In Vivo Imaging of Cerebral Hemodynamics Using High-Frequency Micro-Ultrasound
Bibliographic record
Abstract
Assessment of cerebral vascular response is important in neuroscience research. Some imaging modalities that are commonly used to detect flow and/or vessel diameter changes in the brain include magnetic resonance imaging, positron emission tomography, and optical intrinsic signal imaging. Ultrasound has not typically been used to assess neurovascular response but recent advances in the technology have led to the development of micro-ultrasound systems with significant potential for this application. The state of the art in high frequency (15-50 MHz) micro-ultrasound is based on linear arrays specifically designed for small animal imaging. These systems can achieve axial resolution ranging from 30 to 200 microm. They are capable of quantifying brain hemodynamics in terms of red blood cell (RBC) velocity, flow, and vascular density in real time, up to 35 mm below the cortical surface, and can achieve temporal resolution of up to 1000 frames per second. This protocol describes imaging of the rat brain using various ultrasound imaging modes (power Doppler, color Doppler, pulsed-wave Doppler, and nonlinear contrast-enhanced imaging) to assess the state of cerebral microcirculation.
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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 | 0.000 |
| 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".