Dataset Information

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.

ABSTRACT: Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients-Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7-15% improvement). This result was observed for both recording types (high-quality microphone and telephone).

SUBMITTER: Jeancolas L

PROVIDER: S-EPMC7935511 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.

Jeancolas Laetitia L Petrovska-Delacrétaz Dijana D Mangone Graziella G Benkelfat Badr-Eddine BE Corvol Jean-Christophe JC Vidailhet Marie M Lehéricy Stéphane S Benali Habib H

Frontiers in neuroinformatics 20210219

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Ou ...[more]

PMID: 33679361

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Project description:ObjectiveWe analyzed the longitudinal profile of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers in early Parkinson's disease (PD) compared with healthy controls (HCs) and tested baseline CSF biomarkers for prediction of clinical decline in PD.MethodsAmyloid-β 1 to 42 (Aβ42 ), total tau (t-tau) and phosphorylated tau (p-tau) at the threonine 181 position were measured using the high-precision Roche Elecsys electrochemiluminescence immunoassay in all available CSF samples from longitudinally studied patients with PD (n = 416) and HCs (n = 192) followed for up to 3 years in the Parkinson's Progression Markers Initiative (PPMI). Longitudinal CSF and clinical data were analyzed with linear-mixed effects models.ResultsWe found patients with PD had lower CSF t-tau (median = 157.7 pg/mL; range = 80.9-467.0); p-tau (median = 13.4 pg/mL; range = 8.0-40.1), and Aβ42 (median = 846.2 pg/mL; range = 238.8-3,707.0) than HCs at baseline (CSF t-tau median = 173.5 pg/mL; range = 82.0-580.8; p-tau median = 15.4 pg/mL; range = 8.1-73.6; and Aβ42 median = 926.5 pg/mL; range = 239.1-3,297.0; p < 0.05-0.001) and a moderate-to-strong correlation among these biomarkers in both patients with PD and HCs (Rho = 0.50-0.97; p < 0.001). Of the patients with PD, 31.5% had pathologically low levels of CSF Aβ42 at baseline and these patients with PD had lower p-tau levels (median = 10.8 pg/mL; range = 8.0-32.8) compared with 27.7% of HCs with pathologically low CSF Aβ42 (CSF p-tau median = 12.8 pg/mL; range 8.2-73.6; p < 0.03). In longitudinal CSF analysis, we found patients with PD had greater decline in CSF Aβ42 (mean difference = -41.83 pg/mL; p = 0.03) and CSF p-tau (mean difference = -0.38 pg/mL; p = 0.03) at year 3 compared with HCs. Baseline CSF Aβ42 values predicted small but measurable decline on cognitive, autonomic, and motor function in early PD.InterpretationOur data suggest baseline CSF AD biomarkers may have prognostic value in early PD and that the dynamic change of these markers, although modest over a 3-year period, suggest biomarker profiles in PD may deviate from healthy aging. ANN NEUROL 2020;88:574-587.

Project description:Clinical and biochemical diversity of Parkinson's disease (PD) and numerous demographic, clinical, and pathological measures influencing cognitive function and its decline in PD create problems with the determination of effects of individual measures on cognition in PD. This is particularly the case where these measures significantly interrelate with each other producing intricate networks of direct and indirect effects on cognition. Here, we use generalized structural equation modelling (GSEM) to identify and characterize significant paths for direct and indirect effects of 14 baseline measures on global cognition in PD at baseline and at 4 years later. We consider 269 drug-naïve participants from the Parkinson's Progression Marker Initiative database, diagnosed with idiopathic PD and observed for at least 4 years after baseline. Two GSEM networks are derived, highlighting the possibility of at least two different molecular pathways or two different PD sub-types, with either CSF p-tau181 or amyloid beta (1-42) being the primary protein variables potentially driving progression of cognitive decline. The models provide insights into the interrelations between the 14 baseline variables, and determined their total effects on cognition in early PD. High CSF amyloid concentrations (> 500 pg/ml) are associated with nearly full protection against cognitive decline in early PD in the whole range of baseline age between 40 and 80 years, and irrespectively of whether p-tau181 or amyloid beta (1-42) are considered as the primary protein variables. The total effect of depression on cognition is shown to be strongly amplified by PD, but not at the time of diagnosis or at prodromal stages. CSF p-tau181 protein could not be a reliable indicator of cognitive decline because of its significantly heterogeneous effects on cognition. The outcomes will enable better understanding of the roles of the clinical and pathological measures and their mutual effects on cognition in early PD.

Dataset Information

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.

Publications

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.

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