Predicting clinical scores in Huntington's disease: a lightweight speech test

Abstract

Objectives: Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington's Disease (HD), an inherited Neurodegenerative disease (NDD).

Methods: We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington's disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27-88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington's disease rating scale. We provided correlation between speech variables and striatal volumes.

Results: Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5-0.6, respectively).

Interpretation: Brief and examiner-free speech recording and analysis may become in the future an efficient method for remote evaluation of the individual condition in HD and likely in other NDD.

Keywords: Huntington’s disease; Machine learning; Speech.

Conflict of interest statement

Nothing to report.

© 2022. The Author(s).

Figures

Fig. 1

Extraction of individual clinical scores from the speech samples. (Top panel) Examples of portions of the speech signal and various types of vocalizations and segmentation are provided. Similar speech features were extracted separately from the forward and backward counting tasks yielding to 60 features (30 × 2). (Bottom panel) Illustration of the methods developments, Machine learning training and evaluation of the predictions of the clinical scores. N CAG number of CAG repeats on the Huntingtin gene, DBS Disease Burden Score. TFC Total Functional capacity, TMS Total motor score, SDMT Symbol digit modality, UHDRS IS UHDRS Independence Scale, MAE Mean absolute error, ICC Intraclass correlation coefficient, cUHDRS composite UHDRS

Fig. 2

Illustration of individual predictions of the cUHDRS (Left) and the TMS (Right) based on the speech features. Each individual blue dot is the difference between the predicted and the observed score for a particular assessment of an individual of the test set. The red dashed line is the line ‘y = x’. The black line is the individual contribution of a point (individual absolute error) to obtain the Mean Absolute Error (MAE)

Fig. 3

Boxplots of mean-absolute-error (MAE) on the test set for the repeated-learning testing experiment. A MAE at zero means that the predicted value equals the observed one. Horizontal lines are the medians, boxes are upper and lower quartiles, and whiskers are 1.5 × IQR (Interquartile Range). First row displays the cUHDRS, functional, and motor predicted scores; whereas the second row displays the predicted Cognitive Scores. Statistical Significance was assessed with Wilcoxon-test and was Bonferroni-corrected

Fig. 4

Boxplots of intraclass correlation coefficients (ICC) on the test set for the repeated-learning testing experiment. An ICC at 1 means that the predicted value equals the observed one. Horizontal lines are the medians, boxes are upper and lower quartiles, and whiskers are 1.5 × IQR (Interquartile Range). First row displays the cUHDRS, functional, and motor predicted scores; whereas the second row displays the predicted Cognitive Scores. Statistical Significance was assessed with Wilcoxon-test and was Bonferroni-corrected. The dashed lines figure the ICCs obtained between Neurologists for the clinical scores namely: (1) ICC for cUHDRS ICC = 0.92 [49], (2) for TMS ICC = 0.847 [3], (3) for TFC ICC = 0.938, and for UHDRS IS ICC = 0.842 [4]. The ICC cannot be computed for the Mean Cohort Performance as its standard deviation is zero

Fig. 5

Coefficient importance of the different speech features for the predictions of the clinical scores. Each line represents a feature of Table 2 and the rank is the order introduced in Table 2. These mean weights are obtained with a linear Elastic Net model for interpretability. The weights are z-scored per clinical score to be one the same scale. The weights for the clinical scores are reversed, so that a higher feature weight can be interpreted as a higher clinical impairment