Combining EEG and fMRI: A multimodal tool for epilepsy research
Bibliographic record
Abstract
Patients with epilepsy often present in their electroencephalogram (EEG) short electrical potentials (spikes or spike-wave bursts) that are not accompanied by clinical manifestations but are of important diagnostic significance. They result from a population of abnormally hyperactive and hypersynchronous neurons. It is not easy to determine the location of the cerebral generators and the other brain regions that may be involved as a result of this abnormal activity. The possibility to combine EEG recording with functional MRI (fMRI) scanning opens the opportunity to uncover the regions of the brain showing changes in the fMRI signal in response to epileptic spikes seen in the EEG. These regions are presumably involved in the abnormal neuronal activity at the origin of epileptic discharges. This paper reviews the methodology involved in performing such studies, particularly the challenge of recording a good quality EEG inside the MR scanner while scanning is taking place, and the methods required for the statistical analysis of the combined EEG and fMRI time series. We review the results obtained in patients with different types of epileptic disorders and discuss the difficult theoretical problems raised by the interpretation of an increase (activation) and decrease (deactivation) in blood oxygen level dependent (BOLD) signal, both frequently seen in response to spikes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".