Variability, regularity and coupling measures distinguish PD tremor from voluntary 5Hz tremor

A characteristic of Parkinson's disease (PD) is the development of tremor within the 4–6 Hz range. One method used to better understand pathological tremor is to compare the responses to tremor-type actions generated intentionally in healthy adults. This study was designed to investigate the similarities and differences between voluntarily generated 4–6 Hz tremor and PD tremor in regards to their amplitude, frequency and coupling characteristics. Tremor responses for 8 PD individuals (on- and off-medication) and 12 healthy adults were assessed under postural and resting conditions. Results showed that the voluntary and PD tremor were essentially identical with regards to the amplitude and peak frequency. However, differences between the groups were found for the variability (SD of peak frequency, proportional power) and regularity (Approximate Entropy, ApEn) of the tremor signal. Additionally, coherence analysis revealed strong inter-limb coupling during voluntary conditions while no bilateral coupling was seen for the PD persons. Overall, healthy participants were able to produce a 5 Hz tremulous motion indistinguishable to that of PD patients in terms of peak frequency and amplitude. However, differences in the structure of variability and level of inter-limb coupling were found for the tremor responses of the PD and healthy adults. These differences were preserved irrespective of the medication state of the PD persons. The results illustrate the importance of assessing the pattern of signal structure/variability to discriminate between different tremor forms, especially where no differences emerge in standard measures of mean amplitude as traditionally defined.

The pattern of coupling dynamics between postural motion, isotonic hand movements and physiological tremor

This study was designed to examine differences in the coupling dynamics between upper limb motion, physiological tremor and whole body postural sway in young healthy adults. Acceleration of the hand and fingers, forearm EMG activity and postural sway data were recorded. Estimation of the degree of bilateral and limb motion-postural sway coupling was determined by cross correlation, coherence and Cross-ApEn analyses. The results of the analysis revealed that, under postural tremor conditions, there was no significant coupling between limbs, muscles or sway across all metrics of coupling. In contrast, performing a rapid alternating flexion/extension movement about the wrist joint (with one or both limbs) resulted in stronger coupling between limb motion and postural sway. These results support the view that, for physiological tremor responses, the control of postural sway is maintained independent to tremor in the upper limb. However, increasing the level of movement about a distal segment of one arm (or both) leads to increased coupling throughout the body. The basis for this increased coupling would appear to be related to the enhanced neural drive to task-specific muscles within the upper limb.

Up-regulation of GABA transporters and GABAA Receptor α1 subunit in tremor rat hippocampus

The loss of GABAergic neurotransmission has been closely linked with epileptogenesis. The modulation of the synaptic activity occurs both via the removal of GABA from the synaptic cleft and by GABA transporters (GATs) and by modulation of GABA receptors. The tremor rat (TRM; tm/tm) is the parent strain of the spontaneously epileptic rat (SER; zi/zi, tm/tm), which exhibits absence-like seizure after 8 weeks of age. However, there are no reports that can elucidate the effects of GATs and GABAA receptors (GABARs) on TRMs. The present study was conducted to detect GATs and GABAR a1 subunit in TRMs hippocampus at mRNA and protein levels. In this study, total synaptosomal GABA content was significantly decreased in TRMs hippocampus compared with control Wistar rats by high performance liquid chromatography (HPLC); mRNA and protein expressions of GAT-1, GAT-3 and GABAR a1 subunit were all significantly increased in TRMs hippocampus by real time PCR and western blot, respectively; GAT-1 and GABAR a1 subunit proteins were localized widely in TRMs and control rats hippocampus including CA1, CA3 and dentate gyrus (DG) regions whereas only a wide distribution of GAT-3 was observed in CA1 region by immunohistochemistry. These data demonstrate that excessive expressions of GAT-1 as well as GAT-3 and GABAR a1 subunit in TRMs hippocampus may provide the potential therapeutic targets for genetic epilepsy.

Abnormal changes in voltage-gated sodium channels NaV1.1, NaV1.2, NaV1.3, NaV1.6 and in Calmodulin/calmodulin-dependent protein kinase II, within the brains of spontaneously epileptic rats and tremor rats

Voltage-gated sodium channels (VGSCs) play a crucial role in epilepsy. The expressions of different VGSCs subtypes are varied in diverse animal models of epilepsy that may reflect their multiple phenotypes or the complexity of the mechanisms of epilepsy. In a previous study, we reported that NaV1.1 and NaV1.3 were up-regulated in the hippocampus of the spontaneously epileptic rat (SER). In this study, we further analyzed both the expression and distribution of the typical VGSC subtypes NaV1.1, NaV1.2, NaV1.3 and NaV1.6 in the hippocampus and in the cortex of the temporal lobe of two genetic epileptic animal models: the SER and the tremor rat (TRM). The expressions of calmodulin (CaM) and calmodulin-dependent protein kinase II (CaMKII) were also analyzed with the purpose of assessing the effect of the CaM/CaMKII pathway in these two models of epilepsy. Increased expression of the four VGSC subtypes and CaM, accompanied by a decrease in CaMKII was observed in the hippocampus of both the SERs and the TRM rats. However, the changes observed in the expression of VGSC subtypes and CaM were decreased with an elevated CaMKII in the cortex of their temporal lobes. Double-labeled immunofluorescence data suggested that in SERs and TRM rats, the four subtypes of the VGSC proteins were present throughout the CA1, CA3 and dentate gyrus regions of the hippocampus and temporal lobe cortex and these were co-localized in neurons with CaM. These data represent the first evidence of abnormal changes in expression of four VGSC subtypes (NaV1.1, NaV1.2, NaV1.3 and NaV1.6) and CaM/CaMKII in the hippocampus and temporal lobe cortex of SERs and TRM rats. These changes may be involved in the generation of epileptiform activity and underlie the observed seizure phenotype in these rat models of genetic epilepsy.

Abnormal alterations in the Ca2+/CaV1.2/calmodulin/caMKII signaling pathway in a tremor rat model and in cultured hippocampal neurons exposed to Mg2+-free solution

Voltage-dependent calcium channels (VDCCs) are key elements in epileptogenesis. There are several binding-sites linked to calmodulin (CaM) and several potential CaM-dependent protein kinase II (CaMKII)-mediated phosphorylation sites in CaV1.2. The tremor rat model (TRM) exhibits absence‑like seizures from 8 weeks of age. The present study was performed to detect changes in the Ca2+/CaV1.2/CaM/CaMKII pathway in TRMs and in cultured hippocampal neurons exposed to Mg2+‑free solution. The expression levels of CaV1.2, CaM and phosphorylated CaMKII (p‑CaMKII; Thr‑286) in these two models were examined using immunofluorescence and western blotting. Compared with Wistar rats, the expression levels of CaV1.2 and CaM were increased, and the expression of p‑CaMKII was decreased in the TRM hippocampus. However, the expression of the targeted proteins was reversed in the TRM temporal cortex. A significant increase in the expression of CaM and decrease in the expression of CaV1.2 were observed in the TRM cerebellum. In the cultured neuron model, p‑CaMKII and CaV1.2 were markedly decreased. In addition, neurons exhibiting co‑localized expression of CaV1.2 and CaM immunoreactivities were detected. Furthermore, intracellular calcium concentrations were increased in these two models. For the first time, o the best of our knowledge, the data of the present study suggested that abnormal alterations in the Ca2+/CaV1.2/CaM/CaMKII pathway may be involved in epileptogenesis and in the phenotypes of TRMs and cultured hippocampal neurons exposed to Mg2+‑free solution.

LINGO1 and LINGO2 variants are associated with essential tremor and Parkinson disease.

Genetic variation in the leucine-rich repeat and Ig domain containing 1 gene (LINGO1) was recently associated with an increased risk of developing essential tremor (ET) and Parkinson disease (PD). Herein, we performed a comprehensive study of LINGO1 and its paralog LINGO2 in ET and PD by sequencing both genes in patients (ET, n=95; PD, n=96) and by examining haplotype-tagging single-nucleotide polymorphisms (tSNPs) in a multicenter North American series of patients (ET, n=1,247; PD, n= 633) and controls (n=642). The sequencing study identified six novel coding variants in LINGO1 (p.S4C, p.V107M, p.A277T, p.R423R, p.G537A, p.D610D) and three in LINGO2 (p.D135D, p.P217P, p.V565V), however segregation analysis did not support pathogenicity. The association study employed 16 tSNPs at the LINGO1 locus and 21 at the LINGO2 locus. One variant in LINGO1 (rs9652490) displayed evidence of an association with ET (odds ratio (OR) =0.63; P=0.026) and PD (OR=0.54; P=0.016). Additionally, four other tSNPs in LINGO1 and one in LINGO2 were associated with ET and one tSNP in LINGO2 associated with PD (P<0.05). Further analysis identified one tSNP in LINGO1 and two in LINGO2 which influenced age at onset of ET and two tSNPs in LINGO1 which altered age at onset of PD (P<0.05). Our results support a role for LINGO1 and LINGO2 in determining risk for and perhaps age at onset of ET and PD. Further studies are warranted to confirm these findings and to determine the pathogenic mechanisms involved.

La leyenda de Chevagnes (Los tremor)

UANL

Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network

Deep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.

Upper limb tremor suppression in ADL via an orthosis incorporating a controllable double viscous beam actuator

Neuromuscular disorders affect millions of people world-wide. Upper limb tremor is a common symptom, and due to its complex aetiology it is difficult to compensate for except, in particular cases by surgical intervention or drug therapy. Wearable devices that mechanically compensate for limb tremor could benefit a considerable number of patients, but the technology to assist suffers in this way is under-developed. In this paper we propose an innovative orthosis that can dynamically suppress pathological tremor, by applying viscous damping to the affected limb in a controlled manner. The orthosis design utilises a new actuator design based on Magneto-Rheological Fluids that efficiently deliver damping action in response to the instantaneous tremor frequency and amplitude.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.