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

BACKGROUND: Patients with synucleinopathies frequently display language abnormalities. However, whether patients with isolated rapid eye movement sleep behavior disorder (iRBD) have prodromal language impairment remains unknown. OBJECTIVE: We examined whether the linguistic abnormalities in iRBD can serve as potential biomarkers for conversion to synucleinopathy, including the possible effect of mild cognitive impairment (MCI), speaking task, and automation of analysis procedure. METHODS: We enrolled 139 Czech native participants, including 40 iRBD without MCI and 14 iRBD with MCI, compared with 40 PD without MCI, 15 PD with MCI, and 30 healthy control subjects. Spontaneous discourse and story-tale narrative were transcribed and linguistically annotated. A quantitative analysis was performed computing three linguistic features. Human annotations were compared with fully automated annotations. RESULTS: Compared with control subjects, patients with iRBD showed poorer content density, reflecting the reduction of content words and modifiers. Both PD and iRBD subgroups with MCI manifested less occurrence of unique words and a higher number of n-grams repetitions, indicating poorer lexical richness. The spontaneous discourse task demonstrated language impairment in iRBD without MCI with an area under the curve of 0.72, while the story-tale narrative task better reflected the presence of MCI, discriminating both PD and iRBD subgroups with MCI from control subjects with an area under the curve of up to 0.81. A strong correlation between manually and automatically computed results was achieved. CONCLUSIONS: Linguistic features might provide a reliable automated method for detecting cognitive decline caused by prodromal neurodegeneration in subjects with iRBD, providing critical outcomes for future therapeutic trials. © 2022 International Parkinson and Movement Disorder Society.

• Keywords
• Parkinson's disease, discourse, lexical features, prodromal synucleinopathy biomarker, spoken language,
• MeSH
• Cognitive Dysfunction * diagnosis   MeSH
• Humans MeSH
• Linguistics MeSH
• Parkinson Disease * complications   MeSH
• REM Sleep Behavior Disorder * diagnosis   MeSH
• Synucleinopathies * MeSH
• Language Development Disorders * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

• Keywords
• Automated speech analysis, Cross-linguistic validity, Linguistic assessments, Morphology, Parkinson's disease,
• MeSH
• Language * MeSH
• Cognition MeSH
• Humans MeSH
• Parkinson Disease * MeSH
• Speech MeSH
• Machine Learning MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH