Pathological Aging of Patients With Amyotrophic Lateral Sclerosis: A Preliminary Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: This longitudinal study investigated pathological brain aging in amyotrophic lateral sclerosis (ALS) by evaluating disparities between chronological age and deep learning-derived brain structure age (BSA) and exploring associations with cognitive and functional decline. METHODS: Ten limb-onset ALS patients (seven males) and 10 demographically matched healthy controls (HCs) underwent structural magnetic resonance imaging (sMRI) and cognitive assessments at baseline and follow-up. The BSA was estimated using the validated volBrain platform. Cognitive domains (language, verbal fluency, executive function, memory, and visuospatial skills) and global cognition (Persian adaptive Edinburgh Cognitive and Behavioral ALS Screen [ECAS] total score) were assessed along with functional status (ALSFRS-R). RESULTS: ALS patients exhibited significant BSA-chronological age disparities at baseline (Δ = +7.31 years, p = 0.009) and follow-up (Δ = +8.39 years, p = 0.003), with accelerated BSA progression over time (p = 0.004). The HCs showed no such disparities (p = 0.931). Longitudinal BSA increases were correlated with executive function decline (r = -0.651, p = 0.042). Higher education predicted preserved language (r = 0.831, p = 0.003) and verbal fluency (r = 0.738, p = 0.015). ALSFRS-R decline paralleled visuospatial (r = 0.642, p = 0.045) and global cognitive deterioration (r = 0.667, p = 0.035). CONCLUSIONS: ALS is characterized by accelerated structural brain aging that progresses independently of chronological age and is correlated with executive dysfunction. Education may mitigate cognitive decline, while motor functional deterioration aligns with visuospatial and global cognitive impairments. BSA has emerged as a potential biomarker for tracking pathological aging trajectories in ALS, warranting validation using larger cohorts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".