fMRI Activation in Continuous and Spike‐triggered EEG–fMRI Studies of Epileptic Spikes
Bibliographic record
Abstract
PURPOSE: To evaluate functional magnetic resonance imaging (fMRI) with simultaneous EEG for finding metabolic sources of epileptic spikes. To find the localizing value of activated regions and factors influencing fMRI responses. METHODS: Patients with focal epilepsy and frequent spikes were subjected to spike-triggered or continuous fMRI with simultaneous EEG. Results were analyzed in terms of fMRI activation, concordance with the location of EEG spiking and anatomic MRI abnormalities, and other EEG and clinical variables. In four patients, results also were compared with those of intracerebral EEG. RESULTS: Forty-eight studies were performed on 38 patients. Seventeen studies were not analyzed, primarily because no spikes occurred during scanning. Activation was obtained in 39% of 31 studies, with an activation volume of 2.55 +/- 4.84 cc. Activated regions were concordant with EEG localization in almost all studies and confirmed by intracerebral EEG in four patients. Forty percent of patients without an MRI lesion showed activation; 37.5% of patients with a lesion had an activation; the activation was near or inside the lesion. Bursts of spikes were more likely to generate an fMRI response than were isolated spikes (76 vs. 11%; p < 0.05). CONCLUSIONS: Combining EEG and fMRI in focal epilepsy yields regions of activation that are presumably the source of spiking activity. These regions are highly linked with epileptic foci and epileptogenic lesions in a significant number of patients. Activation also is found in patients with no visible MRI lesion. Intracerebral recordings largely confirm that these activation regions represent epileptogenic areas. It is still unclear why many patients show no activation.
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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 | 0.003 |
| 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) | 0.001 | 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".