Patient-Related Outcome Measures for Oculomotor Symptoms in the Cerebellar Ataxias: Insights from Non-Cerebellar Disorders.

In patients with cerebellar ataxia (CA), symptoms related to oculomotor dysfunction significantly affect quality of life (QoL). This study aimed to analyze the literature on patient-related outcome measures (PROMs) assessing QoL impacts of vestibular and cerebellar oculomotor abnormalities in patients with CA to identify the strengths and limitations of existing scales and highlight any areas of unmet need. A systematic review was conducted (Medline, Embase) of English-language original articles reporting on QoL measures in patients with vertigo, dizziness or CA. Pre-specified parameters were retrieved, including diseases studied, scales applied and conclusions drawn. Our search yielded 3671 articles of which 467 studies (n = 111,606 participants) were deemed relevant. The most frequently studied disease entities were (a) non-specific dizziness/gait imbalance (114 studies; 54,581 participants), (b) vestibular schwannomas (66; 15,360), and (c) vestibular disorders not further specified (66; 10,259). The Dizziness Handicap Inventory (DHI) was the most frequently used PROM to assess QoL (n = 91,851), followed by the Penn Acoustic Neuroma Quality-of-Life Scale (n = 12,027) and the Activities-Specific Balance Confidence Scale (n = 2'471). QoL-scores capturing symptoms related to oculomotor abnormalities in CA were rare, focused on visual impairments (e.g., National-Eye-Institute Visual Function Questionnaire, Oscillopsia Functional Impact, oscillopsia severity score) and were unvalidated. The DHI remains the most widely used and versatile scale for evaluating dizziness. A lack of well-established PROMs for assessing the impact of oculomotor-related symptoms on QoL in CA was noted, emphasizing the need for developing and validating a new QoL-score dedicated to the oculomotor domain for individuals with CA.

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.

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.

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.

A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling.

Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

Machine Learning Based Diagnosis of Vertigo using Video Head Impulse Test.

This work utilises the strength of state space based dynamic modelling and the ability of machine learning based segmentation of SRM standard descriptors to reach superior diagnostic capabilities. Dynamic modelling ensured vHIT input-output characteristics generated SRM standard descriptors, which were consequently used in formation of ML classification models.The best ML model was Linear SVM when built on left and right sided data with the SRM standard descriptors: rise time, settling time, settling minimum, settling maximum, overshoot and undershoot. The model was able to classify individuals to patient or control groups with an accuracy of 100% and a sensitivity and specificity of 1.Clinical Relevance- Dizziness is one of the most common presentations to family physicians and emergency departments. It is associated with significant medical complications such as falls as well as economic costs to both the individual and the community. Vestibular diseases comprise the bulk of dizzy disorders and are often associated with dysfunction of the vestibular or inner ear balance apparatus. This is most commonly the result of hypo-function of the semi-circular canals. Clinically, the most commonly employed objective test of semicircular function is the video Head Impulse Test (vHIT). Here we provide a machine learning approach to a more comprehensible and accurate interpretation of the results obtained by the vHIT to more robustly establish the presence and severity of VOR dysfunction, and ultimately aid in the diagnosis of vestibular disorders.

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.

Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks.

The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman's rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework's usefulness for identifying and monitoring Ataxic Speech.

A patient with neuropathy and ataxia: what do I have to consider?

PURPOSE OF REVIEW: An increasing number of peripheral neuro(no)pathies are identified as involving other components of the neurological system, particularly those that further impair balance. Here we aim to outline an evidence-based approach to the diagnosis of patients who present with a somatosensory disorder which also involves at least one other area of neurological impairment such as the vestibular, auditory, or cerebellar systems. RECENT FINDINGS: Detailed objective investigation of patients who present with sensory impairment, particularly where the degree of imbalance is greater than would be expected, aids the accurate diagnosis of genetic, autoimmune, metabolic, and toxic neurological disease. SUMMARY: Diagnosis and management of complex somatosensory disorders benefit from investigation which extends beyond the presenting sensory impairment.

Interrater Reliability of the Scale for the Assessment and Rating of Ataxia, Berg Balance Scale, and Functional Independence Measure Motor Domain in Individuals With Hereditary Cerebellar Ataxia.

OBJECTIVE: To determine the interrater reliability of the Scale for the Assessment and Rating of Ataxia (SARA), Berg Balance Scale (BBS), and motor domain of the FIM (m-FIM) administered by physiotherapists in individuals with a hereditary cerebellar ataxia (HCA). DESIGN: Participants were assessed by 1 of 4 physiotherapists. Assessments were video-recorded and the remaining 3 physiotherapists scored the scales for each participant. Raters were blinded to each other's scores. SETTING: Assessments were administered at 3 clinical locations in separate states in Australia. PARTICIPANTS: Twenty-one individuals (mean age=47.63 years; SD=18.42; 13 male and 8 female) living in the community with an HCA were recruited (N=21). MAIN OUTCOME MEASURES: Total and single-item scores of the SARA, BBS, and m-FIM were examined. The m-FIM was conducted by interview. RESULTS: Intraclass coefficients (2,1) for the total scores of the m-FIM (0.92; 95% confidence interval [CI], 0.85-0.96), SARA (0.92; 95% CI, 0.86-0.96), and BBS (0.99; 95% CI, 0.98-0.99) indicated excellent interrater reliability. However, there was inconsistent agreement with the individual items, with SARA item 5 (right side) and item 7 (both sides) demonstrating poor interrater reliability and items 1 and 2 demonstrating excellent reliability. CONCLUSIONS: The m-FIM (by interview), SARA, and BBS have excellent interrater reliability for use when assessing individuals with an HCA. Physiotherapists could be considered for administration of the SARA in clinical trials. However, further work is required to improve the agreement of the single-item scores and to examine the other psychometric properties of these scales.

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.

An intronic GAA repeat expansion in FGF14 causes the autosomal-dominant adult-onset ataxia SCA50/ATX-FGF14.

Adult-onset cerebellar ataxias are a group of neurodegenerative conditions that challenge both genetic discovery and molecular diagnosis. In this study, we identified an intronic (GAA) repeat expansion in fibroblast growth factor 14 (FGF14). Genetic analysis of 95 Australian individuals with adult-onset ataxia identified four (4.2%) with (GAA)>300 and a further nine individuals with (GAA)>250. PCR and long-read sequence analysis revealed these were pure (GAA) repeats. In comparison, no control subjects had (GAA)>300 and only 2/311 control individuals (0.6%) had a pure (GAA)>250. In a German validation cohort, 9/104 (8.7%) of affected individuals had (GAA)>335 and a further six had (GAA)>250, whereas 10/190 (5.3%) control subjects had (GAA)>250 but none were (GAA)>335. The combined data suggest (GAA)>335 are disease causing and fully penetrant (p = 6.0 × 10-8, OR = 72 [95% CI = 4.3-1,227]), while (GAA)>250 is likely pathogenic with reduced penetrance. Affected individuals had an adult-onset, slowly progressive cerebellar ataxia with variable features including vestibular impairment, hyper-reflexia, and autonomic dysfunction. A negative correlation between age at onset and repeat length was observed (R2 = 0.44, p = 0.00045, slope = -0.12) and identification of a shared haplotype in a minority of individuals suggests that the expansion can be inherited or generated de novo during meiotic division. This study demonstrates the power of genome sequencing and advanced bioinformatic tools to identify novel repeat expansions via model-free, genome-wide analysis and identifies SCA50/ATX-FGF14 as a frequent cause of adult-onset ataxia.

The Electrophysiological Findings in Spinocerebellar Ataxia Type 6: Evidence From 24 Patients.

PURPOSE: Peripheral neuropathy has been reported commonly in several spinocerebellar ataxia (SCA) types. To date, there is a lack of robust evidence for neuropathy or neuronopathy in SCA type 6 (SCA6). Here, we aim to evaluate the presence of neuropathy or neuronopathy in a cohort of SCA6 patients. METHODS: Twenty-four individuals with genetically confirmed SCA6 underwent detailed neurophysiological assessment. This included nerve conduction studies, and in some, cutaneous silent periods, blink reflexes, tilt table tests, quantitative sudomotor axon reflex tests, and somatosensory (median and tibial) evoked potentials. RESULTS: Mean age was 56.1 years (range, 22-94 years) at the time of testing. Four patients were presymptomatic of SCA6 at recruitment. The mean disease duration of symptomatic patients was 11.9 years (range, 1-40 years). Most patients (79.2%, 19/24) had no neurophysiological evidence of a peripheral neuropathy. One with impaired glucose tolerance had mild, large, and small fiber sensorimotor polyneuropathy. One elderly patient had length-dependent axonal sensorimotor polyneuropathy. Two had minor sensory abnormalities (one had type II diabetes and previous chemotherapy). One other had minor small fiber abnormalities. Ten patients (41.7%) had median neuropathies at the wrist. All somatosensory evoked potential (15/15), and most autonomic function tests (13/14) were normal. CONCLUSIONS: A large proportion of subjects (79.2%) in our cohort had no evidence of large or small fiber neuropathy. This study does not support the presence of neuropathy or neuronopathy as a common finding in SCA6 and confirms the importance of considering comorbidities as the cause of neurophysiological abnormalities.

Bilateral vestibulopathy: a clinical update and proposed diagnostic algorithm.

Bilateral vestibulopathy (BVP) is characterized by its heterogeneous and chronic nature with various clinical presentations and multiple etiologies. This current narrative review reflects on the main insights and developments regarding clinical presentation. In addition, it proposes a new diagnostic algorithm, and describes available and potential future therapeutic modalities.

Overview of the Clinical Approach to Individuals With Cerebellar Ataxia and Neuropathy.

Increasingly, cerebellar syndromes are recognized as affecting multiple systems. Extracerebellar features include peripheral neuropathies affecting proprioception; cranial neuropathies such as auditory and vestibular; and neuronopathies, for example, dorsal root and vestibular. The presence of such features, which in and of themselves may cause ataxia, likely contribute to key disabilities such as gait instability and falls. Based on the evolving available literature and experience, we outline a clinical approach to the diagnosis of adult-onset ataxia where a combination of cerebellar and peripheral or cranial nerve pathology exists. Objective diagnostic modalities including electrophysiology, oculomotor, and vestibular function testing are invaluable in accurately defining an individual's phenotype. Advances in MRI techniques have led to an increased recognition of disease-specific patterns of cerebellar pathology, including those conditions where neuronopathies may be involved. Depending on availability, a stepwise approach to genetic testing is suggested. This is guided by factors such as pattern of inheritance and age at disease onset, and genetic testing may range from specific genetic panels through to whole-exome and whole-genome sequencing. Management is best performed with the involvement of a multidisciplinary team, aiming at minimization of complications such as falls and aspiration pneumonia and maximizing functional status.

RFC1-Related Disease: Molecular and Clinical Insights.

In 2019, a biallelic pentanucleotide repeat expansion in the gene encoding replication factor C subunit 1 (RFC1) was reported as a cause of cerebellar ataxia with neuropathy and vestibular areflexia syndrome (CANVAS). In addition, biallelic expansions were shown to account for up to 22% of cases with late-onset ataxia. Since this discovery, the phenotypic spectrum reported to be associated with RFC1 expansions has extended beyond the initial conditions to include pure cerebellar ataxia, isolated somatosensory impairment, combinations of the 2, and parkinsonism, leading to a potentially broad differential diagnosis. Genetic studies suggest RFC1 expansions may be the most common genetic cause of ataxia and are likely underdiagnosed. This review summarizes the current molecular and clinical knowledge of RFC1-related disease, with a focus on the evaluation of recent phenotype associations and highlighting the current challenges in clinical pathways to diagnosis and molecular testing.

Blockchained Federated Learning for Privacy and Security Preservation: Practical Example of Diagnosing Cerebellar Ataxia.

Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.