The effect of transcranial focused ultrasound target location on the acoustic feedback control performance during blood-brain barrier opening with nanobubbles
Bibliographic record
Abstract
Abstract Real-time acoustic feedback control based on harmonic emissions of stimulated microbubbles may be important for facilitating the clinical adoption of focused ultrasound (FUS)-induced blood-brain barrier (BBB) opening, both to ensure safe acoustic exposures, and to achieve repeatable and consistent opening. Previously our group demonstrated that successful BBB opening was achievable with both commercially available microbubbles and custom-made nanobubbles under acoustic feedback control. In a recent study, we demonstrated the acoustic control performance was not sensitive to the nanobubble concentration within 10 9 –10 11 bubbles/ml. The goal of this study was to examine the effect of the ultrasound target location in the rat brain on the acoustic control quality during BBB opening with nanobubbles. Temporal analysis of the received acoustic signals during each ultrasound pulse indicated that stable nanobubble oscillation was present throughout the entire 10 ms ultrasound exposure. The acoustic feedback control signals were very sensitive to the brain spatial location in rats. There appears to be a shared pattern of acoustic control stability in the brain across different animals, suggesting anatomical features are an underlying cause. The findings emphasize the importance of tuning acoustic feedback control algorithms for specific rodent brain regions of interest to ensure optimal performance.
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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 | 0.002 |
| 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".