The Putative Functional SCN10A Gene Variant Predicts Functional Status and Cerebello-Thalamic Circuit Integrity in Multiple Sclerosis (P6.123)
Bibliographic record
Abstract
OBJECTIVE: We investigated whether a putative functional SCN10A variant (rs6801957) affects motor coordination and cerebellar functional-connectivity in individuals with Multiple Sclerosis (MS). BACKGROUND: Cerebellar dysfunction is a major determinant of disability in people with MS. Recent evidence indicates that cerebellar Purkinje neurons ectopically express the voltage-gated sodium channel Nav1.8 (encoded by SCN10A gene) in MS and its animal models, and suggests that this acquired channelopathy contributes to motor deficits in MS by distorting normal neuronal firing pattern. DESIGN/METHODS: We recruited 161 individuals with relapse-onset MS, aged 18-57 years. All subjects were assessed using expanded disability status scale (EDSS) and MS functional composite (MSFC), were genotyped for rs6801957 polymorphism, and underwent structural MRI. In a subsample of subjects who also underwent resting-state functional MRI (n=62), we further investigated the effect of rs6801957 genotype on cerebellar functional-connectivity across the brain. RESULTS: MS patients with rs6801957-TT genotype performed significantly worse than C-allele carriers on MSFC (P=8.2×10-4) and all of its subtests (25-feet walking test: P=0.0012; 9-hole peg test: P=0.006; paced auditory serial addition test: P=0.048). The association between MSFC and SCN10A genotype was more evident in individuals with lower lesion burden and was independent of EDSS and cerebellar atrophy. Although no significant difference in regional brain volumes were detected between the two groups, patients with rs6801957-TT genotype demonstrated diminished cerebello-thalamic functional-connectivity. CONCLUSIONS: Our findings clearly suggest that SCN10A variation substantially influences functional status and integrity of cerebello-thalamic circuitry in people with MS and should be considered in future disease-modifying or Nav1.8-targeting therapeutic efforts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".