A longitudinal study of tremor frequencies in Parkinson's disease and essential tremor

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation

OBJECTIVE

A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom.

METHODS

We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection.

RESULTS

The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor.

CONCLUSION

The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics.

SIGNIFICANCE

Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Tracking motion kinematics and tremor with intrinsic oscillatory property of instrumental mechanics

Tracking kinematic details of motor behaviors is a foundation to study the neuronal mechanism and biology of motor control. However, most of the physiological motor behaviors and movement disorders, such as gait, balance, tremor, dystonia, and myoclonus, are highly dependent on the overall momentum of the whole-body movements. Therefore, tracking the targeted movement and overall momentum simultaneously is critical for motor control research, but it remains an unmet need. Here, we introduce the intrinsic oscillatory property (IOP), a fundamental mechanical principle of physics, as a method for motion tracking in a force plate. The overall kinetic energy of animal motions can be transformed into the oscillatory amplitudes at the designed IOP frequency of the force plate, while the target movement has its own frequency features and can be tracked simultaneously. Using action tremor as an example, we reported that force plate-based IOP approach has superior performance and reliability in detecting both tremor severity and tremor frequency, showing a lower level of coefficient of variation (CV) compared with video- and accelerometer-based motion tracking methods and their combination. Under the locomotor suppression effect of medications, therapeutic effects on tremor severity can still be quantified by dynamically adjusting the overall locomotor activity detected by IOP. We further validated IOP method in optogenetic-induced movements and natural movements, confirming that IOP can represent the intensity of general rhythmic and nonrhythmic movements, thus it can be generalized as a common approach to study kinematics.

Longitudinal study of clinical and neurophysiological features in essential tremor

BACKGROUND AND PURPOSE

Essential tremor (ET) is a common and heterogeneous disorder characterized by postural/kinetic tremor of the upper limbs and other body segments and by non-motor symptoms, including cognitive and psychiatric abnormalities. Only a limited number of longitudinal studies have comprehensively and simultaneously investigated motor and non-motor symptom progression in ET. Possible soft signs that configure the ET-plus diagnosis are also under-investigated in follow-up studies. We aimed to longitudinally investigate the progression of ET manifestations by means of clinical and neurophysiological evaluation.

METHODS

Thirty-seven ET patients underwent evaluation at baseline (T0) and at follow-up (T1; mean interval ± SD = 39.89 ± 9.83 months). The assessment included the clinical and kinematic evaluation of tremor and voluntary movement execution, as well as the investigation of cognitive and psychiatric disorders.

RESULTS

A higher percentage of patients showed tremor in multiple body segments and rest tremor at T1 as compared to T0 (all p-values < 0.01). At T1, the kinematic analysis revealed reduced finger-tapping movement amplitude and velocity as compared to T0 (both p-values < 0.001). The prevalence of cognitive and psychiatric disorders did not change between T0 and T1. Female sex, absence of family history, and rest tremor at baseline were identified as predictive factors of worse disease progression.

CONCLUSIONS

ET progression is characterized by the spread of tremor in multiple body segments and by the emergence of soft signs. We also identified possible predictors of disease worsening. The results contribute to a better understanding of ET classification and pathophysiology.

A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor

Background

Ordinal scales based on qualitative observation are the mainstay in the clinical assessment of tremor, but are limited by inter-rater reliability, measurement precision, range, and ceiling effects. Quantitative tremor evaluation is well-developed in research, but clinical application has lagged, in part due to cumbersome mathematical application and lack of established standards.

Objectives

To develop a novel method for evaluating tremor that integrates a standardized clinical exam, wrist-watch accelerometers, and a software framework for data analysis that does not require advanced mathematical or computing skills. The utility of the method was tested in a sequential cohort of patients with predominant postural and action tremor presenting to a specialized surgical clinic with the presumptive diagnosis of Essential Tremor (ET).

Methods

Wristwatch accelerometry was integrated with a standardized clinical exam. A MATLAB application was developed for automated data analysis and graphical representation of tremor. Measures from the power spectrum of acceleration of tremor in different upper limb postures were derived in 25 consecutive patients. The linear results from accelerometry were correlated with the commonly used non-linear Clinical Rating Scale for Tremor (CRST).

Results

The acceleration power spectrum was reliably produced in all consecutive patients. Tremor frequency was stable in different postures and across patients. Both total and peak power of acceleration during postural conditions correlated well with the CRST. The standardized clinical examination with integrated accelerometry measures was therefore effective at characterizing tremor in a population with predominant postural and action tremor. The protocol is also illustrated on repeated measures in an ET patient who underwent Magnetic Resonance-Guided Focused Ultrasound thalamotomy.

Conclusion

Quantitative assessment of tremor as a continuous variable using wristwatch accelerometry is readily applicable as a clinical tool when integrated with a standardized clinical exam and a user-friendly software framework for analysis. The method is validated for patients with predominant postural and action tremor, and can be adopted for characterizing tremor of different etiologies with dissemination in a wide variety of clinical and research contexts in ageing populations.

Changes in elbow flexion EMG morphology during adjustment of deep brain stimulator in advanced Parkinson’s disease

Objective

Deep brain stimulation (DBS) is an effective treatment for motor symptoms of advanced Parkinson’s disease (PD). Currently, DBS programming outcome is based on a clinical assessment. In an optimal situation, an objectively measurable feature would assist the operator to select the appropriate settings for DBS. Surface electromyographic (EMG) measurements have been used to characterise the motor symptoms of PD with good results; with proper methodology, these measurements could be used as an aid to program DBS.

Methods

Muscle activation measurements were performed for 13 patients who had advanced PD and were treated with DBS. The DBS pulse voltage, frequency, and width were changed during the measurements. The measured EMG signals were analysed with parameters that characterise the EMG signal morphology, and the results were compared to the clinical outcome of the adjustment.

Results

The EMG signal correlation dimension, recurrence rate, and kurtosis changed significantly when the DBS settings were changed. DBS adjustment affected the signal recurrence rate the most. Relative to the optimal settings, increased recurrence rates (median ± IQR) 1.1 ± 0.5 (−0.3 V), 1.3 ± 1.1 (+0.3 V), 1.7 ± 0.4 (−30 Hz), 1.7 ± 0.8 (+30 Hz), 2.0 ± 1.7 (+30 μs), and 1.5 ± 1.1 (DBS off) were observed. With optimal stimulation settings, the patients’ Unified Parkinson’s Disease Rating Scale motor part (UPDRS-III) score decreased by 35% on average compared to turning the device off. However, the changes in UPRDS-III arm tremor and rigidity scores did not differ significantly in any settings compared to the optimal stimulation settings.

Conclusion

Adjustment of DBS treatment alters the muscle activation patterns in PD patients. The changes in the muscle activation patterns can be observed with EMG, and the parameters calculated from the signals differ between optimal and non-optimal settings of DBS. This provides a possibility for using the EMG-based measurement to aid the clinicians to adjust the DBS.

Fitts Law-Based Performance Metrics to Quantify Tremor in Individuals with Essential Tremor

Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasksa spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts law), path efficiency (PE), and completion time (CT)). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R²=0.57 and R²=0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R²=0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individuals tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.

Clinical Characteristics and Electrophysiological Biomarkers of Parkinson's Disease Developed From Essential Tremor

Background and Objective: Parkinson's disease developed from essential tremor (ET-PD) is a distinct clinical syndrome that is different from essential tremor (ET) and Parkinson's disease (PD). There is currently a lack of research on ET-PD. Tremor characteristics (amplitude and frequency) are primary quantitative indexes for diagnosing and monitoring of tremors. In this study, we aimed to explore specific clinical and electrophysiological biomarkers for the identification of ET-PD. Methods: The study included patients with ET-PD (n = 22), ET (n = 42), and tremor-dominant PD (t-PD, n = 47). We collected demographic data, clinical characteristics (including motor and non-motor symptoms), and tremor analysis. The frequency, amplitude, contracting patterns of resting tremor and postural tremor were collected. The analysis of ET-PD and ET/t-PD was compared. The receiver operating characteristic (ROC) curve was used to analyze the electrophysiological features in distinguishing ET-PD from ET or t-PD. Results: Compared with ET, hyposmia, bradykinesia, rigidity, postural abnormality, and resting tremor were more common in the ET-PD group (P = 0.01, 0.003, 0.001, 0.001, 0.019, respectively). The postural tremor frequencies of the head, upper limbs, and lower limbs were significantly lower in the ET-PD than in the ET (P = 0.007, 0.003, 0.035, respectively), which were the most appropriate variables for distinguishing ET-PD from ET (AUC: 0.775, 0.727, and 0.701, respectively). Compared with t-PD, bradykinesia, rigidity, postural abnormality (both P < 0.001), and resting tremor (P = 0.024) were less common in the ET-PD. The postural tremor amplitudes of the head and upper limbs were significantly higher in the ET-PD than in the t-PD (P = 0.022, 0.001, respectively), which were the most appropriate variables for distinguishing ET-PD from t-PD (AUC: 0.793 and 0.716). Conclusions: Hyposmia and electrophysiological biomarkers (postural tremor frequencies and amplitudes) help early recognition of ET-PD.

Optimal Control Perspective on Parkinson's Disease: Increased Delay Between State Estimator and Controller Produces Tremor

Parkinson's disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson's disease. The thalamus relays signals from the cerebellum to the primary motor cortex: previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex). Simulations demonstrate realistic tremor in a neuromuscular model of the wrist. In addition, changes to effort sensitivity in the optimal controller may account for some clinical features of parkinsonian tremor, including the characteristics of re-emergent tremor and the time-varying amplitude and frequency of tremor. Contextualization of the optimal control model with physiological models and clinical observations provides insight into the potential role of the basal ganglia and cerebello-thalamo-cortical circuit and how treatments like dopaminergic medications and deep brain stimulation reduce tremor.

Tremor analysis with wearable sensors correlates with outcome after thalamic deep brain stimulation

INTRODUCTION

Thalamic deep brain stimulation (DBS) provides excellent tremor control in most patients with essential tremor (ET). However, not all tremor patients show clinically significant improvement after DBS surgery. Currently, there is no reliable clinical or instrument-based measure to predict how patients respond to DBS. Therefore, we set out to provide a method for tremor outcome prediction prior to surgery.

METHODS

We retrospectively analysed quantitative tremor data collected with inertial measurement units (IMU) in 13 patients who underwent DBS surgery in the ventral intermediate nucleus of the thalamus (VIM). All patients were diagnosed with either ET or ET-plus according to current diagnostic criteria of the movement disorder society. We used linear and logistic regression models to evaluate the influence of different tremor characteristics on tremor outcome.

RESULTS

We found that the ratio between the amplitude of the first overtone and the amplitude of the fundamental frequency, denoted as the Harmonic Index, has a significant influence on tremor reduction after DBS surgery. This measure shows a strong correlation with the post-operative improvement of tremor outcome based on the Whiget Tremor Rating Scale.

CONCLUSION

Based on these findings, we propose a novel approach to predict tremor outcome after DBS surgery. Quantitative tremor assessment adds to the preoperative prediction of DBS response and might therefore have a relevant clinical impact in the management of patients suffering from pharmacoresistant tremor.

On the Use of Non-Contact Capacitive Sensors for the Assessment of Postural Hand Tremor of Individuals with Parkinson's Disease

Parkinsonian tremor manifests in different types: rest, postural, and action tremors. The postural tremor occurs while a body part is held straight out from the body in a stable position against gravity. The Unified Parkinson's Disease Rating Scale (UPDRS), which is a subjective assessment performed by the qualitative judgment of neurologists, is the clinical standard for parkinsonian tremor assessment. Despite the common use of subjective methods, inertial measurement unit (IMU) sensors are largely used in many studies as a motion capture system to objective assessment of tremors. However, this kind of sensor must be attached to the patient's body, it limits the patient's movements and requires specific techniques for correct positioning in the limb. In this sense, non-contact capacitive (NCC) sensors are an alternative proposed in this research to record the motor activity of the hand and wrist during a pose against gravity. In order to assess the postural tremor and evaluate this novel sensing technology, data from ten subjects, five with Parkinson's disease (PD) and five neurologically healthy (H) matched in age and sex, were collected. We analyzed the instantaneous mean frequency (IMNF) of the signals from NCC and gyroscope sensors for both groups. The selected descriptive statistical variables allowed discrimination (p <; 0.05) among subjects from H and PD groups while using the gyroscope or the NCC sensor. The obtained results indicate that the NCC sensor can measure the postural hand tremor, and also that frequency features extracted from the collected signals can be used to discriminate subjects from both groups.

Population-scale hand tremor analysis via anonymized mouse cursor signals

Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope and depend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in short-duration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods are presented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as self-reports, the findings of published clinical studies, and a target selection study where participants self-report hand tremors and known causes. The results demonstrate significant alignment between estimated tremors and both self-reports and clinical findings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor frequencies vary as expected for different medical conditions. Overall, the study finds evidence for the validity of harnessing anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications include opt-in services for screening and for monitoring the progression of illness.

Need for mechanically and ergonomically enhanced tremor-suppression orthoses for the upper limb: a systematic review

INTRODUCTION

Tremor is the most common movement disorder, affecting 5.6% of the population with Parkinson's disease or essential tremor over the age of 65. Conventionally, tremor diseases like Parkinson's are treated with medication. An alternative non-invasive symptom treatment is the mechanical suppression of the oscillation movement. The purpose of this review is to identify the weaknesses of past wearable tremor-suppression orthoses for the upper limb and identify the need for further research and developments.

METHOD

A systematic literature search was conducted by performing a keyword combination search of the title, abstract and keyword sections in the four databases Web of Science, MedLine, Scopus, and ProQuest. Initially, the retrieved articles were selected by title and abstract using selection criteria. The same criteria were then applied to the full publication text. After the selection process, relevant information on the retrieved orthoses was isolated, sorted and analysed systematically.

RESULTS

Forty-six papers, representing 21 orthoses, were identified and analysed according to the mechanical and ergonomic properties. The identified orthoses can be divided into 5 concepts and 16 functional prototypes, then subdivided further based upon their use of passive, semi-active, or active suppression mechanisms. Most of the orthoses concentrate on the wrist and elbow flexion and extension. They mainly rely on rigid structures and actuators while having tremor-suppression efficacies for tremorous subjects from 30 to 98% using power spectral density or other methods.

CONCLUSION

The comparison of tremor-suppression orthoses considered and mapped their various mechanical and ergonomic properties, including the degrees of freedom, weight, suppression characteristics, and efficacies. This review shows that most of the orthoses are bulky and heavy, with a non-adapted human-machine interface which can cause rejection by the user. The main challenge of the design of an effective, minimally intrusive and portable tremor-suppressing orthosis is the integration of compact, powerful, lightweight, and non-cumbersome suppression mechanisms. None of the existing prototypes combine all the desired characteristics. Future research should focus on novel suppression orthoses and mechanisms with compact dimensions and light weight in order to be less cumbersome while giving a good tremor-suppression performance.

On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder that remains incurable. The available treatments for the disorder include pharmacologic therapies and deep brain stimulation (DBS). These approaches may cause distinct side effects and motor responses. This work presents the application of t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for nonlinear dimensionality reduction and data visualization, for the problem of discriminating neurologically healthy individuals from those suffering from PD (treated with levodopa and DBS). Furthermore, the assessment of classification methods is presented. Inertial and electromyographic data were collected while the subjects executed a sequence of four motor tasks. The results were focused on the comparison of the classification performance of a support vector machine (SVM) while discriminating two-dimensional feature sets estimated from Principal Component Analysis (PCA), Sammon's mapping, and t-SNE. The results showed visual and statistical differences for all three investigated groups. Classification accuracy for PCA, Sammon's mapping, and t-SNE was, respectively, 73.5%, 78.6%, and 96.9% for the training set and 67.8%, 74.1%, and 76.6% for the test set. The possibility of discriminating healthy individuals from those with PD treated with levodopa and DBS highlights the fact that each treatment method produces distinct motor behavior. The scatter plots resulting from t-SNE could be used in the clinical practice as an objective tool for measuring the discrepancy between normal and abnormal motor behaviors, being thus useful for the adjustment of treatments and the follow-up of the disorder.

Characterization of Parkinsonian Hand Tremor and Validation of a High-Order Tremor Estimator

Recent progress in wearable technology has made wearable tremor suppression devices (WTSDs) for Parkinson's patients a potentially viable alternative solution for tremor management. So far, in contrast to wrist and elbow tremor, finger tremors have not been studied in depth despite the huge impact that they have on a patient's daily life. In addition, more evidence has been found showing that the performance of current tremor estimators may be limited by their model order due to the multiple harmonics present in tremor. The aim of this paper is to characterize finger and wrist tremor in both the time and frequency domains, and to propose a high-order tremor estimation algorithm. Tremor magnitudes are reported in the forms of linear acceleration, angular velocity, and angular displacement. The activation of forearm flexor and extensor muscles is also investigated. The frequency analysis shows that Parkinsonian tremors produce oscillations of the hand with pronounced harmonics. At last, a high-order weighted-frequency Fourier linear combiner (WFLC)-based Kalman filter is proposed. The percentage estimation accuracy achieved from the proposed estimator is 96.3 ± 1.7%, showing average improvements of 28.5% and 48.9% over its lower-order counterpart and the WFLC. The proposed estimator shows promise for use in a WTSD.

Could Wearable and Mobile Technology Improve the Management of Essential Tremor?

Essential tremor (ET) is the most common movement disorder. Individuals exhibit postural and kinetic tremor that worsens over time and patients may also exhibit other motor and non-motor symptoms. While millions of people are affected by this disorder worldwide, several barriers impede an optimal clinical management of symptoms. In this paper, we discuss the impact of ET on patients and review major issues to the optimal management of ET; from the side-effects and limited efficacy of current medical treatments to the limited number of people who seek treatment for their tremor. Then, we propose seven different areas within which mobile and wearable technology may improve the clinical management of ET and review the current state of research in these areas.

Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor

Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

Analysis of 3D spatial trajectories in Parkinsonian, essential and physiological tremors

The clinical differentiation of the tremor in Parkinson's disease (PD) and essential tremor (ET) can sometimes be challenging, especially in the early stage of the disease. As different neural oscillators are involved in the generation of these two types of tremor, their trajectorial analysis could also be different. The goal of this study was to investigate whether some disease-specific patterns related to their tremor trajectories in fact exist. The three-axial accelerometer signals of the hand tremor obtained from a total of 369 participants [49 with PD, 25 with isolated resting tremor (iRT), 133 with ET, and finally 162 normal subjects with physiological tremor (Ph)] were subjected to vector analysis using a custom-made mathematical program. Subsequently, detailed trajectorial analysis was performed. The key discrimination ability between the PD and ET groups was represented by the ratio of the vector in the y-z plane and the spatial vector. The great majority of the patients with PD and iRT showed significantly higher values as compared to those with ET. The differences between the PD and iRT groups and between ET and Ph were not statistically significant. We suggest that the newly introduced three-axial accelerometry with analysis of tremor trajectories could be beneficial in differentiating between tremors in PD and ET.

An Ambulatory Tremor Score for Parkinson's Disease

BACKGROUND

While tremor in Parkinson's Disease (PD) can be characterised in the consulting room, its relationship to treatment and fluctuations can be clinically helpful.

OBJECTIVE

To develop an ambulatory assessment of tremor of PD.

METHODS

Accelerometry data was collected using the Parkinson's KinetiGraph System (PKG, Global Kinetics). An algorithm was developed, which could successfully distinguish been subjects with a resting or postural tremor that involved the wrist whose frequency was greater than 3 Hz. Percent of time that tremor was present (PTT) between 09 : 00 and 18 : 00 was calculated.

RESULTS

This algorithm was applied to 85 people with PD who had been assessed clinically for the presence and nature of tremor. The Sensitivity and Selectivity of a PTT ≥0.8% was 92.5% and 92.9% in identifying tremor, providing that the tremor was not a fine kinetic and postural tremor or was not in the upper limb. A PTT >1% provide high likely hood of the presence of clinical meaningful tremor. These cut-offs were retested on a second cohort (n = 87) with a similar outcome. The Sensitivity and Selectivity of the combined group was 88.7% and 89.5% respectively. Using the PTT, 50% of 22 newly diagnosed patients had a PTT >1.0%.The PKG's simultaneous bradykinesia scores was used to find a threshold for the emergence of tremor. Tremor produced artefactual increase in the PKG's dyskinesia score in 1% of this sample.

CONCLUSIONS

We propose this as a means of assessing the presence of tremor and its relationship to bradykinesia.

Longitudinal wearable tremor measurement system with activity recognition algorithms for upper limb tremor

Assessments of tremor characteristics by movement disorder physicians are usually done at single time points in clinic settings, so that the description of the tremor does not take into account the dependence of the tremor on specific behavioral situations. Moreover, treatment-induced changes in tremor or behavior cannot be quantitatively tracked for extended periods of time. We developed a wearable tremor measurement system with tremor and activity recognition algorithms for long-term upper limb behavior tracking, to characterize tremor characteristics and treatment effects in their daily lives. In this pilot study, we collected sensor data of arm movement from three healthy participants using a wrist device that included a 3-axis accelerometer and a 3-axis gyroscope, and classified tremor and activities within scenario tasks which resembled real life situations. Our results show that the system was able to classify the tremor and activities with 89.71% and 74.48% accuracies during the scenario tasks. From this results, we expect to expand our tremor and activity measurement in longer time period.

Clinical Assessments in Parkinson's Disease: Scales and Monitoring

Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.

Feature visualization and classification for the discrimination between individuals with Parkinson's disease under levodopa and DBS treatments

Background

Over the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments, which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy.

Methods

In this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N = 10), subjects with PD treated with DBS (N = 12), and subjects with PD treated with levodopa (N = 16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which the high-dimensional feature space was reduced to a two-dimensional space using the nonlinear Sammon’s map. Non-parametric analysis of variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification.

Results

The results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81 ± 6% (mean ± standard deviation) and 71 ± 8%, for training and test groups respectively.

Conclusions

This research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.

Tremor frequency characteristics in Parkinson's disease under resting-state and stress-state conditions

Tremor characteristics-amplitude and frequency components-are primary quantitative clinical factors for diagnosis and monitoring of tremors. Few studies have investigated how different patient's conditions affect tremor frequency characteristics in Parkinson's disease (PD). Here, we analyzed tremor characteristics under resting-state and stress-state conditions. Tremor was recorded using an accelerometer on the finger, under resting-state and stress-state (calculation task) conditions, during rest tremor and postural tremor. The changes of peak power, peak frequency, mean frequency, and distribution of power spectral density (PSD) of tremor were evaluated across conditions. Patients whose tremors were considered more than "mild" were selected, for both rest (n=67) and postural (n=25) tremor. Stress resulted in both greater peak powers and higher peak frequencies for rest tremor (p<0.001), but not for postural tremor. Notably, peak frequencies were concentrated around 5 Hz under stress-state condition. The distributions of PSD of tremor were symmetrical, regardless of conditions. Tremor is more evident and typical tremor characteristics, namely a lower frequency as amplitude increases, are different in stressful condition. Patient's conditions directly affect neural oscillations related to tremor frequencies. Therefore, tremor characteristics in PD should be systematically standardized across patient's conditions such as attention and stress levels.

Intrinsic signature of essential tremor in the cerebello-frontal network

Essential tremor is a movement disorder characterized by tremor during voluntary movements, mainly affecting the upper limbs. The cerebellum and its connections to the cortex are known to be involved in essential tremor, but no task-free intrinsic signatures of tremor related to structural cerebellar defects have so far been found in the cortical motor network. Here we used voxel-based morphometry, tractography and resting-state functional MRI at 3 T to compare structural and functional features in 19 patients with essential tremor and homogeneous symptoms in the upper limbs, and 19 age- and gender-matched healthy volunteers. Both structural and functional abnormalities were found in the patients' cerebellum and supplementary motor area. Relative to the healthy controls, the essential tremor patients' cerebellum exhibited less grey matter in lobule VIII and less effective connectivity between each cerebellar cortex and the ipsilateral dentate nucleus. The patient's supplementary motor area exhibited (i) more grey matter; (ii) a lower amplitude of low-frequency fluctuation of the blood oxygenation level-dependent signal; (iii) less effective connectivity between each supplementary motor area and the ipsilateral primary motor hand area, and (iv) a higher probability of connection between supplementary motor area fibres and the spinal cord. Structural and functional changes in the supplementary motor area, but not in the cerebellum, correlated with clinical severity. In addition, changes in the cerebellum and supplementary motor area were interrelated, as shown by a correlation between the lower amplitude of low-frequency fluctuation in the supplementary motor area and grey matter loss in the cerebellum. The structural and functional changes observed in the supplementary motor area might thus be a direct consequence of cerebellar defects: the supplementary motor area would attempt to reduce tremor in the motor output by reducing its communication with M1 hand areas and by directly modulating motor output via its corticospinal projections.See Raethjen and Muthuraman (doi:10.1093/brain/awv238) for a scientific commentary on this article.

Dynamic tension EMG to characterize the effects of DBS treatment of advanced Parkinson's disease

Deep brain stimulation (DBS) is an effective treatment method for motor symptoms of advanced Parkinson's disease. DBS-electrode is implanted to subthalamic nucleus to give precisely allocated electrical stimuli to brain. The optimal stimulus type has to be adjusted individually. Disease severity, main symptoms and biological factors play a role in correctly setting up the device. Currently there are no objective methods to assess the efficacy of DBS, hence the adjustment is based solely on clinical assessment. In optimal case an objectively measurable feature would point the right settings of DBS. Surface electromyographic and kinematic measurements have been used in Parkinson's disease research. As Parkinson's disease symptoms are known to change the EMG signal properties, these methods could be helpful aid in the clinical adjustment of DBS. In this study, 13 patients with advanced Parkinson's disease who received DBS treatment were measured. The patients were measured with seven different settings of the DBS in clinical range including changes in stimulation amplitude, frequency and pulse width. The EMG analysis was based on parameters that characterize EMG signal morphology. Correlation dimension and recurrence rate made the most significant difference in relation to optimal settings. In conclusion, EMG analysis is able to detect differences between the DBS setups, and can help in finding the correct parameters.

A neuroprosthesis for tremor management through the control of muscle co-contraction

Background

Pathological tremor is the most prevalent movement disorder. Current treatments do not attain a significant tremor reduction in a large proportion of patients, which makes tremor a major cause of loss of quality of life. For instance, according to some estimates, 65% of those suffering from upper limb tremor report serious difficulties during daily living. Therefore, novel forms for tremor management are required. Since muscles intrinsically behave as a low pass filter, and tremor frequency is above that of volitional movements, the authors envisioned the exploitation of these properties as a means of developing a novel treatment alternative. This treatment would rely on muscle co-contraction for tremor management, similarly to the strategy employed by the intact central nervous system to stabilize a limb during certain tasks.

Methods

We implemented a neuroprosthesis that regulated the level of muscle co-contraction by injecting current at a pair of antagonists through transcutaneous neurostimulation. Co-contraction was adapted to the instantaneous parameters of tremor, which were estimated from the raw recordings of a pair of solid state gyroscopes with a purposely designed adaptive algorithm. For the experimental validation, we enrolled six patients suffering from parkinsonian or essential tremor of different severity, and evaluated the effect of the neuroprosthesis during standard tasks employed for neurological examination.

Results

The neuroprosthesis attained significant attenuation of tremor (p<0.001), and reduced its amplitude up to a 52.33±25.48%. Furthermore, it alleviated both essential and parkinsonian tremor in spite of their different etiology and symptomatology. Tremor severity was not a limiting factor on the performance of the neuroprosthesis, although there was a subtle trend towards larger attenuation of more severe tremors. Tremor frequency was not altered during neurostimulation, as expected from the central origin of Parkinson’s disease and essential tremor. All patients showed a good tolerance to neurostimulation in terms of comfort and absence of pain, and some spontaneously reported that they felt that tremor was reduced when the neuroprosthesis was activated.

Conclusions

The results presented herein demonstrate that the neuroprosthesis provides systematic attenuation of the two major types of tremor, irrespectively from their severity. This study sets the basis for the validation of the neuroprosthesis as an alternative, non-invasive means for tremor management.

Hierarchical safety supervisory control strategy for robot-assisted rehabilitation exercise

Clinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subject's safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

Tremor modulations across periods with and without voluntary motion and limb load task demands using movement quantification

Understanding the neurobiological mechanisms underlying different types of tremor and the altered functional connectivity of the involved areas is a timely goal in clinical neuroscience. If successful, this quest may open new perspectives on how to achieve tremor modulation, which is notably relevant, in Parkinson's disease (PD). Tremor can be characterized by simple parameters such as frequency and amplitude. It is therefore prone to be objectively targeted by neuromodulation and quantitatively investigated using multimodal techniques, such as, accelerometry, EMG and functional Magnetic Resonance Imaging (fMRI). Embarking on the latter challenge requires an a priori knowledge of how effective functional connectivity is altered in PD tremor. This works aims to ascertain which postural and voluntary movement tasks with distinct types of physical load are suitable for designing efficient fMRI protocols, by performing an accelerometry analysis to measure spontaneous and imposed tremor modulation on cohorts of PD patients, essential tremor patients and a group of voluntary healthy controls.

Dynamic variability of isometric action tremor in precision pinching

Evolutionary development of isometric force impulse frequencies, power, and the directional concordance of changes in oscillatory tremor during performance of a two-digit force regulation task was examined. Analyses compared a patient group having tremor confounding volitional force regulation with a control group having no neuropathological diagnosis. Dependent variables for tremor varied temporally and spatially, both within individual trials and across trials, across individuals, across groups, and between digits. Particularly striking findings were magnitude increases during approaches to cue markers and shifts in the concordance phase from pinching toward rigid sway patterns as the magnitude increased. Magnitudes were significantly different among trace line segments of the task and were characterized by differences in relative force required and by the task progress with respect to cue markers for beginning, reversing force change direction, or task termination. The main systematic differences occurred during cue marker approach and were independent of trial sequence order.