Near‐infrared spectroscopy shows preictal haemodynamic changes in temporal lobe epilepsy
Bibliographic record
Abstract
AIM: Growing evidence suggests that focal seizures are preceded by haemodynamic changes. Specifically, changes in cerebral blood flow, blood oxygen level-dependent magnetic resonance imaging, and near-infrared spectroscopy measurements of haemoglobin have been observed in the seizure focus and other brain regions many minutes prior to the onset of spontaneous seizures. The purpose of this study was to detect preictal haemodynamic changes using near-infrared spectroscopy, a portable and non-invasive optical technique that measures changes in cerebral haemoglobin. METHODS: Five subjects with temporal lobe seizures were studied using near-infrared spectroscopy until a seizure was observed, as confirmed by electroencephalography or clinical symptoms. Relative changes in oxy- and deoxyhaemoglobin, total haemoglobin, and blood oxygen saturation were assessed in the anterior frontal lobes between 15 minutes and one minute prior to seizure onset. RESULTS: In all subjects, a decrease in oxyhaemoglobin, total haemoglobin, and oxygen saturation was observed in the frontal lobe, ipsilateral to the presumed seizure focus. On the contralateral side, all subjects showed a decrease in relative oxyhaemoglobin content. No consistent change in deoxyhaemoglobin was seen on either side. CONCLUSIONS: Preictal haemodynamic changes can be detected in the frontal lobes using near-infrared spectroscopy. Our results suggest that a decrease in metabolic rate, and thus neuronal activity, occurs in the ipsilateral frontal lobe prior to the onset of temporal lobe seizures. Extratemporal haemodynamic changes may therefore be an important marker for seizure anticipation.
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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 | 0.000 |
| 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".