Objective Assessment of Cerebellar Ataxia: A Comprehensive and Refined Approach

Comparison of Two Therapeutic Approaches of Cerebellar Transcranial Direct Current Stimulation in a Sardinian Family Affected by Spinocerebellar Ataxia 38: a Clinical and Computerized 3D Gait Analysis Study

Spinocerebellar ataxia 38 (SCA 38) is a very rare autosomal dominant inherited disorder caused by a mutation in ELOV5 gene, specifically expressed in cerebellar Purkinje cells, encoding an enzyme involved in the synthesis of fatty acids. Seven symptomatic SCA 38 patients of a Sardinian family were administered 15 sessions of cerebellar anodal transcranial direct current stimulation (tDCS) in a cross-over study, employing deltoid cerebellar-only (C-tDCS) and cerebello-spinal (CS-tDCS) cathodal montage. Clinical evaluation was performed at baseline (T0), after 15 sessions of tDCS (T1) and after 1 month of follow-up (T2). Modified International Cooperative Ataxia Rating Scale (MICARS) and the Robertson dysarthria profile were used to rate ataxic and dysarthric symptoms, respectively. Alertness and split attention tests from Zimmermann test battery for attentional performance were employed to rate attentive functions. Moreover, 3D computerized gait analysis was employed to obtain a quantitative measure of efficacy of tDCS on motor symptoms. While clinical data showed that both CS and C-tDCS improved motor, dysarthric, and cognitive scores, the quantitative analysis of gait revealed significant improvement in spatio-temporal parameters only for C-tDCS treatment. Present findings, yet preliminary and limited by the small size of the tested sample, confirm the therapeutic potential of cerebellar tDCS in improving motor and cognitive symptoms in spinocerebellar ataxias and underline the need to obtain quantitative and objective measures to monitor the efficacy of a therapeutic treatment and to design tailored rehabilitative interventions. ClinicalTrials.gov identifier: NCT05951010.

Multifeature quantitative motor assessment of upper limb ataxia including drawing and reaching

OBJECTIVE

Voluntary upper limb movements are an ecologically important yet insufficiently explored digital-motor outcome domain for trials in degenerative ataxia. We extended and validated the trial-ready quantitative motor assessment battery "Q-Motor" for upper limb movements with clinician-reported, patient-focused, and performance outcomes of ataxia.

METHODS

Exploratory single-center cross-sectional assessment in 94 subjects (46 cross-genotype ataxia patients; 48 matched controls), comprising five tasks measured by force transducer and/or position field: Finger Tapping, diadochokinesia, grip-lift, and-as novel implementations-Spiral Drawing, and Target Reaching. Digital-motor measures were selected if they discriminated from controls (AUC >0.7) and correlated-with at least one strong correlation (rho ≥0.6)-to the Scale for the Assessment and Rating of Ataxia (SARA), activities of daily living (FARS-ADL), and the Nine-Hole Peg Test (9HPT).

RESULTS

Six movement features with 69 measures met selection criteria, including speed and variability in all tasks, stability in grip-lift, and efficiency in Target Reaching. The novel drawing/reaching tasks best captured impairment in dexterity (|rho9HPT| ≤0.81) and FARS-ADL upper limb items (|rhoADLul| ≤0.64), particularly by kinematic analysis of smoothness (SPARC). Target hit rate, a composite of speed and endpoint precision, almost perfectly discriminated ataxia and controls (AUC: 0.97). Selected measures in all tasks discriminated between mild, moderate, and severe impairment (SARA upper limb composite: 0-2/>2-4/>4-6) and correlated with severity in the trial-relevant mild ataxia stage (SARA ≤10, n = 20).

INTERPRETATION

Q-Motor assessment captures multiple features of impaired upper limb movements in degenerative ataxia. Validation with key clinical outcome domains provides the basis for evaluation in longitudinal studies and clinical trial settings.

Oxidative stress and ion channels in neurodegenerative diseases

Numerous neurodegenerative diseases result from altered ion channel function and mutations. The intracellular redox status can significantly alter the gating characteristics of ion channels. Abundant neurodegenerative diseases associated with oxidative stress have been documented, including Parkinson's, Alzheimer's, spinocerebellar ataxia, amyotrophic lateral sclerosis, and Huntington's disease. Reactive oxygen and nitrogen species compounds trigger posttranslational alterations that target specific sites within the subunits responsible for channel assembly. These alterations include the adjustment of cysteine residues through redox reactions induced by reactive oxygen species (ROS), nitration, and S-nitrosylation assisted by nitric oxide of tyrosine residues through peroxynitrite. Several ion channels have been directly investigated for their functional responses to oxidizing agents and oxidative stress. This review primarily explores the relationship and potential links between oxidative stress and ion channels in neurodegenerative conditions, such as cerebellar ataxias and Parkinson's disease. The potential correlation between oxidative stress and ion channels could hold promise for developing innovative therapies for common neurodegenerative diseases.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

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.

Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia

Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics.

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.

Evolution of disability in spinocerebellar ataxias type 1, 2, 3, and 6

Objective

The aim was to study the evolution of disability in spinocerebellar ataxias (SCAs) type 1, 2, 3, and 6 (SCA1, 2, 3, 6).

Methods

We analyzed data of two longitudinal cohorts (RISCA, EUROSCA) which recruited ataxic and non‐ataxic SCA1, SCA2, SCA3, and SCA6 mutation carriers. To study disability, we used a five‐stage system for ataxia defined by walking ability (stages 0–3) and death (stage 4). Transitions were analyzed using a multi‐state model with proportional transition hazards. Based on the hazard estimates, transition probabilities and the expected lengths of stay in each stage were calculated. We further studied the effect of sex and CAG repeat length on progression.

Results

Data of 3138 visits in 677 participants were analyzed. Median SARA scores for SCA1, SCA2, SCA3, and SCA6 ranged from 1.5 (interquartile range [IQR] = 0.0–3.5) to 3.5 (IQR = 1.4–6.1) in stage 0, 11.5 (IQR = 9.6–14.0) to 13.8 (IQR = 11.0–16.0) in stage 1, 19.0 (IQR = 17.0–21.0) to 23.8 (IQR = 19.5–27.0) in stage 2, and 28.5 (IQR = 26.0–32.5) to 34.0 (IQR = 32.6–37.1) in stage 3. Modeling allowed to calculate the subtype‐specific probability to be in a certain stage at a given age and duration of each stage. CAG repeat length was associated with faster progression in SCA1 (HR, 95% CI: 1.1, 1.1–1.2), SCA2 (1.2, 1.1–1.3), and SCA3 (1.1, 1.0–1.2). In SCA6, female sex was associated with faster progression (1.7, 1.1–2.6).

Interpretation

Our data are important for counselling of patients, assessment of the relevance of outcome markers, and design of clinical trials.

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.

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.

Evaluation of upper extremity ataxia through image processing in individuals with multiple sclerosis

BACKGROUND

Impaired dexterity is a frequently reported disability among people with ataxic multiple sclerosis (MS).

OBJECTIVE

To quantify and standardize the evaluation of upper extremity coordination disorder among patients with multiple sclerosis (MS), using the Tablet Ataxia Assessment Program (TAAP).

METHODS

The X and Y axis movements of 50 MS patients and 30 healthy individuals who were evaluated using the International Cooperative Ataxia Rating Scale (ICARS) were also assessed using TAAP. The functional times of the participants' right and left hands were recorded using the nine-hole peg test (NHPT). The upper extremity coordination of individuals with MS was evaluated using the upper extremity kinetic functions section of ICARS.

RESULTS

The deviations for the X and Y axis movements of the MS group were greater than those of the control group (p<0.05). Significant correlations were shown between TAAP scores and NHPT and ICARS scores. The strongest correlation was found between NHPT and ICARS in the dominant hand (rnhpt=0.356, pnhpt=0.001; ricars=0.439, picars=0.000). In correlating the Y axis with ICARS, the deviations in the Y axis were found to be greater in the non-dominant hand than those in the X axis (ryright=0.402, pyright=0.004; ryleft=0.691, pyleft=0.000).

CONCLUSION

Measurement using TAAP is more sensitive than other classical and current methods for evaluating ataxia. We think that TAAP is an objective tool that will allow neurorehabilitation professionals and clinicians to evaluate upper extremity coordination.

Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity

Effective monitoring of the progression of neurodegenerative conditions can be significantly improved by objective assessments. Clinical assessments of conditions such as Friedreich's Ataxia (FA), currently rely on subjective measures commonly practiced in clinics as well as the ability of the affected individual to perform conventional tests of the neurological examination. In this study, we propose an ataxia measuring device, in the form of a pressure canister capable of sensing certain kinetic and kinematic parameters of interest to quantify the impairment levels of participants particularly when engaged in an activity that is closely associated with daily living. In particular, the functional task of simulated drinking was utilised to capture characteristic features of disability manifestation in terms of diagnosis (separation of individuals with FA and controls) and severity assessment of individuals diagnosed with the debilitating condition of FA. Time and frequency domain analysis of these biomarkers enabled the classification of individuals with FA and control subjects to reach an accuracy of 98% and a correlation level reaching 96% with the clinical scores.

Instrumented Objective Clinical Examination of Cerebellar Ataxia: the Upper and Lower Limb-a Review

Cerebellar dysfunction results in impairments in co-ordination or 'ataxia'. Bedside examination of cerebellar function has changed little since the early nineteenth century with the exception being the oculomotor examination which has become instrumented. Otherwise, competence and confidence in performing the clinical assessment relies heavily on the skill and experience of the clinician. Potentially, instrumented objective measurement will more accurately assess the severity of ataxia and the changes brought about by advancing therapies in pharmaceutical trials and in rehabilitation intervention. This study describes instrumented versions of several bedside tests of cerebellar function, including rhythmic tapping of the hand (RTH), finger-nose test (FNT), dysdiadochokinesia (DDK), ramp tracking (RMT), ballistic tracking (BT), rhythmic tapping of the foot (RTF) and the heel shin (HST) examination which were validated against scores from Ataxia Rating Scales (ARS) such as the Scale of Assessment and Rating of Ataxia (SARA). While all of the instrumented tests accurately distinguished between ataxic subjects and controls, there was a difference in performance, with the best four performing upper limb tests being RTH, FNT, DDK and BT. A combination of BT plus RTH provided the best correlation with the SARA and outperformed a combination of all the bedside tests (Spearman 0.8; p < 0.001 compared to 0.68; p < 0.001 for the combined set) in identifying the presence and severity of ataxia. This indicates that there is redundancy in the information provided by the bedside tests and that adding other tests to BT plus RTH does not add accuracy to the assessment of ataxia. This analysis highlighted the need for metrics that could be generalised to each of the assessments of ataxia, so, in turn, domains of stability, timing, accuracy and rhythmicity (STAR domains) were developed and compared to the SARA. The STAR criteria could potentially influence the future of instrumented assessment in CA and pave the way for further research into the objective measurement of the cerebellar examination.

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.

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of 〈0,30 〉 Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.