Emotion expression through spoken language in Huntington disease.

Patients with Huntington's disease suffer from disturbances in the perception of emotions; they do not correctly read the body, vocal and facial expressions of others. With regard to the expression of emotions, it has been shown that they are impaired in expressing emotions through face but up until now, little research has been conducted about their ability to express emotions through spoken language. To better understand emotion production in both voice and language in Huntington's Disease (HD), we tested 115 individuals: 68 patients (HD), 22 participants carrying the mutant HD gene without any motor symptoms (pre-manifest HD), and 25 controls in a single-centre prospective observational follow-up study. Participants were recorded in interviews in which they were asked to recall sad, angry, happy, and neutral stories. Emotion expression through voice and language was investigated by comparing the identifiability of emotions expressed by controls, preHD and HD patients in these interviews. To assess separately vocal and linguistic expression of emotions in a blind design, we used machine learning models instead of a human jury performing a forced-choice recognition test. Results from this study showed that patients with HD had difficulty expressing emotions through both voice and language compared to preHD participants and controls, who behaved similarly and above chance. In addition, we did not find any differences in expression of emotions between preHD and healthy controls. We further validated our newly proposed methodology with a human jury on the speech produced by the controls. These results are consistent with the hypothesis that emotional deficits in HD are caused by impaired sensori-motor representations of emotions, in line with embodied cognition theories. This study also shows how machine learning models can be leveraged to assess emotion expression in a blind and reproducible way.

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

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.