Bundle-specific fornix reconstruction for dual-tracer PET-tractometry
Bibliographic record
Abstract
Abstract Tractography is known to have problems reconstructing white matter bundles that are narrow, have high curvature, or go through partial volume voxels contaminated by CSF or gray matter. One such bundle is the fornix, the major output tract of the hippocampus, which is especially problematic with aging. Hippocampal atrophy and ventricular expansion make the fornix even harder (often impossible) to track with current state-of-the-art techniques. In this work, a bundle-specific tractography algorithm is proposed to fully reconstruct the fornix. By injecting shape, position, and orientation priors, fornix reconstruction is markedly is improved. We report an increase in spatial coverage and better reproducibility across test-retest. These improvements over classical tractography algorithms also enable tractometry of the fornix to be combined with dual-tracer positron emission tomography (PET) data in participants with mild cognitive impairment (MCI). MCI participants underwent a multi-modal brain imaging before and after a 6-month daily ketogenic supplement. We report, for the first time, significant diffusion measures and 18F-fluorodeoxyglucose (FDG) uptake differences in specific sub-sections of the fornix after the ketogenic supplement.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".