Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states
Bibliographic record
Abstract
OBJECTIVE: The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI. METHODS: We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs. RESULTS: The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05). INTERPRETATION: IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".