Cerebellar ataxia with sensory ganglionopathy; does autoimmunity have a role to play?

Background and purpose: Cerebellar ataxia with sensory ganglionopathy (SG) is a disabling combination of neurological dysfunction usually seen as part of some hereditary ataxias. However, patients may present with this combination without a genetic cause. Methods: We reviewed records of all patients that have been referred to the Sheffield Ataxia Centre who had neurophysiological and imaging data suggestive of SG and cerebellar ataxia respectively. We excluded patients with Friedreich's ataxia, a common cause of this combination. All patients were screened for genetic causes and underwent extensive investigations. Results: We identified 40 patients (45% males, mean age at symptom onset 53.7 ± 14.7 years) with combined cerebellar ataxia and SG. The majority of patients (40%) were initially diagnosed with cerebellar dysfunction and 30% were initially diagnosed with SG. For 30% the two diagnoses were made at the same time. The mean latency between the two diagnoses was 6.5 ± 8.9 years (range 0-44). The commonest initial manifestation was unsteadiness (77.5%) followed by patchy sensory loss (17.5%) and peripheral neuropathic pain (5%).Nineteen patients (47.5%) had gluten sensitivity, of whom 3 patients (7.5%) had biopsy proven coeliac disease. Other abnormal immunological tests were present in another 15 patients. Six patients had malignancy, which was diagnosed within 5 years of the neurological symptoms. Only 3 patients (7.5%) were classified as having a truly idiopathic combination of cerebellar ataxia with SG. Conclusion: Our case series highlights that amongst patients with the unusual combination of cerebellar ataxia and SG, immune pathogenesis plays a significant role

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

Objective Assessment of Progression and Disease Characterization of Friedreich Ataxia via an Instrumented Drinking Cup: Preliminary Results

The monitoring of disease progression in certain neurodegenerative conditions can significantly be quantified with the help of objective assessments. The severity assessment of diseases like Friedreich ataxia (FRDA) are usually based on different subjective measures. The ability of a participant with FRDA to perform standard neurological tests is the most common way of assessing disease progression. In this feasibility study, an Ataxia Instrumented Measurement-Cup (AIM-C) is proposed to quantify the disease progression of 10 participants (mean age 39 years, onset of disease 16.3 years) in longitudinal timepoints. The device consists of a sensing system with the provision of extracting both kinetic and kinematic information while engaging in an activity closely associated with activities of daily living (ADL). A common functional task of simulated drinking was used to capture features that possesses disease progression information as well as certain other features which intrinsically correlate with commonly used clinical scales such as the modified Friedreich Ataxia Rating Scale (mFARS), the Functional Staging of Ataxia score and the ADL scale. Frequency and time-frequency domain features allowed the longitudinal assessment of participants with FRDA. Furthermore, both kinetic and kinematic measures captured clinically relevant features and correlated 85% with clinical assessments

Modeling the Progression of Speech Deficits in Cerebellar Ataxia using a Mixture Mixed-effect Machine Learning Framework

Background: Accurate and reliable prediction of changes in the severity of cerebellar ataxia (CA) will be necessary for trials of disease-modifying therapies. Cerebellar dysarthria (CD) is a common feature of CA. This study demonstrated that objective acoustic measures were more sensitive than perceptive analysis through the Scale for the Assessment and Rating of Ataxia (SARA) in assessing the progression of CD, within a time window of two years (mean). Method: Thirty-seven people with CA were tested at baseline (time point 1, TP1) and two years later (time point 2, TP2). A machine-learning framework with a robust three-step feature selection criterion and a Bayesian data-driven clustering technique based on the multivariate mixture extension of the generalized linear mixed model (GLMM) was used. The outcomes included two (time and cepstral-based) objective speech parameters recorded at TP1 and TP2. Subject testing involved dynamic prediction and was conducted using samples from the posterior distributions of parameter estimates and random effects. This study further employed the penalized expected deviance (PED) criterion for model comparison and the selection of the number of groups in the clustering procedure. Results: First, the selected objective speech metrics in the individual patients showed a significant worsening of the speech impairment (p<0.001, Kolmogorov–Smirnov test) between TP1 and TP2. Second, the cluster analysis divided the CA patients into two distinct subgroups showing a strong association between objective speech measures and disease duration, with ~96% of observed values falling within the 95% credible intervals. Third, for the training data, our multivariate model ( $PED_{Fea1+Fea2}=5175$ ; number of groups = 2) performed more reliably than the univariate models ( $PED_{Fea1}=4225$ , $PED_{Fea2}=3850$ ; number of groups = 2) in discriminating the CA patients. Fourth, the individual-level predictions of the change in profiles of the objective measures over time were performed for the testing data. Conclusion: Such a framework using objective speech metrics indeed holds promise to predict the rate of clinical progression of CD in individuals with CA