Linguistic Abnormalities in Isolated Rapid Eye Movement Sleep Behavior Disorder

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease

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.

From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease

Speech impairment is a common and disabling symptom in Parkinson's disease (PD), affecting communication and quality of life. Advances in digital speech processing and artificial intelligence have revolutionized objective speech analysis. Given the complex nature of speech impairment, acoustic speech analysis offers unique biomarkers for neuroprotective treatments from the prodromal stages of PD. Digital speech biomarkers can monitor levodopa-induced motor complications, detect the effects of deep brain stimulation, and provide feedback for behavioral speech therapy. This review updates the mechanisms underlying speech impairment, the impact of speech phenotypes, and the effects of interventions on speech. We evaluate the strengths, potential weaknesses, and suitability of promising digital speech biomarkers in PD for capturing disease progression and treatment efficacy. Additionally, we explore the translational potential of PD speech biomarkers to other neuropsychiatric diseases, offering insights into motion, cognition, and emotion. Finally, we highlight knowledge gaps and suggest directions for future research to enhance the use of quantitative speech measures in disease-modifying clinical trials. The findings demonstrate that one year is sufficient to detect disease progression in early PD through speech biomarkers. Voice quality, pitch, loudness, and articulation measures appear to capture the efficacy of treatment interventions most effectively. Certain speech features, such as loudness and articulation rate, behave oppositely in different neurological diseases, offering valuable insights for differential diagnosis. In conclusion, this review highlights speech as a biomarker in tracking disease progression, especially in the prodromal stages of PD, and calls for further longitudinal studies to establish its efficacy across diverse populations.

Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study

OBJECTIVE

This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).

METHODS

Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years.

RESULTS

Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17).

INTERPRETATION

Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.

Narrative discourse production in Parkinson's disease: Decoupling the role of cognitive-linguistic and motor speech changes

Introduction

the interplay between neuropsychological and communicative abilities in Parkinson's disease (PD) has been relatively overlooked, and it is not entirely understood which difficulties are consequent to impaired motor control, and which have a linguistic/cognitive basis. Here, we examined narrative discourse in PD using a multi-level analysis procedure considering sentence-level (productivity, lexical-grammatical processing) and discourse-level processes (narrative organization, informativeness), and partialling out patients' motor speech impairments. The interaction between cognitive (i.e. linguistic and executive) and communication abilities was also investigated.

Methods

Twenty-nine PD subjects in the mild stage of the disease were compared to 29 matched healthy comparators (HC) on quantitative measures of narrative discourse derived from two picture description tasks. Multivariate (considering articulation rate and educational attainment as covariates) and univariate (with group membership as independent variable) analyses of variance were conducted on separate linguistic domains. The contribution of executive/linguistic abilities to PD's narrative performance was explored by multiple regression analyses on narrative measures significantly differentiating patients from HC.

Results

significant reductions in patients were observed on measures of productivity (less well-formed words, shorter sentences) and informativeness (fewer conceptual units, less informative elements, lower number of details) and these alterations were explained by variations in linguistic abilities (action and object naming) rather than executive abilities. Articulation rate and educational attainment did not impact the observed reduced productivity and under-informativeness.

Conclusion

referential narrative discourse is altered in PD, regardless of motor impairments in speech production. The observed reductions in productivity/informativeness aspects of narratives were related to naming abilities and in particular to verbs processing, consistently with the neurocognitive model of motor language coupling. Since narratives are amenable to recurrent and automated analysis for the identification of linguistic patterns potentially anticipating the development of PD and the onset of cognitive deterioration, discourse abilities should be quantitatively and repeatedly profiled in the disorder.

Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis

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.