Towards disease-specific speech markers for differential diagnosis in Parkinsonism

International audienceParkinsonism refers to Parkinson's Disease (PD) and Atyp-ical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within APS groups in early disease stages is a very challenging task. Interestingly, speech disorder is frequently an early and prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of an objective marker to assist neurologists in their diagnosis. This paper is a continuation of a recent work on speech-based differential diagnosis within APS. We address the difficult problem of defining disease-specific speech features which is crucial in the perspective of early differential diagnosis. We investigate this problem by considering the constraint that only a small amount of training data can be available in this setting. To do so, we perform univariate statistical analysis followed by a supervised learning that forces the designed new features to be 1-dimensional. We carry out experiments using speech recordings of MSA and PSP patients. We show that linear classification models allow the definition of new scalar variables which can be considered as speech features which are specific to each disease, MSA and PSP

Linear classification in speech-based objective differential diagnosis of parkinsonism

International audienceParkinsonism refers to Parkinsons disease (PD) and Atyp-ical parkinsonian syndromes (APS). Speech disorder is a common and early symptom in Parkinsonism which makes speech analysis a very important research area for the purpose of early diagnosis. Most of research have however focused on discrimination between PD and healthy controls. Such research does not take into account the fact that PD and APS syndromes are very similar in early disease stages. The main problem that has to be addressed first is then differential diagnosis: discrimination between PD and APS and within APS. This paper is a continuation of an earlier pioneer work in differential diagnosis where we mostly address the machine learning problem due to the small amount of training data. We show that classical linear and generalized linear models can provide interpretable and robust classifiers in term of accuracy and generalization ability

Objective discrimination between Progressive Supranuclear Palsy and Multiple System Atrophy using speech analysis

International audienceWe propose a fully reproducible speech-based technique for objective differential diagnosis between progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). Our technique yields a classification mean accuracy of 86.1% which is a significant improvement as compared to a recent pioneer study on this task

Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

International audienceWhile speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches

Towards disease-specific speech markers for differential diagnosis in Parkinsonism

International audienceParkinsonism refers to Parkinson's Disease (PD) and Atyp-ical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within APS groups in early disease stages is a very challenging task. Interestingly, speech disorder is frequently an early and prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of an objective marker to assist neurologists in their diagnosis. This paper is a continuation of a recent work on speech-based differential diagnosis within APS. We address the difficult problem of defining disease-specific speech features which is crucial in the perspective of early differential diagnosis. We investigate this problem by considering the constraint that only a small amount of training data can be available in this setting. To do so, we perform univariate statistical analysis followed by a supervised learning that forces the designed new features to be 1-dimensional. We carry out experiments using speech recordings of MSA and PSP patients. We show that linear classification models allow the definition of new scalar variables which can be considered as speech features which are specific to each disease, MSA and PSP

Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

International audienceWhile speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches

Automated Vowel Articulation Analysis in Connected Speech Among Progressive Neurological Diseases, Dysarthria Types, and Dysarthria Severities.

PURPOSE Although articulatory impairment represents distinct speech characteristics in most neurological diseases affecting movement, methods allowing automated assessments of articulation deficits from the connected speech are scarce. This study aimed to design a fully automated method for analyzing dysarthria-related vowel articulation impairment and estimate its sensitivity in a broad range of neurological diseases and various types and severities of dysarthria. METHOD Unconstrained monologue and reading passages were acquired from 459 speakers, including 306 healthy controls and 153 neurological patients. The algorithm utilized a formant tracker in combination with a phoneme recognizer and subsequent signal processing analysis. RESULTS Articulatory undershoot of vowels was presented in a broad spectrum of progressive neurodegenerative diseases, including Parkinson's disease, progressive supranuclear palsy, multiple-system atrophy, Huntington's disease, essential tremor, cerebellar ataxia, multiple sclerosis, and amyotrophic lateral sclerosis, as well as in related dysarthria subtypes including hypokinetic, hyperkinetic, ataxic, spastic, flaccid, and their mixed variants. Formant ratios showed a higher sensitivity to vowel deficits than vowel space area. First formants of corner vowels were significantly lower for multiple-system atrophy than cerebellar ataxia. Second formants of vowels /a/ and /i/ were lower in ataxic compared to spastic dysarthria. Discriminant analysis showed a classification score of up to 41.0% for disease type, 39.3% for dysarthria type, and 49.2% for dysarthria severity. Algorithm accuracy reached an F-score of 0.77. CONCLUSIONS Distinctive vowel articulation alterations reflect underlying pathophysiology in neurological diseases. Objective acoustic analysis of vowel articulation has the potential to provide a universal method to screen motor speech disorders. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.23681529

Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

International audienceWhile speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches

Is retina affected in Huntington's disease? Is optical coherence tomography a good biomarker?

Aim of the studyComparative cross-sectional study of retinal parameters in Huntington's disease and their evaluation as marker of disease progression.Clinical rationale for the studyHuntington's disease (HD) is a neurodegenerative disorder with dominant motor and neuropsychiatric symptoms. Involvement of sensory functions in HD has been investigated, however studies of retinal pathology are incongruent. Effect sizes of previous findings were not published. OCT data of the subjects in previous studies have not been published. Additional examination of structural and functional parameters of retina in larger sample of patients with HD is warranted.Materials and methodsThis is a prospective cross-sectional study that included: peripapillary retinal nerve fiber layer thickness (RNFL) and total macular volume (TMV) measured by spectral domain optical coherence tomography (OCT) of retina, Pelli-Robson Contrast Sensitivity test, Farnsworth 15 Hue Color discrimination test, ophthalmology examination and Unified Huntington's disease Rating Scale (UHDRS). Ninety-four eyes of 41 HD patients examined in total 47 visits and 82 eyes of 41 healthy controls (HC) examined in total 41 visits were included. Analyses were performed by repeated measures linear mixed effects model with age and gender as covariates. False discovery rate was corrected by Benjamini-Hochberg procedure.ResultsHD group included 21 males and 20 females (age 50.6±12.0 years [mean ± standard deviation], disease duration 7.1±3.6 years, CAG triplet repeats 44.1±2.4). UHDRS Total Motor Score (TMS) was 30.0±12.3 and Total Functional Capacity 8.2±3.2. Control group (HC) included 19 males and 22 females with age 48.2±10.3 years. There was no statistically significant difference between HD and HC in age. The effect of the disease was not significant in temporal segment RNFL thickness. It was significant in the mean RNFL thickness and TMV, however not passing false discovery rate adjustment and with small effect size. In the HD group, the effect of disease duration and TMS was not significant. The Contrast Sensitivity test in HD was within normal limits and the 15-hue-test in HD did not reveal any specific pathology.ConclusionsThe results of our study support possible diffuse retinal changes in global RNFL layer and in macula in Huntington's disease, however, these changes are small and not suitable as a biomarker for disease progression. We found no other structural or functional changes in retina of Huntington's disease patients using RNFL layer and macular volume spectral domain OCT and Contrast Sensitivity Test and 15-hue-test.Clinical implicationsCurrent retinal parameters are not appropriate for monitoring HD disease progression

Is retina affected in Huntington’s disease? Is optical coherence tomography a good biomarker?

Aim of the study Comparative cross-sectional study of retinal parameters in Huntington’s disease and their evaluation as marker of disease progression. Clinical rationale for the study Huntington’s disease (HD) is a neurodegenerative disorder with dominant motor and neuropsychiatric symptoms. Involvement of sensory functions in HD has been investigated, however studies of retinal pathology are incongruent. Effect sizes of previous findings were not published. OCT data of the subjects in previous studies have not been published. Additional examination of structural and functional parameters of retina in larger sample of patients with HD is warranted. Materials and methods This is a prospective cross-sectional study that included: peripapillary retinal nerve fiber layer thickness (RNFL) and total macular volume (TMV) measured by spectral domain optical coherence tomography (OCT) of retina, Pelli-Robson Contrast Sensitivity test, Farnsworth 15 Hue Color discrimination test, ophthalmology examination and Unified Huntington’s disease Rating Scale (UHDRS). Ninety-four eyes of 41 HD patients examined in total 47 visits and 82 eyes of 41 healthy controls (HC) examined in total 41 visits were included. Analyses were performed by repeated measures linear mixed effects model with age and gender as covariates. False discovery rate was corrected by Benjamini-Hochberg procedure. Results HD group included 21 males and 20 females (age 50.6±12.0 years [mean ± standard deviation], disease duration 7.1±3.6 years, CAG triplet repeats 44.1±2.4). UHDRS Total Motor Score (TMS) was 30.0±12.3 and Total Functional Capacity 8.2±3.2. Control group (HC) included 19 males and 22 females with age 48.2±10.3 years. There was no statistically significant difference between HD and HC in age. The effect of the disease was not significant in temporal segment RNFL thickness. It was significant in the mean RNFL thickness and TMV, however not passing false discovery rate adjustment and with small effect size. In the HD group, the effect of disease duration and TMS was not significant. The Contrast Sensitivity test in HD was within normal limits and the 15-hue-test in HD did not reveal any specific pathology. Conclusions The results of our study support possible diffuse retinal changes in global RNFL layer and in macula in Huntington’s disease, however, these changes are small and not suitable as a biomarker for disease progression. We found no other structural or functional changes in retina of Huntington’s disease patients using RNFL layer and macular volume spectral domain OCT and Contrast Sensitivity Test and 15-hue-test. Clinical implications Current retinal parameters are not appropriate for monitoring HD disease progression