Abstract 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.
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 (N = 169) from participants with parkinsonism (N = 78) and from controls (N = 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. Classification models in ataxia archived areas under the receiver-operating curve of around 0.91, and regression models estimating disease severity obtained correlation coefficients around r = 0.64. Classification and prediction model coefficients were examined and they not only were in accordance, but were in line with clinical observations of ataxia phenotypes where rate and rhythm are altered during upper extremity motor movement.
Abstract Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and response to therapies. Assessments are limited as they require an in-person evaluation by a neurology subspecialist or specialized and expensive equipment. We tested the hypothesis that important eye movement abnormalities in cerebellar disorders (i.e., ataxias) could be captured from iPhone video. Videos of the face were collected from individuals with ataxia (n = 102) and from a comparative population (Parkinson’s disease or healthy participants, n = 61). Computer vision algorithms were used to track the position of the eye which was transformed into high temporal resolution spectral features. Machine learning models trained on eye movement features were able to identify abnormalities in smooth pursuit (a key eye behavior) and accurately distinguish individuals with abnormal pursuit from controls (sensitivity = 0.84, specificity = 0.77). A novel machine learning approach generated severity estimates that correlated well with the clinician scores. We demonstrate the feasibility of capturing eye movement information using an inexpensive and widely accessible technology. This may be a useful approach for disease screening and for measuring severity in clinical trials.
A 20-year-old man presented to the emergency department with 1 week of headaches and double vision following 2 days of fever (102°F), nausea, and vomiting. His headache was progressively worsening, throbbing behind his right eye, nonpositional, and associated with photophobia, blurry vision, and pain with eye movement. Occasionally, it was severe enough to wake him up from sleep. Horizontal double vision ensued soon after the headache. His diplopia was worse looking at a distance, improved on leftward gaze, worsened on rightward gaze, and resolved with closing one eye. He denied neck stiffness, focal weakness, numbness, or other neurologic symptoms. He denied recent rashes, infections, or tick bites. He lives on a farm in central Brazil. He arrived in Massachusetts 2 months before his presentation to visit family members. He had no significant medical history and took no medications. He did not smoke or use drugs.
Background: Studies of movement of the upper extremity for stroke patients currently require assessments with special equipment and trained assessors, limiting the accessibility. Hevelius is an experimental online platform designed to study human interaction with technology at a large scale. Our aim was to demonstrate the feasibility of using Hevelius for testing arm kinematics in stroke patients. Methods: Stroke patients (time from stroke 6 weeks to 1 year) with upper extremity weakness with an NIH Stroke Scale contralesional arm motor (5A or 5B) score of 2 or less were tested on Hevelius. Participants engaged in a Point-And-Click task. Thirty-two kinematic features of movement from continuous, target-driven mouse movement were collected in the arms contralesional and ipsilesional to stroke and compared to data from with health controls. Upper extremity Fugl-Meyer (UE-FM), NIH Stroke Scale (NIHSS), 9-Hole-Peg as well as patient reported outcomes (via Stroke Impact Scale) were collected during the same research visits. Results: In a total sample of N = 19 patients with upper extremity weakness after stroke who performed Hevelius testing, the median age was 66 (range 47 - 81) with 70% male participants. Nine participants had strokes affecting their dominant arm. LASSO method was used for regression to simultaneously performs feature selection and fitting of a linear model. Score estimates on Hevelius platform correlated strongly on linear regression modeling with clinical scores (from r=0.675 for arm portion of NIHSS ). There was also correlation with 9-Hole-Peg (r=0.581) and no clear correlation with UE-FM, modified Rankin score and Stroke Impact Scale. Five of the 9 patients with dominant arm affected by stroke had NIHSS of 0 and UE-FM of 66. Abnormal movement kinematics were detected in both the contralesional and ipsilesional arms when compared to healthy controls. Conclusions: Characteristics of arm movement are essential to the understanding of motor recovery after stroke. Our study demonstrates subtle deficits of arm movement in task-directed testing that were not captured with traditional measures of stroke recovery.
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.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)