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.

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. Eighty-nine manuscripts identified reported on 1541 patients, including spinocerebellar ataxias (SCA2, n = 421), SCA3 (n = 268), SCA6 (n = 117), other SCAs (n = 97), Friedreich ataxia (FRDA, n = 178), Niemann-Pick disease type C (NPC, n = 57), and ataxia-telangiectasia (n = 85) as largest cohorts. Whereas most studies reported discriminatory power of oculomotor assessments in diagnostics, few explored their value for monitoring genotype-specific disease progression (n = 2; SCA2) or treatment response (n = 8; SCA2, FRDA, NPC, ataxia-telangiectasia, episodic-ataxia 4). Oculomotor parameters correlated with disease severity measures including clinical scores (n = 18 studies (SARA: n = 9)), chronological measures (e.g., age, disease duration, time-to-symptom onset; n = 17), genetic stratification (n = 9), and imaging measures of atrophy (n = 5). Recurrent correlations across many ataxias (SCA2/3/17, FRDA, NPC) suggest saccadic eye movements as potentially generic quantitative oculomotor outcome. Recommendation of other paradigms was limited by the scarcity of cross-validating correlations, except saccadic intrusions (FRDA), pursuit eye movements (SCA17), and quantitative head-impulse testing (SCA3/6). This work aids in understanding the current knowledge of quantitative oculomotor parameters in hereditary ataxias, and identifies gaps for validation as potential trial outcome measures in specific ataxia genotypes.

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. One week of continuous wrist accelerometer data were collected from 31 individuals with ataxia-telangiectasia and 27 controls aged 2-20 years old. Longitudinal wrist sensor data were collected in 14 ataxia-telangiectasia participants and 13 controls. A novel algorithm was developed to extract wrist submovements from the velocity time series. Wrist sensor features were compared with caregiver-reported motor function on the Caregiver Priorities and Child Health Index of Life with Disabilities survey and ataxia severity on the neurologist-performed Brief Ataxia Rating Scale. Submovements became smaller, slower, and less variable in ataxia-telangiectasia compared to controls. High-frequency oscillations in submovements were increased, and more variable and low-frequency oscillations were decreased and less variable in ataxia-telangiectasia. Wrist movement features correlated strongly with ataxia severity and caregiver-reported function, demonstrated high reliability, and showed significant progression over a 1-year interval. These results show that passive wrist sensor data produces interpretable and reliable measures that are sensitive to disease change, supporting their potential as ecologically valid motor biomarkers. The ability to obtain these measures from a low-cost sensor that is ubiquitous in smartwatches could help facilitate neurological care and participation in research regardless of geography and socioeconomic status.

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.

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. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.

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. MEDLINE was searched for articles reporting on oculomotor/vestibular properties in ataxia patients and a study-tailored quality-assessment was performed. One-hundred-and-seventeen articles reporting on subjects with genetically confirmed (n=1134) or suspected hereditary ataxia (n=198), and degenerative ataxias with sporadic presentation (n=480) were included and subject to data extraction. Based on robust discrimination from controls, correlation with disease-severity, sensitivity to change, and feasibility in international multicenter settings as prerequisite for clinical trials, we prioritize a core-set of five eye-movement types: (i) pursuit eye movements, (ii) saccadic eye movements, (iii) fixation, (iv) eccentric gaze holding, and (v) rotational vestibulo-ocular reflex. We provide detailed guidelines for their acquisition, and recommendations on the quantitative parameters to extract. Limitations include low study quality, heterogeneity in patient populations, and lack of longitudinal studies. Standardization of quantitative oculomotor assessments will facilitate their implementation, interpretation, and validation in clinical trials, and ultimately advance our understanding of the evolution of oculomotor network dysfunction in hereditary ataxias.

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. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen's d = 1.48) and fewer high intensity movements (p < 0.001; Cohen's d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen's d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71-0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67-0.92; p < 0.01), demonstrated high reliability (r: 0.86-0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

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 ± 8.6 years) and 4 healthy controls (mean age 49.0 ± 16.4 years) were recruited through the Massachusetts General Hospital (MGH) Ataxia Center. Participants donned seven inertial sensors while performing two independent trials of gait and balance assessments from the Scale for the Assessment and Rating of Ataxia (SARA) and Brief Ataxia Rating Scale (BARS2). Univariate analysis was used to identify sensor-derived metrics from wearable sensors that discriminate motor function between the SCA and control groups. Multivariate linear regression models were used to estimate the subjective in-person SARA/BARS2 ratings. Spearman correlation coefficients were used to evaluate the performance of the model. RESULTS: Stride length variability, stride duration, cadence, stance phase, pelvis sway, and turn duration were different between SCA and controls (p < 0.05). Similarly, sway and sway velocity of the ankle, hip, and center of mass differentiated SCA and controls (p < 0.05). Using these features, linear regression models showed moderate-to-strong correlation with clinical scores from the in-person rater during SARA assessments of gait (r = 0.73, p = 0.003) and stance (r = 0.90, p < 0.001) and the BARS2 gait assessment (r = 0.74, p = 0.003). CONCLUSION: This study demonstrates that sensor-derived metrics can potentially be used to estimate the level of motor impairment in patient with SCA quickly and objectively. Thus, digital biomarkers from wearable sensors have the potential to be an integral tool for SCA clinical trials and care.

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. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson's disease, and to provide estimates of disease severity.

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. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants' assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland-Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia.

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. Patients with low neuropsychiatric scores had slower speech (22 words/min, P = 0.01), longer word duration (0.09 seconds/word, P = 0.01), and used fewer particles (0.85% fewer, P = 0.01). Patients with a history of overt HE had slower speech (23 words/min, P = 0.005) and longer word duration (0.09 seconds/word, P = 0.005). DISCUSSION: HE is associated with slower speech.

Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia.

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants' decomposed wrist velocity time-series. A machine learning regression model was trained to estimate overall ataxia severity, as measured by the Brief Ataxia Rating Scale (BARS). Classification models were trained to distinguish between ataxia participants and controls and between ataxia and parkinsonism phenotypes. Movement decomposition revealed expected and novel characteristics of the ataxia phenotype. The distance, speed, duration, morphology, and temporal relationships of decomposed movements exhibited strong relationships with disease severity. The regression model estimated BARS with a root mean square error of 3.6 points, r2 = 0.69, and moderate-to-excellent reliability. Classification models distinguished between ataxia participants and controls and ataxia and parkinsonism phenotypes with areas under the receiver-operating curve of 0.96 and 0.89, respectively. Movement decomposition captures core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments.

Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection.

BACKGROUND: Objective assessments of movement impairment are needed to support clinical trials and facilitate diagnosis. The objective of the current study was to determine if a rapid web-based computer mouse test (Hevelius) could detect and accurately measure ataxia and parkinsonism. METHODS: Ninety-five ataxia, 46 parkinsonism, and 29 control participants and 229,017 online participants completed Hevelius. We trained machine-learning models on age-normalized Hevelius features to (1) measure severity and disease progression and (2) distinguish phenotypes from controls and from each other. RESULTS: Regression model estimates correlated strongly with clinical scores (from r = 0.66 for UPDRS dominant arm total to r = 0.83 for the Brief Ataxia Rating Scale). A disease change model identified ataxia progression with high sensitivity. Classification models distinguished ataxia or parkinsonism from healthy controls with high sensitivity (≥0.91) and specificity (≥0.90). CONCLUSIONS: Hevelius produces a granular and accurate motor assessment in a few minutes of mouse use and may be useful as an outcome measure and screening tool. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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. We studied an Exploratory cohort (2002-2014) of 88 MSA-C and 78 other cerebellar ataxia patients, and a Validation cohort (2015-2021) of 49 MSA-C, 13 multiple system atrophy of the parkinsonian type (MSA-P), 99 other cerebellar ataxia patients and 314 non-ataxia patients. We measured anterior-posterior pons and middle cerebellar peduncle diameters on baseline and subsequent MRIs, and correlated results with Brief Ataxia Rating Scale scores. We assessed midbrain:pons and middle cerebellar peduncle:pons ratios over time. The normative anterior-posterior pons diameter was 23.6 ± 1.6 mm, and middle cerebellar peduncle diameter 16.4 ± 1.4 mm. Pons diameter correlated with volume, r = 0.94, P < 0.0001. The anterior-posterior pons and middle cerebellar peduncle measures were smaller at first scan in MSA-C compared to all other ataxias; anterior-posterior pons diameter: Exploratory, 19.3 ± 2.6 mm versus 20.7 ± 2.6 mm, Validation, 19.9 ± 2.1 mm versus 21.1 ± 2.1 mm; middle cerebellar peduncle transverse diameter, Exploratory, 12.0 ± 2.6 mm versus 14.3 ±2.1 mm, Validation, 13.6 ± 2.1 mm versus 15.1 ± 1.8 mm, all P < 0.001. The anterior-posterior pons and middle cerebellar peduncle rates of change were faster in MSA-C than in all other ataxias; anterior-posterior pons diameter rates of change: Exploratory, -0.87 ± 0.04 mm/year versus -0.09 ± 0.02 mm/year, Validation, -0.89 ± 0.48 mm/year versus -0.10 ± 0.21 mm/year; middle cerebellar peduncle transverse diameter rates of change: Exploratory, -0.84 ± 0.05 mm/year versus -0.08 ± 0.02 mm/year, Validation, -0.94 ± 0.64 mm/year versus -0.11 ± 0.27 mm/year, all values P < 0.0001. Anterior-posterior pons and middle cerebellar peduncle diameters were indistinguishable between Possible, Probable and Definite MSA-C. The rate of anterior-posterior pons atrophy was linear, correlating with ataxia severity. Using a lower threshold anterior-posterior pons diameter decrease of -0.4 mm/year to balance sensitivity and specificity, area under the curve analysis discriminating MSA-C from other ataxias was 0.94, yielding sensitivity 0.92 and specificity 0.87. For the middle cerebellar peduncle, with threshold decline -0.5 mm/year, area under the curve was 0.90 yielding sensitivity 0.85 and specificity 0.79. The midbrain:pons ratio increased progressively in MSA-C, whereas the middle cerebellar peduncle:pons ratio was almost unchanged. Anterior-posterior pons and middle cerebellar peduncle diameters were smaller in MSA-C than in MSA-P, P < 0.001. We conclude from this 20-year longitudinal clinical and imaging study that anterior-posterior pons and middle cerebellar peduncle diameters are phenotypic imaging biomarkers of MSA-C. In the correct clinical context, an anterior-posterior pons and transverse middle cerebellar peduncle diameter decline of ∼0.8 mm/year is sufficient for and diagnostic of MSA-C.

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. Interclass correlation coefficients (ICC) and Bland-Altman plots were used to assess agreement between staff-administered and self-reported ALSFRS-R pairs. Longitudinal change was evaluated using ANCOVA models and linear mixed models, including impact of mood and time of day. Impact of frequency of administration of the ALSFRS-RSE on precision of the estimate slope was tested using a mixed effects model. Results: In our ALS cohort, digital assessments were well-accepted and adherence was robust, with completion rates of 86%. There was excellent agreement between the digital self-entry and staff-administered scores computing multiple ICCs (ICC range = 0.925-0.961), with scores on the ALSFRS-RSE slightly higher (1.304 points). Digital assessments were associated with increased precision of the slope, resulting in higher standardized response mean estimates for higher frequencies, though benefit appeared to diminish at biweekly and weekly frequency. Effects of participant mood and time of day on total ALSFRS-RSE score were evaluated but were minimal and not statistically significant. Conclusion: Remote collection of digital patient-reported outcomes of functional status such as the ALSFRS-RSE yield more accurate estimates of change over time and provide a broader understanding of the lived experience of people with ALS.

At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (-0.86 ± 0.70 SD/year versus -0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.

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.