New and Emerging Drug and Gene Therapies for Friedreich Ataxia

The life shortening nature of Friedreich Ataxia (FRDA) demands the search for therapies that can delay, stop or reverse its relentless trajectory. This review provides a contemporary position of drug and gene therapies for FRDA currently in phase 1 clinical trials and beyond. Despite significant scientific advances in the specificity of both compounds and targets developed and investigated, challenges remain for the advancement of treatments in a limited recruitment population. Currently therapies focus on reducing oxidative stress and improving mitochondrial function, modulating frataxin controlled metabolic pathways and gene replacement and editing. Approval of omaveloxolone, the first treatment for individuals with FRDA aged 16 years and over, has created much excitement for both those living with FRDA and those that care for them. The process of approval of omaveloxolone by the US Food and Drug Administration highlighted the importance of sensitive outcome measures and the significant role of data from natural history studies.

A Study of Upper-Limb Motion using Kinematic Measures for Clinical Assessment of Cerebellar Ataxia

Cerebellar Ataxia (CA) is a group of diseases affecting the cerebellum, which is responsible for movement coordination. It causes uncoordinated movements and can also impact balance, speech, and eye movements. There are no approved disease-modifying medications for CA, so clinical studies to assess potential treatments are crucial. These studies require robust, objective measurements of CA severity to reflect changes in the progression of the disease due to medication. In recent years, studies have used kinematic measures to evaluate CA severity, but the current method relies on subjective clinical observations and is insufficient for telehealth. There is a need for a non-intrusive system that can monitor people with CA regularly to better understand the disease and develop an automated assessment system. In this study, we analyzed kinematic measures of upper-limb movements during a ballistic tracking test, which primarily involves movements at the shoulder joint. We aimed to understand the challenges of identifying CA and evaluating its severity when measuring such movements. Statistical features of the kinematic signals were used to develop machine learning models for classification and regression. The Gradient Boosting Classifier model had a maximum accuracy of 74%, but the models had low specificity and performed poorly in regression, suggesting that kinematic measures from shoulder-dominated movements during ballistic tracking are not as viable for CA assessment as other measures.

A Novel Feature from Instrumented Utensils for Clinical Assessment of Friedreich Ataxia

Friedreich Ataxia (FRDA) is an inherited disorder that affects the cerebellum and other regions of the human nervous system. It causes impaired movement that affects quality and reduces lifespan. Clinical assessment of movement is a key part of diagnosis and assessment of severity. Recent studies have examined instrumented measurement of movement to support clinical assessments. This paper presents a frequency domain approach based on Average Band Power (ABP) estimation for clinical assessment using Inertial Measurement Unit (IMU) signals. The IMUs were attached to a 3D printed spoon and a cup. Participants used them to mimic eating and drinking activities during data collection. For both activities, the ABP of frequency components from individuals with FRDA clustered in 0 to 0.2Hz band. This suggests that the ABP of this frequency is affected by FRDA irrespective of the device or activity. The ABP in this frequency band was used to distinguish between FRDA and non-ataxic participants using the Area Under the Receiver-Operating-Characteristic Curve (AUC) which produced peak values greater than 0.8. The machine learning models (logistic regression and neural networks) produced accuracy greater than 80% with these features common to both devices.

Modified Recurrence Quantification Analysis for Objective Assessment of Cerebellar Ataxia

Cerebellar Ataxia (CA) is a neurological condition that affects coordination, balance and speech. Assessing its severity is important for developing effective treatment and rehabilitation plans. Traditional assessment methods involve a clinician instructing a person with ataxia to perform tests and assigning a severity score based on their performance. However, this approach is subjective as it relies on the clinician's experience, and can vary between clinicians. To address this subjectivity, some researchers have developed automated assessment methods using signal processing and data-driven approaches, such as supervised machine learning. These methods still rely on subjective ground truth and can perform poorly in real-world scenarios. This research proposed an alternative approach that uses signal processing to modify recurrence plots and compare the severity of ataxia in a person with CA to a control cohort. The highest correlation score obtained was 0.782 on the back sensor with the feet-apart and eyes-open test. The contributions of the research include modifying the recurrence plot as a measurement tool for assessing CA severity, proposing a new approach to assess severity by comparing kinematic data between people with CA and a control reference group, and identifying the best subtest and sensor position for practical use in CA assessments.

Digital health metrics reveal upper limb impairment profiles in ARSACS

OBJECTIVE

Adults with autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) often present with reduced upper limb coordination affecting their independence in daily life. Previous studies in ARSACS identified reduced performance in clinical assessments requiring fine and gross dexterity as well as prehension. However, the kinematic and kinetic aspects underlying reduced upper limb coordination in ARSACS have not been systematically investigated yet. In this work, we aimed to provide a detailed characterization of alterations in upper limb movement patterns and hand grip forces in 57 participants with ARSACS.

METHODS

We relied on a goal-directed technology-aided assessment task, which provides eight previously validated digital health metrics describing movement efficiency, smoothness, speed, and grip force control.

RESULTS

First, we observed that 98.3% of the participants were impaired in at least one of the metrics, that all metrics are significantly impaired on a population level, and that grip force control during precise manipulations is most commonly and strongly impaired. Second, we identified high inter-participant variability in the kinematic and kinetic impairment profiles, thereby capturing different clinical profiles subjectively observed in this population. Lastly, abnormal goal-directed task performance in ARSACS could be best explained by reduced movement speed, efficiency, and especially force control during precise manipulations, while abnormal movement smoothness did not have a significant effect.

INTERPRETATION

This work helped to refine the clinical profile of ARSACS and highlights the need for characterizing individual kinematic and kinetic impairment profiles in clinical trials in ARSACS.

Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder

OBJECTIVES

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS.

METHODS

We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session.

RESULTS

Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed.

INTERPRETATION

This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-crafted Feature Extractor

Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.

Analysis of Gait Sub-Movements to Estimate Ataxia Severity using Ankle Inertial Data

Objective: Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia. Methods: A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features. Results: Estimated total BARS showed strong agreement with clinician-evaluated scores with r² = 0.72 and a root mean square error of 2.6 BARS points. Movement element-based models significantly outperformed conventional, spatiotemporal gait feature-based models. Conclusion: The proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task. Significance: Our work could support fine-grained monitoring of disease progression and patients' responses to medical/clinical interventions.

Self-Feeding Kinematics in an Ecological Setting: Typically Developing Children and Children With Cerebral Palsy

Assessment of self-feeding kinematics is seldom performed in an ecological setting. In preparation for development of an instrumented spoon for measurement of self-feeding in children with cerebral palsy (CP), the current work aimed to evaluate upper extremity kinematics of self-feeding in young children with typical development (TD) and a small, age-matched group of children with CP in a familiar setting, while eating with a spoon.

METHODS

Sixty-five TD participants and six children diagnosed with spastic CP, aged 3-9 years, fed themselves while feeding was measured using miniature three-dimensional motion capture sensors (trakStar). Kinematic variables associated with different phases of self-feeding cycle (movement time, curvature, time to peak velocity and smoothness) were compared across age-groups in the TD sample and between TD children and those with CP.

RESULTS

Significant between-age group differences were identified in movement times, time to peak velocity and curvature. Children with CP demonstrated slower, less smooth self-feeding movements, potentially related to activity limitations.

CONCLUSIONS

The identified kinematic variables form a basis for implementation of self-feeding performance assessment in children of different ages, including those with CP, which can be deployed via an instrumented spoon.

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.

Developing an Instrumented Measure of Upper Limb Function in Friedreich Ataxia

Upper limb function for people with Friedreich ataxia determines capacity to participate in daily activities. Current upper limb measures available do not fully capture impairments related to Friedreich ataxia. We have developed an objective measure, the Ataxia Instrumented Measure-Spoon (AIM-S), which consists of a spoon equipped with a BioKin wireless motion capture device, and algorithms that analyse these signals, to measure ataxia of the upper limb during the pre-oral phase of eating. The aim of this study was to evaluate the AIM-S as a sensitive and functionally relevant clinical outcome for use in clinical trials. A prospective longitudinal study evaluated the capacity of the AIM-S to detect change in upper limb function over 48 weeks. Friedreich ataxia clinical severity, performance on the Nine-Hole Peg Test and Box and Block Test and responses to a purpose-designed questionnaire regarding acceptability of AIM-S were recorded. Forty individuals with Friedreich ataxia and 20 control participants completed the baseline assessment. Thirty individuals with Friedreich ataxia completed the second assessment. The sensitivity of the AIM-S to detect deterioration in upper limb function was greater than other measures. Patient-reported outcomes indicated the AIM-S reflected a daily activity and was more enjoyable to complete than other assessments. The AIM-S is a more accurate, less variable measure of upper limb function in Friedreich ataxia than existing measures. The AIM-S is perceived by individuals with Friedreich ataxia to be related to everyday life and will permit individuals who are non-ambulant to be included in future clinical trials.

Assessment of Disease Progression in Friedreich Ataxia using an Instrumented Self Feeding Activity

Friedreich ataxia (FRDA), the most common of the inherited ataxias, is a degenerative disease that progressively affects walking and other functions leading to significant impairment associated with a shortened lifespan. It is important to monitor the progression of ataxia over periods of time for clinical and therapeutic interventions. This study was aimed at investigating the use of our instrumented measurement scheme of utilizing a motion detecting spoon in a self-feeding activity to quantify the longitudinal effect of FRDA on upper limb function. Forty individuals diagnosed with FRDA (32.8±14.9 years old) were recruited in a 12-month longitudinal study consisting of equal number of males and females (20). A set of biomarkers was extracted from the temporal and texture analysis of the movement time series data that objectively detected subtle changes during follow-up testing. The results indicated that both analyses generated features that resembled clinical ratings. Although the diagnosis and severity related performances were readily observed by temporal features, the longitudinal progression was better captured by the textural features (p = 0.029). The estimation of severity by mean of random forest regression model and LASSO exhibited a high degree of parity with the standard clinical scale (rho = 0.73, p < 0.001).