A Guide to Delineate the Logic of Neurovascular Signaling in the Brain
Bibliographic record
Abstract
The neurovascular system may be viewed as a distributed nervous system within the brain. It transforms local neuronal activity into a change in the tone of smooth muscle that lines the walls of arterioles and microvessels. We review the current state of neurovascular coupling, with an emphasis on signaling molecules that convey information from neurons to neighboring vessels. At the level of neocortex, this coupling is mediated by: (i) a likely direct interaction with inhibitory neurons, (ii) indirect interaction, via astrocytes, with excitatory neurons, and (iii) fiber tracts from subcortical layers. Substantial evidence shows that control involves competition between signals that promote vasoconstriction versus vasodilation. Consistent with this picture is evidence that, under certain circumstances, increased neuronal activity can lead to vasoconstriction rather than vasodilation. This confounds naïve interpretations of functional brain images. We discuss experimental approaches to detect signaling molecules in vivo with the goal of formulating an empirical basis for the observed logic of neurovascular control.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.038 |
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".