Neurovascular coupling in humans: Physiology, methodological advances and clinical implications
Bibliographic record
Abstract
Neurovascular coupling reflects the close temporal and regional linkage between neural activity and cerebral blood flow. Although providing mechanistic insight, our understanding of neurovascular coupling is largely limited to non-physiologicalex vivopreparations and non-human models using sedatives/anesthetics with confounding cerebrovascular implications. Herein, with particular focus on humans, we review the present mechanistic understanding of neurovascular coupling and highlight current approaches to assess these responses and the application in health and disease. Moreover, we present new guidelines for standardizing the assessment of neurovascular coupling in humans. To improve the reliability of measurement and related interpretation, the utility of new automated software for neurovascular coupling is demonstrated, which provides the capacity for coalescing repetitive trials and time intervals into single contours and extracting numerous metrics (e.g., conductance and pulsatility, critical closing pressure, etc.) according to patterns of interest (e.g., peak/minimum response, time of response, etc.). This versatile software also permits the normalization of neurovascular coupling metrics to dynamic changes in arterial blood gases, potentially influencing the hyperemic response. It is hoped that these guidelines, combined with the newly developed and openly available software, will help to propel the understanding of neurovascular coupling in humans and also lead to improved clinical management of this critical physiological function.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".