Explore the vast range of titles available.

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.

X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson’s Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.

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.

Background Adrenomyeloneuropathy (AMN) is a neurodegenerative disease phenotype of X-linked adrenoleukodystrophy (ALD), resulting in progressive myeloneuropathy causing spastic paraparesis, sensory ataxia, and bowel/bladder symptoms. We conducted a retrospective cohort study using two large administrative databases to characterize mortality and the burden of illness in adult men with AMN in the US. Results Healthcare resource use was assessed using a national commercial insurance claims database (2006–2021). Males with AMN ages 18–64 years and no evidence of cerebral ALD or other peroxisomal disorders were included and 1:4 matched on demographic characteristics to individuals without AMN. All study participants were followed for as long as observable. Patients with AMN were also identified in the Medicare Limited Dataset (2017–2022); mortality and age at death were compared with all Medicare enrollees. We identified 303 commercially insured men with AMN. Compared with non-AMN, individuals with AMN had significantly more inpatient hospital admissions (0.44 vs. 0.04 admissions/patient/year), outpatient clinic (8.88 vs. 4.1 visits/patient/year), outpatient hospital (5.33 vs. 0.99 visits/patient/year), and home healthcare visits (4.66 vs. 0.2 visits/patient/year), durable medical equipment claims (0.7 vs. 0.1 claims/patient/year), and prescription medication fills (18.1 vs. 5.4 fills/patient/year) (all p  < 0.001). Average length-of-stay per hospitalization was also longer in AMN (8.88 vs. 4.3 days; p  < 0.001). Rates of comorbidities were significantly more common in AMN compared to controls, including peripheral vascular disease (4.6% vs. 0.99%), chronic pulmonary disease (6.3% vs. 2.6%), and liver disease (5.6% vs. 0.88%), all p  < 0.001. Among individuals age < 65 with Medicare disability coverage, mortality rates were 5.3x higher for adult AMN males (39.3% vs. 7.4%) and the age at death significantly younger (47.0 ± 11.3 vs. 56.5 ± 7.8 years), both p  < 0.001. Among Medicare beneficiaries ages ≥ 65 mortality rates were 2.2x higher for men with AMN vs. those without AMN (48.6% vs. 22.4%), p  < 0.001. Conclusion AMN imposes a substantial and underrecognized health burden on men, with higher healthcare utilization, greater medical comorbidity, higher mortality rates, and younger age at death.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common heritable form of vascular dementia in adults. It is well-established that CADASIL results in neurocognitive dysfunction and mood disturbance. There is also cumulative evidence that CADASIL patients are more susceptible to ischemic heart disease. The aim of this study is to review the current literature regarding the incidence of coronary artery disease in CADASIL patients with a focus on the various management options and the clinical challenges associated with each of these treatment strategies. We conducted a literature search using Cochrane, MEDLINE, and EMBASE for papers that reported the occurrence of coronary artery disease in patients with CADASIL. We supplemented the search with a manual search in Google Scholar. Only case reports, case series, and original articles were included. The search resulted in six reports indicating the association between coronary artery disease and CADASIL and its management. Evidence suggests that extracranial manifestations of CADASIL may include coronary artery disease, presenting as a more extensive burden of disease in younger patients. Surgical and percutaneous revascularization strategies are feasible, but the incidence of peri-procedural stroke remains significant and should be weighed against the potential benefit derived from either of these strategies. A multidisciplinary approach to therapy, with perspectives from neurologists, cardiologists, and cardiac surgeons, is needed to provide the appropriate treatment to the CADASIL patient with severe coronary artery disease. Future studies should be directed toward the development of targeted therapies that may help with the early detection and prevention of disease progress in these patients.

Objective Functional dystonia (FD) is a disabling and diagnostically challenging functional movement disorder (FMD). We sought to identify historical predictors of FD vs. other primary dystonias (ODs) and develop a practical prediction algorithm to guide neurologists. Methods 1475 consecutive new patient medical records were reviewed at an adult/pediatric tertiary‐referral dystonia clinic from 2005 to 2017. Ninety‐nine met criteria for clinically established FD (85 adults and 14 pediatric), paired with 99 age/dystonia distribution‐matched OD. Univariate and multivariate regression analyses were performed to identify predictors of FD and disability. We formed a prediction algorithm, assessed using the area under the receiver operating curve (AUC). Results Multivariate logistic regression analysis investigating independent predictors of FD (P < 0.001) followed by development of a prediction algorithm showed that the most robust predictors included abrupt onset, spontaneous resolution/recurrence, pain, cognitive complaints, being on or pursuing disability, lifetime mood/anxiety disorder, comorbid functional somatic disorders, and having ≥3 medication allergies. The prediction algorithm had utility for both adult and pediatric FD, with excellent sensitivity/specificity (89%/92%) and an area under the curve (AUC) 0.95 (0.92‐0.98). Greater disability (modified Rankin Scale) independently correlated with a number of functional examination features, unemployment/not attending school, number of medication allergies, and younger age of presentation. FD patients were high health‐care utilizers and were more frequently prescribed opiates/opioids and benzodiazepines (P < 0.003). Interpretation This case‐control study provides an algorithm to guide clinicians in gauging their index of suspicion for a FD, with diagnostic confirmation subsequently informed by neurological examination. While this algorithm requires prospective validation, health‐care utilization data underscore the importance and need for more research in FD.

Objective Late‐onset GM2 gangliosidosis (LOGG) subtypes late‐onset Tay‐Sachs (LOTS) and Sandhoff disease (LOSD) are ultra‐rare neurodegenerative lysosomal storage disorders presenting with weakness, ataxia, and neuropsychiatric symptoms. Previous studies considered LOTS and LOSD clinically indistinguishable; recent studies have challenged this. We performed a scoping review to ascertain whether imaging and clinical features may differentiate these diseases. Methods We examined MEDLINE/non‐MEDLINE databases up to May 2022. Articles reporting brain imaging findings in genetically/enzymatically confirmed LOGG, symptom onset at age ≥ 10 years (or evaluated at least once ≥18 years) were included, yielding 170 LOGG patients (LOTS = 127, LOSD = 43) across 68 papers. We compared LOTS versus LOSD and performed regression analyses. Results were corrected for multiple comparisons. Results Age of onset was lower in LOTS versus LOSD (17.9 ± 8.2 vs. 23.9 ± 14.4 years, p = 0.017), although disease duration was similar (p = 0.34). LOTS more commonly had psychosis/bipolar symptoms (35.0% vs. 9.30%, p = 0.011) but less frequent swallowing problems (4.10% vs. 18.60%, p = 0.041). Cerebellar atrophy was more common in LOTS (89.0%) versus LOSD (60.5%), p < 0.0001, with more severe atrophy in LOTS (p = 0.0005). Brainstem atrophy was documented only in LOTS (14.2%). Independent predictors of LOTS versus LOSD (odds ratio [95% confidence interval]) included the presence of psychosis/bipolar symptoms (4.95 [1.59–19.52], p = 0.011), no swallowing symptoms (0.16 [0.036–0.64], p = 0.011), and cerebellar atrophy (5.81 [2.10–17.08], p = 0.0009). Lower age of onset (0.96 [0.93–1.00], p = 0.075) and tremor (2.50 [0.94–7.43], p = 0.078) were marginally statistically significant but felt relevant to include in the model. Interpretation These data suggest significant differences in symptomatology, disease course, and imaging findings between LOTS and LOSD.

Oculomotor abnormalities are common in the spinocerebellar ataxias (SCAs). In studies of SCAs 1, 2, 3, and 6, eye movement abnormalities correlate with disease severity. Oculomotor abnormalities may be the sole motor manifestation of early and/or premanifest disease; however, not all ataxia rating scales include oculomotor assessment. We sought to identify the prevalence and characteristics of oculomotor abnormalities at first presentation in a large SCA cohort, including those in earlier stages of disease. We performed a retrospective assessment of initial clinical examinations of SCA patients followed in the Massachusetts General Hospital Ataxia Unit and assessed with the Brief Ataxia Rating Scale (BARS). One hundred thirty-four SCA patients were assessed: 17 SCA1, 13 SCA2, 55 SCA3, 2 SCA5, 22 SCA6, 11 SCA7, 9 SCA8, and 5 SCA17, mainly in the early stages of disease (67.2% stage 0–1). Oculomotor abnormalities were present on initial assessment in 94.8%, including 7/9 stage 0 and 77/81 stage 1 patients. Stage 0/1 patients had frequent saccadic intrusions, nystagmus, and hypo/hypermetric saccades. Saccadic slowing was present even in early stage SCA7 and SCA2, eventually leading to ophthalmoplegia. The burden of oculomotor abnormalities correlated with disease stage, duration, and severity, remaining highly significant even when controlling for age. The ubiquitous presence of oculomotor abnormalities in the SCAs, particularly early in the course, underscores the importance of oculomotor assessment in ataxia rating scales such as BARS. These findings highlight the potential for quantitative physiological oculomotor measures as clinical biomarkers in natural history studies and clinical trials.

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 65-year-old woman with a history of measles encephalitis was evaluated for rapidly progressive weakness in the right arm and recurrent falls. MRI of the head showed restricted diffusion and hyperintensity in the left frontal and left parietal lobes. Her father had died 7 months after weakness developed in his right hand and right leg. A diagnostic test was performed.