Bibliographic record
Abstract
Brain perfusion is tightly coupled to neuronal activity, is commonly used to monitor normal or pathological brain function, and is a direct reflection of the interactions that occur between neuronal signals and blood vessels. Cerebral blood vessels at the surface and within the brain are surrounded by nerve fibers that originate, respectively, from peripheral nerve ganglia and intrinsic brain neurons. Although of different origin and targeting distinct vascular beds, these "perivascular nerves" fulfill similar roles related to cerebrovascular functions, a major one being to regulate their tone and, therein, brain perfusion. This utmost function, which underlies the signals used in functional neuroimaging techniques and which can be jeopardized in pathologies such as Alzheimer's disease, stroke, and migraine headache, is thus regulated at several levels. Recently, new insights into our understanding of how neural input regulate cerebrovascular tone resulted in the rediscovery of the functional "neurovascular unit." These remarkable advances suggest that neuron-driven changes in vascular tone result from interactions that involve all components of the neurovascular unit, transducing neuronal signals into vasomotor responses not only through direct interaction between neurons and vessels but also indirectly via the perivascular astrocytes. Neurovascular coupling is thus determined by chemical signals released from activated perivascular nerves and astrocytes that alter vascular tone to locally adjust perfusion to the spatial and temporal changes in brain activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".