Epileptic networks in action: Synchrony between distant hemodynamic responses
Bibliographic record
Abstract
OBJECTIVE: Structural and functional imaging studies in focal epilepsy often reveal distributed regions of abnormality. These are interpreted as representing the existence of epileptic networks, but the presence of actual neuronal interactions between these regions has not been demonstrated. We sought to determine whether the distributed hemodynamic responses often seen in functional magnetic resonance imaging (fMRI) studies of scalp interictal epileptic discharges (IEDs) actually correspond to synchronized neuronal activities when examining the intracerebral electroencephalogram (iEEG) at distant nodes of the network. METHODS: We studied 28 patients who underwent first EEG-fMRI and then iEEG, and had significant hemodynamic responses in the gray matter. We coregistered the hemodynamic responses to the iEEG electrode contact positions and analyzed synchrony, measured by correlation, between IEDs recorded by iEEG in regions with and without hemodynamic responses. RESULTS: The synchrony of intracerebral IED activity between pairs of regions showing a hemodynamic response was higher compared to that between pairs of regions without (p < 0.0001) and between pairs of regions, one with and one without hemodynamic response (p < 0.0001). These differences were found during the interictal periods with IEDs but were absent during the interictal periods without IEDs. Higher synchrony was also observed between regions involved at seizure onset (p < 0.0001). INTERPRETATION: EEG-fMRI studies are unique in their ability to reveal hemodynamic concomitants of IEDs anywhere in the brain. This study proves that iEEG activity is synchronized between these regions of hemodynamic response, thus demonstrating the existence of an actual neuronally based interictal epileptic network. This also validates the EEG-fMRI approach to reveal this network noninvasively. Ann Neurol 2017;82:57-66.
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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 | 0.002 |
| 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".