Dipole Localization for Identification of Neuronal Generators in Independent Neighboring Interictal EEG Spike Foci
Bibliographic record
Abstract
PURPOSE: We evaluated dipole localizations of independent neighboring interictal spike foci using scalp electroencephalogram (EEG) to identify neuronal generators of epileptic discharges. METHODS: Three pediatric patients with extratemporal lobe epilepsy who had two independent neighboring interictal spike foci on scalp EEG were studied. Prolonged video EEG was digitally recorded from 19 scalp electrodes, whose positions were registered using a three-dimensional digitizer. Interictal spikes were visually selected based on negative phase reversals on bipolar montages. We analyzed the dipole position and moment of each spike using a single moving dipole and three-shell spherical head model. The dipoles were overlaid onto magnetic resonance (MR) images and divided into two groups based on two spike foci. RESULTS: The dipoles of the two groups were oriented either tangentially or radially to the scalp in close proximity to each other. The dipoles oriented radially were located underneath the electrode with a negative peak; those oriented tangentially were between electrodes with a negative and positive peak. The positions of tangential dipoles were more concentrated than those of radial dipoles. The epileptogenic regions corresponded to the dipole localizations. Surgical excisions were performed based on the results of electrocorticography. After surgery, two patients were seizure free, and one had rare seizures (follow-up period, 13-31 months). CONCLUSIONS: We showed that dipoles in close proximity but with different orientations projected two negative maxima on scalp EEG in three patients with extratemporal localization-related epilepsy. Equivalent current dipole analysis of individual interictal spikes can provide useful information about the epileptogenic zone in these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".