Spreading depression and near-infrared spectroscopy during seizures in patients with medically intractable epilepsy
Bibliographic record
Abstract
Cortical spreading depression (SD), a pathological cortical negative DC potential, is associated with various brain abnormalities. SD is a significant transient and localized relocation of ions within the neurons and spreads slowly like a wave in the brain tissue. SD results from a high extracellular K+ concentration, increasing neuronal excitability and, consequently, brain oxygen consumption. In our previous studies, we developed an electroencephalography (EEG) system capable of recording the SD from the surface scalp of epileptic patients. We demonstrated that SD is associated with seizures in patients with medically intractable epilepsy. In this paper, in addition to EEG measurements, near-infrared spectroscopy (NIRS) was used to measure local brain oxygen consumption during SD and seizures. NIRS is a non-invasive method to measure the hemodynamics of the tissue, such as oxy and deoxyhemoglobin concentrations, representing the gray matter's local neuronal metabolisms. By applying two or more wavelengths in the near-infrared window and measuring the attenuation variations of the relative change in the concentration of deoxyhemoglobin (HHb) and oxyhemoglobin (HbO2), the local oxygen consumption can be estimated. Method: We recorded SD and NIRS simultaneously during epileptiform EEG activities from twelve epileptic patients. Main result: SD occurred in the scalp of epileptic patients and preceded seizures with a varying time lag (0-30 minutes). HHb concentration increased during the SD duration. While HbO2 concentration decreased during the SD duration. Both returned to normal values after the SD event.
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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) | 0.000 | 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".