Damage of the lateral geniculate nucleus in MS
Bibliographic record
Abstract
Objective To study if the thalamic lateral geniculate nucleus (LGN) is affected in multiple sclerosis (MS) due to anterograde degeneration from optic neuritis (ON) or retrograde degeneration from optic radiation (OR) pathology, and if this is relevant for visual function. Methods In this cross-sectional study, LGN volume of 34 patients with relapsing-remitting MS and 33 matched healthy controls (HC) was assessed on MRI using atlas-based automated segmentation (MAGeT). ON history, thickness of the ganglion cell–inner plexiform layer (GC-IPL), OR lesion volume, and fractional anisotropy (FA) of normal-appearing OR (NAOR-FA) were assessed as measures of afferent visual pathway damage. Visual function was tested, including low-contrast letter acuity (LCLA) and Hardy-Rand-Rittler (HRR) plates for color vision. Results LGN volume was reduced in patients vs HC (165.5 ± 45.5 vs 191.4 ± 47.7 mm3, B = −25.89, SE = 5.83, p < 0.001). It was associated with GC-IPL thickness (B = 0.95, SE = 0.33, p = 0.006) and correlated with OR lesion volume (Spearman ρ = −0.53, p = 0.001), and these relationships remained after adjustment for normalized brain volume. There was no association between NAOR-FA and LGN volume (B = −133.28, SE = 88.47, p = 0.137). LGN volume was not associated with LCLA (B = 5.5 × 10−5, SE = 0.03, p = 0.998), but it correlated with HRR color vision (ρ = 0.39, p = 0.032). Conclusions LGN volume loss in MS indicates structural damage with potential functional relevance. Our results suggest both anterograde degeneration from the retina and retrograde degeneration from the OR lesions as underlying causes. LGN volume is a promising marker reflecting damage of the visual pathway in MS, with the advantage of individual measurement per patient on conventional MRI.
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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 | 0.000 |
| 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 | 0.000 |
| 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".