Human cervical spinal cord funiculi: Investigation with magnetic resonance diffusion tensor imaging
Bibliographic record
Abstract
PURPOSE: To use spinal cord diffusion tensor imaging (DTI) for investigating human cervical funiculi, acquire axial diffusion magnetic resonance imaging (MRI) data with an in-plane resolution sufficient to delineate subquadrants within the spinal cord, obtain corresponding DTI metrics, and assess potential regional differences. MATERIALS AND METHODS: Healthy volunteers were studied with a 3 T Siemens Trio MRI scanner. DTI data were acquired using a single-shot spin echo EPI sequence. The spatial resolution allowed for the delineation of regions of interest (ROIs) in the ventral, dorsal, and lateral spinal cord funiculi. ROI-based and tractography-based analyses were performed. RESULTS: Significant fractional anisotropy (FA) differences were found between ROIs in the dorsal and ventral funiculi (P = 0.0001), dorsal and lateral funiculi (P = 0.015), and lateral and ventral funiculi (P = 0.0002). Transverse diffusivity was significantly different between ROIs in the ventral and dorsal funiculi (P = 0.003) and the ventral and lateral funiculi (P = 0.004). Tractography-based quantifications revealed DTI parameter regional differences that were generally consistent with the ROI-based analysis. CONCLUSION: Original contributions are: 1) the use of a tractography-based method to quantify DTI metrics in the human cervical spinal cord, and 2) reported DTI values in various funiculi at 3 T.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".