A new diagnostic test to distinguish tremulous Parkinson's disease from advanced essential tremor

Distinguishing essential tremor from Parkinson's disease: clinical and experimental tools

INTRODUCTION

Essential tremor (ET) and Parkinson's disease (PD) are the most common causes of tremor and the most prevalent movement disorders, with overlapping clinical features that can lead to diagnostic challenges, especially in the early stages.

AREAS COVERED

In the present paper, the authors review the clinical and experimental studies and emphasized the major aspects to differentiate between ET and PD, with particular attention to cardinal phenomenological features of these two conditions. Ancillary and experimental techniques, including neurophysiology, neuroimaging, fluid biomarker evaluation, and innovative methods, are also discussed for their role in differential diagnosis between ET and PD. Special attention is given to investigations and tools applicable in the early stages of the diseases, when the differential diagnosis between the two conditions is more challenging. Furthermore, the authors discuss knowledge gaps and unsolved issues in the field.

EXPERT OPINION

Distinguishing ET and PD is crucial for prognostic purposes and appropriate treatment. Additionally, accurate diagnosis is critical for optimizing clinical and experimental research on pathophysiology and innovative therapies. In a few years, integrated technologies could enable accurate, reliable diagnosis from early disease stages or prodromal stages in at-risk populations, but further research combining different techniques is needed.

Validation and application of computer vision algorithms for video-based tremor analysis

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55-0.86, p≤ .01) as well as an accuracy of up to 2.60 mm (95% CI [-3.13, 8.23]) and ≤0.21 Hz (95% CI [-0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings.

The role of laboratory investigations in the classification of tremors

INTRODUCTION

Tremor is the most common movement disorder. Although clinical examination plays a significant role in evaluating patients with tremor, laboratory tests are useful to classify tremors according to the recent two-axis approach proposed by the International Parkinson and Movement Disorders Society.

METHODS

In the present review, we will discuss the usefulness and applicability of the various diagnostic methods in classifying and diagnosing tremors. We will evaluate a number of techniques, including laboratory and genetic tests, neurophysiology, and neuroimaging. The role of newly introduced innovative tremor assessment methods will also be discussed.

RESULTS

Neurophysiology plays a crucial role in tremor definition and classification, and it can be useful for the identification of specific tremor syndromes. Laboratory and genetic tests and neuroimaging may be of paramount importance in identifying specific etiologies. Highly promising innovative technologies are being developed for both clinical and research purposes.

CONCLUSIONS

Overall, laboratory investigations may support clinicians in the diagnostic process of tremor. Also, combining data from different techniques can help improve understanding of the pathophysiological bases underlying tremors and guide therapeutic management.

Development of a New Wearable Device for the Characterization of Hand Tremor

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson's Disease Versus Essential Tremor

Upper limb tremor is a prominent symptom of both Parkinson's disease and essential tremor. Its kinematic parameters overlap substantially for these two pathological conditions, thus leading to high rate of misdiagnosis, especially for community doctors. Several groups have proposed various methods for improving differential diagnosis. These prior studies have attempted to identify better kinematic parameters, however they have mainly focused on single limb features including tremor intensity, tremor frequency, and tremor variability. In this paper, we propose a wearable system for multi-segment assessment of upper limb tremor and differential diagnosis of Parkinson's disease versus essential tremor. The proposed system collected tremor data from both wrist and fingers simultaneously. From this data, we extracted multi-segment features in the form of phase relationships between limb segments. Using support vector machine classifiers, we then performed differential diagnosis from the extracted features. We evaluated the performance of the proposed system on 19 Parkinson's disease patients and 12 essential tremor patients. Moreover, we also assessed the performance cost associated with reducing task load and sensor array size. The proposed system reached perfect accuracy in leave-one-out cross validation. Task reduction and sensor array reduction were associated with penalties of 2% and 9-10% respectively. The results demonstrated that the proposed system could be simplified for clinical applications, and successfully applied to the differential diagnosis of Parkinson's disease versus essential tremor in real-world setting.

Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits

Introduction

Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors.

Methods

Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability.

Results

Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important.

Conclusion

Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.

Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices

BACKGROUND

Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors.

METHODS

The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device".

RESULTS

Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy.

CONCLUSIONS

Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

Clinical neurophysiology of Parkinson's disease and parkinsonism

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This review is part of the series on the clinical neurophysiology of movement disorders and focuses on Parkinson’s disease and parkinsonism.

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The pathophysiology of cardinal parkinsonian motor symptoms and myoclonus are reviewed.

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The recordings from microelectrode and deep brain stimulation electrodes are reported in detail.

This review is part of the series on the clinical neurophysiology of movement disorders. It focuses on Parkinson’s disease and parkinsonism. The topics covered include the pathophysiology of tremor, rigidity and bradykinesia, balance and gait disturbance and myoclonus in Parkinson’s disease. The use of electroencephalography, electromyography, long latency reflexes, cutaneous silent period, studies of cortical excitability with single and paired transcranial magnetic stimulation, studies of plasticity, intraoperative microelectrode recordings and recording of local field potentials from deep brain stimulation, and electrocorticography are also reviewed. In addition to advancing knowledge of pathophysiology, neurophysiological studies can be useful in refining the diagnosis, localization of surgical targets, and help to develop novel therapies for Parkinson’s disease.

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.

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury

DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.

Differentiation of Parkinson’s disease tremor and essential tremor based on a novel hand posture

Background

Tremor is one of the most common movement disorders but the correct diagnosis of tremor disorders, especially the differentiation between Parkinson’s disease tremor (PT) and essential tremor (ET) remains a challenge for clinicians.

Method

We examined a novel hand position to distinguish PT from ET. We prospectively collected accelerometric tremor data in 14 ET patients and 14 PT patients with arms and hands fully stretched against arms stretched and hands relaxed, i. e. hanging down. The total acceleration from the three pairwise-perpendicular accelerometric axes during the 1-minute blocks of the two hand positions were computed and high-passed filtered at 2 Hz. The power spectral density during each hand position was calculated and summed up over the frequency domain.

Results

Our results showed a significantly higher occurrence of tremor in the hands hanging down position in PT patients compared to ET patients (p = 0.0262). Moreover, in PT patients the tremor intensity significantly increased when transitioning from the stretched hand position to the hanging-down position (83 % of cohort) and vice versa in ET patients (75 % of cohort).

Conclusion

In conclusion, the new hand posture can differentiate between PT and ET with high accuracy (sensitivity 83 %, specificity 75 % for PT) and may be a helpful tool in the clinical assessment of tremor.

The diagnostic value of clinical neurophysiology in hyperkinetic movement disorders: A systematic review

INTRODUCTION

To guide the neurologist and neurophysiologist with interpretation and implementation of clinical neurophysiological examinations, we aim to provide a systematic review on evidence of electrophysiological features used to differentiate between hyperkinetic movement disorders.

METHODS

A PRISMA systematic search and QUADAS quality evaluation has been performed in PubMed to identify diagnostic test accuracy studies comparing electromyography and accelerometer features. We included papers focusing on tremor, dystonia, myoclonus, chorea, tics and ataxia and their functional variant. The features were grouped as 1) basic features (e.g., amplitude, frequency), 2) the influence of tasks on basic features (e.g., entrainment, distraction), 3) advanced analyses of multiple signals, 4) and diagnostic tools combining features.

RESULTS

Thirty-eight cross-sectional articles were included discussing tremor (n = 28), myoclonus (n = 5), dystonia (n = 5) and tics (n = 1). Fifteen were rated as 'high quality'. In tremor, the basic and task-related features showed great overlap between clinical tremor syndromes, apart from rubral and enhanced physiological tremor. Advanced signal analyses were best suited for essential, parkinsonian and functional tremor, and cortical, non-cortical and functional jerks. Combinations of electrodiagnostic features could identify essential, enhanced physiological and functional tremor.

CONCLUSION

Studies into the diagnostic accuracy of electrophysiological examinations to differentiate between hyperkinetic movement disorders have predominantly been focused on clinical tremor syndromes. No single feature can differentiate between them all; however, a combination of analyses might improve diagnostic accuracy.

Different effects of essential tremor and Parkinsonian tremor on multiscale dynamics of hand tremor

OBJECTIVE

Essential tremor (ET) and Parkinsonian tremor (PT) are often clinically misdiagnosed due to the overlapping characteristics of their hand tremor. We aim to examine if ET and PT influence the multiscale dynamics of hand tremor, as quantified using complexity, differently, and if such complexity metric is of promise to help identify ET from PT.

METHODS

Forty-eight participants with PT and 48 with ET performed two 30-second tests within each of the following conditions: sitting while resting arms or outstretching arms horizontally. The hand tremor was captured by accelerometers secured to the dorsum of each hand. The complexity was quantified using multiscale entropy.

RESULTS

Compared to PT group, ET group had lower complexity of both hands across conditions (F > 34.2, p < 0.001). Lower complexity was associated with longer disease duration (r² > 0.15, p < 0.009) in both PT and ET, and within PT, greater Unified Parkinson's Disease Rating Scale-III UPDRS-III scores (r² > 0.18, p < 0.009). Receiver-operating-characteristic curves revealed that the complexity metric can distinguish ET from PT (area-under-the-curve > 0.77, cut-off value = 48 (postural), 49 (resting)), which was confirmed in a separate dataset with ET and PT that were clearly diagnosed in prior work.

CONCLUSIONS

The PT and ET have different effects on hand tremor complexity, and this metric is promising to help the identification of ET and PT, which still needs to be confirmed in future studies.

SIGNIFICANCE

The characteristics of multiscale dynamics of the hand tremor, as quantified by complexity, provides novel insights into the different pathophysiology between ET and PT.

The Characteristics of Tremor Motion Help Identify Parkinson's Disease and Multiple System Atrophy

Background/Objectives: Distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA) is challenging in the clinic because patients with these two conditions present with similar symptoms in motor dysfunction. Here, we aimed to determine whether tremor characteristics can serve as novel markers for distinguishing the two conditions.

Methods: Ninety-one subjects with clinically diagnosed PD and 93 subjects with MSA were included. Tremor of the limbs was measured in different conditions (such as resting, postural, and weight-holding) using electromyography (EMG) surface electrodes and accelerometers. The dominant frequency, tremor occurrence rate, and harmonic occurrence rate (HOR) of the tremor were then calculated.

Results: Our results demonstrated that the tremor dominant frequency in the upper limbs of the MSA group was significantly higher than that in the PD group across all resting (F = 5.717, p = 0.023), postural (F = 13.409, p < 0.001), and weight-holding conditions (F = 9.491, p < 0.001) and that it was not dependent on the patient's age or disease course. The tremor occurrence rate (75.6 vs. 14.9%, χ² = 68.487, p < 0.001) and HOR (75.0 vs. 4.5%, χ² = 46.619, p < 0.001) in the resting condition were significantly lower in the MSA group than in the PD group. The sensitivity of the harmonic for PD diagnosis was 75.0% and the specificity was relatively high, in some cases up to 95.5%. The PPV and NPV were 95.2 and 75.9%, respectively.

Conclusion: Our study confirmed that several tremor characteristics, including the dominant tremor frequency and the occurrence rate in different conditions, help detect PD and MSA. The presence of harmonics may serve as a novel marker to help distinguish PD from MSA with high sensitivity and specificity.

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.

β-Adrenoceptor Drugs and Parkinson's Disease: A Nationwide Nested Case-Control Study

BACKGROUND

Potential relationships between β-adrenergic drugs and α-synuclein synthesis in Parkinson's disease (PD) have been recently suggested.

OBJECTIVE

This study investigated the putative association between β-adrenoceptor drug exposure and PD occurrence.

METHODS

A nested case-control study was performed in the Echantillon Généraliste des Bénéficiaires (EGB) (a 1/97th random sample of affiliates to the French Insurance System). Incident PD patients diagnosed between 01/01/2008 and 31/12/2017 (index date) were matched 1:1 to controls by gender, birth year, and insurance scheme. Exposure to any β-agonist and to any β-antagonist was compared between cases and controls within 1-2 years before the index date, and exposure to salbutamol and to propranolol was individualized. The association between PD and β-adrenoceptor drugs was investigated through conditional logistic regression models adjusted for potential confounding factors. Because of a statistical interaction between β-agonists and diabetes, results were stratified according to the presence of diabetes.

RESULTS

Among the 2225 incident PD patients identified in the EGB (mean age 75.6 ± 10.2 years, sex ratio 1.04), no significant association was found between PD and β-antagonists (adjusted odds ratio [aOR] 1.05 [95% confidence interval 0.91-1.20]), except for propranolol (aOR 2.11 [1.38-3.23]). For β-agonists, a protective association in non-diabetic patients (aOR 0.75 [0.60-0.93]) and an opposite and significant association in diabetic patients (aOR 1.61 [1.02-2.55]) were observed. Similar results were found with salbutamol.

CONCLUSION

This study did not identify an increased risk of PD occurrence after β-antagonist exposure, except for propranolol (potential protopathic bias). The discordant results observed with β-agonists in patients with or without diabetes deserve further exploration of the influence of diabetic comorbidity on PD occurrence and evolution.

Essential tremor severity and anatomical changes in brain areas controlling movement sequencing

Objective

Although the cerebello‐thalamo‐cortical network has often been suggested to be of importance in the pathogenesis of essential tremor (ET), the origins of tremorgenic activity in this disease are not fully understood. We used a combination of cortical thickness imaging and neurophysiological studies to analyze whether the severity of tremor was associated with anatomical changes in the brain in ET patients.

Methods

Magnetic resonance imaging (MRI) and a neurophysiological assessment were performed in 13 nondemented ET patients. High field structural brain MRI images acquired in a 3T scanner and analyses of cortical thickness and surface were carried out. Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer image analysis software. We used high‐density surface electromyography (hdEMG) and inertial measurement units (IMUs) to quantify the tremor severity in upper extrimities of patients. In particular, advanced computer tool was used to reliably identify discharge patterns of individual motor units from surface hdEMG and quantify motor unit synchronization.

Results

We found significant association between increased motor unit synchronization (i.e., more severe tremor) and cortical changes (i.e., atrophy) in widespread cerebral cortical areas, including the left medial orbitofrontal cortex, left isthmus of the cingulate gyrus, right paracentral lobule, right lingual gyrus, as well as reduced left supramarginal gyrus (inferior parietal cortex), right isthmus of the cingulate gyrus, left thalamus, and left amygdala volumes.

Interpretation

Given that most of these brain areas are involved in controlling movement sequencing, ET tremor could be the result of an involuntary activation of a program of motor behavior used in the genesis of voluntary repetitive movements.

[Pathophysiology of tremor]

Tremor is clinically defined as a rhythmic, oscillating movement of parts of the body, which functionally leads to impairment of the coordination and execution of targeted movements. It can be a symptom of a primary disease, such as resting tremor in Parkinson's disease or occur as an independent disease, such as essential or orthostatic tremor. For the development of tremor, cerebral components as well as mechanisms at the spinal and muscular level play an important role. This review presents the results of new imaging and electrophysiological studies that have led to important advances in our understanding of the pathophysiology of tremor. We discuss pathophysiological models for the development of resting tremor in Parkinson's disease, essential and orthostatic tremor. We describe recent developments starting from the classical generator model, with an onset of pathological oscillations in distinct cerebral regions, to a network perspective in which tremor arises and spreads through existing anatomical or newly emerged pathological brain networks. In particular translational approaches are presented and discussed. These could serve in the future as a basis for the development of new therapeutic strategies.

Tremor stability index: a new tool for differential diagnosis in tremor syndromes

See Vidailhet et al. (doi:10.1093/brain/awx140) for a scientific commentary on this article. Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 55 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.

Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer

Background

The differential diagnosis between patients with essential tremor (ET) and those with Parkinson’s disease (PD) whose main manifestation is tremor may be difficult unless using complex neuroimaging techniques such as ¹²³I-FP-CIT SPECT. We considered that using smartphone’s accelerometer to stablish a diagnostic test based on time-frequency differences between PD an ET could support the clinical diagnosis.

Methods

The study was carried out in 17 patients with PD, 16 patients with ET, 12 healthy volunteers and 7 patients with tremor of undecided diagnosis (TUD), who were re-evaluated one year after the first visit to reach the definite diagnosis. The smartphone was placed over the hand dorsum to record epochs of 30 s at rest and 30 s during arm stretching. We generated frequency power spectra and calculated receiver operating characteristics curves (ROC) curves of total spectral power, to establish a threshold to separate subjects with and without tremor. In patients with PD and ET, we found that the ROC curve of relative energy was the feature discriminating better between the two groups. This threshold was then used to classify the TUD patients.

Results

We could correctly classify 49 out of 52 subjects in the category with/without tremor (97.96% sensitivity and 83.3% specificity) and 27 out of 32 patients in the category PD/ET (84.38% discrimination accuracy). Among TUD patients, 2 of 2 PD and 2 of 4 ET were correctly classified, and one patient having PD plus ET was classified as PD.

Conclusions

Based on the analysis of smartphone accelerometer recordings, we found several kinematic features in the analysis of tremor that distinguished first between healthy subjects and patients and, ultimately, between PD and ET patients. The proposed method can give immediate results for the clinician to gain valuable information for the diagnosis of tremor. This can be useful in environments where more sophisticated diagnostic techniques are unavailable.

Rest tremor revisited: Parkinson's disease and other disorders

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.

A system to monitor tremors in patients with Parkinson's disease

In this work the design and development of a system to monitor tremors in patients with Parkinson's disease based on Arduino open-source prototyping platform is presented. For processing data tremors acquired by the sensor device we have developed an Android application which allows an evaluation of the state of PD patients based on three types of tests that are in the Unified Parkinson's Disease Rating Scale recommended by the Movement Disorder Society (MDS-UPDRS); the tests performed in the application are: postural tremor of the hands, kinetic tremors of the hands and resting tremor amplitude. The results of PD Patients showed that despite receiving medication to minimize symptoms of their disease, patients have a considerable tremor amplitude, which affects the normal development of their daily activities. In addition, the spectral analysis of the tremors shows that two of the patients were correctly diagnosed with PD while the third patient showed spectral characteristics which led us to suggest to the treating physician reconsider the diagnosis.

The Clinical Evaluation of Parkinson's Tremor

Parkinson’s disease harbours many different tremors that differ in distribution, frequency, and context in which they occur. A good clinical tremor assessment is important for weighing up possible differential diagnoses of Parkinson’s disease, but also to measure the severity of the tremor as a basis for further tailored treatment. This can be challenging, because Parkinson’s tremor amplitude is typically very variable and context-dependent. Here, we outline how we investigate Parkinson’s tremor in the clinic. We describe a simple set of clinical tasks that can be used to constrain tremor variability (cognitive and motor co-activation, several specific limb postures). This may help to adequately characterize the tremor(s) occurring in a patient with Parkinson’s disease.

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.

Automatic recognition of Parkinson's disease using surface electromyography during standardized gait tests

Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for PD.

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.

Dopamine-responsive pattern in tremor patients

BACKGROUND

Diagnosis and treatment of tremor are largely based on clinical assessment. Whereas in some patients tremor may respond to dopaminergic treatment, in general l-Dopa response to tremor varies considerably. The aim of this study was to predict l-Dopa response by accelerometry.

METHODS

We included 60 tremor patients and measured harmonic oscillations by accelerometry. In addition to neurological assessment, we performed l-Dopa challenge tests and the individual tremor response was compared to the amount of harmonic oscillations.

RESULTS

We found a strong correlation between harmonic oscillations and clinical l-Dopa response. Similarly, harmonic oscillations were significantly greater in patients with subjective tremor reduction upon l-Dopa administration.

CONCLUSIONS

We conclude that harmonic oscillations are a measure for l-Dopa response to tremor irrespective of the underlying disease. Because of the observational character of the study, any causal relation remains speculative. Nevertheless, we propose a novel, non-invasive approach to predict l-Dopa response in tremor patients.

Smart watch accelerometry for analysis and diagnosis of tremor

BACKGROUND

Distinguishing the postural re-emergent tremor of Parkinson disease from essential tremor can be difficult clinically. Use of accelerometry to aid diagnosis is limited to laboratory settings. We sought to record and differentiate these tremors using a smart watch device in an outpatient clinic.

NEW METHOD

41 patients were enrolled. Recordings were made with a smart watch device on the predominantly affected hand (all patients), and simultaneously with an analog accelerometer (10 patients) with hands at rest and outstretched. Tremor peak frequency, peak power, and power of the first four harmonics was calculated and compared between the two devices. Mean power at the first four harmonics was calculated and used to classify tremor as parkinsonian or essential. Test characteristics were calculated to compare the device and clinical diagnoses.

RESULTS

Mean harmonic peak power was both highly sensitive and specific for distinction of Parkinson disease postural tremor from essential tremor with an optimal threshold for our sample (sensitivity 90.9%, 95% CI 58.7-99.8%; specificity 100%, 95% CI 76.8-100%; Cohen's kappa=0.91, SE=0.08).

COMPARISON WITH EXISTING METHODS

The smart watch and analog devices had nearly perfect concordance of peak frequency and proportional harmonic power. The smart watch recordings in clinic took 3-6 min.

CONCLUSIONS

A smart watch device can provide accurate and diagnostically relevant information about postural tremor. Its portability and ease of use could help translate such techniques into routine clinic use or to the community.

Waveform analysis of tremor may help to differentiate Parkinson's disease from drug-induced parkinsonism

In this study, we analyzed the waveform characteristics of resting tremor by accelerometer recordings in patients with drug-induced parkinsonism (DIP) and Parkinson's disease (PD). We prospectively recruited 12 patients with tremulous PD and 12 patients with DIP presenting with resting tremor. Tremor was recorded from the more affected side and was recorded twice for a 60 s period in each patient. Peak frequency, amplitude and all harmonic peaks were obtained, and the asymmetry of the decay of the autocorrelation function, third momentum and time-reversal invariance were also computed using a mathematical algorithm. Among the parameters used in the waveform analysis, the harmonic ratio, time-reversal invariance and asymmetric decay of the autocorrelation function were different between PD and DIP at a statistically significant level (all p < 0.01). The total harmonic peak power and third momentum in the time series were not significantly different. The clinical characteristics of DIP patients may be similar to those of PD patients in some cases, which makes the clinical differentiation between DIP and PD challenging. Our study shows that the identification of parameters reflecting waveform asymmetry might be helpful in differentiating between DIP and PD.

EMG signal morphology in essential tremor and Parkinson's disease

The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.

Epidemiology, diagnosis and differential diagnosis in Parkinson's disease tremor

The epidemiology of tremor in Parkinson's disease is not well examined. The prevalence of Parkinson's disease is about 100-300 per 100,000, and the majority (70-100%) of these patients may develop tremor during the course of the disorder. The expression of tremor is also influenced by the genetic background of selected patients. On the other hand, Parkinson patients with a predominant tremor phenotype may have a more favourable prognosis in terms of mortality and the development of motor and non-motor complications. The diagnosis of Parkinson tremor is based on a clinical diagnosis of both underlying Parkinson's disease and on the tremor itself. Tremor is a rhythmical, involuntary oscillatory movement of a body part, and includes resting tremor, action tremor including postural and kinetic tremor. The classical type is resting tremor, but other phenotypes may also occur. Misdiagnoses between Parkinson tremor and essential tremor are relatively common. Electrophysiological and functional imaging examinations can be useful in the distinction of the two, but both approaches suffer from some limitations. In general, essential tremor and other tremor forms can be distinguished from Parkinson tremor by their frequency and their expression with different activation.

The phenomenology of parkinsonian tremor

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.