Hyperspectral Near-Infrared Spectroscopy for the Clinical Monitoring of the Brain: Can We Measure the Brain of Patients Experiencing Severe Changes in the Entire Body?
Bibliographic record
Abstract
Near Infrared Spectroscopy (NIRS) uses near infrared light to measure the concentrations of different tissue chromophores such as oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (HHb), and cytochrome c oxidase (Cyt-ox). However, NIRS measurements in adult humans are highly sensitive to extracranial tissue layers and thus less sensitive to the brain. The aim of this work was to assess the effectiveness of measuring specific brain hemodynamic response during breath-holding respiratory challenge. We used hyperspectral NIRS with combined short and long source-detector distance channels at 1 cm, 3 cm, and 4 cm. We used two approaches for signal processing: we measured the absolute hemoglobin concentrations using the analytical solution to the light diffusion equation for the semi-infinite homogeneous medium, and the changes in tissue layers using the modified Lambert-Beer law for a two-layer medium. Linear regression in the time domain of long distance and short distance channel signals allowed us to assess differences in brain responses and extracranial changes. We found that using the short-range channel was important for measuring cerebral response to breath-holding. We also found that the optimal wavelength band was between 750 and 900 nm, with the largest partial change between 800 and 850 nm. We also found that Cyt-ox at the 4 cm channel exhibited the most brain-specific changes compared to HbO2 and HHb.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".