Neocortical and medial temporal seizures have distinct impacts on brain responsiveness
Bibliographic record
Abstract
Focal epileptic seizures are characterized by abnormal neuronal discharges that can spread to other cortical areas and interfere with brain activity, thereby altering the patient's experience and behavior. The origin of these pathological neuronal discharges encompasses various mechanisms that converge toward similar clinical manifestations. Recent studies have suggested that medial temporal lobe (MTL) and neocortical (NC) seizures are often underpinned by two characteristic onset patterns, which, respectively, affect and spare synaptic transmission in cortical slices. However, these synaptic alterations and their effects have never been confirmed or studied in intact human brains. To fill this gap, we here evaluate whether responsiveness of MTL and NC are differentially affected by focal seizures, using a unique data set of cortico-cortical evoked potentials (CCEPs) collected during seizures triggered by single-pulse electrical stimulation (SPES). We find that responsiveness is abruptly reduced by the onset of MTL seizures, despite increased spontaneous activity, whereas it is preserved in the case of NC seizures. The present results provide an extreme example of dissociation between responsiveness and activity and show that brain networks are diversely affected by the onset of MTL and NC seizures, thus extending at the whole brain level the evidence of synaptic alteration found in vitro.
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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 | 0.001 |
| 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".