Typical versus Atypical Absence Seizures: Network Mechanisms of the Spread of Paroxysms
Bibliographic record
Abstract
PURPOSE: Typical absence seizures differ from atypical absence seizures in terms of semiology, EEG morphology, network circuitry, and cognitive outcome, yet have the same pharmacological profile. We have compared typical to atypical absence seizures, in terms of the recruitment of different brain areas. Our initial question was whether brain areas that do not display apparent paroxysmal discharges during typical absence seizures, are affected during the ictal event in terms of synchronized activity, by other, distant areas where seizure activity is evident. Because the spike-and-wave paroxysms in atypical absence seizures invade limbic areas, we then asked whether an alteration in inhibitory processes in hippocampi may be related to the spread seizure activity beyond thalamocortical networks, in atypical seizures. METHODS: We used two models of absence seizures in rats: one of typical and the other of atypical absence seizures. We estimated phase synchronization, and evaluated inhibitory transmission using a paired-pulse paradigm. RESULTS: In typical absence seizures, we observed an increase in synchronization between hippocampal recordings when spike-and-wave discharges occurred in the cortex and thalamus. This indicates that seizure activity in the thalamocortical circuitry enhances the propensity of limbic areas to synchronize, but is not sufficient to drive hippocampal circuitry into a full paroxysmal discharge. Lower paired-pulse depression was then found in hippocampus of rats that displayed atypical absence seizures. CONCLUSIONS: These observations suggest that circuitries in brain areas that do not display apparent seizure activity become synchronized as seizures occur within thalamocortical circuitry, and that a weakened hippocampal inhibition may predispose to develop synchronization into full paroxysms during atypical absence seizures.
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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) | 0.000 | 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".