Diffusion tensor imaging tractography reveals altered fornix in all diagnostic subtypes of multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Diffusion tensor imaging (DTI) has shown abnormalities of the fornix and other limbic white matter tracts in multiple sclerosis (MS), mainly focusing on relapsing-remitting MS. METHODS: The goal here was to evaluate the fornix, cingulum, and uncinate fasciculus with DTI tractography at 1.7 mm isotropic resolution in three MS subgroups (11 relapsing-remitting (RRMS), nine secondary progressive (SPMS), eight primary progressive (PPMS)) versus 11 controls, and assess correlations with cognitive and clinical scores. RESULTS: The MS group overall showed extensive diffusion abnormalities of the fornix with less volume, lower fractional anisotropy (FA), and higher mean and radial diffusivities, which were similarly affected in all three MS subgroups. The uncinate fasciculus had lower FA only in the secondary progressive subgroup, and the cingulum had no DTI differences in any MS subgroup. The FA and/or volumes of these tracts correlated negatively with larger total lesion volume. The only DTI-cognitive correlation was lower right cingulum FA and greater depression over the entire MS cohort. CONCLUSIONS: Diffusion tractography identified abnormalities in the fornix that appears to be affected early and consistently across all three primary MS phenotypes of RRMS, SPMS, and PPMS regardless of Expanded Disability Status Scale, time since diagnosis, or cognitive scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it