Clinical and methodological advances in EEG-fMRI for epilepsy: a focused review
Bibliographic record
Abstract
Simultaneous EEG-fMRI is a unique, noninvasive neuroimaging technique that enables high spatial resolution mapping of metabolic changes linked to EEG epileptic discharges in focal and generalized epilepsy, reflected through fMRI signals. It is increasingly recognized as a valuable tool in the presurgical evaluation of drug-resistant epilepsy, supporting the localization of epileptogenic zones, guiding electrode implantation, and informing surgical strategies and outcome prediction, while also revealing important insights into the networks involved in epileptic activity. Advances in artifact removal, automated spike detection, and statistical modeling have improved EEG-fMRI's data quality and clinical utility. It is particularly valuable in diagnostically challenging cases where standard EEG is not localizing, or MRI findings are negative. However, its routine clinical adoption is limited by the complexity of the procedure, the lack of standardized protocols, interpretation criteria, and broader validation across diverse epilepsy populations. This review highlights EEG-fMRI's evolving role in localizing epileptic discharges, emphasizing both methodological and clinical aspects. It covers the process from data acquisition through analysis to statistical interpretation and decision-making, with its application in distinguishing generalized from widespread activity, assessing thalamic involvement in focal epilepsy, evaluating status epilepticus, mapping blood oxygen-level dependent responses in relation to structural lesions, and supporting presurgical planning in complex cases, demonstrating its potential to improve diagnostic precision and treatment 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".