Non-Invasive Ultrasound Detection of Cerebrovascular Changes in a Mouse Model of Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) can induce changes in vascular architecture. Although ultrasound metrics such as pulsatility index (PI) are sensitive to changes in hemodynamic resistance downstream from major arteries, these metrics depend on features unrelated to vessel architecture, such as blood pressure and heart rate. In contrast, input impedance and reflection coefficient that are derived from wave reflection theory seek to minimize the effects of altered cardiac output or heart rate. In this article, we investigate the use of ultrasound to assess changes in vascular impedance and wave reflection in the common carotid arteries of mice exposed to a controlled cortical impact. Focusing on the first harmonics of the reflected waves, the impedance phase was increased ipsilaterally in impacted mice compared with shams, whereas the magnitude of the impedance was unchanged. In contrast, PI was reduced bilaterally. Interestingly, PI and the first harmonic magnitude of input impedance in the carotid artery were correlated on the contralateral but not ipsilateral side. We investigated the use of these metrics to classify mice as sham or TBI, finding an area under the receiver operating characteristic curve ipsilaterally of 0.792 (confidence interval [CI]: 0.648-0.936) for correct classification with first harmonic impedance magnitude and phase as predictors and 0.716 (CI: 0.553-0.879) using carotid artery PI and diameter as predictors. Overall, the findings support the use of wave reflection analysis as a more specific measure of vascular changes following TBI and motivate the translation of this approach for monitoring vascular changes in humans affected by TBI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".