Sensitive Quantification of Cerebellar Speech Abnormalities Using Deep Learning Models.

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. This work aims to develop models that can accurately identify and quantify clinical signs of ataxic speech.

At-home wearables and machine learning capture motor impairment and progression in adult ataxias.

A significant barrier to developing disease-modifying therapies for spinocerebellar ataxias (SCAs) and multiple system atrophy of the cerebellar type (MSA-C) is the scarcity of tools to sensitively measure disease progression in clinical trials. Wearable sensors worn continuously during natural behavior at home have the potential to produce ecologically valid and precise measures of motor function by leveraging frequent and numerous high-resolution samples of behavior. Here we test whether movement-building block characteristics (i.

Longitudinal comparison of the self-administered ALSFRS-RSE and ALSFRS-R as functional outcome measures in ALS.

Objective: Test the feasibility, adherence rates and optimal frequency of digital, remote assessments using the ALSFRS-RSE via a customized smartphone-based app.

Methods: This fully remote, longitudinal study was conducted over a 24-week period, with virtual visits every 3 months and weekly digital assessments. 19 ALS participants completed digital assessments via smartphone, including a digital version of the ALSFRS-RSE and mood survey.

Rates of change of pons and middle cerebellar peduncle diameters are diagnostic of multiple system atrophy of the cerebellar type.

Definitive diagnosis of multiple system atrophy of the cerebellar type (MSA-C) is challenging. We hypothesized that rates of change of pons and middle cerebellar peduncle diameters on MRI would be unique to MSA-C and serve as diagnostic biomarkers. We defined the normative data for anterior-posterior pons and transverse middle cerebellar peduncle diameters on brain MRI in healthy controls, performed diameter-volume correlations and measured intra- and inter-rater reliability.

Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers.

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains.

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.

Quantitative Oculomotor Assessment in Hereditary Ataxia: Systematic Review and Consensus by the Ataxia Global Initiative Working Group on Digital-motor Biomarkers.

Oculomotor deficits are common in hereditary ataxia, but disproportionally neglected in clinical ataxia scales and as outcome measures for interventional trials. Quantitative assessment of oculomotor function has become increasingly available and thus applicable in multicenter trials and offers the opportunity to capture severity and progression of oculomotor impairment in a sensitive and reliable manner. In this consensus paper of the Ataxia Global Initiative Working Group On Digital Oculomotor Biomarkers, based on a systematic literature review, we propose harmonized methodology and measurement parameters for the quantitative assessment of oculomotor function in natural-history studies and clinical trials in hereditary ataxia.

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.

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.

Real-life ankle submovements and computer mouse use reflect patient-reported function in adult ataxias.

Novel disease-modifying therapies are being evaluated in spinocerebellar ataxias and multiple system atrophy. Clinician-performed disease rating scales are relatively insensitive for measuring disease change over time, resulting in large and long clinical trials. We tested the hypothesis that sensors worn continuously at home during natural behaviour and a web-based computer mouse task performed at home could produce interpretable, meaningful and reliable motor measures for potential use in clinical trials.

Quantitative Oculomotor Assessment in Hereditary Ataxia: Discriminatory Power, Correlation with Severity Measures, and Recommended Parameters for Specific Genotypes.

Characterizing bedside oculomotor deficits is a critical factor in defining the clinical presentation of hereditary ataxias. Quantitative assessments are increasingly available and have significant advantages, including comparability over time, reduced examiner dependency, and sensitivity to subtle changes. To delineate the potential of quantitative oculomotor assessments as digital-motor outcome measures for clinical trials in ataxia, we searched MEDLINE for articles reporting on quantitative eye movement recordings in genetically confirmed or suspected hereditary ataxias, asking which paradigms are most promising for capturing disease progression and treatment response.

Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches.

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement.

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.

Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors.

The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient's wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores.

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos.

Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to distinguish participants with Ataxia ( = 169) from participants with parkinsonism ( = 78) and from controls ( = 58), and predict their upper extremity and overall disease severity. Features were extracted from the time series representing the distance between the index and thumb and its derivatives.

Real-life Wrist Movement Patterns Capture Motor Impairment in Individuals with Ataxia-Telangiectasia.

Sensitive motor outcome measures are needed to efficiently evaluate novel therapies for neurodegenerative diseases. Devices that can passively collect movement data in the home setting can provide continuous and ecologically valid measures of motor function. We tested the hypothesis that movement patterns extracted from continuous wrist accelerometer data capture motor impairment and disease progression in ataxia-telangiectasia.

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.

Assessment of gait and balance impairment in people with spinocerebellar ataxia using wearable sensors.

Objective: To explore the use of wearable sensors for objective measurement of motor impairment in spinocerebellar ataxia (SCA) patients during clinical assessments of gait and balance.

Methods: In total, 14 patients with genetically confirmed SCA (mean age 61.6 ± 8.

Hepatic Encephalopathy is Associated With Slow Speech on Objective Assessment.

Introduction: There are no available low-burden, point-of-care tests to diagnose, grade, and predict hepatic encephalopathy (HE).

Methods: We evaluated speech as a biomarker of HE in 76 English-speaking adults with cirrhosis.

Results: Three speech features significantly correlated with the following neuropsychiatric scores: speech rate, word duration, and use of particles.

Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.

With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home.