Detection of epileptogenic zones in people with epilepsy using optimized EEG-fMRI
Bibliographic record
Abstract
PURPOSE: Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to assist in the presurgical localization of seizure foci in people with epilepsy. Our study aimed to examine the clinical feasibility of an optimized concurrent EEG-fMRI protocol. METHODS: The optimized protocol employed a fast-fMRI sequence (sampling rate = 10 Hz) with a spare arrangement, which allowed a time window of 1.9 s for EEG recording without radio frequency noise. Patients with a diagnosis of drug-resistant epilepsy who were candidates for surgical intervention were enrolled and underwent concurrent EEG-fMRI studies to map fMRI blood oxygen level-dependent (BOLD) signal changes related to interictal epileptiform discharges. The BOLD signals were compared to those in the epileptogenic zone determined by resective cavities or radiofrequency thermocoagulation lesions. Postoperative seizure outcomes were classified according to the ILAE classification. RESULTS: The EEG-related BOLD results indicated that 15 of the 19 patients (78.9 %) had concordant findings in the epileptogenic zone determined by surgical intervention. The percentage of patients who achieved good surgical outcomes was significantly greater in the concordant group than in the discordant group (n = 9, 60.0 % vs. n = 0, 0 %, p = 0.033). CONCLUSIONS: Using fast MRI scan, the optimized protocol provides satisfactory accuracy (78.9 %) for detecting epileptogenic zones. A concordant BOLD signal and epileptogenic zone can predict good surgical outcomes.
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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) | 0.000 | 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".