1,721,121 research outputs found
Process for reducing neuromuscular fatigue caused by exercise
No full textA process for reducing neuromuscular fatigue caused by exercise comprises applying an anodal trans-cranial direct current stimulation over the right motor cortex of a person
An external portable device for adaptive deep brain stimulation (aDBS) clinical research in advanced Parkinson's Disease
No full textCompared to conventional deep brain stimulation (DBS) for patients with Parkinson's Disease (PD), the newer approach of adaptive DBS (aDBS), regulating stimulation on the basis of the patient's clinical state, promises to achieve better clinical outcomes, avoid adverse-effects and save time for tuning parameters. A remaining challenge before aDBS comes into practical use is to prove its feasibility and its effectiveness in larger groups of patients and in more ecological conditions. We developed an external portable aDBS system prototype designed for clinical testing in freely-moving PD patients with externalized DBS electrodes. From a single-channel bipolar artifact-free recording, it analyses local field potentials (LFPs), during ongoing DBS for tuning stimulation parameters, independent from the specific feedback algorithm implemented. We validated the aDBS system in vitro, by testing both its sensing and closed-loop stimulation capabilities, and then tested it in vivo, focusing on the sensing capabilities. By applying the aDBS system prototype in a patient with PD, we provided evidence that it can track levodopa and DBS-induced LFP spectral power changes among different patient's clinical states. Our system, intended for testing LFP-based feedback strategies for aDBS, should help understanding how and whether aDBS therapy works in PD and indicating future technical and clinical advances
Evidence for minimal abnormalities of motor axonal excitability in restless leg syndrome
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Sex, genes, hormones, and nigral neurodegeneration: two different Parkinson’s diseases in males and females
No full textEvidence from clinical, anatomical, neurophysiological, endocrinological and genetic studies demonstrates that Parkinson's disease (PD) is a gender-specific pathology. The study by Cantuti-Castelvetri et al. points out the central role of sexual dimorphisms in gene expression for understanding the pathophysiology of gender differences in PD. They identified several genes differentially expressed in female and male PD patients. Their results support the hypothesis that PD in females and in males are two different pathologies possibly associated with specific therapies. Gender differences in PD could be seen in the wider perspective of gender medicine for neurological disorders and their results suggest that basic and clinical research should examine sex differences for both understanding the disease and optimizing treatments
The adaptive deep brain stimulation challenge
No full textSub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson's Disease (PD) have boosted the development of new solutions to improve DBS therapy. Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters according to the patient's clinical state, promises to achieve this goal and is attracting increasing interest in overcoming all of the challenges posed by its development and adoption. In the design, implementation, and application of aDBS, the choice of the control variable and of the control algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the control variable and control policy, in the system design and its technological limits, in the patient's target symptom, and in the surgical procedure needed. Here, we review the current proposals for aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks, thus providing a general overview of the possible pathways for the clinical translation of aDBS with its benefits, limitations and unsolved issues
From Mocap data to inertial data through a biomechanical model to classify countermeasure exercises performed on ISS
Full text linkAbstract— On board the International Space Station (ISS) resistive training is essential to reduce the effects of musculoskeletal system deconditioning due to weightlessness. However, it could be equally dangerous or not useful if performed with inappropriate techniques. Thus, a system based on inertial sensors able to monitor astronauts has been thought. In this work, an OpenSim biomechanical model was used to reproduce motion of countermeasure target exercises and to simulate inertial sensors put on the model. This was done starting from kinematic data collected with motion capture system (mocap), because no inertial data were available. Then, it was explored a possible approach to build the classifier able to automatically recognize ‘correct’ and ‘wrong’ techniques of execution. Two machine learning algorithms were compared and results in terms of accuracy were encouraging
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