The relationship of white matter tract orientation to vascular geometry in the human brain
Bibliographic record
Abstract
The white matter of the human brain exhibits highly ordered anisotropic structures of both axonal nerve fibers and cerebral vasculature. Separately, the anisotropic nature of white matter axons and white matter vasculature have been shown to cause an orientation dependence on various MRI contrasts used to study the structure and function of the brain; however, little is known of the relationship between axonal and vascular orientations. Thus, the aim of this study is to compare the orientation between nerve fibers and vasculature within the white matter. To do this, we use diffusion MRI and susceptibility weighted imaging acquired in the same healthy young adult volunteers and analyze the alignment between white matter fibers and blood vessels in different brain regions, and along different pathways, to determine the degree of alignment between these structures. We first describe vascular orientation throughout the brain and note several regions with consistent orientations across individuals. Next, we find that vasculature does not necessarily align with the dominant direction of white matter in many regions, but, due to the presence of crossing fiber populations, does align with at least some white matter within each MRI voxel. Even though the spatial patterns of blood vessels run in parallel to several white matter tracts, they do not do so along the entire pathway, nor for all pathways, suggesting that vasculature does not supply/drain blood in a tract-specific manner. Overall, these findings suggest that the vascular architecture within the white matter is closely related to, but not the same as, the organization of neural pathways. This study contributes to a better understanding of the microstructural arrangement of the brain and may have implications for interpreting neuroimaging data in health and disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".