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Updated: May 23, 2026
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
A machine learning-derived speech index as a biomarker for Huntington's disease severity
Lishan Lin1,2,3, Da Huang4,5, Ya Zhang1,2,3
1Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, No.58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
Background:
The development of disease-modifying therapies for Huntington's disease (HD) necessitates sensitive, scalable, and objective biomarkers for patient stratification and tracking. Current clinical scales are rater-dependent and time-consuming, while neuroimaging is costly and inaccessible. We aim to develop and validate a novel Speech Index, derived from automated acoustic analysis, to stratify HD stages and predict the severity of disease.
Methods:
We recruited 141 HTT gene carriers (37 premanifest, 104 manifest HD) and 69 healthy controls. Participants read a standardized passage, and the recordings were processed to extract 28 speech features. A composite Speech Index was constructed using Bootstrap LASSO Regression for feature selection. Its performance was validated against the HD stages, clinical scale scores and the volume of the caudate and putamen from MRI.
Results:
The Speech Index significantly increased across HD stages (p < 0.001). The Index showed strong correlations with clinical measures (cUHDRS: ρ = -0.67; TMS: ρ = 0.57; SDMT: ρ = -0.63; all p < 0.001) and neuroimaging biomarkers (caudate: ρ = -0.55; putamen: ρ = -0.62; all p < 0.001). Generalized additive models confirmed the Index's high predictive value for these outcomes (pseudo-R2 from 0.430 to 0.596).
Conclusion:
We developed a fully automated, interpretable Speech Index that serves as a valid digital biomarker for HD severity. It holds promise for remote monitoring, clinical trial enrichment, and objective assessment of therapeutic efficacy.
