Tractography at 3T MRI of Corpus Callosum Tracts Crossing White Matter Hyperintensities
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The impact of white matter hyperintensities on the diffusion characteristics of crossing tracts is unclear. This study used quantitative tractography at 3T MR imaging to compare, in the same individuals, the diffusion characteristics of corpus callosum tracts that crossed white matter hyperintensities with the diffusion characteristics of corpus callosum tracts that did not pass through white matter hyperintensities. MATERIALS AND METHODS: Brain T2 fluid-attenuated inversion recovery-weighted and diffusion tensor 3T MR imaging scans were acquired in 24 individuals with white matter hyperintensities. Tractography data were generated by the Fiber Assignment by Continuous Tracking method. White matter hyperintensities and corpus callosum tracts were manually segmented. In the corpus callosum, the fractional anisotropy, radial diffusivity, and mean diffusivity of tracts crossing white matter hyperintensities were compared with the fractional anisotropy, radial diffusivity, and mean diffusivity of tracts that did not cross white matter hyperintensities. The cingulum, long association fibers, corticospinal/bulbar tracts, and thalamic projection fibers were included for comparison. RESULTS: Within the corpus callosum, tracts that crossed white matter hyperintensities had decreased fractional anisotropy compared with tracts that did not pass through white matter hyperintensities (P = .002). Within the cingulum, tracts that crossed white matter hyperintensities had increased radial diffusivity compared with tracts that did not pass through white matter hyperintensities (P = .001). CONCLUSIONS: In the corpus callosum and cingulum, tracts had worse diffusion characteristics when they crossed white matter hyperintensities. These results support a role for white matter hyperintensities in the disruption of crossing tracts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".