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While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses. [ABSTRACT FROM AUTHOR]

Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson's disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson's disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started. [ABSTRACT FROM AUTHOR]

In everyday life, we encounter situations that require tradeoffs between potential rewards and associated costs, such as time and (physical) effort. The literature indicates a prominent role for dopamine in discounting of both delay and effort, with mixed findings for delay discounting in humans. Moreover, the reciprocal antagonistic interaction between dopaminergic and cholinergic transmission in the striatum suggests a potential opponent role of acetylcholine in these processes. We found opposing effects of dopamine D2 (haloperidol) and acetylcholine M1 receptor (biperiden) antagonism on specific components of effort-based decision-making in healthy humans: haloperidol decreased, whereas biperiden increased the willingness to exert physical effort. In contrast, delay discounting was reduced under haloperidol, but not affected by biperiden. Together, our data suggest that dopamine, acting at D2 receptors, modulates both effort and delay discounting, while acetylcholine, acting at M1 receptors, appears to exert a more specific influence on effort discounting only. Tradeoffs between potential rewards and associated costs involves the dopaminergic system, which antagonistically interacts with the cholinergic system. This study describes that dopamine and acetylcholine have opposing effects on the modulation of specific components of cost-benefit decision making in humans. [ABSTRACT FROM AUTHOR]

Background: Previous research suggested that quadripulse (QPS)-induced synaptic plasticity is associated with both cognitive and motor function in patients with multiple sclerosis (MS) and does not appear to be reduced compared to healthy controls (HCs). Objective: This study aimed to explore the relationship between the degree of QPS-induced plasticity and clinically significant decline in motor and cognitive functions over time. We hypothesized that MS patients experiencing functional decline would exhibit lower levels of baseline plasticity compared to those without decline. Methods: QPS-induced plasticity was evaluated in 80 MS patients (56 with relapsing-remitting MS and 24 with progressive MS), and 69 age-, sex-, and education-matched HCs. Cognitive and motor functions, as well as overall disability status were evaluated annually over a median follow-up period of 2 years. Clinically meaningful change thresholds were predefined for each outcome measure. Linear mixed-effects models, Cox proportional hazard models, logistic regression, and receiver-operating characteristic analysis were applied to analyse the relationship between baseline plasticity and clinical progression in the symbol digit modalities test, brief visuospatial memory test revised (BVMT-R), nine-hole peg test (NHPT), timed 25-foot walk test, and expanded disability status scale. Results: Overall, the patient cohort showed no clinically relevant change in any functional outcome over time. Variability in performance was observed across time points in both patients and HCs. MS patients who experienced clinically relevant decline in manual dexterity and/or visuospatial learning and memory had significantly lower levels of synaptic plasticity at baseline compared to those without such decline (NHPT: β = -0.25, p = 0.02; BVMT-R: β = -0.50, p = 0.005). Receiver-operating characteristic analysis underscored the predictive utility of baseline synaptic plasticity in discerning between patients experiencing functional decline and those maintaining stability only for visuospatial learning and memory (area under the curve = 0.85). Conclusion: Our study suggests that QPS-induced plasticity could be linked to clinically relevant functional decline in patients with MS. However, to solidify these findings, longer follow-up periods are warranted, especially in cohorts with higher prevalences of functional decline. Additionally, the variability in cognitive performance in both patients with MS and HCs underscores the importance of conducting further research on reliable change based on neuropsychological tests. [ABSTRACT FROM AUTHOR]

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches. [ABSTRACT FROM AUTHOR]