Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor

Deep learning of Parkinson's movement from video, without human-defined measures

BACKGROUND

The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures.

OBJECTIVES

(1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model.

METHODS

152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips.

RESULTS

For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group.

CONCLUSIONS

A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.

Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors

Background

Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge.

Methods

We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques.

Results

Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88.

Conclusion

We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.

Clinical and kinematic characterization of parkinsonian soft signs in essential tremor

BACKGROUND

Subtle parkinsonian signs, i.e., rest tremor and bradykinesia, are considered soft signs for defining essential tremor (ET) plus.

OBJECTIVES

Our study aimed to further characterize subtle parkinsonian signs in a relatively large sample of ET patients from a clinical and neurophysiological perspective.

METHODS

We employed clinical scales and kinematic techniques to assess a sample of 82 ET patients. Eighty healthy controls matched for gender and age were also included. The primary focus of our study was to conduct a comparative analysis of ET patients (without any soft signs) and ET-plus patients with rest tremor and/or bradykinesia. Additionally, we investigated the asymmetry and side concordance of these soft signs.

RESULTS

In ET-plus patients with parkinsonian soft signs (56.10% of the sample), rest tremor was clinically observed in 41.30% of cases, bradykinesia in 30.43%, and rest tremor plus bradykinesia in 28.26%. Patients with rest tremor had more severe and widespread action tremor than other patients. Furthermore, we observed a positive correlation between the amplitude of action and rest tremor. Most ET-plus patients had an asymmetry of rest tremor and bradykinesia. There was no side concordance between these soft signs, as confirmed through both clinical examination and kinematic evaluation.

CONCLUSIONS

Rest tremor and bradykinesia are frequently observed in ET and are often asymmetric but not concordant. Our findings provide a better insight into the phenomenology of ET and suggest that the parkinsonian soft signs (rest tremor and bradykinesia) in ET-plus may originate from distinct pathophysiological mechanisms.

Subtle changes in central dopaminergic tone underlie bradykinesia in essential tremor

INTRODUCTION

In this research, our primary objective was to explore the correlation between basal ganglia dopaminergic neurotransmission, assessed using 123I-FP-CIT (DAT-SPECT), and finger movements abnormalities in patients with essential tremor (ET) and Parkinson's disease (PD).

METHODS

We enrolled 16 patients with ET, 17 with PD, and 18 healthy controls (HC). Each participant underwent comprehensive clinical evaluations, kinematic assessments of finger tapping. ET and PD patients underwent DAT-SPECT imaging. The DAT-SPECT scans were subjected to both visual and semi-quantitative analysis using DaTQUANT®. We then investigated the correlations between the clinical, kinematic, and DAT-SPECT data, in patients.

RESULTS

Our findings confirm that individuals with ET exhibited slower finger tapping than HC. Visual evaluation of radiotracer uptake in both striata demonstrated normal levels within the ET patient cohort, while PD patients displayed reduced uptake. However, there was notable heterogeneity in the quantification of uptake within the striata among ET patients. Additionally, we found a correlation between the amount of radiotracer uptake in the striatum and movement velocity during finger tapping in patients. Specifically, lower radioligand uptake corresponded to decreased movement velocity (ET: coef. = 0.53, p-adj = 0.03; PD: coef. = 0.59, p-adj = 0.01).

CONCLUSION

The study's findings suggest a potential link between subtle changes in central dopaminergic tone and altered voluntary movement execution, in ET. These results provide further insights into the pathophysiology of ET. However, longitudinal studies are essential to determine whether the slight reduction in dopaminergic tone observed in ET patients represents a distinct subtype of the disease or could serve as a predictor for the clinical progression into PD.

Parkinsonian Hand or Clinician's Eye? Finger Tap Bradykinesia Interrater Reliability for 21 Movement Disorder Experts

BACKGROUND

Bradykinesia is considered the fundamental motor feature of Parkinson's disease (PD). It is central to diagnosis, monitoring, and research outcomes. However, as a clinical sign determined purely by visual judgement, the reliability of humans to detect and measure bradykinesia remains unclear.

OBJECTIVE

To establish interrater reliability for expert neurologists assessing bradykinesia during the finger tapping test, without cues from additional examination or history.

METHODS

21 movement disorder neurologists rated finger tapping bradykinesia, by Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS), in 133 videos of hands: 73 from 39 people with idiopathic PD, 60 from 30 healthy controls. Each neurologist rated 30 randomly-selected videos. 19 neurologists were also asked to judge whether the hand was PD or control. We calculated intraclass correlation coefficients (ICC) for absolute agreement and consistency of MDS-UPDRS ratings, using standard linear and cumulative linked mixed models.

RESULTS

There was only moderate agreement for finger tapping MDS-UPDRS between neurologists, ICC 0.53 (standard linear model) and 0.65 (cumulative linked mixed model). Among control videos, 53% were rated >  0 by MDS-UPDRS, and 24% were rated as bradykinesia by MBRS subscore combination. Neurologists correctly identified PD/control status in 70% of videos, without strictly following bradykinesia presence/absence.

CONCLUSION

Even experts show considerable disagreement about the level of bradykinesia on finger tapping, and frequently see bradykinesia in the hands of those without neurological disease. Bradykinesia is to some extent a phenomenon in the eye of the clinician rather than simply the hand of the person with PD.

Wearable sensors during drawing tasks to measure the severity of essential tremor

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r² = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Computerised analysis of writing and drawing by essential tremor phenotype

We investigated whether computerised analysis of writing and drawing could discriminate essential tremor (ET) phenotypes according to the 2018 Consensus Statement on the Classification of Tremors. The Consensus scheme emphasises soft additional findings, mainly motor, that do not suffice to diagnose another tremor syndrome. Ten men and nine women were classified by blinded assessors according to Consensus Axis 1 definitions of ET and ET plus. Blinded scoring of tremor severity and alternating limb movement was also conducted. Twenty healthy participants acted as controls. Four writing and three drawing tasks were performed on a Wacom Intuos Pro Large digital tablet with a pressure-sensor mounted ink pen. Sixty-seven computerised measurements were obtained, comprising static (dimensional and temporal), kinematic and pen pressure features. The mean age of ET participants was 67.2±13.0 years and mean tremor duration was 21.7±19.0 years. Six were classified as ET, five had one plus feature and eight had two plus features. The computerised analysis could predict the presence and number of ET plus features. Measures of acceleration and variation of pen pressure performed strongly to separate ET phenotypes (p<0.05). Plus features were associated with higher scores on the Fahn-Tolosa-Marin Tremor Rating Scale (p=0.001) and it appeared that ET groups were mainly being separated according to severity of tremor and by compensatory manoeuvres used by participants with more severe tremor. There were, in addition, a small number of negative kinematic correlations suggesting some slowness with ET plus. Abnormal repetitive limb movement was also correlated with tremor severity (R=0.57) by clinical grading. Critics of the Consensus Statement have drawn attention to weaknesses of the ET plus concept in relation to duration and severity of ET. This classification of ET may be too biased towards tremor severity to assist in distinguishing underlying biological differences by clinical measurement.

Parkinsonism and tremor syndromes

Tremor, the most common movement disorder, may occur in isolation or may co-exist with a variety of other neurologic and movement disorders including parkinsonism, dystonia, and ataxia. When associated with Parkinson's disease, tremor may be present at rest or as an action tremor overlapping in phenomenology with essential tremor. Essential tremor may be associated not only with parkinsonism but other neurological disorders, suggesting the possibility of essential tremor subtypes. Besides Parkinson's disease, tremor can be an important feature of other parkinsonian disorders, such as atypical parkinsonism and drug-induced parkinsonism. In addition, tremor can be a prominent feature in patients with other movement disorders such as fragile X-associated tremor/ataxia syndrome, and Wilson's disease in which parkinsonian features may be present. This article is part of the Special Issue "Parkinsonism across the spectrum of movement disorders and beyond" edited by Joseph Jankovic, Daniel D. Truong and Matteo Bologna.

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.

Emerging concepts on bradykinesia in non-parkinsonian conditions

BACKGROUND AND PURPOSE

Bradykinesia is one of the cardinal motor symptoms of Parkinson's disease. However, clinical and experimental studies indicate that bradykinesia may also be observed in various neurological diseases not primarily characterized by parkinsonism. These conditions include hyperkinetic movement disorders, such as dystonia, chorea, and essential tremor. Bradykinesia may also be observed in patients with neurological conditions that are not seen as "movement disorders," including those characterized by the involvement of the cerebellum and corticospinal system, dementia, multiple sclerosis, and psychiatric disorders.

METHODS

We reviewed clinical reports and experimental studies on bradykinesia in non-parkinsonian conditions and discussed the major findings.

RESULTS

Bradykinesia is a common motor abnormality in non-parkinsonian conditions. From a pathophysiological standpoint, bradykinesia in neurological conditions not primarily characterized by parkinsonism may be explained by brain network dysfunction.

CONCLUSION

In addition to the pathophysiological implications, the present paper highlights important terminological issues and the need for a new, more accurate, and more widely used definition of bradykinesia in the context of movement disorders and other neurological conditions.

Supervised classification of bradykinesia in Parkinson's disease from smartphone videos

BACKGROUND

Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.

AIM

We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.

METHODS

We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0-1) or mild/moderate/severe bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis.

RESULTS

A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.

CONCLUSION

The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

A Matlab toolbox for analyzing repetitive movements: application in gait and tapping experiments

Coordination and timing in repetitive movements have been intensively investigated in diverse experimental settings for understanding the underlying basic mechanisms in healthy controls. On this basic research side, there are mainly two theoretical models: the Wing-Kristofferson (WK) model and the Haken-Kelso-Bunz (HKB) model. On the clinical side of the research, several efforts have been spent on quantitatively assessing gait and other repetitive movements such as tapping, especially as an outcome measure of clinical trials in diverse neurological disorders. Nevertheless, Parkinson's disease (PD) remains the predominant disorder in the clinical literature in this context, as the tremor activity and the changes in the gait are both common symptoms in PD. Although there are motion recording systems for data acquisition in clinical settings, the tools for analysis and quantification of the extracted time-series offered by these systems are severely restricted. Therefore, we introduce a toolbox which enables the analysis of repetitive movements within the framework of the two main theoretical models of motor coordination, which explicitly focuses on varying clinical and experimental settings such as self-paced vs. cued or uni-manual vs. bi-manual measurements. The toolbox contains particular pipelines for digital signal processing. Licensed under the GNU General Public License (GNU-GPL), the open source toolbox is freely available and can be downloaded from the Github link: https://github.com/MehmetEylemKirlangic/RepetitiveMovementAnalysis. We illustrate the application of the toolbox on sample experiments of gait and tapping with a control subject, as well as with a Parkinson's patient. The patient has gone through a brain surgery for deep brain stimulation (DBS); hence, we present the results for both stimulation ON and stimulation OFF modes. Sample data are freely accessible at: https://github.com/MehmetEylemKirlangic/DATA.

New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease

Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.

Rest tremor quantification based on fuzzy inference systems and wearable sensors

BACKGROUND

Currently the most consistent, widely accepted and detailed instrument to rate Parkinson's disease (PD) is the Movement Disorder Society sponsored Unified Parkinson Disease Rating Scale (MDS-UPDRS). However, the motor examination is based upon subjective human interpretation trying to capture a snapshot of PD status. Wearable sensors and machine learning have been broadly used to analyze PD motor disorder, but still most ratings and examinations lay outside MDS-UPDRS standards. Moreover, logical connections between features and output ratings are not clear and complex to derive from the model, thus limiting the understanding of the structure in the data.

METHODS

Fifty-seven PD patients underwent a full motor examination in accordance to the MDS-UPDRS on twelve different sessions, gathering 123 measurements. Overall, 446 different combinations of limb features correlated to rest tremors amplitude are extracted from gyroscopes, accelerometers, and magnetometers and feed into a fuzzy inference system to yield severity estimations.

RESULTS

A method to perform rest tremor quantification fully adhered to the MDS-UPDRS based on wearable sensors and fuzzy inference system is proposed, which enables a reliable and repeatable assessment while still computing features suggested by clinicians in the scale. This quantification is straightforward and scalable allowing clinicians to improve inference by means of new linguistic statements. In addition, the method is immediately accessible to clinical environments and provides rest tremor amplitude data with respect to the timeline. A better resolution is also achieved in tremors rating by adding a continuous range.

Technologies Assessing Limb Bradykinesia in Parkinson's Disease

Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues.

Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016.

Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016.

Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase.

Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.

Time measurement characterization of stand-to-sit and sit-to-stand transitions by using a smartphone

The aim of this study is to analyze a common method to measure the acceleration of a daily activity pattern by using a smartphone. In this sense, a numerical approach is proposed to transform the relative acceleration signal, recorded by a triaxial accelerometer, into an acceleration referred to an inertial reference. The integration of this acceleration allows to determine the velocity and position with respect to an inertial reference. Two different kinematic parameters are suggested to characterize the profile of the velocity during the sit-to-stand and stand-to-sit transitions for Parkinson and control subjects. The results show that a dimensionless kinematic parameter, which is linked to the time of sit-to-stand and stand-to-sit transitions, has the potential to differentiate between Parkinson and control subjects.

The application of information theory for the research of aging and aging-related diseases

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

Digitomotography in Parkinson's disease: a cross-sectional and longitudinal study

Motor symptoms in Parkinson’s disease (PD) are usually assessed with semi-quantitative tests such as the Unified PD Rating Scale (UPDRS) which are limited by subjectivity, categorical design, and low sensitivity. Particularly bradykinesia as assessed e.g. with speeded index finger tapping exhibits low validity measures. This exploratory study set out to (i) assess whether force transducer-based objective and quantitative analysis of motor coordination in index finger tapping is able to distinguish between PD patients and controls, and (ii) assess longitudinal changes. Sixteen early-stage and 17 mid-stage PD patients as well as 18 controls were included in the cross-sectional part of the study; thirteen, 16 and 16 individuals of the respective groups agreed in a reassessment 12 months later. Frequency, force, rhythmicity, regularity and laterality of speeded and metronome paced tapping were recorded by digitomotography using a quantitative motor system ("Q-Motor"). Analysis of cross-sectional data revealed most consistent differences between PD patients and controls in variability of tap performance across modalities assessed. Among PD patients, variability of taps and the ability to keep a given rhythm were associated with UPDRS motor and finger tapping scores. After 12 months, laterality parameters were reduced but no other parameters changed significantly. This data suggests that digitomotography provides quantitative and objective measures capable to differentiate PD from non-PD in a small cohort, however, the value of the assessment to track PD progression has to be further evaluated in larger cohorts of patients.

The relationship between Parkinson's disease and essential tremor: review of clinical, epidemiologic, genetic, neuroimaging and neuropathological data, and data on the presence of cardinal signs of parkinsonism in essential tremor

BACKGROUND

The possible relationship between essential tremor (ET) and Parkinson's disease (PD) has been controversial since the first description of PD. However, there is increasing evidence suggesting an overlap between these two disorders. The aim of this review is to examine the relationship between PD and ET, focusing on clinical, epidemiologic, genetic, neuroimaging, and neuropathological data, and the presence of cardinal parkinsonism symptoms in ET.

METHODS

We conducted a PubMed search for articles published between 1966 and November 2011 regarding the relationship between ET and PD and the presence of postural tremor in PD patients; the presence of rest tremor, rigidity, and slowed movements in ET patients is reviewed.

RESULTS

Clinical series, follow-up studies of ET patients, and case-control and genetic epidemiological studies indicate that ET is associated with increased risk for PD. Some neuroimaging studies and neuropathological reports suggest an association between the two diseases. ET patients show high prevalence of rest tremor, and at least seven studies described slowed movements (possibly related to cerebellar dysfunction and/or bradykinesia) in patients with ET.

DISCUSSION

There is reasonable epidemiological and clinical evidence to support a link between ET and PD, although it is not clear what factors predict ET patient risk for developing PD or, more rarely, of PD patients developing ET. Future multicentric and multidisciplinary studies including epidemiological, clinical, neuroimaging, genetic, and neuropathological assessments are required to understand these associations.

Feasibility of home-based automated Parkinson's disease motor assessment

Patients with Parkinson's disease (PD) receive therapies aimed at addressing a diverse range of motor symptoms. Motor complications in the form of symptom fluctuations and dyskinesias that commonly occur with chronic PD medication use may not be effectively captured by Unified Parkinson's Disease Rating Scale (UPDRS) assessments performed in the clinic. Therefore, home monitoring may be a viable adjunct tool to provide insight into PD motor symptom response to treatment. In this pilot study, we sought to evaluate the feasibility of capturing PD motor symptoms at home using a computer-based assessment system. Ten subjects diagnosed with idiopathic PD used the system at home and ten non-PD control subjects used the system in a laboratory. The Kinesia system consists of a wireless finger-worn motion sensor and a laptop computer with software for automated tremor and bradykinesia severity score assessments. Data from control subjects were used to develop compliance algorithms for rejecting motor tasks performed incorrectly. These algorithms were then applied to data collected from the PD subjects who used the Kinesia system at home to complete motor exams 3-6 times per day over 3-6 days. Motor tasks not rejected by the compliance algorithms were further processed for symptom severity. PD subjects successfully completed motor assessments at home, with approximately 97% of all motor task data files (1222/1260) accepted. These findings suggest that objective home monitoring of PD motor fluctuations is feasible.