Importance of depth-enhanced optical methods for measuring cerebral hemodynamics during transient hypotension
Bibliographic record
Abstract
Optical methods are well-suited for non-invasive bedside brain imaging: near-infrared spectroscopy (NIRS) for measuring brain oxygenation and diffuse correlation spectroscopy (DCS) for measuring cerebral blood flow. However, data obtained with those optical techniques are prone to signal contamination from extracerebral tissue. This study aimed to evaluate extracerebral signal contamination in trNIRS/multidistance DCS data acquired during transient hypotension and assess suitable means of separating scalp and brain signals. An in-house built hybrid system was used to acquire oxygenation and blood flow data simultaneously during transient orthostatic hypotension induced by rapid-onset lower body negative pressure (LBNP). In nine healthy young adults, LBNP significantly decreased arterial blood pressure (-18 ± 14%), scalp blood flow (-36 ± 25%), and scalp tissue oxygenation (all p ≤ 0.04 vs baseline). In contrast, LBNP had no significant effect on cerebral blood flow or oxygenation. This finding was confirmed by transcranial Doppler ultrasound, which found no significant changes (-8 ± 16%) in middle cerebral artery blood velocity during LBNP. These results demonstrate the importance of using depth-enhanced methods when applying these optical technologies to physiological paradigms designed to test cerebral autoregulation that also affect systemic physiology.
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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 | 0.001 | 0.002 |
| 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".