Impaired temporal processing in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that damages grey and white matter and reduces neural transmission efficiency. Volumetric MRI studies indicate early neurodegeneration in subcortical structures, including the basal ganglia (BG), with microstructural damages and altered tissue anisotropy reported across all MS subtypes, affecting BG functional connectivity while also being linked to fatigue. Given the BG's central role in temporal processing, we hypothesized that people with MS (pwMS) would show impaired perceptual and motor timing. Twenty-two pwMS (14 females) with relapsing-remitting MS completed the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA) on a tablet, performing perceptual tasks and finger-tapping motor tasks. Compared to normative data, pwMS exhibited increased motor variability during unpaced tapping and reduced synchronization consistency to rhythmic auditory cues. Perceptual deficits included poorer detection of metronome alignment with musical beats and reduced sensitivity to deviations from a regular beat. These perceptual impairments correlated with higher patient-reported Expanded Disability Status Scale (prEDSS) scores and perceived fatigue levels, as evaluated with the Multidimensional Fatigue Inventory (MFI). These findings suggest timing measures as a potential candidate for behavioral biomarkers of disease progression and fatigue in MS.
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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 | 0.002 |
| 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) | 0.002 | 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".