Using depth-enhanced diffuse correlation spectroscopy and near-infrared spectroscopy to isolate cerebral hemodynamics during transient hypotension
Bibliographic record
Abstract
SignificanceCombining diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS) permits simultaneous monitoring of multiple cerebral hemodynamic parameters related to cerebral autoregulation; however, interpreting these optical measurements can be confounded by signal contamination from extracerebral tissue.AimWe aimed to evaluate extracerebral signal contamination in NIRS/DCS data acquired during transient hypotension and assess suitable means of separating scalp and brain signals.ApproachA hybrid time-resolved NIRS/multidistance DCS system was used to simultaneously acquire cerebral oxygenation and blood flow data during transient orthostatic hypotension induced by rapid-onset lower body negative pressure (LBNP) in nine young, healthy adults. Changes in microvascular flow were verified against changes in middle cerebral artery velocity (MCAv) measured by transcranial Doppler ultrasound.ResultsLBNP significantly decreased arterial blood pressure (−18 % ± 14 % ), scalp blood flow (>30 % ), and scalp tissue oxygenation (all p ≤ 0.04 versus baseline). However, implementing depth-sensitive techniques for both DCS and time-resolved NIRS indicated that LBNP did not significantly alter microvascular cerebral blood flow and oxygenation relative to their baseline values (all p ≥ 0.14). In agreement, there was no significant reduction in MCAv (8 % ± 16 % ; p = 0.09).ConclusionTransient hypotension caused significantly larger blood flow and oxygenation changes in the extracerebral tissue compared to the brain. We demonstrate the importance of accounting for extracerebral signal contamination within optical measures of cerebral hemodynamics during physiological paradigms designed to test cerebral autoregulation.
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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 | 0.000 | 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".