Symmetry of the fornix using diffusion tensor imaging
Bibliographic record
Abstract
PURPOSE: To: 1) Present fornix tractography in its entirety for 20 healthy individuals to assess variability. 2) Provide individual and groupwise whole tract diffusion parameter symmetry assessments prior to clinical application. 3) Compare whole tract diffusion parameter assessments with tract-based spatial statistics (TBSS). MATERIALS AND METHODS: Diffusion tensor imaging (DTI) data were acquired on a 3T Siemens magnetic resonance imaging (MRI) system using a single-shot spin echo planar imaging (EPI) sequence. Individual fornix tractography was conducted and whole tract diffusion parameter symmetries assessed. Whole tract results were compared with asymmetry contrasts conducted with voxelwise statistical analysis of diffusion parameters using TBSS. RESULTS: The fornix tract could be visualized in its entirety including the columns, body, crura, and fimbria. Contrary to the crus and body, there were some tractography inconsistencies of the columns and fimbria across subjects. Although whole tract diffusion parameter asymmetries were nonsignificant, fractional anisotropy (FA) values bordered on statistical significance (P = 0.052). Using TBSS, significant FA asymmetries were identified (P ≤ 0.01, corrected). CONCLUSION: The findings demonstrate consistency of fornix tractography as well as some variability in the columns and fimbria. While parametric assessment demonstrates diffusion parameter symmetry, permutation-based TBSS analysis reveals significant FA asymmetries in the crura and fimbriae.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".