搜索结果：Dystonia

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a signifi

Robust characterization of dynamic causal interactions in multivariate biomedical signals is essential for advancing computational and algorithmic methods in biomedical imaging. Conventional approaches, such as Dynamic Bayesian Networks (DBNs), often assume linear or simple statistical dependencies, while manifold based techniques like Convergent Cross Mapping (CCM) capture nonlinear, lagged interactions but lack probabilistic quantification and interventional modeling. We introduce a DBN informed CCM framework that integrates geometric manifold reconstruction with probabilistic temporal modeling. Applied to multimodal EEG-EMG recordings from dystonic and neurotypical children, the method quantifies uncertainty, supports interventional simulation, and reveals distinct frequency specific reorganization of corticomuscular pathways in dystonia. Experimental results show marked improvements in predictive consistency and causal stability as compared to baseline approaches, demonstrating the potential of causality aware multimodal modeling for developing quantitative biomarkers and guiding targeted neuromodulatory interventions.

Background: Mutations in KMT2B are a recognized cause of early-onset complex dystonia, with deep brain stimulation (DBS) of the internal globus pallidus (GPi-DBS) being an effective treatment. However, gait impairment, particularly freezing of gait (FOG), remains a significant challenge in DYT-KMT2B patients post-DBS. Objectives: To characterize the emergence of FOG in DYT-KMT2B patients treated with GPi-DBS and explore potential underlying mechanisms, including striatonigral degeneration. Methods: Five patients (four females) with KMT2B-related dystonia and protein-truncating variants (PTVs) were retrospectively analyzed. Clinical progression, response to GPi-DBS, and the presence of FOG were documented. Dopaminergic function was assessed using DaTscan (SPECT for ^123I-ioflupane) in four patients. Results: FOG developed in all patients, with onset ranging from 1 to 15.5 years post-DBS. DaTscan abnormalities, indicative of bilateral striatal dopaminergic denervation, were observed in four cases. Prior to DBS, all patients exhibited dystonia unresponsive to L-dopa, and post-DBS, FOG remained refractory to dopaminergic treatment in most cases. Despite initial improvements in gait pos

Objective: To develop and externally test a video-based framework for simultaneous detection of hyperkinetic MDs phenomenologies: dystonia, tremor, myoclonus, chorea, athetosis, ballismus, stereotypies, and tics using routine clinical recordings, with explicit testing of external, cross-cohort transfer from adult to pediatric populations. Methods: In this proof-of-concept study, the framework combines markerless pose estimation, kinematic descriptors, and a pretrained fondation model. A shared predictive backbone was developed on 21 adults with confirmed hyperkinetic MDs and 4 healthy controls assessed under a standardized protocol. External validation was performed on an independent external cohort: a real-world pediatric sample (n=12, monogenic combined MDs). For the external dataset, the backbone was deployed without retraining; lightweight calibration adjusted only the final subject-level decision step using a small labeled subset of patients selected by clinicians as representative of the cohort's phenotypic range. Results: After local calibration of the decision layer on the clinician-selected subset, performance improved consistently on the held-out pediatric patients (n=7):

Hyperkinetic movement disorders (HMDs) such as dystonia, tremor, chorea, myoclonus, and tics are disabling motor manifestations across childhood and adulthood. Their fluctuating, intermittent, and frequently co-occurring expressions hinder clinical recognition and longitudinal monitoring, which remain largely subjective and vulnerable to inter-rater variability. Objective and scalable methods to distinguish overlapping HMD phenotypes from routine clinical videos are still lacking. Here, we developed a pose-based machine-learning framework that converts standard outpatient videos into anatomically meaningful keypoint time series and computes kinematic descriptors spanning statistical, temporal, spectral, and higher-order irregularity-complexity features.

Hyperkinetic movement disorders (HMDs) in children, including dystonia (abnormal twisting) and chorea (irregular, random movements), pose significant diagnostic challenges due to overlapping clinical features. The prevalence of dystonia ranges from 2 to 50 per million, and chorea from 5 to 10 per 100,000. These conditions are often diagnosed with delays averaging 4.75 to 7.83 years. Traditional diagnostic methods depend on clinical history and expert physical examinations, but specialized tests are ineffective due to the complex pathophysiology of these disorders. This study develops a neural network model to differentiate between dystonia and chorea from video recordings of paediatric patients performing motor tasks. The model integrates a Graph Convolutional Network (GCN) to capture spatial relationships and Long Short-Term Memory (LSTM) networks to account for temporal dynamics. Attention mechanisms were incorporated to improve model interpretability. The model was trained and validated on a dataset of 50 videos (31 chorea-predominant, 19 dystonia-predominant) collected under regulatory approval from Guy's and St Thomas' NHS Foundation Trust. The model achieved 85% accuracy, 81%

We consider functional linear regression models where functional outcomes are associated with scalar predictors by coefficient functions with shape constraints, such as monotonicity and convexity, that apply to sub-domains of interest. To validate the partial shape constraints, we propose testing a composite hypothesis of linear functional constraints on regression coefficients. Our approach employs kernel- and spline-based methods within a unified inferential framework, evaluating the statistical significance of the hypothesis by measuring an $L^2$-distance between constrained and unconstrained model fits. In the theoretical study of large-sample analysis under mild conditions, we show that both methods achieve the standard rate of convergence observed in the nonparametric estimation literature. Through numerical experiments of finite-sample analysis, we demonstrate that the type I error rate keeps the significance level as specified across various scenarios and that the power increases with sample size, confirming the consistency of the test procedure under both estimation methods. Our theoretical and numerical results provide researchers the flexibility to choose a method based

Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the Pearson correlation matrix, which is not well-suited to describing the relationships between non-Gaussian variables. We propose a generalized approach to PVA enabling the use of different types of correlation, and we explore using Spearman, Gaussian copula, and polychoric correlations as alternatives to Pearson correlation when performing PVA. We compare performance in simulation studies varying the form of the true multivariate distribution over a wide range of possibilities. Our results show that on continuous non-Gaussian data, using generalized PVA with Gaussian copula or Spearman correlations provides a major improvement in performance compared to Pearson. Meanwhile, on ordinal data, generalized PVA with polychoric correlations outperforms the rest by a wide margin. We apply generalized PVA to a dataset of 102 clinical variables measured on individuals with X-linked dystonia parkinsonism (XDP), a rare neurodegenerative disorder, and we find that using different types of correlation y

The needle bio-potential sensors for measuring muscle and brain activity need invasive surgical targeted muscle reinnervation (TMR) and a demanding process to maintain, but surface bio-potential sensors lack clear bio-signal reading (Signal-Interference). In this research, a novel power-optimized complementary metal-oxide-semiconductor (CMOS) Surface Electromyography (sEMG) is developed to improve the efficiency and quality of captured bio-signal for biomedical application: The early diagnosis of neurological disorders (Dystonia) and a novel compatible mind-controlled prosthetic leg with human daily activities. A novel sEMG composed of CMOS Op-Amp based PIC16F877A 8-bit CMOS Flash-based Microcontroller is utilized to minimize power consumption and data processing time. sEMG Circuit is implemented with developed analog filter along with infinite impulse response (IIR) digital filter via Fast Fourier Transform (FFT), Z-transform, and difference equations. The analysis shows a significant improvement of 169.2% noise-reduction in recorded EMG signal using developed digital filter compared to analog one according to numerical root mean square error (RMSE). Moreover, digital IIR was test

The aim of this study is to investigate bursts- related EEG signals in a focal hand dystonia patient. Despite of considering time domain and frequency domain techniques as mutually exclusive analysis, in this contribution we have taken advantage from both of them: particularly, in the frequency domain, coherence was used to identify the most likely frequency bands of interaction between brain and muscles, then, in the time domain, cross-correlation was exploited to verify the physiological reliability of such a relationship in terms of signal transmission delay from the centre to the periphery. Our preliminary results suggest - in line with recent literature - that activity in the high beta band (around 30 Hz) could represent an electroencephalographic correlate for the pathological electromyographic bursts affecting the focal hand dystonia condition. Even though a future study on a larger sample is needed to statistically support these preliminary findings, this contribution allows to think of new kinds of rehabilitation from focal hand dystonia that could target the actual electroencephalographic correlate of the pathology, i.e. phenotypically expressed by bursts, with the conseq

Many people living with neurological disorders, such as cerebral palsy, stroke, muscular dystrophy or dystonia experience upper limb impairments (muscle spasticity, loss of selective motor control, muscle weakness or tremors) and have difficulty to eat independently. The general goal of this project is to develop a new device to assist with eating, aimed at stabilizing the movement of people who have movement disorders. A first iteration of the device was validated with children living with cerebral palsy and showed promising results. This validation however pointed out important drawbacks. This paper presents an iteration of the design which includes a new mechanism reducing the required arm elevation, improving safety through a compliant utensil attachment, and improving damping and other static balancing factors.

Parkinson's disease (PD) is debilitating, progressive, and clinically marked by motor symptoms. As the second most common neurodegenerative disease in the world, it affects over 10 million lives globally. Existing diagnoses methods have limitations, such as the expense of visiting doctors and the challenge of automated early detection, considering that behavioral differences in patients and healthy individuals are often indistinguishable in the early stages. However, micrographia, a handwriting disorder that leads to abnormally small handwriting, tremors, dystonia, and slow movement in the hands and fingers, is commonly observed in the early stages of PD. In this work, we apply machine learning techniques to extract signs of micrographia from drawing samples gathered from two open-source datasets and achieve a predictive accuracy of 94%. This work also sets the foundations for a publicly available and user-friendly web portal that anyone with access to a pen, printer, and phone can use for early PD detection.

Deep brain stimulation (DBS) is an established method for treating pathological conditions such as Parkinson's disease, dystonia, Tourette syndrome, and essential tremor. While the precise mechanisms which underly the effectiveness of DBS are not fully understood, theoretical studies of populations of neural oscillators stimulated by periodic pulses suggest that this may be related to clustering, in which subpopulations of the neurons are synchronized, but the subpopulations are desynchronized with respect to each other. The details of the clustering behavior depend on the frequency and amplitude of the stimulation in a complicated way. In the present study, we investigate how the number of clusters, their stability properties, and their basins of attraction can be understood in terms of one-dimensional maps defined on the circle. Moreover, we generalize this analysis to stimuli that consist of pulses with alternating properties, which provide additional degrees of freedom in the design of DBS stimuli. Our results illustrate how the complicated properties of clustering behavior for periodically forced neural oscillator populations can be understood in terms of a much simpler dynami

This paper presents the development of a new passive assistive handwriting device, which aims to stabilize the motion of people living with movement disorders. Many people living with conditions such as cerebral palsy, stroke, muscular dystrophy or dystonia experience upper limbs impairments (muscle spasticity, unselective motor control, muscle weakness or tremors) and are unable to write or draw on their own. The proposed device is designed to be fixed on a table. A pen is attached to the device using a pen holder, which maintains the pen in a fixed orientation. The user interacts with the device using a handle while mechanical dampers and inertia contribute to the stabilization of the user's movements. The overall mechanical design of the device is first presented, followed by the design of the pen holder mechanism.

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