Simultaneous EEG-fMRI in Human Epilepsy
Bibliographic record
Abstract
Electroencephalography (EEG) has been used to study and characterize epilepsy for decades, but has a limited ability to localize epileptiform activity to a specific brain region. With recent technological advances, high-quality EEG can now be recorded during functional magnetic resonance imaging (fMRI), which characterizes brain activity through local changes in blood oxygenation. By combining these techniques, the specific timing of interictal events can be identified on the EEG at millisecond resolution and spatially localized with fMRI at millimeter resolution. As a result, simultaneous EEG-fMRI provides the opportunity to better investigate the spatiotemporal mechanisms of the generation of epileptiform activity in the brain. This article discusses the technical considerations and their solutions for recording simultaneous EEG-fMRI and the results of studies to date. It also addresses the application of EEG-fMRI to epilepsy in humans, including clinical applications and ongoing challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".