Longitudinal Imaging Biomarkers Correlate with Progressive Motor Deficit in the Mouse Model of Charlevoix‐Saguenay Ataxia
Bibliographic record
Abstract
OBJECTIVE: In autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) disease, severity and age of onset vary greatly, hindering to objectively measure and predict clinical progression. Thickening of the retinal nerve fiber layer is distinctive of ARSACS patients, as assessed by optical coherence tomography, whereas conventional brain magnetic resonance imaging findings include both supratentorial and infratentorial changes. Because longitudinal imaging studies in ARSACS patients are not available to define these changes as biomarkers of disease progression, we aimed to address this issue in the ARSACS mouse model. METHODS: ARSACS mouse model, alongside motor and coordination assessment in the beam walking test. We also investigated visual function and the molecular mechanisms underlying RNFL increased thickness by histology and immunofluorescence. RESULTS: mouse model, reflecting the progression of motor impairment, and later reaches a plateau when thinning of the posterior corpus callosum becomes detectable by MRI. Mechanistically, we unveiled that RNFL thickening is associated with aberrant accumulation of non-phosphorylated neurofilament H and glial fibrillary acidic protein. We also uncovered mild signs of myelin pathology coherent with increased latency of visual evoked potentials, and altered retinal activation by photopic electroretinography. INTERPRETATION: mouse model. Our data gathers knowledge instrumental to clinical studies, holding potential as readout for treatment efficacy. ANN NEUROL 2025;97:425-434.
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.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".