Brain dissection photogrammetry: a tool for studying human white matter connections integrating ex vivo and in vivo multimodal datasets
Bibliographic record
Abstract
Understanding the architecture of the white matter of the human brain is central to neuroscience and clinics. Despite major advances in tractography and white matter dissection, integrating these complementary techniques remains a longstanding challenge. Here, we introduce BraDiPho (Brain Dissection Photogrammetry), an open-access resource of high-resolution, fully textured 3D digital models of layer-by-layer white matter microdissection. The models are registered to their radiological space, allowing direct alignment of dissection and neuroimaging data. BraDiPho includes eight hemispheres, enriched with sample tractography bundles, anatomical annotations, and cortical atlases, establishing a unified framework for multimodal analyses. Four case studies demonstrate how BraDiPho supports anatomically grounded investigations, moving beyond classical side-by-side comparisons toward accurate integration of ex vivo dissection and in vivo tractography. All data, tools, and scripts are openly available, enabling customized research and educational applications. BraDiPho offers a framework to support multimodal investigations of human brain connectivity in both research and educational contexts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".