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.

• Publication type
• Journal Article MeSH

BACKGROUND AND OBJECTIVES: The impact of dopaminergic medication on language in Parkinson's disease (PD) remains poorly understood. This observational, naturalistic study aimed to investigate the effects of long-term dopaminergic therapy on language performance in patients with de-novo PD based on a high-level linguistic analysis of natural spontaneous discourse. METHODS: A fairy-tale narration was recorded at baseline and a 12-month follow-up. The speech samples were automatically analyzed using six representative lexical and syntactic features based on automatic speech recognition and natural language processing. RESULTS: We enrolled 109 de-novo PD patients compared to 68 healthy controls. All subjects completed the 12-month follow-up; 92 PD patients were on stable dopaminergic medication (PD-treated), while 17 PD patients remained without medication (PD-untreated). At baseline, the PD-treated group exhibited abnormalities in syntactic domains, particularly in sentence length (p = 0.018) and sentence development (p = 0.042) compared to healthy controls. After 12 months of dopaminergic therapy, PD-treated showed improvements in the syntactic domain, including sentence length (p = 0.012) and sentence development (p = 0.030). Of all PD-treated patients, 37 were on monotherapy with dopamine agonists and manifested improvement in sentence length (p = 0.048), while 32 were on monotherapy with levodopa and had no language amelioration. No changes in language parameters over time were seen in both the PD-untreated group and healthy controls. DISCUSSION: Initiation of dopaminergic therapy improved high-language syntactic deficits in de-novo PD, confirming the role of dopamine in cognitive-linguistic processing. Automated linguistic analysis of spontaneous speech via natural language processing can assist in improving the prediction and management of language deficits in PD.

• Keywords
• Discourse, Levodopa, Linguistic analysis, Natural language processing, Speech,
• MeSH
• Dopamine Agonists * therapeutic use   MeSH
• Antiparkinson Agents * therapeutic use   MeSH
• Dopamine Agents * therapeutic use   MeSH
• Middle Aged MeSH
• Humans MeSH
• Follow-Up Studies MeSH
• Parkinson Disease * drug therapy   complications   MeSH
• Speech * drug effects   MeSH
• Aged MeSH
• Treatment Outcome MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Observational Study MeSH
• Names of Substances
• Dopamine Agonists * MeSH
• Antiparkinson Agents * MeSH
• Dopamine Agents * 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.

• Keywords
• Automated linguistic analysis, Language, Multiple system atrophy, Natural language processing, Spontaneous discourse,
• MeSH
• Diagnosis, Differential MeSH
• Middle Aged MeSH
• Humans MeSH
• Multiple System Atrophy * diagnosis   complications   MeSH
• Parkinson Disease * diagnosis   complications   MeSH
• Aged MeSH
• Natural Language Processing * MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored. OBJECTIVES: We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features. METHODS: We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. RESULTS: Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p < 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r > 0.88, p < 0.001). CONCLUSION: Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.

• Keywords
• automated linguistic analysis, language, multiple sclerosis, nature language processing, spontaneous discourse,
• Publication type
• Journal Article MeSH