Noninvasive continuous functional near‐infrared spectroscopy combined with electroencephalography recording of frontal lobe seizures
Bibliographic record
Abstract
PURPOSE: To investigate spatial and metabolic changes associated with frontal lobe seizures. METHODS: Functional near-infrared spectroscopy combined with electroencephalography (EEG-fNIRS) recordings of patients with confirmed nonlesional refractory frontal lobe epilepsy (FLE). KEY FINDINGS: Eighteen seizures from nine patients (seven male, mean age 27 years, range 13-46 years) with drug-refractory FLE were captured during EEG-fNIRS recordings. All seizures were coupled with significant hemodynamic variations that were greater with electroclinical than with electrical seizures. fNIRS helped in the identification of seizures in three patients with more subtle ictal EEG abnormalities. Hemodynamic changes consisted of local increases in oxygenated (HbO) and total hemoglobin (HbT) but heterogeneous deoxygenated hemoglobin (HbR) behavior. Furthermore, rapid hemodynamic alterations were observed in the homologous contralateral region, even in the absence of obvious propagated epileptic activity. The extent of HbO activation adequately lateralized the epileptogenic side in the majority of patients. SIGNIFICANCE: EEG-fNIRS reveals complex spatial and metabolic changes during focal frontal lobe seizures. Further characterization of these changes could improve seizure detection, localization, and understanding of the impact of focal seizures.
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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 | 0.000 | 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".