Neural Fragility of the Intracranial EEG Network Decreases after Surgical Resection of the Epileptogenic Zone
Bibliographic record
Abstract
Abstract Over 15 million patients with epilepsy worldwide do not respond to medical therapy and may benefit from surgical treatment. In focal epilepsy, surgical treatment requires complete removal or disconnection of the epileptogenic zone (EZ). However, despite detailed multimodal pre-operative assessment, surgical success rates vary and may be as low as 30% in the most challenging cases. Here we demonstrate that neural fragility, a dynamical networked-system biomarker of epileptogenicity, decreases following successful surgical resection. Moreover, neural fragility increases or remains constant when seizure-freedom is not achieved. We demonstrate this retrospectively in a virtual patient with epilepsy using the Virtual Brain neuroinformatics platform, and subsequently on six children with epilepsy with pre- and post-resection intra-operative recordings. Finally, we compare neural fragility as a putative biomarker of epileptogenicity against established spectral metrics, such as high frequency oscillations and find that neural fragility is a superior biomarker of epileptogenicity.
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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 | 0.002 |
| 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) | 0.002 | 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".