Distinguishing essential tremor from Parkinson's disease: bedside tests and laboratory evaluations

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.

Motor Output Variability in Movement Disorders: Insights From Essential Tremor

Findings on individuals with essential tremor suggest that tremor (within-trial movement unsteadiness) and inconsistency (trial-to-trial movement variance) stem from distinct pathologies and affect function uniquely. Nonetheless, the intricacies of inconsistency in movement disorders remain largely unexplored, as exemplified in ataxia where inconsistency below healthy levels is associated with greater pathology. We advocate for clinical assessments that quantify both tremor and inconsistency.

Brain structure comparison among Parkinson disease, essential tremor, and healthy controls using 7T MRI

Both Parkinson disease (PD) and Essential tremor (ET) are movement disorders causing tremors in elderly individuals. Although PD and ET are different disease, they often present with similar initial symptoms, making their differentiation challenging with magnetic resonance imaging (MRI) techniques. This study aimed to identify structural brain differences among PD, ET, and health controls (HCs) using 7-Tesla (T) MRI. We assessed the whole-brain parcellation in gray matter volume, thickness, subcortical volume, and small regions of basal ganglia in PD (n = 18), ET (n = 15), and HCs (n = 18), who were matched for age and sex. Brain structure analysis was performed automatic segmentation through Freesurfer software. Small regions of basal ganglia were manually segmented by ITK-SNAP. Additionally, we examined the associations between clinical indicators (symptom duration, unified Parkinson diseases rating scale (UPDRS), and clinical rating scale for tremor (CRST)) and brain structure. PD showed a significant reduction in gray matter volume in the postcentral region compared to ET. ET showed a significant reduction in cerebellum volume compared to HCs. There was a negative correlation between CRST scores (B and C) and gray matter thickness in right superior frontal in ET. This study demonstrated potential of 7T MRI in differentiating brain structure differences among PD, ET, and HCs. Specific findings, such as parietal lobe atrophy in PD compared to ET and cerebellum atrophy in ET compared to HCs, the importance of advanced imaging techniques in accurately diagnosing and distinguishing between movement disorders that present with similar initial symptoms.

Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

Inflammatory bowel disease induces pathological α-synuclein aggregation in the human gut and brain

AIMS

According to Braak's hypothesis, it is plausible that Parkinson's disease (PD) originates in the enteric nervous system (ENS) and spreads to the brain through the vagus nerve. In this work, we studied whether inflammatory bowel diseases (IBDs) in humans can progress with the emergence of pathogenic α-synuclein (α-syn) in the gastrointestinal tract and midbrain dopaminergic neurons.

METHODS

We have analysed the gut and the ventral midbrain from subjects previously diagnosed with IBD and form a DSS-based rat model of gut inflammation in terms of α-syn pathology.

RESULTS

Our data support the existence of pathogenic α-syn in both the gut and the brain, thus reinforcing the potential role of the ENS as a contributing factor in PD aetiology. Additionally, we have analysed the effect of a DSS-based rat model of gut inflammation to demonstrate (i) the appearance of P-α-syn inclusions in both Auerbach's and Meissner's plexuses (gut), (ii) an increase in α-syn expression in the ventral mesencephalon (brain) and (iii) the degeneration of nigral dopaminergic neurons, which all are considered classical hallmarks in PD.

CONCLUSION

These results strongly support the plausibility of Braak's hypothesis and emphasise the significance of peripheral inflammation and the gut-brain axis in initiating α-syn aggregation and transport to the substantia nigra, resulting in neurodegeneration.

Explainable artificial intelligence for searching frequency characteristics in Parkinson's disease tremor

The distinction between Parkinson's disease (PD) and essential tremor (ET) tremors is subtle, posing challenges in differentiation. To accurately classify the PD and ET, BiLSTM-based recurrent neural networks are employed to classify between normal patients (N), PD patients, and ET patients using accelerometry data on their lower arm (L), hand (H), and upper arm (U) as inputs. The trained recurrent neural network (RNN) has reached 80% accuracy. The neural network is analyzed using layer-wise relevance propagation (LRP) to understand the internal workings of the neural network. A novel explainable AI method, called LRP-based approximate linear weights (ALW), is introduced to identify the similarities in relevance when assigning the class scores in the neural network. The ALW functions as a 2D kernel that linearly transforms the input data directly into the class scores, which significantly reduces the complexity of analyzing the neural network. This new classification method reconstructs the neural network's original function, achieving a 73% PD and ET tremor classification accuracy. By analyzing the ALWs, the correlation between each input and the class can also be determined. Then, the differentiating features can be subsequently identified. Since the input is preprocessed using short-time Fourier transform (STFT), the differences between the magnitude of tremor frequencies ranging from 3 to 30 Hz in the mean N, PD, and ET subjects are successfully identified. Aside from matching the current medical knowledge on frequency content in the tremors, the differentiating features also provide insights about frequency contents in the tremors in other frequency bands and body parts.

MRI-based multivariate gray matter volumetric distance for predicting motor symptom progression in Parkinson's disease

While Parkinson's disease (PD)-related neurodegeneration is associated with structural changes in the brain, conventional magnetic resonance imaging (MRI) has proven less effective for clinical diagnosis due to its inability to reliably identify subtle changes early in the disease course. In this study, we aimed to develop a structural MRI-based biomarker to predict the rate of progression of motor symptoms in the early stages of PD. The study included 88 patients with PD and 120 healthy controls from the Parkinson's Progression Markers Initiative database; MRI at baseline and motor symptom scores assessed using the MDS-UPDRS-III at two time points (baseline and 48 months) were selected. Group-level volumetric analyses revealed that the volumetric reductions in the left striatum were associated with the decline in motor functioning. Then, we developed a patient-specific multivariate gray matter volumetric distance and demonstrated that it could significantly predict changes in motor symptom scores (P < 0.05). Further, we classified patients as relatively slower and faster progressors with 89% accuracy using a support vector machine classifier. Thus, we identified a promising structural MRI-based biomarker for predicting the rate of progression of motor symptoms and classifying patients based on motor symptom severity.

Electrophysiology-aided DBS targeting the ventral intermediate nucleus in an essential tremor patient with MRI-incompatible lead: A case report

Essential tremor (ET) is a common disease in the elderly population. Severe, medication-refractory ET may require surgical intervention via ablation or deep brain stimulation (DBS). Thalamic Vim (Ventral intermediate nucleus), targeted indirectly using atlas-based coordinates, is the classical target in these procedures. We present a case of an ET patient with a non-MR-compatible cardiac orphaned leads who was a candidate for DBS surgery. Due to the lead constraints of MR use, we used a head computed tomography (CT) with contrast media as the reference exam to define the AC, PC, and midline, and to register and indirectly target the Vim. For target validation, we used intraoperative electrophysiological recordings and intraoperative CT. We implanted bilateral directional leads at the target location. We used the-essential-tremor-rating-assessment-scale (TETRAS) pre and postoperatively to clinically evaluate tremor. Intraoperative micro-electrode recordings (MERs) showed individual tremor cells and a robust increase in normalized root mean square (NRMS) indicating entry to the Vim. Postoperative visualization using lead-DBS along with dramatic clinical improvements show that we were able to accurately target the Vim. Our results show that CT-only registration and planning for thalamic Vim DBS is feasible, and that MERs and intraoperative CT are useful adjuncts for Vim target validation.

Objective measurement versus clinician-based assessment for Parkinson's disease

INTRODUCTION

Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients.

AREAS COVERED

The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management.

EXPERT OPINION

In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.

Clinical Heterogeneity of Essential Tremor: Understanding Neural Substrates of Action Tremor Subtypes

Essential tremor (ET) is a common movement disorder affecting millions of people. Studies of ET patients and perturbations in animal models have provided a foundation for the neural networks involved in its pathophysiology. However, ET encompasses a wide variability of phenotypic expression, and this may be the consequence of dysfunction in distinct subcircuits in the brain. The cerebello-thalamo-cortical circuit is a common substrate for the multiple subtypes of action tremor. Within the cerebellum, three sets of cerebellar cortex-deep cerebellar nuclei connections are important for tremor. The lateral hemispheres and dentate nuclei may be involved in intention, postural and isometric tremor. The intermediate zone and interposed nuclei could be involved in intention tremor. The vermis and fastigial nuclei could be involved in head and proximal upper extremity tremor. Studying distinct cerebellar circuitry will provide important framework for understanding the clinical heterogeneity of ET.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor

This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.

Transcranial low-intensity ultrasound stimulation for treating central nervous system disorders: A promising therapeutic application

Transcranial ultrasound stimulation is a neurostimulation technique that has gradually attracted the attention of researchers, especially as a potential therapy for neurological disorders, because of its high spatial resolution, its good penetration depth, and its non-invasiveness. Ultrasound can be categorized as high-intensity and low-intensity based on the intensity of its acoustic wave. High-intensity ultrasound can be used for thermal ablation by taking advantage of its high-energy characteristics. Low-intensity ultrasound, which produces low energy, can be used as a means to regulate the nervous system. The present review describes the current status of research on low-intensity transcranial ultrasound stimulation (LITUS) in the treatment of neurological disorders, such as epilepsy, essential tremor, depression, Parkinson's disease (PD), and Alzheimer's disease (AD). This review summarizes preclinical and clinical studies using LITUS to treat the aforementioned neurological disorders and discusses their underlying mechanisms.

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.

4D printing of soft orthoses for tremor suppression

Tremor is an involuntary and oscillatory movement disorder that makes daily activities difficult for affected patients. Hand tremor-suppression orthoses are noninvasive, wearable devices designed to mitigate tremors. Various studies have shown that these devices are effective, economical, and safe; however, they have drawbacks such as large weight, awkward shape, and rigid parts. This study investigates different types of tremor-suppression orthoses and discusses their efficiency, mechanism, benefits, and disadvantages. First, various orthoses (with passive, semi-active, and active mechanisms) are described in detail. Next, we look at how additive manufacturing (AM) has progressed recently in making sensors and actuators for application in tremor orthoses. Then, the materials used in AM are further analyzed. It is found that traditional manufacturing problems can be solved with the help of AM techniques, like making orthoses that are affordable, lighter, and more customizable. Another concept being discussed is using smart materials and AM methods, such as four-dimensional (4D) printing, to make orthoses that are more comfortable and efficient.

Graphic abstract

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity.

Aging-associated changes in motor function are ovarian somatic tissue-dependent, but germ cell and estradiol independent in post-reproductive female mice exposed to young ovarian tissue

A critical mediator of evolution is natural selection, which operates by the divergent reproductive success of individuals and results in conformity of an organism with its environment. Reproductive function has evolved to support germline transmission. In mammalian ovaries, this requires healthy, active gonad function, and follicle development. However, healthy follicles do not contribute to germline transmission in a dead animal. Therefore, support of the health and survival of the organism, in addition to fertility, must be considered as an integral part of reproductive function. Reproductive and chronological aging both impose a burden on health and increase disease rates. Tremors are a common movement disorder and are often correlated with increasing age. Muscle quality is diminished with age and these declines are gender-specific and are influenced by menopause. In the current experiments, we evaluated aging-associated and reproduction-influenced changes in motor function, utilizing changes in tremor amplitude and grip strength. Tremor amplitude was increased with aging in normal female mice. This increase in tremor amplitude was prevented in aged female mice that received ovarian tissue transplants, both in mice that received germ cell-containing or germ cell-depleted ovarian tissue. Grip strength was decreased with aging in normal female mice. This decrease in grip strength was prevented in aged female mice that received either germ cell-containing or germ cell-depleted tissue transplants. As expected, estradiol levels decreased with aging in normal female mice. Estradiol levels did not change with exposure to young ovarian tissues/cells. Surprisingly, estradiol levels were not increased in aged females that received ovaries from actively cycling, young donors. Overall, tremor amplitude and grip strength were negatively influenced by aging and positively influenced by exposure to young ovarian tissues/cells in aged female mice, and this positive influence was independent of ovarian germ cells and estradiol levels. These findings provide a strong incentive for further investigation of the influence of ovarian somatic tissue on health. In addition, changes in tremor amplitude may serve as an additional marker of biological age.

Motion characteristics of subclinical tremors in Parkinson's disease and normal subjects

The characteristics of the Parkinson’s disease tremor reported previously are not applicable to the full spectrum of severity. The characteristics of high- and low-amplitude tremors differ in signal regularity and frequency dispersion, a phenomenon that indicates characterisation should be studied separately based on the severity. The subclinical tremor of Parkinson’s disease is close to physiological tremor in terms of amplitude and frequency, and their distinctive features are still undetermined. We aimed to determine joint motion characteristics that are unique to subclinical Parkinson’s disease tremors. The tremors were characterised by four hand–arm motions based on displacement and peak frequencies. The rest and postural tremors of 63 patients with Parkinson’s disease and 62 normal subjects were measured with inertial sensors. The baseline was established from normal tremors, and the joint motions were compared within and between the two subject groups. Displacement analysis showed that pronation–supination and wrist abduction–adduction are the most and least predominant tremor motions for both Parkinson’s disease and normal tremors, respectively. However, the subclinical Parkinson’s disease tremor has significant greater amplitude and peak frequency in specific predominant motions compared with the normal tremor. The flexion–extension of normal postural tremor increases in frequency from the proximal to distal segment, a phenomenon that is explainable by mechanical oscillation. This characteristic is also observed in patients with Parkinson’s disease but with amplification in wrist and elbow joints. The contributed distinctive characteristics of subclinical tremors provide clues on the physiological manifestation that is a result of the neuromuscular mechanism of Parkinson’s disease.

Tremor and Rigidity in Patients with Parkinson's Disease: Emphasis on Epidemiology, Pathophysiology and Contributing Factors

Parkinson's disease (PD) is the second most prominent neurodegenerative movement disorder after Alzheimer's disease, involving 2-3% of the population aged above 65 years. This is mainly triggered by the depletion of dopaminergic neurons located in substantia nigra pars compacta (SNpc) in the region of basal ganglia. At present, diagnosis for symptoms of PD is clinical, contextual, unspecified and therapeutically incomprehensive. Analysis of various causes of PD is essential for an accurate examination of the disease. Among the different causes, such as tremors and rigidity, unresponsiveness to the current treatment approach contributes to mortality. In the present review article, we describe various key factors of pathogenesis and physiology associated with tremors and rigidity necessary for the treatment of PI (postural instability) in patients with PD. Additionally, several reports showing early tremor and rigidity causes, particularly age, cortex lesions, basal ganglia lesions, genetic abnormalities, weakened reflexes, nutrition, fear of fall, and altered biomechanics, have been explored. By summarizing the factors that contribute to the disease, histopathological studies can assess rigidity and tremor in PD. With a clear understanding of the contributing factors, various prospective studies can be done to assess the incidence of rigidity and tremors.

Disorders of Movement due to Acquired and Traumatic Brain Injury

Purpose of Review

Both traumatic and acquired brain injury can result in diffuse multifocal injury affecting both the pyramidal and extrapyramidal tracts. Thus, these patients may exhibit signs of both upper motor neuron syndrome and movement disorder simultaneously which can further complicate diagnosis and management. We will be discussing movement disorders following acquired and traumatic brain injury.

Recent Findings

Multiple functions including speech, swallowing, posture, mobility, and activities of daily living can all be affected. Medical treatment and rehabilitation-based therapy can be especially challenging due to accompanying cognitive deficits and severity of the disorder which can involve multiple limbs in addition to muscles of the face and axial skeleton. Tremor and dystonia are the most reported movement disorders following traumatic brain injury. Dystonia and myoclonus are well documented following hypoxic ischemic brain injuries. Electrophysiological studies such as dynamic surface poly-electromyography can assist with identifying phenomenology, especially differentiating between jerk-like phenomenon and help guide further work up and management. Management with medications remains challenging due to potential adverse effects. Surgical interventions including stereotactic surgery, deep brain stimulation, and intrathecal baclofen pumps have been reported, but most of the evidence supporting them has been limited to primarily case reports except for post-traumatic tremor.

Summary

Brain injury can lead to motor disorders, movement disorders, visual (processing) deficits, and vestibular deficits which often coexist with cognitive deficits making it challenging to treat and rehabilitate these patients. Unfortunately, the evidence regarding the medical management and rehabilitation of brain injury patients with movement disorders is sparse and leaves much to be desired.

A Non-Invasive IR Sensor Technique to Differentiate Parkinson's Disease from Other Neurological Disorders Using Autonomic Dysfunction as Diagnostic Criterion

Early diagnosis of Parkinson's disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, an early symptom in PD patients, is caused by α-synuclein pathogenesis in the central nervous system and can be diagnosed using skin vasomotor response to cold stimuli. In this study, the investigations were performed using data collected from 20 PD, 20 ET and 20 healthy subjects. Infrared thermography was used for the cold stress test to observe subjects' hand temperature before and after cold stimuli. The results show that the recovery rate of hand temperature was significantly different between the groups. The data obtained in the cold stress test were verified using Pearson's cross-correlation technique, which showed that few disease parameters like medication and motor rating score had an impact on the recovery rate of hand temperature in PD subjects. The characteristics of the three groups were compared and classified using the k-means clustering algorithm. The sensitivity and specificity of these techniques were analyzed using an Receiver Operating Characteristic (ROC) curve analyzer. These results show that this non-invasive technique can be used as an effective tool in the diagnosis and differentiation of PD in its early stage.

Approach to Tremor Disorders

Tremor disorders are diverse and complex. Historical clues and examination features play a major role in diagnosing these disorders, but diagnosis can be challenging due to phenotypic overlap. Ancillary testing, such as neuroimaging or laboratory testing, is driven by the history and examination, and should be performed particularly when there are other neurological or systemic manifestations. The pathophysiology of tremor is not entirely understood, but likely involves multiple networks along with the cerebello-thalamo-cortical pathways. Treatment options include medications, botulinum toxin, surgery, and nonpharmacologic interventions utilizing physical and occupational therapies and assistive devices. Further work is needed in developing accurate diagnostic tests and better treatment options for tremor disorders.

Essential tremor

Essential tremor (ET) is one of the most common movement disorders, with a reported >60 million affected individuals worldwide. The definition and underlying pathophysiology of ET are contentious. Patients present primarily with motor features such as postural and action tremors, but may also have other non-motor features, including cognitive impairment and neuropsychiatric symptoms. Genetics account for most of the ET risk but environmental factors may also be involved. However, the variable penetrance and challenges in validating data make gene-environment analysis difficult. Structural changes in cerebellar Purkinje cells and neighbouring neuronal populations have been observed in post-mortem studies, and other studies have found GABAergic dysfunction and dysregulation of the cerebellar-thalamic-cortical circuitry. Commonly prescribed medications include propranolol and primidone. Deep brain stimulation and ultrasound thalamotomy are surgical options in patients with medically intractable ET. Further research in post-mortem studies, and animal and cell-based models may help identify new pathophysiological clues and therapeutic targets and, together with advances in omics and machine learning, may facilitate the development of precision medicine for patients with ET.

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.

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.

Movement analysis of pick-and-place virtual reality exergaming in patients with Parkinson's disease

BACKGROUND

Reduced coordination of precise small movements of the hand, wrist and fingers in Parkinson's disease (PD) has been mostly solved by medications and deep brain stimulation. The effects have been evaluated by clinical tests only.

OBJECTIVE

Virtual reality-based exergaming may enhance fine movements, decrease the medications dosage and provide an additional non-subjective evaluation.

METHODS

3D pick-and-place task (10Cubes) has been developed in a virtual world. The person placed the virtual cubes by the virtual hand, an avatar of the real hand tracked by a Leap Motion Controller (LMC). The system computed the time of manipulating the cube, the total time, the average time, the speed, and the distance. It counted and managed the number of cubes touched, and calculated the hand shake level, i.e. the average tremor index. A pilot test was carried out in a healthy neurologically intact person and a patient with PD using a 3D head-mounted device (HMD) or LCD screen.

RESULTS

The results indicate that substantial and also statistically significant (p< 0.05) differences exist between both participants in all objective parameters; the most noteworthy is the average tremor index. However, we found the parameters only marginally different with 2D equipment.

CONCLUSIONS

The evaluation system of 10Cubes has proved applicable at an unchanged medication plan, but its clinical effectiveness could be confirmed with a randomized study.

Myrtenol complexed with β-cyclodextrin ameliorates behavioural deficits and reduces oxidative stress in the reserpine-induced animal model of Parkinsonism

Current pharmacological approaches to treat Parkinson's disease have low long-term efficacy and important adverse side effects. The development of new pharmacological therapies has focused on novel plant-derived phytochemicals. The alcoholic monoterpene myrtenol has been isolated from several plant species, and has anxiolytic, analgesic, anti-inflammatory and antioxidant actions. Our study evaluated the neuroprotective potential of myrtenol complexed with β-cyclodextrin (MYR) on a progressive parkinsonism model induced by reserpine (RES) in mice. The complexation with cyclodextrins enhances the pharmacological action of monoterpenes. Male Swiss mice were treated daily with MYR (5 mg/kg, p.o.) and with RES (0.1 mg/kg, s.c.) every other day during 28 days. Behavioural evaluations were conducted across treatment. At the end of the treatment, immunohistochemistry for tyrosine hydroxylase (TH) and oxidative stress parameters were evaluated. Chronic MYR-treatment protected against olfactory sensibility loss, restored short-term memory and decreased RES-induced motor impairments. Moreover, this treatment prevented dopaminergic depletion and reduced the oxidative status index in the dorsal striatum. Therefore, MYR ameliorated motor and non-motor impairments in the progressive animal model of parkinsonism, possibly by an antioxidant action. Additional research is needed to investigate the mechanisms involved in this neuroprotective effect.

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.

A deep explainable artificial intelligent framework for neurological disorders discrimination

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

Tremor-Suppression Orthoses for the Upper Limb: Current Developments and Future Challenges

Introduction: Pathological tremor is the most common motor disorder in adults and characterized by involuntary, rhythmic muscular contraction leading to shaking movements in one or more parts of the body. Functional Electrical Stimulation (FES) and biomechanical loading using wearable orthoses have emerged as effective and non-invasive methods for tremor suppression. A variety of upper-limb orthoses for tremor suppression have been introduced; however, a systematic review of the mechanical design, algorithms for tremor extraction, and the experimental design is still missing.

Methods: To address this gap, we applied a standard systematic review methodology to conduct a literature search in the PubMed and PMC databases. Inclusion criteria and full-text access eligibility were used to filter the studies from the search results. Subsequently, we extracted relevant information, such as suppression mechanism, system weights, degrees of freedom (DOF), algorithms for tremor estimation, experimental settings, and the efficacy.

Results: The results show that the majority of tremor-suppression orthoses are active with 47% prevalence. Active orthoses are also the heaviest with an average weight of 561 ± 467 g, followed by semi-active 486 ± 395 g, and passive orthoses 191 ± 137 g. Most of the orthoses only support one DOF (54.5%). Two-DOF and three-DOF orthoses account for 33 and 18%, respectively. The average efficacy of tremor suppression using wearable orthoses is 83 ± 13%. Active orthoses are the most efficient with an average efficacy of 83 ± 8%, following by the semi-active 77 ± 19%, and passive orthoses 75 ± 12%. Among different experimental setups, bench testing shows the highest efficacy at 95 ± 5%, this value dropped to 86 ± 8% when evaluating with tremor-affected subjects. The majority of the orthoses (92%) measured voluntary and/or tremorous motions using biomechanical sensors (e.g., IMU, force sensor). Only one system was found to utilize EMG for tremor extraction.

Conclusions: Our review showed an improvement in efficacy of using robotic orthoses in tremor suppression. However, significant challenges for the translations of these systems into clinical or home use remain unsolved. Future challenges include improving the wearability of the orthoses (e.g., lightweight, aesthetic, and soft structure), and user control interfaces (i.e., neural machine interface). We also suggest addressing non-technical challenges (e.g., regulatory compliance, insurance reimbursement) to make the technology more accessible.

Visualisation of Parkinsonian, essential and physiological tremor planes in 3Dspace

Based on the fact that tremors display some distinct 3D spatial characteristics, we decided to visualise tremor planes in 3D space. We obtained 3-axial linear accelerometer signals of hand tremors from 58 patients with Parkinson´s disease (PD), 37 with isolated resting tremor (iRT), 75 with essential tremor (ET), and 44 healthy volunteers with physiological tremor (Ph). For each group analysis was done with subsequent spatial 3D regression of the input data i.e. along the x, y and z axes; the projected vector lengths in the individual (vertical transversal XY, vertical longitudinal XZ and horizontal YZ) reference frame planes and their angles. Most meaningful and statistically significant differences were found in the analyses of the 3D vector lengths. The tremor of the PD and the iRT group was oriented mainly in the horizontal YZ plane. The tremors of the patients with ET and Ph were oriented approximately in the midway between the all three referential planes with less tilt toward the vertical longitudinal XZ plane.

Video electroencephalogram combined with electromyography in the diagnosis of hyperkinetic movement disorders with an unknown cause

OBJECTIVES

Diagnosis of hyperkinetic movement disorders with an unknown cause is usually challenging. The objective of this study is to learn about video electroencephalogram (VEEG) combined with electromyography (EMG) in the diagnosis of hyperkinetic movement disorders with an unknown cause.

METHODS

We performed an observational cohort study by recruiting consecutive patients with hyperkinetic movements as the main manifestation with an unknown cause for VEEG combined with EMG evaluations.

RESULTS

A total of 77 consecutive patients were enrolled for VEEG-EMG examination. Among them, 57 patients changed their diagnosis after VEEG-EMG assessment, with a mean final diagnosis age of 35.4 ± 20.3 years (range, 4-74 years). The mean duration between initial and final diagnosis was 54.8 ± 71.3 months (range 0.5-300 months). The most common misdiagnosed hyperkinetic movement disorders were myoclonus (40.4%), followed by tremors (24.6%), dystonia (15.8%), psychogenic movement disorders (10.5%), and periodic leg movement syndrome (PLMS) (7.0%). Outcomes of therapy were significantly improved after VEEG-EMG examination (p = 0.000).

CONCLUSIONS

Simultaneous VEEG and EMG are important in the diagnosis of hyperkinetic movement disorders with an unknown cause.

Drawing lines and circles in Parkinson's Disease: The lateralized symptoms interfere with the movements of the unaffected hand

INTRODUCTION

Evidence about altered bimanual coordination has been reported in Parkinson's Disease. However, no previous study has explored such an alteration quantifying the interference effect that the trajectory of each hand might impose on the other one. Thus, in the present research, we applied the traditional Circles-Lines Coupling Task, which allowed assessing the motor coordination of the two hands, in the context of Parkinson's Disease.

METHODS

Thirty-six individuals affected by Parkinson's Disease were consecutively recruited and assigned to two groups according to their symptoms' lateralization. Moreover, eighteen age-matched healthy controls participated in the study. We capitalized on the Circles-Lines Coupling Task, in which the performance during incongruent movements (drawing lines with one hand and circles with the other hand) was compared with the performance during congruent movements (drawing lines with both hands). A bimanual coupling index was computed to compare the interference effect of each hand on the other one.

RESULTS

In healthy controls, the bimanual coupling index did not differ between the two hands. Crucially, in both groups of individuals affected by Parkinson's Disease, the less affected hand showed a significantly higher bimanual coupling index, due to the abnormal interference exerted by the most affected one, than vice versa.

CONCLUSIONS

Our results highlighted an altered spatial bimanual coupling in Parkinson's disease, depending on the symptoms' lateralization. We offered different explanations of our results according to the theoretical frameworks about the mechanisms subserving bimanual coordination.

Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

BACKGROUND

Parkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.

METHODS

This retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores.

RESULTS

Machine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor.

CONCLUSIONS

This study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.

The clinical application of nigrosome 1 detection on high-resolution susceptibility-weighted imaging in the evaluation of suspected Parkinsonism: The real-world performance and pitfalls

Purpose

To evaluate the real-world diagnostic performance of high-resolution susceptibility-weighted imaging (HR-SWI) and investigate whether the reader’s predictions can be used to find cases where HR-SWI finding and final clinical diagnosis matched.

Methods

This retrospective study enrolled patients with suspected Parkinsonism (n = 48) or volunteers with other intracranial pathologies (n = 31) who underwent brain magnetic resonance imaging (MRI) including HR-SWI, which was used to evaluate nigrosome 1 (NG1). All patients with suspected Parkinsonism underwent N-3-fluoropropyl-2-carbomethoxy-3-4-iodophenyl nortropane (FP-CIT) positron emission tomography and a clinical diagnosis was made by a neurologist. The HR-SWI data were qualitatively analyzed by two independent reviewers. A consensus reading was performed and a diagnostic confidence score was assigned. According to final clinical diagnosis, diagnostic sensitivity, specificity, and accuracy were calculated. Receiver operating characteristic (ROC) curve analysis was used to examine whether the diagnostic confidence score could be used to identify HR-SWI finding—final clinical diagnosis matched cases.

Results

Of the 48 patients with suspected Parkinsonism, 31 were diagnosed with idiopathic Parkinson’s disease, and three with multiple system atrophy. The remaining 14 patients were included in the disease control group. Of the 31 volunteers, 10 subjects were excluded due to possibility of nigrostriatal degeneration and finally 21 subjects were enrolled as controls with non-Parkinsonism pathology (non-PD control). After consensus reading, 25 subjects were classified as true positive and 28 as true negative, according to HR-SWI findings. The calculated diagnostic sensitivity, specificity, and accuracy were 73.5%, 80.0%, and 76.8%, respectively. With using diagnostic concordance score, the area under the ROC curve for the detection of concordance case was 0.83 (95% CI: 0.72–0.91, p < 0.05).

Conclusion

The diagnostic performance of NG1 detection using HR-SWI with 3T MRI was within acceptable range. Using the reader's diagnostic confidence could be helpful to find cases which HR-SWI finding and final clinical conclusion match. So HR-SWI may be of added value in the evaluation of suspected Parkinsonism.

Advantages of using 3D virtual reality based training in persons with Parkinson's disease: a parallel study

BACKGROUND

Parkinson's disease (PD) is a slowly progressive neurodegenerative disease. There are mixed reports on success of physiotherapy in patients with PD. Our objective was to investigate the functional improvements, motivation aspects and clinical effectiveness when using immersive 3D virtual reality versus non-immersive 2D exergaming.

METHODS

We designed a randomized parallel study with 97 patients, but only 20 eligible participants were randomized in 2 groups; the one using 3D Oculus Rift CV1 and the other using a laptop. Both groups participated in the 10-session 3 weeks training with a pick and place task in the virtual world requiring precise hand movement to manipulate the virtual cubes. The kinematics of the hand was traced with Leap motion controller, motivation effect was assessed with modified Intrinsic Motivation Inventory and clinical effectiveness was evaluated with Box & Blocks Test (BBT) and shortened Unified Parkinson's disease rating scale (UPDRS) before and after the training. Mack-Skilling non-parametrical statistical test was used to identify statistically significant differences (p < 0.05) and Cohen's U3 test to find the effect sizes.

RESULTS

Participants in the 3D group demonstrated statistically significant and substantially better performance in average time of manipulation (group x time, p = 0.009), number of successfully placed cubes (group x time, p = 0.028), average tremor (group x time, p = 0.002) and UPDRS for upper limb (U3 = 0.35). The LCD and 3D groups substantially improved their BBT score with training (U3 = 0.7, U3 = 0.6, respectively). However, there were no statistically significant differences in clinical tests between the groups (group x time, p = 0.2189, p = 0.2850, respectively). In addition the LCD group significantly decreased the pressure/tension (U3 = 0.3), the 3D did not show changes (U3 = 0.5) and the differences between the groups were statistically different (p = 0.037). The 3D group demonstrated important increase in effort (U3 = 0.75) and perceived competences (U3 = 0.9).

CONCLUSIONS

The outcomes of the study demonstrated that the immersive 3D technology may bring increased interests/enjoyment score resulting in faster and more efficient functional performance. But the 2D technology demonstrated lower pressure/tension score providing similar clinical progress. A study with much larger sample size may also confirm the clinical effectiveness of the approaches.

TRIAL REGISTRATION

The small scale randomized pilot study has been registered at ClinicalTrials.gov Identifier: NCT03515746 , 4 May 2018.

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.

Essential tremor: a nuanced approach to the clinical features

Click here to listen to the Podcast Essential tremor is the most common form of tremor in humans. Given neurologists' high exposure to this condition, and its seemingly straightforward phenotype, it might seem easy to diagnose. However, 30%-50 % of patients labelled as having 'essential tremor' have other diagnoses, mostly Parkinson's disease and dystonia. The tremor of essential tremor is neither non-descript nor featureless but is multifaceted and highly patterned. This review focuses on its clinical features, beginning with a discussion of tremors and then briefly discussing its additional motor features, and presents several aids to help distinguish essential tremor from Parkinson's disease and dystonia. Careful attention to certain clinical nuances will aid the diagnosis and care of patients with essential tremor.

Kinematic Metrics from a Wireless Stylus Quantify Tremor and Bradykinesia in Parkinson's Disease

A fundamental challenge in the clinical care of Parkinson disease (PD) is the current dependence on subjective evaluations of tremor and bradykinesia. New technologies offer the ability to evaluate motor deficits using purely objective measures. The aim of this study was to develop and evaluate the efficacy of a wireless stylus (Cleveland Clinic Stylus) with an embedded motion sensor to quantitatively assess tremor and bradykinesia in patients with PD with subthalamic nucleus (STN) deep brain stimulation (DBS). Twenty-one subjects were tested in various on and off DBS conditions while holding the Cleveland Clinic Stylus while at rest, maintaining a postural hold, and during a movement task. Kinematic metrics were calculated from the motion sensor data, including 3D angular velocity and 3D acceleration data, and were compared between the on and off conditions. Generalized estimating equations (GEEs) were used to determine the relationship between kinematic metrics and MDS-Unified Parkinson's Disease Rating Scale Motor III (UPDRS-III) subscores. Kinematic metrics from the rest and postural tasks were significantly related to the UPDRS-III subscores of tremor (p < 0.001 for all metrics), and kinematic metrics from the movement task were significantly related to the UPDRS-III subscore of bradykinesia (p < 0.001 for 3/7 metrics). Kinematic metrics (7/9) showed a significant effect of stimulation setting (range: p < 0.03– < 0.0001) across the three tasks, indicating significant improvements from DBS in these measures. The Cleveland Clinic Stylus provided quantitative kinematic measures of tremor and bradykinesia severity and detected significant improvements in these measures from medication and DBS therapy. This low-cost, easy-to-use tool can provide objective measures that will improve clinical care of PD patients by providing a more reliable and objective evaluation of movement symptoms, disease progression, and effects of therapy in and outside the clinical setting.

A role for accelerometry in the differential diagnosis of tremor syndromes

Accelerometry is a reliable tool for gauging the occurrence, amplitude and frequency of tremor. However, there is no consensus on criteria for accelerometric diagnosis of tremor syndromes. We enrolled 20 patients with essential tremor (ET), 20 with dystonic tremor (DT), and 20 with classic parkinsonian tremor (PD-T), all meeting accepted clinical criteria. All the patients underwent dopamine transporter imaging (by means of single-photon emission computed tomography) and triaxial accelerometric tremor analysis. The latter revealed groupwise differences in tremor frequency, peak dispersion, spectral coherence, unilaterality and resting vs action tremor amplitude. From the above, five diagnostic criteria were extrapolated for each condition. Receiver operating characteristic curves, depicting criteriabased scoring of each tremor type, showed negligible declines in specificity for scores ≥4 in patients with ET or DT and scores ≥3 in patients with PD-T, thus providing a simple scoring method (accelerometrically derived) for differential diagnosis of the principal tremor syndromes.

Are Clinical Certainty Ratings Helpful in the Diagnosis of Parkinson's Disease?

Background

Clinical diagnostic criteria for PD rely on rest tremor, bradykinesia, and rigidity. These features are non-specific and neuropathological confirmation remains the gold standard for diagnosis. This study presents data on clinical certainty ratings in autopsy-proven PD.

Methods

Subjects were assessed annually by a movement disorders specialist and assigned to a clinical certainty group for PD based on multiple clinical features before autopsy. The three groups considered for analysis are as follows: Group I 0-49% certainty, Group II 50-89% certainty, and Group III 90-100% certainty. All subjects were autopsied and had a standardized neuropathological assessment.

Results

275 subjects were assigned a PD certainty at their last visit before death. Group I had 80 subjects, Group II 56 subjects, and Group III 139 subjects. The clinical features recorded in Group I, II, and III, were as follows: rest tremor, bradykinesia, rigidity, postural instability, asymmetric onset, persistent asymmetry, current response to dopaminergic treatment, motor fluctuations, and dyskinesia. Rigidity, postural instability, asymmetric onset, current response to dopaminergic treatment, motor fluctuation, and dyskinesia were more likely to be present in the group which was rated with higher certainty. The final diagnosis of PD was confirmed by neuropathological assessment in 85% of the patients in Group III as compared to 30% in Group II and 5% in Group I.

Conclusions

High certainty (90-100%) had strong positive predictive value (85%) for autopsy-proven PD as compared to either lower certainty groups (0-49% and 50-89%) which had lower predictive value (5% and 30% respectively).

Animal Models of Tremor: Relevance to Human Tremor Disorders

Background

Tremor is the most common movement disorder; however, the pathophysiology of tremor remains elusive. While several neuropathological alterations in tremor disorders have been observed in post-mortem studies of human brains, a full understanding of the relationship between brain circuitry alterations and tremor requires testing in animal models. Additionally, tremor animal models are critical for our understanding of tremor pathophysiology, and/or to serve as a platform for therapy development.

Methods

A PubMed search was conducted in May 2018 to identify published papers for review.

Results

The methodology used in most studies on animal models of tremor lacks standardized measurement of tremor frequency and amplitude; instead, these studies are based on the visual inspection of phenotypes, which may fail to delineate tremor from other movement disorders such as ataxia. Of the animal models with extensive tremor characterization, harmaline-induced rodent tremor models provide an important framework showing that rhythmic and synchronous neuronal activities within the olivocerebellar circuit can drive action tremor. In addition, dopamine-depleted monkey and mouse models may develop rest tremor, highlighting the role of dopamine in rest tremor generation. Finally, other animal models of tremor have involvement of the cerebellar circuitry, leading to altered Purkinje cell physiology.

Discussion

Both the cerebellum and the basal ganglia are likely to play a role in tremor generation. While the cerebellar circuitry can generate rhythmic movements, the nigrostriatal system is likely to modulate the tremor circuit. Tremor disorders are heterogeneous in nature. Therefore, each animal model may represent a subset of tremor disorders, which collectively can advance our understanding of tremor.