Diffusion imaging of fornix and interconnected limbic deep grey matter is linked to cognitive impairment in multiple sclerosis
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) and volumetric magnetic resonance imaging (MRI) have shown white matter (WM) and deep grey matter (GM) abnormalities in the limbic system of multiple sclerosis (MS) participants. Structures like the fornix have been associated with cognitive impairment (CI) in MS, but the diffusion metrics are often biased by partial volume effects from cerebrospinal fluid (CSF) due to its small bundle size and intraventricular location. These errors in DTI parameter estimation worsen with atrophy in MS. The goal here was to evaluate DTI parameters and volumes of the fornix, as well as associated deep GM structures like the thalamus and hippocampus, with high-resolution fluid-attenuated inversion recovery (FLAIR)-DTI at 3T in 43 MS patients, with and without CI, versus 43 controls. The fornix, thalamus and hippocampus displayed atrophy and/or abnormal diffusion metrics, with the fornix showing the most extensive changes within the structures studied here, mainly in CI MS. The affected fornix volumes and diffusion metrics were associated with thalamic atrophy and atypical diffusion metrics in interconnected limbic GM, larger total lesion volume and global brain atrophy. Lower fractional anisotropy (FA) and higher mean and radial diffusivity in the fornix, lower hippocampus FA and lower thalamus volume were strongly correlated with CI in MS. Hippocampus FA and thalamus atrophy were negatively correlated with fatigue and longer time since MS symptoms onset, respectively. FLAIR-DTI and volumetric analyses provided methodologically superior evidence for microstructural abnormalities and extensive atrophy of the fornix and interconnected deep GM in MS that were associated with cognitive deficits.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".