Features of speech and swallowing dysfunction in pre-ataxic spinocerebellar ataxia type 2

Quantitative Speech Assessment in Ataxia-Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e. pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials.

COVID-19 Impacts the Mental Health and Speech Function in Spinocerebellar Ataxia Type 2: Evidences from a Follow-Up Study

Limited evidence suggests that the SARS-CoV-2 infection can accelerate the progression of neurodegenerative diseases, but this has been not verified in the spinocerebellar ataxias (SCA). The objective of this study is to assess the impact of COVID-19 on the mental health and motor features of SCA2. A follow-up study was carried out in 170 Cuban SCA2 subjects and 87 community controls between 2020 and 2021. All subjects underwent a structured questionnaire to assess the risks of exposure to COVID-19, the confirmation of COVID-19 diagnosis, and the Hospital Anxiety and Depression Scale (HADS). Moreover, 36 subjects underwent the Scale for the Assessment and Rating of ataxia (SARA). The risk of exposure to SARS-CoV-2 and the frequency of COVID-19 were similar between the ataxia cohort and the community controls. Within the ataxia group, significantly increased HADS scores existed at the 2nd visit in both groups, but this increase was more evident for the infected group regarding the depression score. Moreover, a significant within-group increase of SARA score was observed in the infected group but not the non-infected group, which was mainly mediated by the significant increase of the speech item score in the infected group. Similar results were observed within the subgroup of preclinical carriers. Our study identified no selective vulnerability nor protection to COVID-19 in SCA2, but once infected, the patients experienced a deterioration of mental health and speech function, even at preclinical disease stage. These findings set rationales for tele-health approaches that minimize the detrimental effect of COVID-19 on SCA2 progression and identify SCA2 individuals as clinical model to elucidate the link between SARS-CoV-2 infection and neurodegeneration.

Expanding the phenotype of Kleefstra syndrome: speech, language and cognition in 103 individuals

OBJECTIVES

Speech and language impairments are core features of the neurodevelopmental genetic condition Kleefstra syndrome. Communication has not been systematically examined to guide intervention recommendations. We define the speech, language and cognitive phenotypic spectrum in a large cohort of individuals with Kleefstra syndrome.

METHOD

103 individuals with Kleefstra syndrome (40 males, median age 9.5 years, range 1-43 years) with pathogenic variants (52 9q34.3 deletions, 50 intragenic variants, 1 balanced translocation) were included. Speech, language and non-verbal communication were assessed. Cognitive, health and neurodevelopmental data were obtained.

RESULTS

The cognitive spectrum ranged from average intelligence (12/79, 15%) to severe intellectual disability (12/79, 15%). Language ability also ranged from average intelligence (10/90, 11%) to severe intellectual disability (53/90, 59%). Speech disorders occurred in 48/49 (98%) verbal individuals and even occurred alongside average language and cognition. Developmental regression occurred in 11/80 (14%) individuals across motor, language and psychosocial domains. Communication aids, such as sign and speech-generating devices, were crucial for 61/103 (59%) individuals including those who were minimally verbal, had a speech disorder or following regression.

CONCLUSIONS

The speech, language and cognitive profile of Kleefstra syndrome is broad, ranging from severe impairment to average ability. Genotype and age do not explain the phenotypic variability. Early access to communication aids may improve communication and quality of life.

SpEx: a German-language dataset of speech and executive function performance

This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.

Uncertainty of Vowel Predictions as a Digital Biomarker for Ataxic Dysarthria

Dysarthria is a common manifestation across cerebellar ataxias leading to impairments in communication, reduced social connections, and decreased quality of life. While dysarthria symptoms may be present in other neurological conditions, ataxic dysarthria is a perceptually distinct motor speech disorder, with the most prominent characteristics being articulation and prosody abnormalities along with distorted vowels. We hypothesized that uncertainty of vowel predictions by an automatic speech recognition system can capture speech changes present in cerebellar ataxia. Speech of participants with ataxia (N=61) and healthy controls (N=25) was recorded during the "picture description" task. Additionally, participants' dysarthric speech and ataxia severity were assessed on a Brief Ataxia Rating Scale (BARS). Eight participants with ataxia had speech and BARS data at two timepoints. A neural network trained for phoneme prediction was applied to speech recordings. Average entropy of vowel tokens predictions (AVE) was computed for each participant's recording, together with mean pitch and intensity standard deviations (MPSD and MISD) in the vowel segments. AVE and MISD demonstrated associations with BARS speech score (Spearman's rho=0.45 and 0.51), and AVE demonstrated associations with BARS total (rho=0.39). In the longitudinal cohort, Wilcoxon pairwise signed rank test demonstrated an increase in BARS total and AVE, while BARS speech and acoustic measures did not significantly increase. Relationship of AVE to both BARS speech and BARS total, as well as the ability to capture disease progression even in absence of measured speech decline, indicates the potential of AVE as a digital biomarker for cerebellar ataxia.

Characterization of central manifestations in patients with Niemann-Pick disease type C

PURPOSE

Niemann-Pick disease type C (NPC) is a rare lysosomal storage disease characterized by progressive neurodegeneration and neuropsychiatric symptoms. This study investigated pathophysiological mechanisms underlying motor deficits, particularly speech production, and cognitive impairment.

METHODS

We prospectively phenotyped 8 adults with NPC and age-sex-matched healthy controls using a comprehensive assessment battery, encompassing clinical presentation, plasma biomarkers, hand-motor skills, speech production, cognitive tasks, and (micro-)structural and functional central nervous system properties through magnetic resonance imaging.

RESULTS

Patients with NPC demonstrated deficits in fine-motor skills, speech production timing and coordination, and cognitive performance. Magnetic resonance imaging revealed reduced cortical thickness and volume in cerebellar subdivisions (lobule VI and crus I), cortical (frontal, temporal, and cingulate gyri) and subcortical (thalamus and basal ganglia) regions, and increased choroid plexus volumes in NPC. White matter fractional anisotropy was reduced in specific pathways (intracerebellar input and Purkinje tracts), whereas diffusion tensor imaging graph theory analysis identified altered structural connectivity. Patients with NPC exhibited altered activity in sensorimotor and cognitive processing hubs during resting-state and speech production. Canonical component analysis highlighted the role of cerebellar-cerebral circuitry in NPC and its integration with behavioral performance and disease severity.

CONCLUSION

This deep phenotyping approach offers a comprehensive systems neuroscience understanding of NPC motor and cognitive impairments, identifying potential central nervous system biomarkers.

Perceptual and Acoustic Analysis of Speech in Spinocerebellar ataxia Type 1

This study characterizes the speech phenotype of spinocerebellar ataxia type 1 (SCA1) using both perceptual and objective acoustic analysis of speech in a cohort of SCA1 patients. Twenty-seven symptomatic SCA1 patients in various disease stages (SARA score range: 3-32 points) and 18 sex and age matched healthy controls underwent a clinical assessment addressing ataxia severity, non-ataxia signs, cognitive functioning, and speech. Speech samples were perceptually rated by trained speech therapists, and acoustic metrics representing speech timing, vocal control, and voice quality were extracted. Perceptual analysis revealed reduced intelligibility and naturalness in speech samples of SCA1 patients. Acoustically, SCA1 patients presented with slower speech rate and diadochokinetic rate as well as longer syllable duration compared to healthy controls. No distinct abnormalities in voice quality in the acoustic analysis were detected at group level. Both the affected perceptual and acoustic variables correlated with ataxia severity. Longitudinal assessment of speech is needed to place changes in speech in the context of disease progression and potential response to treatment.

Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics

Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjected 74 spectral and temporal prosodic features from the speech recordings to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

Sensitive quantification of cerebellar speech abnormalities using deep learning models

Objective

Objective, sensitive, and meaningful disease assessments are critical to support clinical trials and clinical care. Speech changes are one of the earliest and most evident manifestations of cerebellar ataxias. The purpose of this work is to develop models that can accurately identify and quantify these abnormalities.

Methods

We use deep learning models such as ResNet 18 , that take the time and frequency partial derivatives of the log-mel spectrogram representations of speech as input, to learn representations that capture the motor speech phenotype of cerebellar ataxia. We train classification models to separate patients with ataxia from healthy controls as well as regression models to estimate disease severity.

Results

Our model was able to accurately distinguish healthy controls from individuals with ataxia, including ataxia participants with no detectable clinical deficits in speech. Furthermore the regression models produced accurate estimates of disease severity, were able to measure subclinical signs of ataxia, and captured disease progression over time in individuals with ataxia.

Conclusion

Deep learning models, trained on time and frequency partial derivatives of the speech signal, can detect sub-clinical speech changes in ataxias and sensitively measure disease change over time.

Significance

Such models have the potential to assist with early detection of ataxia and to provide sensitive and low-burden assessment tools in support of clinical trials and neurological care.

SARAspeech-Feasibility of automated assessment of ataxic speech disturbance

Ataxias are a group of movement disorders that are characterized by progressive loss of balance, impaired coordination and speech disturbance, which together lead to markedly reduced quality of life. Speech disturbance is clinically diagnosed, but methods for objective assessment of severity are lacking. Using 71 sets of speech recordings from ataxia patients, we developed an automated classification system. With a tolerance of ±1 point, this classification system correctly predicted experts' ratings of speech disturbance according to item 4 of the Scale for Assessment and rating of ataxia (SARA) in 80% of cases. We thereby demonstrate feasibility of computer-assisted voice analysis for automated assessment of severity of speech disturbance.

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS.

Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches

Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric and hypometric saccades) is a common finding in individuals with cerebellar ataxia. In this study, we evaluated a scalable approach for detecting and quantifying oculomotor dysmetria. Eye movement data were extracted from iPhone video recordings of the horizontal saccade task (a standard clinical task in ataxia) and combined with signal processing and machine learning approaches to quantify saccade abnormalities. Entropy-based measures of eye movements during saccades were significantly different in 72 individuals with ataxia with dysmetria compared with 80 ataxia and Parkinson's participants without dysmetria. A template matching-based analysis demonstrated that saccadic eye movements in patients without dysmetria were more similar to the ideal template of saccades. A support vector machine was then used to train and test the ability of multiple signal processing features in combination to distinguish individuals with and without oculomotor dysmetria. The model achieved 78% accuracy (sensitivity= 80% and specificity= 76%). These results show that the combination of signal processing and machine learning approaches applied to iPhone video of saccades, allow for extraction of information pertaining to oculomotor dysmetria in ataxia. Overall, this inexpensive and scalable approach for capturing important oculomotor information may be a useful component of a screening tool for ataxia and could allow frequent at-home assessments of oculomotor function in natural history studies and clinical trials.

Rehabilitation in patients with cerebellar ataxias

ABSTRACT Cerebellar ataxias comprise a heterogeneous group of diseases characterized by motor and non-motor symptoms, which can be acquired, degenerative, or have a genetic cause, such as spinocerebellar ataxias (SCA). Usually, the genetic and neurodegenerative forms of cerebellar ataxias present a progressive and inevitable worsening of the clinical picture so that rehabilitation treatment is fundamental. Rehabilitation treatment includes physical therapy, respiratory therapy, speech, voice and swallowing therapy, occupational therapy, and new technologies, such as the use of exergames. The current treatment of patients with cerebellar ataxias, especially neurodegenerative forms, genetic or not, should include these different forms of rehabilitation, with the main objective of improving the quality of life of patients.

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

PURPOSE

The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis.

METHOD

Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate.

RESULTS

Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions.

DISCUSSION

A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Ataxin-2 gene: a powerful modulator of neurological disorders

PURPOSE OF REVIEW

To provide an update on the role of Ataxin-2 gene (ATXN2) in health and neurological diseases.

RECENT FINDINGS

There is a growing complexity emerging on the role of ATXN2 and its variants in association with SCA2 and several other neurological diseases. Polymorphisms and intermediate alleles in ATXN2 establish this gene as a powerful modulator of neurological diseases including lethal neurodegenerative conditions such as motor neuron disease, spinocerebellar ataxia 3 (SCA3), and peripheral nerve disease such as familial amyloidosis polyneuropathy. This role is in fact far wider than the previously described for polymorphism in the prion protein (PRNP) gene. Positive data from antisense oligo therapy in a murine model of SCA2 suggest that similar approaches may be feasible in humans SCA2 patients.

SUMMARY

ATXN2 is one of the few genes where a single gene causes several diseases and/or modifies several and disparate neurological disorders. Hence, understanding mutagenesis, genetic variants, and biological functions will help managing SCA2, and several human diseases connected with dysfunctional pathways in the brain, innate immunity, autophagy, cellular, lipid, and RNA metabolism.