Reactivity of larger intracranial arteries using 7 T MRI in young adults
Bibliographic record
Abstract
The larger intracranial conduit vessels contribute to the total cerebral vascular resistance, and understanding their vasoreactivity to physiological stimuli is required when attempting to understand regional brain perfusion. Reactivity of the larger cerebral conduit arteries remains understudied due to a need for improved imaging methods to simultaneously assess these vessels in a single stimulus. We characterized reactivity of basal intracranial conduit arteries (basilar, right and left posterior, middle and anterior cerebral arteries) and the right and left internal carotid arteries, to manipulations in end-tidal CO 2 (PetCO 2 ). Cross-sectional area changes (%CSA) were evaluated from high-resolution (0.5 mm isotropic) images collected at 7 T using a T1-weighted 3D SPACE pulse sequence, providing high contrast between vessel lumen and surrounding tissue. Cerebrovascular reactivity was calculated as %CSA/ΔPetCO 2 in eight healthy individuals (18–23 years) during normocapnia (41 ± 4 mmHg), hypercapnia (48 ± 4 mmHg; breathing 5% CO 2 , balance oxygen), and hypocapnia (31 ± 8 mmHg; via hyperventilation). Reactivity to hypercapnia ranged from 0.8%/mmHg in the right internal carotid artery to 2.7%/mmHg in the left anterior cerebral artery. During hypocapnia, vasoconstriction ranged from 0.9%/mmHg in the basilar artery to 2.6%/mmHg in the right posterior cerebral artery. Heterogeneous cerebrovascular reactivity to hypercapnia and hypocapnia was characterized across basal intracranial conduit and internal carotid arteries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".