Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach.

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.

Noise-dependent bias in quantitative STEM-EMCD experiments revealed by bootstrapping.

Electron magnetic circular dichroism (EMCD) is a powerful technique for estimating element-specific magnetic moments of materials on nanoscale with the potential to reach atomic resolution in transmission electron microscopes. However, the fundamentally weak EMCD signal strength complicates quantification of magnetic moments, as this requires very high precision, especially in the denominator of the sum rules. Here, we employ a statistical resampling technique known as bootstrapping to an experimental EMCD dataset to produce an empirical estimate of the noise-dependent error distribution resulting from application of EMCD sum rules to bcc iron in a 3-beam orientation. We observe clear experimental evidence that noisy EMCD signals preferentially bias the estimation of magnetic moments, further supporting this with error distributions produced by Monte-Carlo simulations. Finally, we propose guidelines for the recognition and minimization of this bias in the estimation of magnetic moments.

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.

Essential and dystonic head tremor: More similarities than differences.

INTRODUCTION: Head tremor is a common symptom of essential tremor (ET) and cervical dystonia (CD). In clinical practice, it is often difficult to distinguish between these two conditions, especially in cases where head tremor predominates. OBJECTIVES: To investigate which clinical and instrumental methods best differentiate ET and CD in patients with head tremor. METHODS: 65 patients were included, of which 23 were diagnosed with ET and head tremor (HT+), 21 with ET without head tremor (HT-) and 21 with CD and dystonic head tremor. 22 healthy volunteers served as controls. All patients were examined using the rating scales for ET (TETRAS), cervical dystonia (TWSTRS), and ataxia (SARA). The Somatosensory Temporal Discrimination Threshold (STDT) was defined as the shortest interval in which an individual recognizes two tactile stimuli as temporally separated. RESULTS: TETRAS and SARA scores were higher in the HT+ group compared with HT- and CD, with no significant difference between mild head tremor subscores in HT+ and CD. In most HT+ and CD patients, head tremor disappeared supine. The STDT values were significantly higher in the HT+ group compared with controls. CONCLUSION: While TWSTRS contributed to assess dystonia severity, the scales of tremor and ataxia were not helpful in differentiating head tremor syndromes. The cessation of head tremor in the supine position could be related to the overall mild head tremor scores in both groups. Increased SARA scores and STDT values in HT+ patients suggest a possible role of cerebellar involvement and altered somatosensory timing that merit further verification.

Automated Vowel Articulation Analysis in Connected Speech Among Progressive Neurological Diseases, Dysarthria Types, and Dysarthria Severities.

PURPOSE: Although articulatory impairment represents distinct speech characteristics in most neurological diseases affecting movement, methods allowing automated assessments of articulation deficits from the connected speech are scarce. This study aimed to design a fully automated method for analyzing dysarthria-related vowel articulation impairment and estimate its sensitivity in a broad range of neurological diseases and various types and severities of dysarthria. METHOD: Unconstrained monologue and reading passages were acquired from 459 speakers, including 306 healthy controls and 153 neurological patients. The algorithm utilized a formant tracker in combination with a phoneme recognizer and subsequent signal processing analysis. RESULTS: Articulatory undershoot of vowels was presented in a broad spectrum of progressive neurodegenerative diseases, including Parkinson's disease, progressive supranuclear palsy, multiple-system atrophy, Huntington's disease, essential tremor, cerebellar ataxia, multiple sclerosis, and amyotrophic lateral sclerosis, as well as in related dysarthria subtypes including hypokinetic, hyperkinetic, ataxic, spastic, flaccid, and their mixed variants. Formant ratios showed a higher sensitivity to vowel deficits than vowel space area. First formants of corner vowels were significantly lower for multiple-system atrophy than cerebellar ataxia. Second formants of vowels /a/ and /i/ were lower in ataxic compared to spastic dysarthria. Discriminant analysis showed a classification score of up to 41.0% for disease type, 39.3% for dysarthria type, and 49.2% for dysarthria severity. Algorithm accuracy reached an F-score of 0.77. CONCLUSIONS: Distinctive vowel articulation alterations reflect underlying pathophysiology in neurological diseases. Objective acoustic analysis of vowel articulation has the potential to provide a universal method to screen motor speech disorders. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.23681529.

Single scan STEM-EMCD in 3-beam orientation using a quadruple aperture.

The need to acquire multiple angle-resolved electron energy loss spectra (EELS) is one of the several critical challenges associated with electron magnetic circular dichroism (EMCD) experiments. If the experiments are performed by scanning a nanometer to atomic-sized electron probe on a specific region of a sample, the precision of the local magnetic information extracted from such data highly depends on the accuracy of the spatial registration between multiple scans. For an EMCD experiment in a 3-beam orientation, this means that the same specimen area must be scanned four times while keeping all the experimental conditions same. This is a non-trivial task as there is a high chance of morphological and chemical modification as well as non-systematic local orientation variations of the crystal between the different scans due to beam damage, contamination and spatial drift. In this work, we employ a custom-made quadruple aperture to acquire the four EELS spectra needed for the EMCD analysis in a single electron beam scan, thus removing the above-mentioned complexities. We demonstrate a quantitative EMCD result for a beam convergence angle corresponding to sub-nm probe size and compare the EMCD results for different detector geometries.

The automated screening of speech motor development in children based on the sequential motion rate.

BACKGROUND: Motor skills in children have traditionally been examined via challenging speech tasks such as syllable repetition, and calculating the syllabic rate using a stopwatch or by inspecting the oscillogram followed by a laborious comparison of the scores on a look-up table representing the typical performances of children of the given age and sex. As the commonly used performance tables are over-simplified to allow for manual scoring, we raise the question of whether a computational model of motor skills development could be more informative, and could allow for the automated screening of children to detect underdeveloped motor skills. METHODS: We recruited a total of 275 children aged four to 15 years. All the participants were native Czech speakers with no history of hearing or neurological impairments. We recorded each child's performance of/pa/-/ta/-/ka/syllable repetition. Various parameters of diadochokinesis (DDK; DDK rate, DDK regularity, voice onset time [VOT] ratio, syllable, vowel and VOT duration) were investigated in the acoustic signals using supervised reference labels. Female and male participants were analyzed separately by comparing younger, middle, and older age groups of children via ANOVA. Finally, we implemented a fully automated model that estimated the developmental age of a child based on the acoustic signal, and evaluated its accuracy using Pearson's correlation coefficient and normalized root-mean-squared errors (RMSEs). RESULTS: The DDK rate reflected the ages of the children proportionally (p < 0.001). Other DDK parameters also showed strong sensitivity to age (p < 0.001), with the exception of VOT duration, which had a smaller effect (p = 0.091). The effect of age was found to be sex specific for the syllable length (p < 0.001) and DDK rate (p = 0.003). We observed that females spoke more slowly and had a longer VOT at preschool age (p < 0.001). The DDK rate obtained via the automated algorithm was strongly correlated with the reference (p < 0.001, Pearson's correlation coefficient of 0.97), with a low normalized RMSE of 3.77%. CONCLUSIONS: As children develop their motor skills, they are capable of shortening the vowels to increase the rate of syllabic repetitions. The nonlinear development in childhood and adolescence, with a steady state in adulthood, follows a logistic function for the DDK rate. This study demonstrates that the development of motor skills can be examined sensitively and more appropriately by a fully automated noninvasive procedure that also accounts for the dispersion of values within age brackets.

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.

Acute stimulation with symmetric biphasic pulses induces less ataxia compared to cathodic pulses in DBS for essential tremor.

BACKGROUND: Symmetric biphasic pulses have been shown to acutely increase the therapeutic window of ventralis intermedius deep brain stimulation (Vim-DBS) for essential tremor (ET) compared to cathodic pulses. Acute supratherapeutic stimulation can induce ataxic side effects in Vim-DBS. OBJECTIVE: To investigate the effect on tremor, ataxia and dysarthria of 3 h of biphasic stimulation in patients with DBS for ET. METHODS: A randomized, doubled-blind, cross-over design was used to compare standard cathodic pulses with symmetric biphasic pulses (anode-first) during a 3-h period per pulse shape. During each 3-h period, all stimulation parameters were identical, except for the pulse shape. Tremor (Fahn-Tolosa-Marin Tremor Rating Scale), ataxia (International Cooperative Ataxia Rating Scale) and speech (acoustic and perceptual measures) were assessed hourly during the 3-h periods. RESULTS: Twelve ET patients were included. During the 3-h stimulation period, tremor control was equivalent between the two pulse shapes. Biphasic pulses elicited significantly less ataxia than cathodic pulses (p = 0.006). Diadochokinesis rate of speech was better for the biphasic pulse (p = 0.048), but other measures for dysarthria were not significantly different between the pulses. CONCLUSION: Symmetric biphasic pulses induce less ataxia than conventional pulses after 3 h of stimulation DBS in ET patients.

Speech and gait abnormalities in motor subtypes of de-novo Parkinson's disease.

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.

Quantum-well states at the surface of a heavy-fermion superconductor.

Two-dimensional electronic states at surfaces are often observed in simple wide-band metals such as Cu or Ag (refs. 1-4). Confinement by closed geometries at the nanometre scale, such as surface terraces, leads to quantized energy levels formed from the surface band, in stark contrast to the continuous energy dependence of bulk electron bands2,5-10. Their energy-level separation is typically hundreds of meV (refs. 3,6,11). In a distinct class of materials, strong electronic correlations lead to so-called heavy fermions with a strongly reduced bandwidth and exotic bulk ground states12,13. Quantum-well states in two-dimensional heavy fermions (2DHFs) remain, however, notoriously difficult to observe because of their tiny energy separation. Here we use millikelvin scanning tunnelling microscopy (STM) to study atomically flat terraces on U-terminated surfaces of the heavy-fermion superconductor URu2Si2, which exhibits a mysterious hidden-order (HO) state below 17.5 K (ref. 14). We observe 2DHFs made of 5f electrons with an effective mass 17 times the free electron mass. The 2DHFs form quantized states separated by a fraction of a meV and their level width is set by the interaction with correlated bulk states. Edge states on steps between terraces appear along one of the two in-plane directions, suggesting electronic symmetry breaking at the surface. Our results propose a new route to realize quantum-well states in strongly correlated quantum materials and to explore how these connect to the electronic environment.

Simulations of magnetic Bragg scattering in transmission electron microscopy.

We have simulated the magnetic Bragg scattering in transmission electron microscopy in two antiferromagnetic compounds, NiO and LaMnAsO. This weak magnetic phenomenon was experimentally observed in NiO by Loudon (2012). We have computationally reproduced Loudon's experimental data, and for comparison we have performed calculations for the LaMnAsO compound as a more challenging case, containing lower concentration of magnetic elements and strongly scattering heavier non-magnetic elements. We have also described thickness and voltage dependence of the intensity of the antiferromagnetic Bragg spot for both compounds. We have considered lattice vibrations within two computational approaches, one assuming a static lattice with Debye-Waller smeared potentials, and another explicitly considering the atomic vibrations within the quantum excitations of phonons model (thermal diffuse scattering). The structural analysis shows that the antiferromagnetic Bragg spot appears in between (111) and (000) reflections for NiO, while for LaMnAsO the antiferromagnetic Bragg spot appears at the position of the (010) reflection in the diffraction pattern, which corresponds to a forbidden reflection of the crystal structure. Calculations predict that the intensity of the magnetic Bragg spot in NiO is significantly stronger than thermal diffuse scattering at room temperature. For LaMnAsO, the magnetic Bragg spot is weaker than the room-temperature thermal diffuse scattering, but its detection can be facilitated at reduced temperatures.

Speech biomarkers in Huntington's disease: A cross-sectional study in pre-symptomatic, prodromal and early manifest stages.

BACKGROUND AND PURPOSE: Motor speech alterations are a prominent feature of clinically manifest Huntington's disease (HD). Objective acoustic analysis of speech can quantify speech alterations. It is currently unknown, however, at what stage of HD speech alterations can be reliably detected. We aimed to explore the patterns and extent of speech alterations using objective acoustic analysis in HD and to assess correlations with both rater-assessed phenotypical features and biological determinants of HD. METHODS: Speech samples were acquired from 44 premanifest (29 pre-symptomatic and 15 prodromal) and 25 manifest HD gene expansion carriers, and 25 matched healthy controls. A quantitative automated acoustic analysis of 10 speech dimensions was performed. RESULTS: Automated speech analysis allowed us to differentiate between participants with HD and controls, with areas under the curve of 0.74 for pre-symptomatic, 0.92 for prodromal, and 0.97 for manifest stages. In addition to irregular alternating motion rates and prolonged pauses seen only in manifest HD, both prodromal and manifest HD displayed slowed articulation rate, slowed alternating motion rates, increased loudness variability, and unstable steady-state position of articulators. In participants with premanifest HD, speech alteration severity was associated with cognitive slowing (r = -0.52, p < 0.001) and the extent of bradykinesia (r = 0.43, p = 0.004). Speech alterations correlated with a measure of exposure to mutant gene products (CAG-age-product score; r = 0.60, p < 0.001). CONCLUSION: Speech abnormalities in HD are associated with other motor and cognitive deficits and are measurable already in premanifest stages of HD. Therefore, automated speech analysis might represent a quantitative HD biomarker with potential for assessing disease progression.

Long-Term Averaged Spectrum Descriptors of Dysarthria in Patients With Parkinson's Disease Treated With Subthalamic Nucleus Deep Brain Stimulation.

PURPOSE: This study aimed to evaluate whether long-term averaged spectrum (LTAS) descriptors for reading and monologue are suitable to detect worsening of dysarthria in patients with Parkinson's disease (PD) treated with subthalamic nucleus deep brain stimulation (STN-DBS) with potential effect of ON and OFF stimulation conditions and types of connected speech. METHOD: Four spectral moments based on LTAS were computed for monologue and reading passage collected from 23 individuals with PD treated with bilateral STN-DBS and 23 age- and gender-matched healthy controls. Speech performance of patients with PD was compared in ON and OFF STN-DBS conditions. RESULTS: All LTAS spectral moments including mean, standard deviation, skewness, and kurtosis across both monologue and reading passage were able to significantly distinguish between patients with PD in both stimulation conditions and control speakers. The spectral mean was the only LTAS measure sensitive to capture better speech performance in STN-DBS ON, as compared to the STN-DBS OFF stimulation condition (p < .05). Standardized reading passage was more sensitive compared to monologue in detecting dysarthria severity via LTAS descriptors with an area under the curve of up to 0.92 obtained between PD and control groups. CONCLUSIONS: Our findings confirmed that LTAS is a suitable approach to objectively describe changes in speech impairment severity due to STN-DBS therapy in patients with PD. We envisage these results as an important step toward a continuum development of technological solutions for the automated assessment of stimulation-induced dysarthria. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21644798.

Articulatory undershoot of vowels in isolated REM sleep behavior disorder and early Parkinson's disease.

Imprecise vowels represent a common deficit associated with hypokinetic dysarthria resulting from a reduced articulatory range of motion in Parkinson's disease (PD). It is not yet unknown whether the vowel articulation impairment is already evident in the prodromal stages of synucleinopathy. We aimed to assess whether vowel articulation abnormalities are present in isolated rapid eye movement sleep behaviour disorder (iRBD) and early-stage PD. A total of 180 male participants, including 60 iRBD, 60 de-novo PD and 60 age-matched healthy controls performed reading of a standardized passage. The first and second formant frequencies of the corner vowels /a/, /i/, and /u/ extracted from predefined words, were utilized to construct articulatory-acoustic measures of Vowel Space Area (VSA) and Vowel Articulation Index (VAI). Compared to controls, VSA was smaller in both iRBD (p = 0.01) and PD (p = 0.001) while VAI was lower only in PD (p = 0.002). iRBD subgroup with abnormal olfactory function had smaller VSA compared to iRBD subgroup with preserved olfactory function (p = 0.02). In PD patients, the extent of bradykinesia and rigidity correlated with VSA (r = -0.33, p = 0.01), while no correlation between axial gait symptoms or tremor and vowel articulation was detected. Vowel articulation impairment represents an early prodromal symptom in the disease process of synucleinopathy. Acoustic assessment of vowel articulation may provide a surrogate marker of synucleinopathy in scenarios where a single robust feature to monitor the dysarthria progression is needed.

Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy.

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.

Linguistic Abnormalities in Isolated Rapid Eye Movement Sleep Behavior Disorder.

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.