In Vivo Visualization of Cranial Nerve Pathways in Humans Using Diffusion-Based Tractography
Bibliographic record
Abstract
OBJECTIVE: Diffusion-based tractography has emerged as a powerful technique for 3-dimensional tract reconstruction and imaging of white matter fibers; however, tractography of the cranial nerves has not been well studied. In particular, the feasibility of tractography of the individual cranial nerves has not been previously assessed. METHODS: 3-Tesla magnetic resonance imaging scans, including anatomic magnetic resonance images and diffusion tensor images, were used for this study. Tractography of the cranial nerves was performed using 3D Slicer software. The reconstructed 3-dimensional tracts were overlaid onto anatomic images for determination of location and course of intracranial fibers. RESULTS: Detailed tractography of the cranial nerves was obtained, although not all cranial nerves were imaged with similar anatomic fidelity. Some tracts were imaged in great detail (cranial nerves II, III, and V). Tractography of the optic apparatus allowed tracing from the optic nerve to the occipital lobe, including Meyer's loop. Trigeminal tractography allowed visualization of the gasserian ganglion as well as postganglionic fibers. Tractography of cranial nerve III shows the course of the fibers through the midbrain. Lower cranial nerves (cranial nerves IX, XI, and XII) could not be imaged well. CONCLUSION: Tractography of the cranial nerves is feasible, although technical improvements are necessary to improve the tract reconstruction of the lower cranial nerves. Detailed assessment of anatomy and the ability of overlaying the tracts onto anatomic magnetic resonance imaging scans is essential, particularly in the posterior fossa, to ensure that the tracts have been reconstructed with anatomic fidelity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".