Focal low amplitude periodic spikes on interictal scalp EEG and intracranial EEG for localizing epileptogenic foci
Bibliographic record
Abstract
To reveal the influential factors on scalp EEG recordings and provide valuable information for the localization of the seizure onset zone, we analyzed the low amplitude spikes with continuous focal periodic discharges on the scalp and intracranial EEG. Five patients with refractory epilepsy who had performed pre-surgery scalp EEG and intracranial EEG recordings were studied. The amplitudes of spikes and cortical areas of spike-wave foci were measured using the DaVinci system. Patterns of continuous periodic activity were determined by auto-correlograms, power spectral density, and coherence analysis using Matlab and Spike2 software. t-test was employed to compare the mean amplitudes of spikes on the scalp and intracranial EEG. We found that the amplitudes of spikes recorded on scalp EEG of the five patients were: (22.2±4.8), (30.4±7.1), (20.7±3.2), (58.4±10.1), (23.4±3.9) µV. The amplitudes of spikes recorded on intracranial EEG of the five patients were: (1253.8±199.3), (806.5 4-161.4), (1585.7±305.7), (922.5±140.6), (736.8±70.9) µV. The amplitudes of spikes on scalp EEG were significantly lower than those on intracranial EEG (t=6.394, P<0.05). The cortical areas of spike-wave foci of the five patients were: 4.0, 6.0, 3.5, 5.5, and 6.5 cm2. Power spectral density and auto-correlograms showed 1-3 Hz oscillations in spike-wave foci on intracranial EEG. Cross-correlation and coherence analysis showed synchronized activity in two neighbor intracranial electrodes. Seizures stopped in these five patients after the removal of these continuous periodic spike-wave foci. Pathology showed focal cortical dysplasia. We conclude that the focal low amplitude periodic spikes on interictal scalp EEG provide valuable information for localizing epileptogenic foci.
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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) | 0.001 | 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) | 0.002 | 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".