3D ultrasound localization microscopy of the nonhuman primate brain
Bibliographic record
Abstract
BACKGROUND: Haemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualisation and quantification of cerebral blood flow would help better understand the underlying mechanism of these cerebrovascular diseases. METHODS: 3D ultrasound localization microscopy (ULM) is a recently developed technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. In this study, we tested the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channel programmable ultrasound scanner. FINDINGS: We achieved a highly resolved vascular map of the macaque brain at large depth (down to 3 cm) in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 μm. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60 μm. INTERPRETATION: This work paves the way to clinical applications of 3D ULM. In particular, transcranial 3D ULM in humans could become a tool for the non-invasive study and monitoring of the brain cerebrovascular changes occurring in neurological diseases. FUNDING: This work was supported by the New Frontier in Research Fund (NFRFE-2022-00590), by the Canada Foundation for Innovation under grant 38095, by the Natural Sciences and Engineering Research Council of Canada (NSERC) under discovery grant RGPIN-2020-06786, by Brain Canada under grant PSG2019, and by the Canadian Institutes of Health Research (CIHR) under grant PJT-156047 and MPI-452530. Computing support was provided by the Digital Research Alliance of Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".