Differentiating upper- and lower motor neuron diseases using automated acoustic analysis
Bibliographic record
Abstract
OBJECTIVE: Motor neuron diseases (MNDs) result in a spectrum of motor impairments, including considerable effects on speech function, which manifest as dysarthria-a motor speech disorder. Speech metrics are increasingly recognized as critical biomarkers with potential utility in disease diagnosis and phenotyping. This study aimed to (1) characterize acoustics of upper motor neuron (UMN) and lower motor neuron (LMN) dysarthria presentations in MNDs, and (2) identify relationships between bulbar disease severity scores and acoustic features, as these could collectively enable personalized approaches to management of these diseases. METHODS: Data from 16 individuals with primary lateral sclerosis (PLS) representing UMN disease, 14 individuals with spinal and bulbar muscular atrophy (SBMA) representing LMN disease, and 25 neurologically healthy individuals were analyzed. Clinical measures were also collected from PLS and SBMA groups. All participants were remotely recorded performing passage reading, rapid syllable repetition, and vowel phonation. Fifty-two acoustic features were extracted representing articulation, phonation, prosody, resonance, and overall speech timing. Features were compared using Kruskal-Wallis tests for between-group comparisons and Spearman correlations between acoustic features and clinical scores. RESULTS: Articulatory and prosodic features best differentiated PLS, SBMA and controls. Correlations were observed in the PLS group between the clinical score and various articulatory features, most notably those indexing tongue and jaw movements. CONCLUSIONS: Our study demonstrated that acoustic assessment could capture fingerprints of dysarthrias associated with PLS and SBMA. These findings also demonstrate the potential for remote speech assessment to characterize diverse dysarthria profiles and pave the way for creating ways for personalized disease management approaches in clinical care and trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".