Speech's syllabic rhythm and articulatory features produced under different auditory feedback conditions identify Parkinsonism

Diagnostic tests for Parkinsonism based on speech samples have shown promising results. Although abnormal auditory feedback integration during speech production and impaired rhythmic organization of speech are known in Parkinsonism, these aspects have not been incorporated into diagnostic tests. Thi...

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Bibliographic Details
Published in:Scientific reports 2024-07, Vol.14 (1), p.15787-10, Article 15787
Main Authors: Piña Méndez, Ángeles, Taitz, Alan, Palacios Rodríguez, Oscar, Rodríguez Leyva, Ildefonso, Assaneo, M. Florencia
Format: Article
Language:English
Subjects:
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Aged
Algorithms
Auditory stimuli
Basal ganglia
Case-Control Studies
Central nervous system diseases
Diagnostic tests
Feedback
Feedback, Sensory - physiology
Female
Hearing
Humanities and Social Sciences
Humans
Male
Middle Aged
Movement disorders
multidisciplinary
Neurodegenerative diseases
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Parkinson's disease
Parkinsonian Disorders - physiopathology
Pilot Projects
Rhythms
Science
Science (multidisciplinary)
Speech
Speech - physiology
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Summary:Diagnostic tests for Parkinsonism based on speech samples have shown promising results. Although abnormal auditory feedback integration during speech production and impaired rhythmic organization of speech are known in Parkinsonism, these aspects have not been incorporated into diagnostic tests. This study aimed to identify Parkinsonism using a novel speech behavioral test that involved rhythmically repeating syllables under different auditory feedback conditions. The study included 30 individuals with Parkinson's disease (PD) and 30 healthy subjects. Participants were asked to rhythmically repeat the PA-TA-KA syllable sequence, both whispering and speaking aloud under various listening conditions. The results showed that individuals with PD had difficulties in whispering and articulating under altered auditory feedback conditions, exhibited delayed speech onset, and demonstrated inconsistent rhythmic structure across trials compared to controls. These parameters were then fed into a supervised machine-learning algorithm to differentiate between the two groups. The algorithm achieved an accuracy of 85.4%, a sensitivity of 86.5%, and a specificity of 84.3%. This pilot study highlights the potential of the proposed behavioral paradigm as an objective and accessible (both in cost and time) test for identifying individuals with Parkinson's disease.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-65974-6