Digital profiling of dysarthria in late- and early-onset Parkinson's disease.
Digital profiling of dysarthria in late- and early-onset Parkinson's disease.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Medellín, CO · Author affiliation
GITA Lab, Universidad de Antioquia, Medellín, Colombia.Location evidence
Rio Grande, BR · Author affiliation
Departamento de Cirurgia e Ortopedia (Curso de Fonoaudiologia), Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.Location evidence
Porto Alegre, BR · Author affiliation
Departamento de Cirurgia e Ortopedia (Curso de Fonoaudiologia), Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.Location evidence
Erlangen, DE · Author affiliation
LME Lab, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Erlangen, Germany.Location evidence
US · Author affiliation · country only
Genomic Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.Location evidence
Buenos Aires, AR · Author affiliation
Cognitive Neuroscience Center, Department of Life and Behavioral Sciences, Universidad de San Andrés, Buenos Aires, Argentina.Location evidence
San Francisco, US · Author affiliation
Global Brain Health Institute, University of California, San Francisco, CA, USA; and Trinity College Dublin, Dublin, Ireland.Location evidence
IE · Author affiliation · country only
Global Brain Health Institute, University of California, San Francisco, CA, USA; and Trinity College Dublin, Dublin, Ireland.Location evidence
Santiago, CL · Author affiliation
Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.Location evidence
A plain-language reading has not been prepared for this paper yet.
Original abstract
BackgroundDigital speech analysis affords robust markers of Parkinson's disease (PD). However, most studies target late-onset PD (LOPD), neglecting early-onset PD (EOPD) -an increasingly prevalent subtype. This proof-of-concept study tackles such gap.MethodsWe used machine learning to discriminate persons with EOPD (with symptom onset before age 50) and LOPD (with symptom onset after age 50) from healthy controls (HCs) through prosodic and articulatory features from natural speech.ResultsMaximal classification between patients and HCs was afforded by combined prosodic and articulation features in LOPD (AUC = 0.90) and by articulation alone in EOPD (AUC = 0.79), with chance-level discrimination between patient groups (AUC = 0.55). Motor severity (MDS-UPDRS-III) scores predicted by these features correlated with actual motor severity scores in both LOPD (r = 0.52, p < 0.001) and EOPD (r = 0.27, p < 0.001).ConclusionsDigital speech markers offer markers of PD irrespective of age of onset.Plain language summary titleVoice recordings capture motor symptoms in Parkinson's disease irrespective of age of onset.