have proposed mechanisms involving an evolution of excitability driven specifically by a spatiotemporally progressing seizure wavefront. These mechanisms attempt to align the abnormal propagation of neural activity with well-known neurobiological parameters, such as excitation-inhibition balance and neuronal connectivity patterns. We describe a direct test of these mechanisms by performing real-time, in vivo investigations of excitability in the acutely epileptic state and during seizure propagation. We used all-optical interrogation to test single-neuronal and local-circuit excitability in the epileptic brain. We demonstrate a surprising paradox during the acutely epileptic state, wherein the brain becomes susceptible to large synchronous inputs, yet single-cell excitability is largely maintained at baseline levels. At a finer scale, excitability of neurons at the single-cell level is related to their distance from the seizure wavefront. Local circuit excitability is increased in the distal penumbra but, crucially, we find inhibition in close proximity to the seizure wavefront. This is in contrast with previously suggested notions of widespread inhibition outside the direct area of action during a focal-onset seizure. These experimental results provide the first direct, in vivo evidence for the precise spatial scale over which single-cell excitability dynamics evolve during seizure propagation, providing support for local inhibitory restraint of seizure propagation.
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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".