Computational modeling of seizure onset patterns to underpin their underlying mechanisms
Bibliographic record
Abstract
Abstract In the study of epilepsy, it is of crucial importance to understand the transition from interictal into ictal activities (ictogenesis). Different mechanisms have been suggested for the generation of ictal activity; yet, it remains unclear whether different physiological mechanisms underly different seizure onset patterns. Herein, by implementing a computational model that takes into account some of the most relevant physiological events (e.g., depolarization block, collapse, and recovery of inhibitory activities) and different scenarios of imbalanced excitatory-inhibitory activities, we explored if seizures with different onset patterns stem from different underlying mechanisms. Our model revealed that depending on the excitation level, seizures could be generated due to both enhancement and collapse of inhibition for specific range of parameters. Successfully reproducing some of the commonly observed seizure onset patterns, our findings indicated that different onset patterns can arise from different underlying mechanisms. Significance Statement Various seizure onset patterns have been reported; however, it yet remains unknown whether seizures with distinct onset patterns originate from different underlying mechanisms. The common belief on seizure generation focuses on the imbalance between synaptic excitation and inhibition which has led to the identification of distinct and, in some cases, even contradictory mechanisms for seizure initiation. In this study, by incorporating some of these various physiological mechanisms in a unified framework, we reproduced some commonly observed seizure onset patterns. Our results suggest the existence of different mechanisms responsible for the generation of seizures with distinct onset patterns which can enhance our understanding of seizure generation mechanisms with significant implications on developing therapeutic measures in seizure control.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".