Insight into brain-muscle oxygenation relationship before and after anaerobic threshold using near-infrared spectroscopy: a feasibility study
Bibliographic record
Abstract
Monitoring the oxygenation levels of the prefrontal cortex during exercise is crucial in assessing decision-making abilities and cognitive responsibilities. Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that measures and monitors tissue oxygenation levels in real-time. This study aimed to investigate the feasibility of using NIRS to monitor and compare patterns of cerebral and muscle oxygenation during progressive exercise, both before and after the anaerobic threshold (AT) is reached. A cohort of healthy adults with moderate to high fitness levels participated in an incremental exercise protocol using an indoor exercise bike. Two wearable NIRS sensors were used to monitor tissue oxygenation from the forehead and the thigh vastus lateralis (VL) muscle during the exercise. To estimate the anaerobic threshold (AT) time point, we used the Respiratory Exchange Ratio (RER) value greater than 1.0 as measured by a metabolic cart. The concentration difference between oxygenated and deoxygenated hemoglobin (Hb-diff), which indicates the level of tissue oxygenation, exhibited a significant decrease (p<0.05) in the VL muscle of all participants after the AT was reached. Conversely, there was a significant increase in Hb-diff in the cerebral cortex after the AT (p< 0.05). The results of this study demonstrate the efficient hemodynamics autoregulation of the brain even when the body is affected by metabolic fatigue during high-intensity exercise. This study confirms the feasibility of NIRS to monitor prefrontal cortex and muscle oxygenation during exercise as a unique application in exercise science.
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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 | 0.001 |
| 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".