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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Deep Brain Stimulation, functional neurosurgery for Parkinson's disease treatment, has three parameters: frequency, pulse width, and voltage. They should be selected appropriately to achieve an effective treatment. In this research, we studied some nonlinear dynamics of Parkinsonian tremor under Deep Brain Stimulation. We recorded finger tremor signals of 8 patients at different voltages as data. To study tremor behavior variation at different voltages, we extracted some nonlinear dynamic features of the data. These features were: embedding space, correlation dimension, largest Lyapunov exponent, entropy and variance of embedding space along x, and y axes. We found that the largest Lyapunov exponents were positive and correlation dimensions of data were not integer, and also all embedding spaces at different voltages had attractors. These observations show that the tremor signal has chaotic behaviors. Also, embedding space variance along x, and y axes in all cases and entropy in the most of them had minimum values at highest voltages in comparison with other voltages. We plotted bar graphs of embedding space variances along x, and y axes at every patient's recorded voltages. A reduction procedure was seen after a threshold in all graphs. As a result, all the chaotic features change with the voltage variation. In the future, it could be possible to select the optimum stimulation voltages by using chaotic features of tremor.
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PMID:Study of chaotic behavior of tremor of some Parkinsonians under deep brain stimulation. 2212 47

The definition of Parkinsonian tremor covers all different forms occurring in Parkinson's disease. The most common form is rest tremor, labelled as typical Parkinsonian tremor. Other variants cover also postural and action tremors. Data support the notion that suppression of rest tremor may be more specific for PD tremors. Several differential diagnoses like rest tremor in ET, dystonic tremor, psychogenic tremor and Holmes' tremor may be misinterpreted as PD-tremor. Tests and clinical clues to separate them are presented.
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PMID:The phenomenology of parkinsonian tremor. 2216 65

Parkinsonian tremor is among the most emblematic medical signs and is one of the cardinal manifestations of Parkinson's disease (PD). Its semiology has been extensively addressed by ancient and contemporary medical literature, but more attention has been dedicated to its medical treatment in the past than nowadays. Among the hundreds of studies performed to determine the value of medical and surgical approaches on motor and non motor signs of PD, only a minority specifically considered effect on tremor as an efficacy outcome. Current available guidelines for PD treatment include attempts to specifically address tremor treatment but stress the low level of evidences available. In these conditions, with its still poorly understood pathophysiological basis and variable clinical expression PD tremor treatment is a clinical challenge. Only surgery (lesion or high frequency stimulation) of discrete deep brain targets consistently provides symptomatic long lasting alleviation. Through revision of contemporary scientific evidence, the purpose of this paper is to offer a systematic pragmatic approach to symptomatic management of tremor as one of the distinctive signs of PD that may generate substantial disability.
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PMID:Tremor revisited: treatment of PD tremor. 2216 67

The deep brain stimulation (DBS) of the subthalamic nucleus (STN) is the most effective surgical therapy for Parkinson's disease (PD). The first aim of the study was to locate the STN-DBS electrode by applying source analysis on EEG. Secondly, to identify tremor related areas which are associated with the STN. The Dynamic imaging of coherent sources (DICS) was used to find the coherent sources in the brain. The capability of the source analysis to detect deep sources like STN in the brain using EEG data was tested with two model dipole simulations. The simulations were concentrated on two aspects, the angle of the dipole orientation and the disturbance of the cortical areas on locating subcortical regions. In all the DBS treated Parkinsonian tremor patients the power spectrum showed a clear peak at the stimulated frequency and followed by there harmonics. The DBS stimulated frequency constituted a network of primary sensory motor cortex, supplementary motor area, prefrontal cortex, diencephalon, cerebellum and brainstem. Thus the STN was located in the region of the diencephalon. The resolved network may give better understanding to the pathophysiology of the effected tremor network in PD patients with STN-DBS.
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PMID:Locating the STN-DBS electrodes and resolving their subsequent networks using coherent source analysis on EEG. 2336 97

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3-C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics demonstrated a frequency dependent damping on tremor, which may prevent tremor above 8 Hz to occur.
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PMID:Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease. 2427 89

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.
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PMID:Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials. 2673 29

Tremor is the most common movement disorder characterized by a rhythmical, involuntary oscillatory movement of a body part. Since distinct diseases can cause similar tremor manifestations and vice-versa, it is challenging to make an accurate diagnosis. This applies particularly for tremor at rest. This entity was only rarely studied in the past, although a multitude of clinical studies on prevalence and clinical features of tremor in Parkinson's disease (PD), essential tremor and dystonia, have been carried out. Monosymptomatic rest tremor has been further separated from tremor-dominated PD. Rest tremor is also found in dystonic tremor, essential tremor with a rest component, Holmes tremor and a few even rarer conditions. Dopamine transporter imaging and several electrophysiological methods provide additional clues for tremor differential diagnosis. New evidence from neuroimaging and electrophysiological studies has broadened our knowledge on the pathophysiology of Parkinsonian and non-Parkinsonian tremor. Large cohort studies are warranted in future to explore the nature course and biological basis of tremor in common tremor related disorders.
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PMID:Rest tremor revisited: Parkinson's disease and other disorders. 2863 97

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson's Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson's disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.
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PMID:Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device. 2929 43

Deep brain stimulation (DBS) has markedly changed how we treat movement disorders including Parkinson's disease (PD), dystonia, and essential tremor (ET). However, despite its demonstrable clinical benefit, DBS is often limited by side effects and partial efficacy. These limitations may be due in part to the fact that DBS interferes with both pathological and physiological neural activities. DBS could, therefore, be potentially improved were it applied selectively and only at times of enhanced pathological activity. This form of stimulation is known as closed-loop or adaptive DBS (aDBS). An aDBS approach has been shown to be superior to conventional DBS in PD in primates using cortical neuronal spike triggering and in humans employing local field potential biomarkers. Likewise, aDBS studies for essential and Parkinsonian tremor are advancing and show great promise, using both peripheral or central sensing and stimulation. aDBS has not yet been trialed in dystonia and yet exciting and promising biomarkers suggest it could be beneficial here too. In this chapter, we will review the existing literature on aDBS in movement disorders and explore potential biomarkers and stimulation algorithms for applying aDBS in PD, ET, and dystonia.
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PMID:Adaptive Brain Stimulation for Movement Disorders. 2933 87

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.
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PMID:Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. 3156 35


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