Understanding the transferability of language and gender-based phenotypic expression of specific acoustic measures is essential for applying digital speech biomarkers in potential future clinical trials. This study aimed to identify possible gender- or language-related differences in speech between men and women with multiple system atrophy (MSA). A total of 42 male and 40 female MSA patients, along with 41 male and 41 female age-matched healthy controls, were recruited from two centres representing two distinct languages: Czech and Italian. A quantitative acoustic assessment was performed using 12 distinct speech dimensions. No significant clinical differences in MSA patients were found between men and women in terms of age, disease duration, motor severity, or dysarthria severity. MSA patients exhibited significantly worse performance compared to controls for voice quality, pitch breaks, frequency and amplitude vocal tremor, slow and irregular sequential motion rates, imprecise consonants, dynamics of articulation, monopitch, excessive loudness variation, articulation rate, and inappropriate silences (p < 0.001). Considering the gender-specific patterns, the female MSA patients manifested more impaired voice quality (p < 0.05) and more frequent vocal tremor (p < 0.05) then male MSA, while male MSA patients showed slower diadochokinetic rate (p < 0.01) and higher excessive loudness variability (p < 0.01) than female MSA. The impact of language on disease-related changes appears to be minimal for the majority of acoustic parameters considered. Despite some gender differences, our findings demonstrate that speech-based digital biomarkers in MSA offer high discriminatory power while maintaining good consistency across gender and language.

• Klíčová slova
• Acoustic analyses., Dysarthria, Gender, Multiple system atrophy, Sex,
• Publikační typ
• časopisecké články MeSH

Head tremor is a common symptom in both essential tremor (ET) and cervical dystonia (CD). Distinguishing between these two conditions can be challenging in clinical practice, particularly when head tremor is the dominant feature. Our goal was to explore the potential of speech assessment in recognizing the mechanisms of head tremor in patients with ET and CD. Objective acoustic vocal assessments of oral diadochokinesis, phonatory stability, vocal tremor, and speech timing were performed. Of the 93 patients assessed, 39 had cervical dystonia (CD) with head tremor, 38 had ET with head tremor (ET-HT), and 16 had ET with no head tremor (ET-nHT). Compared to both CD and ET-nHT, ET-HT showed irregular sequential motion rate, excessive pitch fluctuations, increased noise, and higher extent of vocal vibrato. Compared to CD, ET-HT also demonstrated slower sequential motion rate, prolonged pauses, and a slower articulation rate. Additionally, ET-HT had more pronounced vocal tremolo compared to ET-nHT. Speech assessment provided discrimination between the CD and ET-HT groups with an area under curve of 0.80. This study underscores the promising potential of speech analysis in recognizing mechanisms of head tremor in patients with ET or CD, revealing more severe and distinct speech impairments in ET-HT patients compared to those with CD.

• Klíčová slova
• Acoustic analysis, Cervical dystonia, Dysarthria, Essential tremor, Speech disorder,
• MeSH
• dospělí MeSH
• esenciální tremor * komplikace   patofyziologie   diagnóza   MeSH
• hlava * patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• řeč * fyziologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• tortikolis * komplikace   patofyziologie   diagnóza   MeSH
• tremor * patofyziologie   diagnóza   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Speech abnormalities in Parkinson's disease (PD) are heterogeneous and often considered resistant to levodopa. However, human hearing may miss subtle treatment-related speech changes. Digital speech biomarkers offer a sensitive alternative to measure such changes objectively. Speech was recorded in 51 PD patients during ON and OFF medication states and compared to 43 healthy controls matched for language and gender. Acute levodopa effects were significant in prosodic (F0 standard deviation, p = 0.03, effect size = 0.47), respiratory (intensity slope, p = 0.02, effect size = 0.49), and spectral domains (LTAS mean, p = 0.01, effect size = 0.35). Stepwise backward regression identified 8 biomarkers reflecting hypokinetic symptoms, 6 for dyskinetic symptoms, and 7 for medication-state transitions. Hypokinetic compound score correlated strongly with MDS-UPDRS-III changes (r = 0.70; MAE = 6.06/92), and the dyskinetic compound score with dyskinesia ratings (r = 0.50; MAE = 1.81/12). Medication-state transitions were detected with AUC = 0.86. This study highlights the potential of digital speech biomarkers to objectively measure levodopa-induced changes in PD symptoms and medication states.

• Publikační typ
• časopisecké články MeSH

Over the past decade, neuropsychiatric fluctuations in Parkinson's disease (PD) have been increasingly recognized for their impact on patients' quality of life. Speech, a complex function carrying motor, emotional, and cognitive information, offers potential insights into these fluctuations. While previous studies have focused on acoustic analysis to assess motor speech disorders reliably, the potential of linguistic patterns associated with neuropsychiatric fluctuations in PD remains unexplored. This study analyzed the content of spontaneous speech from 33 PD patients in ON and OFF medication states, using machine learning and large language models (LLMs) to predict medication states and a neuropsychiatric state score. The top-performing model, the LLM Gemma-2 (9B), achieved 98% accuracy in differentiating ON and OFF states and its predicted scores were highly correlated with actual scores (Spearman's ρ = 0.81). These methods could provide a more comprehensive assessment of PD treatment effects, allowing remote neuropsychiatric symptom monitoring via mobile devices.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: Research on the possible influence of lateralised basal ganglia dysfunction on speech in Parkinson's disease is scarce. This study aimed to compare speech in de-novo, drug-naive patients with Parkinson's disease (PD) with asymmetric nigral dopaminergic dysfunction, predominantly in either the right or left hemisphere. METHODS: Acoustic analyses of reading passages were performed. Asymmetry of nigral dysfunction was defined using dopamine transporter-single-photon emission CT (DAT-SPECT). RESULTS: From a total of 135 de novo patients with PD assessed, 47 patients had a lower right and 36 lower left DAT availability in putamen based on DAT-SPECT. Patients with PD with lower left DAT availability had higher dysarthria severity via composite dysarthria index compared with patients with lower right DAT availability (p=0.01). CONCLUSION: Our data support the crucial role of DAT availability in the left putamen in speech. This finding might provide important clues for managing speech following deep brain stimulation.

• Klíčová slova
• MOTOR CONTROL, MOVEMENT DISORDERS, PARKINSON'S DISEASE, SPEECH,
• MeSH
• bazální ganglia * patofyziologie   diagnostické zobrazování   MeSH
• dysartrie patofyziologie   diagnostické zobrazování   etiologie   MeSH
• funkční lateralita * fyziologie   MeSH
• jednofotonová emisní výpočetní tomografie MeSH
• lidé středního věku MeSH
• lidé MeSH
• Parkinsonova nemoc * patofyziologie   diagnostické zobrazování   komplikace   MeSH
• proteiny přenášející dopamin přes plazmatickou membránu metabolismus   MeSH
• putamen diagnostické zobrazování   metabolismus   patofyziologie   MeSH
• řeč * fyziologie   MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• proteiny přenášející dopamin přes plazmatickou membránu MeSH

BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD. METHODS: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis. RESULTS: Despite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = -0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved. DISCUSSION: Automated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

• Klíčová slova
• Automated linguistic analysis, Language, Multiple system atrophy, Natural language processing, Spontaneous discourse,
• MeSH
• diferenciální diagnóza MeSH
• lidé středního věku MeSH
• lidé MeSH
• multisystémová atrofie * diagnóza   komplikace   MeSH
• Parkinsonova nemoc * diagnóza   komplikace   MeSH
• senioři MeSH
• zpracování přirozeného jazyka * MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Approximately 90% of Parkinson's patients (PD) suffer from dysarthria. However, there is currently a lack of research on acoustic measurements and speech impairment patterns among Mandarin-speaking individuals with PD. This study aims to assess the diagnosis and disease monitoring possibility in Mandarin-speaking PD patients through the recommended speech paradigm for non-tonal languages, and to explore the anatomical and functional substrates. We examined total of 160 native Mandarin-speaking Chinese participants consisting of 80 PD patients, 40 healthy controls (HC), and 40 MRI controls. We screened the optimal acoustic metric combination for PD diagnosis. Finally, we used the objective metrics to predict the patient's motor status using the Naïve Bayes model and analyzed the correlations between cortical thickness, subcortical volumes, functional connectivity, and network properties. Comprehensive acoustic screening based on prosodic, articulation, and phonation abnormalities allows differentiation between HC and PD with an area under the curve of 0.931. Patients with slowed reading exhibited atrophy of the fusiform gyrus (FDR p = 0.010, R = 0.391), reduced functional connectivity between the fusiform gyrus and motor cortex, and increased nodal local efficiency (NLE) and nodal efficiency (NE) in bilateral pallidum. Patients with prolonged pauses demonstrated atrophy in the left hippocampus, along with decreased NLE and NE. The acoustic assessment in Mandarin proves effective in diagnosis and disease monitoring for Mandarin-speaking PD patients, generalizing standardized acoustic guidelines beyond non-tonal languages. The speech impairment in Mandarin-speaking PD patients not only involves motor aspects of speech but also encompasses the cognitive processes underlying language generation.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

• Publikační typ
• časopisecké články MeSH

AIM: To investigate the presence and relationship of temporal speech and gait parameters in patients with postural instability/gait disorder (PIGD) and tremor-dominant (TD) motor subtypes of Parkinson's disease (PD). METHODS: Speech samples and instrumented walkway system assessments were acquired from a total of 60 de-novo PD patients (40 in TD and 20 in PIGD subtype) and 40 matched healthy controls. Objective acoustic vocal assessment of seven distinct speech timing dimensions was related to instrumental gait measures including velocity, cadence, and stride length. RESULTS: Compared to controls, PIGD subtype showed greater consonant timing abnormalities by prolonged voice onset time (VOT) while also shorter stride length during both normal walking and dual task, while decreased velocity and cadence only during dual task. Speaking rate was faster in PIGD than TD subtype. In PIGD subtype, prolonged VOT correlated with slower gait velocity (r = -0.56, p = 0.01) and shorter stride length (r = -0.59, p = 0.008) during normal walking, whereas relationships were also found between decreased cadence in dual task and irregular alternating motion rates (r = -0.48, p = 0.04) and prolonged pauses (r = -0.50, p = 0.03). No correlation between speech and gait was detected in TD subtype. CONCLUSION: Our findings suggest that speech and gait rhythm disorder share similar underlying pathomechanisms specific for PIGD subtype.

• Klíčová slova
• Parkinson's disease, dysarthria, gait, postural instability gait difficulty, speech disorder,
• MeSH
• chůze (způsob) MeSH
• chůze MeSH
• lidé MeSH
• neurologické poruchy chůze * etiologie   MeSH
• Parkinsonova nemoc * komplikace   MeSH
• posturální rovnováha MeSH
• řeč MeSH
• tremor MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

While 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.

• Publikační typ
• časopisecké články MeSH