Spatiotemporal components of sustained functional hyperemia are differentially modulated by locomotion and silenced with vascular chemogenetics
Bibliographic record
Abstract
ABSTRACT Neural activity underlying sensation, movement or cognition drives regional blood flow enhancement – termed functional hyperemia – to increase the oxygen supply to respiring cells for as long as needed to meet energy demands. However, functional hyperemia is often studied under anesthesia which typically yields response profiles that appear temporally and spatially homogenous. We have insufficient understanding of the underlying kinetics of oxygen delivery in awake animals, especially during specific behaviours that may influence neurally-driven enhancements in cerebral blood flow. Using widefield intrinsic optical signal imaging in awake, head-fixed but active mice, we demonstrated distinct early and late components to changes in intravascular oxygenation in response to sustained (30s) whisker stimulation. We found that the late component (20-30s), but not the early component (1-5s), was strongly influenced by level of whisking/locomotion in the region of highest response and in surrounding regions. Optical flow analyses revealed complex yet stereotyped spatial properties of the early and late components that were related to location within the optical window and the initial state of the cerebral vasculature. In attempt to control these complex response characteristics, we drove a canonical microvasculature constriction pathway using mural cell Gq-chemogenetic mice. A low-dose of systemic C21 strongly limited both the magnitude and spatial extent of the sensory-evoked hemodynamic response, showing that functional hyperemia can be severely limited by direct mural cell activation. These data provide new insights into the cerebral microcirculation in the awake state and may have implications for interpreting functional imaging data.
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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 | 0.000 |
| 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 | 0.001 |
| 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".