Hierarchical Imaging and Computational Analysis of Three-Dimensional Vascular Network Architecture in the Entire Postnatal and Adult Mouse Brain
Bibliographic record
Abstract
Background: The cerebrovascular network plays a vital role in brain development, normal brain function, and various central nervous system (CNS) disorders. Nevertheless, the effects of recently discovered (anti)angiogenic markers, genes, and pharmaceutical targets on cerebrovascular architecture remain largely unknown. Additionally, there is a lack of quantitative data and imaging approaches to study cerebrovascular architecture and morphology at sub-micrometer resolution across the entire brain. Our objective is to bridge this gap between the molecular and morphological levels by establishing a novel neuroimaging pipeline that enables objective quantification and visualization of cerebrovascular network architecture at ultra-high resolution. Methods: Here we present a step-by-step method for hierarchical imaging and computational analysis of vascular networks in entire postnatal and adult mouse brains. The method involves resin-based vascular corrosion casting, synchrotron radiation microcomputed tomography imaging, and 3D computational network analysis. Results: We visualized and quantified the cerebrovascular architecture in early postnatal(n=10, postnatal day 10) and adult (n=10, postnatal day 60) brain vascular corrosion casts, including a separation between capillaries and non-capillary vessels. Our analysis revealed significant changes in cerebrovascular morphology from the early postnatal stage to adulthood, including an increase in vascular volume fraction, branchpoint density, branchpoint degree, and vessel tortuosity, combined with a decrease in vessel segment diameter, length and volume, branchpoint diameter, extravascular distance, and the average adjacent segment angle as a measure of vessel directionality. A genetic knockout model of the known anti-angiogenic molecule Nogo-A confirmed the applicability of our method to study the effects of individual genes on vessel morphology. Conclusion and Future Directions: Using the novel neuroimaging pipeline presented in this thesis we provide a morphological atlas of the entire mouse brain vasculature at both the postnatal and adult stages of development. The presented methodology enables researchers to objectively quantify cerebrovascular morphology before and after pharmacotherapy or intervention in various experimental models of cerebrovascular disease. Targeted graph-based machine-learning approaches, such as link prediction algorithms and arteriovenous identity classification, can be integrated and our approach has the potential to be extended to human ex-vivo resected brain tissue or postmortem settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".