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Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome-wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses.ResultsOur analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Abstract Parkinson’s disease (PD) is a highly heterogeneous disease both with respect to arising symptoms and its progression over time. This hampers the design of disease modifying trials for PD as treatments which would potentially show efficacy in specific patient subgroups could be considered ineffective in a heterogeneous trial cohort. Establishing clusters of PD patients based on their progression patterns could help to disentangle the exhibited heterogeneity, highlight clinical differences among patient subgroups, and identify the biological pathways and molecular players which underlie the evident differences. Further, stratification of patients into clusters with distinct progression patterns could help to recruit more homogeneous trial cohorts. In the present work, we applied an artificial intelligence-based algorithm to model and cluster longitudinal PD progression trajectories from the Parkinson's Progression Markers Initiative. Using a combination of six clinical outcome scores covering both motor and non-motor symptoms, we were able to identify specific clusters of PD that showed significantly different patterns of PD progression. The inclusion of genetic variants and biomarker data allowed us to associate the established progression clusters with distinct biological mechanisms, such as perturbations in vesicle transport or neuroprotection. Furthermore, we found that patients of identified progression clusters showed significant differences in their responsiveness to symptomatic treatment. Taken together, our work contributes to a better understanding of the heterogeneity encountered when examining and treating patients with PD, and points towards potential biological pathways and genes that could underlie those differences.

Progressive supranuclear palsy (PSP) is a rare, relentlessly progressive, ultimately fatal neurodegenerative brain disease. The objective of this study was to assess the burden of PSP on patients, caregivers, and healthcare systems by PSP phenotype. Data were drawn from the Adelphi PSP Disease Specific Programme™, a cross-sectional study of neurologists and people living with PSP in the United States of America, France, Germany, Italy, Spain, and the United Kingdom. All people living with PSP with a reported phenotype were included. PSP phenotype was reported for 242 patients (mean age: 70.2 years, 58% male): PSP-Richardson's syndrome, n = 96; PSP-predominant Parkinsonism, n = 88; PSP-predominant corticobasal syndrome, n = 28; PSP-predominant speech/language disorder, n = 12; PSP-progressive gait freezing, n = 9; PSP-predominant frontal presentation, n = 9. Most patients reported impaired cognitive, motor, behavioral and ocular functionality; 67–100% of patients (across phenotypes) had moderate-to-severe disease at the time of data collection. Post-diagnosis, the majority were provided with a visual and/or mobility aid (55–100%, across phenotypes), and/or required home modification to facilitate their needs (55–78%, across phenotypes). Patients required multiple types of healthcare professionals for disease management (mean 3.6–4.4, across phenotypes), and the majority reported receiving care from at least one caregiver (mean 1.3–1.8, across phenotypes). There is a high burden on patients, caregivers, and healthcare systems across all PSP phenotypes. Although phenotypes manifest different symptoms and are associated with different diagnostic pathways, once diagnosed with PSP, patients typically receive similar care.

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.

Abstract Objective Infantile spasm syndrome (ISS) is an epileptic encephalopathy without established treatment after the failure to standard of care based on steroids and vigabatrin. Converging lines of evidence indicating a role of NR2B subunits of the N‐methyl‐D‐aspartate (NMDA) receptor on the onset of spams in ISS patients, prompted us to test radiprodil, a negative allosteric NR2B modulator in preclinical seizure models and in infants with ISS. Methods Radiprodil has been tested in three models, including pentylenetetrazole‐induced seizures in rats across different postnatal (PN) ages. Three infants with ISS have been included in a phase 1b escalating repeated dose study. Results Radiprodil showed the largest protective seizure effects in juvenile rats (maximum at PN12, corresponding to late infancy in humans). Three infants resistant to a combination of vigabatrin and prednisolone received individually titrated doses of radiprodil for up to 34 days. Radiprodil was safe and well tolerated in all three infants, and showed the expected pharmacokinetic profile. One infant became spasm‐free and two showed clinical improvement without reaching spasm‐freedom. After radiprodil withdrawal, the one infant continued to be spasm‐free, while the two others experienced seizure worsening requiring the use of the ketogenic diet and other antiepileptic drugs. Interpretation Radiprodil showed prominent anti‐seizure effect in juvenile animals, consistent with the prevalent expression of NR2B subunit of the NMDA receptor at this age in both rodents and humans. The clinical testing, although preliminary, showed that radiprodil is associated with a good safety and pharmacokinetic profile, and with the potential to control epileptic spasms.

Abstract Objective Clinical and genetic predictors of response to antiepileptic drugs (AEDs) are largely unknown. We examined predictors of lacosamide response in a real‐world clinical setting. Methods We tested the association of clinical predictors with treatment response using regression modeling in a cohort of people with refractory epilepsy. Genetic assessment for lacosamide response was conducted via genome‐wide association studies and exome studies, comprising 281 candidate genes. Results Most patients (479/483) were treated with LCM in addition to other AEDs. Our results corroborate previous findings that patients with refractory genetic generalized epilepsy (GGE) may respond to treatment with LCM. No clear clinical predictors were identified. We then compared 73 lacosamide responders, defined as those experiencing greater than 75% seizure reduction or seizure freedom, to 495 nonresponders (

Abstract Objective To study the effectiveness and tolerability of antiepileptic drugs (AEDs) commonly used in juvenile myoclonic epilepsy (JME). Methods People with JME were identified from a large database of individuals with epilepsy, which includes detailed retrospective information on AED use. We assessed secular changes in AED use and calculated rates of response (12‐month seizure freedom) and adverse drug reactions (ADRs) for the five most common AEDs. Retention was modeled with a Cox proportional hazards model. We compared valproate use between males and females. Results We included 305 people with 688 AED trials of valproate, lamotrigine, levetiracetam, carbamazepine, and topiramate. Valproate and carbamazepine were most often prescribed as the first AED. The response rate to valproate was highest among the five AEDs (42.7%), and significantly higher than response rates for lamotrigine, carbamazepine, and topiramate; the difference to the response rate to levetiracetam (37.1%) was not significant. The rates of ADRs were highest for topiramate (45.5%) and valproate (37.5%). Commonest ADRs included weight change, lethargy, and tremor. In the Cox proportional hazards model, later start year (1.10 [1.08‐1.13], P