Essential Tremor Subjects Research Articles

Combining salivary α-synuclein seeding activity and miRNA-29a to distinguish Parkinson's disease and multiple system atrophy

IntroductionThe differential diagnosis of early Parkinson's disease (PD) by a single biomarker is still challenging due to its symptomatic overlap with other neurological diseases. Increasing evidences support the use of saliva biomarkers of neurodegeneration, including microRNAs and α-synuclein (α-syn) seeding activity, to diagnose patients with idiopathic PD and multiple system atrophy (MSA). Our previous study confirmed the salivary microRNA-29a-3p (miRNA-29a-3p) and α-syn seeding activity could differentiate PD and MSA from healthy control subjects (HCs) and patients with essential tremor (ET). MethodsWe set up α-syn real-time quaking induced conversion seed amplification assay (α-syn RT-QuIC SAA) in 203 participants from the Peking University First Hospital with PD (n = 101), MSA (n = 32), ET (n = 17) and healthy control subjects (HCs, n = 53). We also determined miRNA-29a-3p in saliva by real time quantitative PCR (RT-qPCR) and, in 155 participants (36HCs, 80PD, 22MSA, 17ET). ResultsSensitivity of RT-QuIC seed amplification assay (SAA) for PD was 70.30 %, for MSA was 56.25 % and specificity for healthy controls was 92.45 %. The expression level of saliva miRNA-29a-3p was significantly decreased in patients with PD (p < 0.001) and MSA (p < 0.0001), and allowed differentiation with HCs (PD vs. HCs, AUC 0.69; MSA vs. HCs, AUC 0.95). Sensitivity of salivary miRNA-29a-3p for PD and MSA were 70.00 % and 95.45 %, respectively, and specificity for PD and MSA were 77.23 % and 80.56 %, respectively. By combining the salivary α-syn RT-QuIC SAA with miRNA-29a-3p, sensitivity for PD vs. HCs increasing to 75.00 %, while sensitivity for MSA vs. HCs increasing to 90.00 %. Specificity was 91.67 % for PD and 88.89 % for MSA after combining assessment of salivary α-syn RT-QuIC SAA. Salivary α-syn RT-QuIC SAA yielded 100.00 % sensitivity and 79.21 % specificity for PD vs. ET, and 100.00 % sensitivity and 65.63 % specificity for MSA vs. ET. Salivary miRNA-29a-3p provied 88.24 % sensitivity and 48.75 % specificity for PD vs. ET and 86.36 % sensitivity and 88.24 % specificity for MSA vs. ET. The combined assessment of saliva markers provided a better diagnostic value for ET vs. synucleinopathies (ET vs. PD: 88.24 % sensitivity and 81.25 % specificity; ET vs. MSA: 94.12 % sensitivity and 90.00 % specificity) than RT-QuIC SAA alone, or miRNA-29a-3p alone. The combination of lag phase and miRNA-29a-3p could add higher specificity (85.71 %) which increased approximately 40 percent (specificity: miRNA-29a-3p 47.50 %, lag phase 48.98 %) for discriminating PD from MSA. However, the sensitivity of combining these two methods was 61.11 %, which was lower than lag phase alone (89.66 %) or miRNA-29a-3p alone (95.45 %). ConclusionsThis study confirmed that saliva, a non-invasive biofluid in synucleinopathies possessed potential diagnostic power between PD, MSA, ET and normal controls. We show the combined value of saliva miRNA-29a-3p and saliva α-syn RT-QuIC SAA in the diagnosis and differential diagnosis of Parkinsonism.

Effect of Propranolol on Motor Cortex Excitability in Essential Tremor: An Exploratory Study.

Essential tremor, the world's most prevalent movement disorder, lacks a clear understanding of its pathophysiology. Propranolol, a non-specific beta-blocker capable of crossing the blood-brain barrier, is a primary choice for essential tremor treatment. While its tremor-reducing effects are generally attributed to peripheral actions, various uses hint at central adrenergic effects. Nevertheless, propranolol's precise impact on the central nervous system in essential tremor subjects remains unexplored. In this study, we employed transcranial magnetic stimulation to assess the influence of propranolol on the excitability of the primary motor cortex (M1) in patients with essential tremor, compared to an age- and sex-matched control group. Cortical excitability parameters were measured following placebo and propranolol administration, encompassing resting and active motor thresholds, motor evoked potential characteristics, cortical silent period, and the input/output curve. Distinct effects were observed across the two cortical hemispheres. Essential tremor patients displayed inhibition of the left M1 cortex and heightened excitability in the right M1 cortex four hours after propranolol administration, but not following placebo. These findings suggest potential differential noradrenergic excitatory and inhibitory modulation. However, comprehensive understanding necessitates further investigations, including left-handed participants and more diverse essential tremor subpopulations. This study underscores the need for continued exploration to unravel propranolol's complex effects on motor cortex excitability in essential tremor.

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.

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

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

Postmortem Cerebellar Volume Is Not Reduced in Essential Tremor: A Comparison with Multiple System Atrophy and Controls.

Essential tremor (ET) is a common movement disorder in which cerebellar microscopic and volume alterations have been repeatedly reported although with disagreement between studies. However, pronounced heterogeneity was found with regard to cerebellar volume alterations. This study aimed to assess postmortem cerebellar volume in subjects with or without ET, as compared with subjects with multiple system atrophy (MSA), a well-established cerebellar neurodegeneration. Cases with ET (n = 29), MSA (n = 7), and non-demented control cases without any movement disorder (n = 22) were selected from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND), a longitudinal clinicopathological study with annual research-dedicated clinical assessments by neuropsychologists, subspecialist movement disorders, and cognitive/behavioral neurologists, with comprehensive neuropathological examinations after death. Group comparisons were controlled for common age-related neurodegenerative and cerebrovascular pathologies. Cerebellar volumes were calculated using digital images of slices taken at the time of autopsy, immediately after brain removal and before fixation. Cerebellar volume was not reduced in ET subjects compared to controls. The two groups did not differ in terms of incidental cerebrovascular and Alzheimer's disease neuropathology. In contrast, cerebellar volume was significantly reduced in subjects with MSA when compared to ET and control subjects. In a well-characterized cohort, postmortem cerebellar volume measurements suggest that there are no volume alterations in ET when compared to controls, in contrast to significant cerebellar atrophy in subjects with MSA.

Wearable sensor-based gait analysis to discriminate early Parkinson’s disease from essential tremor

BackgroundDifferentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning.ObjectiveTo differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors.MethodsGait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components’ weight indices were trained using logistic regression. Several ensemble models’ leave-one-out cross-validation (LOOCV) performances were compared. Independent test data were used to evaluate the model with the best LOOCV performance.ResultsThe best weighted average ensemble classification model LOOCV results included an accuracy of 84%, Kappa of 0.68, sensitivity of 85.9%, specificity of 82.1%, and AUC of 0.912. Thirty-three gait and postural transition parameters, such as Arm–Symbolic Symmetry Index and 180° Turn–Max Angular Velocity, were included in Feature Group III. The independent test data achieved a 75.8% accuracy.ConclusionsOur findings suggest that gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage PD and ET.

Locus coeruleus and substantia nigra neuromelanin magnetic resonance imaging differentiates Parkinson’s disease and essential tremor

BackgroundDifferential diagnosis of essential tremor (ET) and Parkinson’s disease (PD) can still be a challenge in clinical practice. These two tremor disorders may have different pathogenesis related to the substantia nigra (SN) and locus coeruleus (LC). Characterizing neuromelanin (NM) in these structures may help improve the differential diagnosis. MethodsForty-three subjects with tremor-dominant PD (PDTD), 31 subjects with ET, and 30 age- and sex-matched healthy controls were included. All subjects were scanned with NM magnetic resonance imaging (NM-MRI). NM volume and contrast measures for the SN and contrast for the LC were evaluated. Logistic regression was used to calculate predicted probabilities by using the combination of SN and LC NM measures. The discriminative power of the NM measures in detecting subjects with PDTD from ET was assessed with a receiver operative characteristic curve, and the area under the curve (AUC) was calculated. ResultsThe NM contrast-to-noise ratio (CNR) of the LC, the NM volume, and CNR of the SN on the right and left sides were significantly lower in PDTD subjects than in ET subjects or healthy controls (all P < 0.05). Furthermore, when combining the best model constructed from the NM measures, the AUC reached 0.92 in differentiating PDTD from ET. ConclusionThe NM volume and contrast measures of the SN and contrast for the LC provided a new perspective on the differential diagnosis of PDTD and ET, and the investigation of the underlying pathophysiology.

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features.

Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation. The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals. The SSC method is used to obtain morphology-based features of the spectrum of the accelerometer and two surface EMG signals. The SDWD technique is used in this work to obtain the approximate spectral representation of both accelerometer and the two surface EMG signals. Two sets of data (training and test) are used in this paper. The training set consists of 21 ET subjects and 19 PD subjects, while the test set consists of 20 ET and 20 PD subjects. A neural network of the type feed forward back propagation has been used to combine best SSC features and best SDWD features of the accelerometer and EMG signals. Efficiency result of 92.5% was obtained using best hybrid features. The artificial neural network has been used successfully to combine two types of features in an automatic discrimination system between PD and ET.

Diagnostic Evaluation of 99mTc-TRODAT-1 SPECT in Parkinsonism

Background Parkinsonism as a group of movement disorders, exhibit similar clinical presentation. Therefore, clinically differentiating these diseases is difficult. We investigated the diagnostic value of 99m Tc-TRODAT-1 SPECT in this setting. Due to the fact that this modality has some limitations in imaging small organs like the sub-regions of basal ganglia, we also evaluated the use of anatomical MR imaging along with functional SPECT imaging in parkinsonism. Methods This follow-up diagnostic test evaluation study was performed with 40 patients with the clinical presentation of parkinsonism, and 10 healthy subjects as controls. After administration of the radiopharmaceutical, SPECT images were acquired, then co-registered on MRI. Uptake values were evaluated in basal ganglia semi-quantitatively. Results In this study, 99mTc-TRODAT-1 SPECT was able to differentiate essential tremor and healthy subjects from progressive supranuclear palsy (PSP) and Parkinson’s disease (PD) with a sensitivity of 76.47% and specificity of 100% at a cut-off of 0.53; however, findings were not significant in differentiation of PD from PSP (p ˃0.05), and the results were similar in SPECT and co-registered MRI/SPECT images. In evaluation of the uptake pattern in basal ganglia, the lateralization of decreased uptake was only seen in PD; and in PSP, the dysfunction was bilateral in all patients. Conclusion 99mTc-TRODAT-1 SPECT is sensitive and specific in diagnosing basal ganglia dysfunction; however, 99mTc-TRODAT-1 SPECT alone or co-registration on MRI are not adequate in differentiation of the etiologies of basal ganglia dysfunction.

Brain Metabolic Changes with Longitudinal Transcutaneous Afferent Patterned Stimulation in Essential Tremor Subjects.

Background:Non-invasive peripheral nerve stimulation, also referred to as transcutaneous afferent patterned stimulation (TAPS), reduces hand tremor in essential tremor (ET) subjects. However, the mechanism of action of TAPS is unknown. Here, we investigated changes in brain metabolism over three months of TAPS use in ET subjects.Methods:This was an interventional, open label, single group study enrolling 5 ET subjects. They received 40 minutes of TAPS treatment twice daily for 90 days. Brain metabolic activity and tremor severity were measured using 18F-fluorodeoxyglucose (FDG) PET/CT, and the Tremor Research Group Essential Tremor Rating Assessment Scale (TETRAS), respectively, at baseline and after 90 days. Tremor power and frequency was measured before and after all TAPS sessions using an onboard three-axis accelerometer.Results:FDG PET/CT revealed areas of hypermetabolism in ipsilateral cerebellar hemisphere and hypometabolism in contralateral cerebellar hemisphere following 90 days of TAPS treatment, compared to day one (uncorrected p value <0.05). Paired pre-post kinematic measurements over 90 days showed significantly decreased tremor power (p < 0.0001) but no change in tremor frequency. The TETRAS score on day 1 decreased from 6.5 ± 2.5 to 4.1 ± 1.8 following TAPS (p = 0.05). The pre-post TETRAS scores on day 90: 4.9 ± 1.5 and 4.1± 1 were lower than pre-TAPS TETRAS score on day 1 (p = 0.14 and 0.05, respectively).Conclusions:Our results suggest that longitudinal TAPS of the median and radial nerves modulates brain metabolism in areas instrumental to motor coordination and implicated in ET. Clinically, TAPS reduced tremor power, but had no effect on tremor frequency. This study paves the way for comprehensive studies in larger cohorts to further elucidate the mechanism of TAPS.Highlights:Non-invasive peripheral nerve stimulation, also referred to as transcutaneous afferent patterned stimulation (TAPS), reduces hand tremor in essential tremor subjects. Longitudinal TAPS therapy alters cerebellar metabolism, which can be a cause or consequence of tremor reduction. Cerebellar-premotor region connectivity may play a role in the anti-tremor effects of TAPS.

Does Essential Tremor Alter the Axonal Excitability Properties of Lower Motor Neurons?

Automated nerve excitability testing has identified that the altered excitability of lower motor neuron (LMN) axons in central diseases is because of trans-synaptic plasticity. Essential tremor (ET) is considered a central disorder caused by an altered cerebellar circuit. This study aimed to identify alterations in the excitability of distal motor axons in subjects with ET, with the intention of clarifying whether a trans-synaptic mechanism or LMN adaptation for tremor affects the LMNs of subjects with ET. Twenty-one consecutive patients diagnosed with ET underwent a clinical and electrophysiological evaluation. For the enrolled cases and 45 age- and gender-matched healthy controls, automated nerve excitability testing with threshold tracking techniques (QTRACS software with TRONDF multiple-excitability protocol) was used to evaluate multiple nerve excitability indices in distal median nerve motor axons. The automated protocol calculated the strength-duration time constant, parameters of threshold electrotonus and current-threshold relationship, and the recovery cycle of excitability. Comparisons of the automated nerve excitability testing parameters revealed no significant differences between the ET and control groups in any of strength-duration time constant, threshold electrotonus, current-threshold relationship, and recovery cycle, whereas the rheobase was higher in the ET group (3.4 ± 1.1 vs. 2.3 ± 1.1, mean ± standard error mean; P < 0.01). With the exception of an increased rheobase in ET subjects, no significant differences were observed in LMN excitability between the ET subjects and their controls. The extent of plasticity or adaptation in LMNs may be limited to a major change in central processes that exert marked effects on the pool of LMNs.

Hippocampal impairment in patients with Essential Tremor

IntroductionThere is growing evidence that a proportion of patients with Essential Tremor (ET) may develop a memory impairment over time. However, no studies have evaluated whether hippocampal damage really occur in ET. This study investigated the macro and micro-structural integrity of the hippocampus in ET subjects using a multimodal MRI approach. MethodsNeuropsychological and MRI data were acquired from 110 participants (60 patients with ET and 50 age-, sex-, and education-matched healthy controls [HC]). Whole-brain T1-weighted and Diffusion Tensor Imaging (DTI) were performed to assess macro-and microstructural alterations. MRI parameters (volume; mean diffusivity [MD]; fractional anisotropy [FA]) of bilateral hippocampi were obtained. In order to evaluate the relationship between MRI alterations and neurocognitive impairment, hippocampal parameters were also correlated with cognitive test scores. ResultsCompared to controls, ET patients showed a subclinical memory impairment with significantly lower memory scores, but within the normal ranges. Despite the subclinical damage, however, ET patients showed a significant increase in MD values in the bilateral hippocampi in comparison with HC. A significant correlation was also found between MD and memory scores in ET. ConclusionThis study improves the knowledge on memory impairment in ET, as our results demonstrate for the first time the hippocampal microstructural damage related to subclinical memory impairment in ET patients. Further studies are needed before these findings can be considered predictive of a distinct ET subtype or suggestive of a co-occurent dementia.

Deficits in tapping accuracy and variability in tremor patients

BackgroundThe basal ganglia and cerebellum are brain structures involved in movement initiation, execution and termination. They are thought to be involved in the tremor generation and movement deficits in Parkinson’s disease (PD) and essential tremor (ET). Especially in PD, maintaining cyclic movement, such as walking or tapping can be significantly disturbed. Providing external cues improves timing of these movements in PD but its effect on ET has not yet been studied in depth. The aim of this study is to evaluate the usefulness of a bimanual tapping task as a tool during clinical decision making.MethodHand movements and tremor was recorded using accelerometers and EMG (m. extensor carpi ulnaris) from PD and ET patients and healthy controls during a bimanual tapping task as a way to distinguish PD from ET. All subjects performed the tapping task at two different frequencies, 2 Hz and 4 Hz, with and without the presence of auditory cues.ResultsNo significant intra-group differences were found in the patient groups. Acceleration data revealed significantly less accurate tapping and more variable tapping in PD than in ET and healthy controls. ET subjects tapped less accurate and with a greater variability than healthy controls during the 4 Hz tapping task. Most interestingly the tapping accuracy improved in PD patients when kinetic tremor was recorded with EMG during the task.ConclusionProviding ET and PD patients with an external cue results in different tapping performances between patient groups and healthy controls. Furthermore, the findings suggest that kinetic tremor in PD enables patients to perform the task with a greater accuracy. So far this has not been shown in other studies.

Evaluating Mild Cognitive Impairment in Essential Tremor: How Many and Which Neuropsychological Tests?

Essential tremor (ET) confers an increased risk for developing both amnestic and non-amnestic mild cognitive impairment (MCI). Yet, the optimal measures for detecting mild cognitive changes in individuals with this movement disorder have not been established. We sought to identify the cognitive domains and specific motor-free neuropsychological tests that are most sensitive to mild deficits in cognition as defined by a Clinical Dementia Rating (CDR) of 0.5, which is generally associated with a clinical diagnosis of MCI. A total of 196 ET subjects enrolled in a prospective, longitudinal, clinical-pathological study underwent an extensive motor-free neuropsychological test battery and were assigned a CDR score. Logistic regression analyses were performed to identify the neuropsychological tests which best identified individuals with CDR of 0.5 (mild deficits in cognition) versus 0 (normal cognition). In regression models, we identified five tests in the domains of Memory and Executive Function which best discriminated subjects with CDR of 0.5 versus 0 (86.9% model classification accuracy). These tests were the California Verbal Learning Test II Total Recall, Logical Memory II, Verbal-Paired Associates I, Category Switching Fluency, and Color-Word Inhibition. Mild cognitive difficulty among ET subjects is best predicted by combined performance on five measures of memory and executive function. These results inform the nature of cognitive dysfunction in ET and the creation of a brief cognitive battery to assess patients with ET for cognitively driven dysfunction in life that could indicate the presence of MCI. (JINS, 2018, 24, 1084-1098).

Repeated Spiral Drawings in Essential Tremor: a Possible Limb-Based Measure of Motor Learning.

To investigate changes in tremor severity over repeated spiral drawings to assess whether learning deficits can be evaluated directly in a limb in essential tremor (ET). A motor learning deficit in ET, possibly mediated by cerebellar pathways, has been established in eye-blink conditioning studies, but not paradigms measuring from an affected, tremulous limb. Computerized spiral analysis captures multiple characteristics of Archimedean spirals and quantifies performance through calculated indices. Sequential spiral drawing has recently been suggested to demonstrate improvement across trials among ET subjects. One hundred and sixty-one ET and 80 age-matched control subjects drew 10 consecutive spirals on a digitizing tablet. Degree of severity (DoS), a weighted, computational score of spiral execution that takes into account spiral shape and line smoothness, previously validated against a clinical rating scale, was calculated in both groups. Tremor amplitude (Ampl), an independent index of tremor size, measured in centimeters, was also calculated. Changes in DoS and Ampl across trials were assessed using linear regression with slope evaluations. Both groups demonstrated improvement in DoS across trials, but with less improvement in the ET group compared to controls. Ampl demonstrated a tendency to worsen across trials in ET subjects. ET subjects demonstrated less improvement than controls when drawing sequential spirals, suggesting a possible motor learning deficit in ET, here captured in an affected limb. DoS improved independently of Ampl, showing that DoS and Ampl are separable motor physiologic components in ET that may be independently mediated.

Square biphasic pulse deep brain stimulation for essential tremor: The BiP tremor study

BackgroundConventional deep brain stimulation (DBS) utilizes regular, high frequency pulses to treat medication-refractory symptoms in essential tremor (ET). Modifications of DBS pulse shape to achieve improved effectiveness is a promising approach. ObjectivesThe current study assessed the safety, tolerability and effectiveness of square biphasic pulse shaping as an alternative to conventional ET DBS. MethodsThis pilot study compared biphasic pulses (BiP) versus conventional DBS pulses (ClinDBS). Eleven ET subjects with clinically optimized ventralis intermedius nucleus DBS were enrolled. Objective measures were obtained over 3 h while ON BiP stimulation. ResultsThere was observed benefit in the Fahn-Tolosa Tremor Rating Scale (TRS) for BiP conditions when compared to the DBS off condition and to ClinDBS setting. Total TRS scores during the DBS OFF condition (28.5 IQR = 24.5–35.25) were significantly higher than the other time points. Following active DBS, TRS improved to (20 IQR = 13.8–24.3) at ClinDBS setting and to (16.5 IQR = 12–20.75) at the 3 h period ON BiP stimulation (p = 0.001). Accelerometer recordings revealed improvement in tremor at rest (χ2 = 16.1, p = 0.006), posture (χ2 = 15.9, p = 0.007) and with action (χ2 = 32.1, p=<0.001) when comparing median total scores at ClinDBS and OFF DBS conditions to 3 h ON BiP stimulation. There were no adverse effects and gait was not impacted. ConclusionBiP was safe, tolerable and effective on the tremor symptoms when tested up to 3 h. This study demonstrated the feasibility of applying a novel DBS waveform in the clinic setting. Larger prospective studies with longer clinical follow-up will be required.

Essential tremor and depression.

Depression and neuropsychiatric disorders in individuals with essential tremor (ET) are not well characterized in the literature. We compared 104 ET subjects with 481 non-ET controls involved in the Arizona Study of Aging and Neurodegenerative Disorders. An analysis of baseline depression scales and neuropsychiatric inventory (NPI) was done between the two groups. Comparisons were made additionally within the ET group based on tremor severity, duration of tremor and age of onset. There were no significant differences among each of the scales between the ET and non-ET groups. There were no significant differences in the ET group above and below the median tremor duration. Additionally, no differences were found in the ET group based on objective measures of tremor severity, age of onset, or those with subjectively distressing tremor compared with those without. There were no significant differences in depressive symptoms between ET and non-ET groups. Furthermore, no correlation was found between depressive symptoms in ET groups based on tremor severity, duration, or age of onset.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation.

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

Randomized, Blinded Pilot Testing of Nonconventional Stimulation Patterns and Shapes in Parkinson's Disease and Essential Tremor: Evidence for Further Evaluating Narrow and Biphasic Pulses.

ObjectivesEvidence suggests that nonconventional programming may improve deep brain stimulation (DBS) therapy for movement disorders. The primary objective was to assess feasibility of testing the tolerability of several nonconventional settings in Parkinson's disease (PD) and essential tremor (ET) subjects in a single office visit. Secondary objectives were to explore for potential efficacy signals and to assess the energy demand on the implantable pulse‐generators (IPGs).Materials and MethodsA custom firmware (FW) application was developed and acutely uploaded to the IPGs of eight PD and three ET subjects, allowing delivery of several nonconventional DBS settings, including narrow pulse widths, square biphasic pulses, and irregular pulse patterns. Standard clinical rating scales and several objective measures were used to compare motor outcomes with sham, clinically‐optimal and nonconventional settings. Blinded and randomized testing was conducted in a traditional office setting.ResultsOverall, the nonconventional settings were well tolerated. Under these conditions it was also possible to detect clinically‐relevant differences in DBS responses using clinical rating scales but not objective measures. Compared to the clinically‐optimal settings, some nonconventional settings appeared to offer similar benefit (e.g., narrow pulse widths) and others lesser benefit. Moreover, the results suggest that square biphasic pulses may deliver greater benefit. No unexpected IPG efficiency disadvantages were associated with delivering nonconventional settings.ConclusionsIt is feasible to acutely screen nonconventional DBS settings using controlled study designs in traditional office settings. Simple IPG FW upgrades may provide more DBS programming options for optimizing therapy. Potential advantages of narrow and biphasic pulses deserve follow up.

Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor.

BackgroundParkinson’s disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.Methods35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.ResultsThe ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.ConclusionsThis study provides the novel measurement of tremor variation in time domain termed ‘temporal fluctuation’. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.