The MDS consensus tremor classification: The best way to classify patients with tremor at present

Tremor Suppression Using Functional Electrical Stimulation

Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.

Vim-PSA Double-Target DBS for the Treatment of Holmes Tremor Secondary to Brainstem Hemorrhage: A Case Report

We report the case of a 53-year-old man who was diagnosed with Holmes tremor and underwent deep brain stimulation of the ventro-intermediate thalamic nucleus and posterior subthalamic area. We assessed the patients' tremor with the Fahn-Tolosa-Marin Tremor Rating Scale at 1, 3, 6, 12 and 24 months after deep brain stimulation. Deep brain stimulation relieved the patient's tremor during the 24-month follow-up period.

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.

Comparing Essential Tremor with and without Soft Dystonic Signs and Tremor Combined with Dystonia: The TITAN Study

BACKGROUND

Tremor disorders remain as clinical diagnoses and the rate of misdiagnosis between the commonest non-parkinsonian tremors is relatively high.

OBJECTIVES

To compare the clinical features of Essential Tremor without other features (pure ET), ET plus soft dystonic signs (ET + DS), and tremor combined with dystonia (TwD).

METHODS

We compared the clinical features of patients with pure ET, ET + DS, and TwD enrolled in The ITAlian tremor Network (TITAN). Linear regression models were performed to determine factors associated with health status and quality of life.

RESULTS

Three-hundred-eighty-three patients were included. Sex distribution was significantly different between the groups with males being more represented in pure ET and females in TwD. The initial site of tremor was different between the groups with about 40% of TwD having head tremor and ET + DS unilateral upper limb tremor at onset. This pattern mirrored the distribution of overt dystonia and soft dystonic signs at examination. Sensory trick, task-specificity, and position-dependence were more common, but not exclusive, to TwD. Pure ET patients showed the lowest degree of alcohol responsiveness and ET + DS the highest. Midline tremor was more commonly encountered and more severe in TwD than in the other groups. Regression analyses demonstrated that tremor severity, sex, age, and to a lesser degree the variable "group", independently predicted health status and quality of life, suggesting the existence of other determinants beyond tremor.

CONCLUSIONS

Pure ET and TwD manifest with a phenotypic overlap, which calls for the identification of diagnostic biomarkers. ET + DS shared features with both syndromes, suggesting intra-group heterogeneity.

Bedside clinical assessment of patients with common upper limb tremor and algorithmic approach

The diagnostic approach for patients with tremor is challenging due to the complex and overlapping phenotypes among tremor syndromes. The first step in the evaluation of tremor is to identify the tremulous movement and exclude the tremor mimics. The second step is to classify the tremor syndrome based on the characteristics of tremor from historical clues and focused examination (Axis 1). Comprehensive tremor examinations involve the assessment of tremor in different conditions (rest, action or mixed, position or task-specific), distribution of tremor (upper limb, lower limb, head, jaw), positive signs for functional tremor (FT) if suspected (distractibility, entrainment, co-contraction), and associated neurological signs including parkinsonism, dystonic posture, cerebellar/brainstem signs, neuropathy, and cognitive impairment. A pivotal feature in this step is to determine any distinct feature of a specific isolated or combined tremor syndrome. In this review, we propose an algorithm to assess upper limb tremors. Ancillary testing should be performed if clinical evaluation is unclear. The choice of investigation depends on the types of tremors considered to narrow down the spectrum of etiology (Axis 2). Laboratory blood tests are considered for acute onset and acute worsening of tremors, while structural neuroimaging is indicated in unilateral tremors with acute onset, nonclassical presentations, and a combination of neurological symptoms. Neurophysiological study is an important tool that aids in distinguishing between tremor and myoclonus, etiology of tremor and document specific signs of FT. Treatment is mainly symptomatic based depending on the etiology of the tremor and the patient's disabilities.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor

Objective

To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET.

Methods

We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients.

Results

ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms.

Conclusion

This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Diagnostic Uncertainties in Tremor

The approach and diagnosis of patients with tremor may be challenging for clinicians. According to the most recent consensus statement by the Task Force on Tremor of the International Parkinson Movement Disorder Society, the differentiation between action (i.e., kinetic, postural, intention), resting, and other task- and position-specific tremors is crucial to this goal. In addition, patients with tremor must be carefully examined for other relevant features, including the topography of the tremor, since it can involve different body areas and possibly associate with neurological signs of uncertain significance. Following the characterization of major clinical features, it may be useful to define, whenever possible, a particular tremor syndrome and to narrow down the spectrum of possible etiologies. First, it is important to distinguish between physiological and pathological tremor, and, in the latter case, to differentiate between the underlying pathological conditions. A correct approach to tremor is particularly relevant for appropriate referral, counseling, prognosis definition, and therapeutic management of patients. The purpose of this review is to outline the possible diagnostic uncertainties that may be encountered in clinical practice in the approach to patients with tremor. In addition to an emphasis on a clinical approach, this review discusses the important ancillary role of neurophysiology and innovative technologies, neuroimaging, and genetics in the diagnostic process.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Age and Sex Affect Essential Tremor (ET) Plus: Clinical Heterogeneity in ET Based on the National Survey in China

The new term essential tremor (ET) plus was proposed in the 2018 tremor consensus criteria. The National Survey of Essential Tremor Plus in China, a large multicenter registry study, aimed to evaluate the clinical features of pure ET and ET plus and explore possible factors related to ET plus. All patients with ET underwent neurological examination and neuropsychological assessment at 17 clinical sites. The diagnosis was made according to the 2018 consensus criteria. Clinicodemographic characteristics were analyzed. A total of 1160 patients were included, including 546 patients with pure ET and 614 patients with ET plus. The proportion of females was significantly higher in the ET plus than that in the pure ET (P = 0.001). The age at onset (AAO) of pure ET showed a bimodal distribution, with peaks in the 2nd and 5th decades. However, the AAO of the ET plus group demonstrated a skewed distribution, with a single peak in the 6th decade. Female sex (OR=1.645, P<0.001), older age (OR=1.023, P<0.001), lower educational level (OR=0.934, P<0.001), head tremor (OR=1.457, P<0.001), and higher the Tremor Research Group Essential Tremor Rating Assessment Scale (TETRAS)-II scores (OR=1.134, P<0.001) were significantly associated with ET plus. Old age and female sex may contribute to ET plus development. Pure ET showed a bimodal distribution for AAO, whereas ET plus showed a unimodal distribution. It remains unclear whether pure ET and ET plus are merely different stages of a single disease or represent distinct disease entities.

Strain Gauge Measuring System for Subsensory Micromotions Analysis as an Element of a Hybrid Human-Machine Interface

The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis of one set of integrative system components makes it possible to draw conclusions about the state and efficiency of the other components. Thus, to evaluate a person's cognitive properties, we can assess their involuntary motor acts, i.e., a person's subsensory reactions. To measure the parameters of involuntary motor acts, we have developed a strain gauge measuring system. This system provides measurement and estimation of the parameters of involuntary movements against the background of voluntary isometric efforts. The article presents the architecture of the system and shows the organization of the primary signal processing in analog form, in particular the separation of the signal taken from the strain-gauge sensor into frequency and smoothly varying components by averaging and subtracting the analog signals. This transfer to analog form simplifies the implementation of the digital part of the measuring system and allowed for minimizing the response time of the system while displaying the isometric forces in the visual feedback channel. The article describes the realization of the system elements and shows the results of its experimental research.