Multi-modal in vivo imaging of brain blood oxygenation, blood flow and neural calcium dynamics during acute seizures
Bibliographic record
Abstract
Dysfunction of the vascular endothelium has been implicated in the development of epilepsy. To better understand the relation between vascular function and seizure and provide a foundation for interpreting results from functional imaging in chronic disease models, we investigate the relationship between intracellular calcium dynamics and local cerebral blood flow and blood oxygen saturation during acute seizure-like events and pharmacological seizure rescue. To probe the relation between the aforementioned physiological markers in an acute model of epilepsy in rats, we integrated three different optical modalities together with electrophysiological recordings: Laser speckle contrast imaging (LSCI) was used to study changes in flow speeds, Intrinsic optical signal imaging (IOSI) was used to monitor changes in oxygenated, de-oxygenated, and total hemoglobin concentration, and Calcium-sensitive dye imaging was used to monitor intracellular calcium dynamics. We designed a dedicated cortical flow chamber to remove superficial blood and dye resulting from the injection procedure, which reduced spurious artifacts. The near infrared light used for IOSI and LSCI was delivered via a light pipe integrated with the flow chamber to minimize the effect of fluid surface movement on illumination stability. Calcium-sensitive dye was injected via a glass electrode used for recording the local field potential. Our system allowed us to observe and correlate increases in intracellular calcium, blood flow and blood volume during seizure-like events and provide a quantitative analysis of neurovascular coupling changes associated with seizure rescue via injection of an anti-convulsive agent.
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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".