Functional near infrared spectroscopy (fNIRS) in pigmented subjects: a maneuver to confirm sufficient transcutaneous photon transmission for measurement of hemodynamic change in the anterior cortex
Bibliographic record
Abstract
Background: A requisite for fNIRS studies of cortical blood flow is that sufficient photons are transmitted transcutaneously for the fluctuations in cerebral hemoglobin oxygenation that occur during neuronal activation to be detected. Transmission is determined by the specifications of the fNIRS device, but also influenced by the characteristics of the skin. Epidermal pigments can attenuate photon transmission; the literature states that in dark skinned subjects some NIRS devices may not achieve sufficient photon migration to monitor cortical blood flow. Hence, as fNIRS use is spreading, we describe a simple head tilt maneuver where positional redistribution of cerebral blood volume will confirm if photon transmission is sufficient. Methods: A repetitive head tilt maneuver (bending forward from a seated position, hold for 30 seconds, returning to original position X 5) performed by a pigmented (African) subject and a non-pigmented (Caucasian) subject. A 23- channel portable fNIRS system with dual wavelength (750 and 860 nm) emitters and photodiode detectors was worn over the anterior cortex, and changes in oxy, deoxy and total hemoglobin concentration measured at 50 Hz. Results: Data from both subjects were compared and found to have a comparable pattern of change in oxyhemoglobin concentration and temporal response to the effects of head tilt; clear arterial pulsations and minimal noise were also evident. Conclusion: We suggest the head tilt maneuver described as a feasible test to confirm the adequacy of transcutaneous photon transmission where fNIRS studies are to be performed in subjects with pigmented skin to detect hemodynamic change in the cortex.
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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 | 0.001 | 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".