A role for accelerometry in the differential diagnosis of tremor syndromes

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

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

Multimodal imaging of essential tremor and dystonic tremor

Despite recent advances in tremor and dystonia classification, it remains difficult to discriminate essential tremor from dystonic tremor as they are similar in appearance and no biomarker exists. Further, tremor can appear in the same or a different body part than the dystonia. The aim of the current study was to better understand the differential pathophysiology of these tremors. We designed a cross-sectional case-control study and recruited 16 patients with essential tremor, 16 patients with dystonic tremor, and 17 age-matched healthy volunteers. We used multi-modal imaging combining resting-state functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopy. We measured functional connectivity of resting-state fMRI to assess connectivity in the tremor network, fractional anisotropy and mean diffusivity with diffusion tensor imaging, and GABA+, Glutamate/Glutamine, Choline, and N-Acetylaspartate with spectroscopy (adjusted to Creatine). Our results showed reduced functional connectivity of resting-state fMRI between the cerebellum and dentate nucleus bilaterally for the essential tremor group, but not the dystonic tremor group, compared to healthy volunteers. There was higher fractional anisotropy in the middle cerebellar peduncle bilaterally for the dystonic tremor group compared to the essential tremor group as well as for essential tremor group compared to healthy volunteers. There was also higher fractional anisotropy in the red nucleus and corticospinal tract for essential tremor and dystonic tremor groups compared to healthy volunteers. We also showed reduced mean diffusivity in the cerebellum of both essential tremor and dystonic tremor groups compared to healthy volunteers. Finally, we found elevated GABA+/Cr in the cerebellum of the essential tremor and dystonic tremor groups compared to healthy volunteers, but no difference emerged between essential tremor and dystonic tremor groups. We did not find group differences in the other metabolites. Our results indicate cerebellar alterations in essential tremor and dystonic tremor patients compared to healthy volunteers, and further changes in the cerebellum network for the dystonic tremor patients. suggesting that the cerebellum is affected differently in both tremors.

Are smartphones and machine learning enough to diagnose tremor?

BACKGROUND

Patients with essential tremor (ET), Parkinson's disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics.

OBJECTIVES

To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses.

METHODS

A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data.

RESULTS

The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results.

CONCLUSIONS

Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.

Clinical features, pathophysiology, treatment, and controversies of tremor in dystonia

Dystonia and tremor frequently co-occur. In some cases, they have shared biological mechanisms, while in others dystonia and tremor are two comorbid conditions. The term "dystonic tremor" is used to describe tremor in those who have dystonia. Two mutually exclusive definitions of "dystonic tremor" were proposed. According to one definition, dystonic tremor is the tremor in the dystonic body part. An alternate definition of dystonic tremor entails irregular and jerky oscillations that have saw tooth appearance with or without overt dystonia. This paper outlines the differences in two definitions of dystonic tremor and identifies their limitations. Given the diverse views defining "dystonic tremor", this paper will use the term "tremor in dystonia". In addition, we will outline different ways to separate the subtypes of tremor in dystonia. Then we will discuss pathophysiological mechanisms derived from the objective measures and single neuron physiology analyses of tremor in dystonia. This article is part of the Special Issue "Tremor" edited by Daniel D. Truong, Mark Hallett, and Aasef Shaikh.

White Matter Integrity Underlies the Physical-Cognitive Correlations in Subjective Cognitive Decline

Objective: Although previous studies postulated that physical and cognitive decline codeveloped in preclinical dementia, the interconnected relationship among subjective cognitive complaints (SCCs), objective cognitive performance, and physical activity remained hazy. We investigated the mediating roles of physical activity between subjective and objective cognition. Diffusion tensor imaging (DTI) was utilized to test our hypothesis that brain white matter microstructural changes underlie the physical-cognitive decline in subjective cognitive decline (SCD).

Methods: We enrolled cognitively normal older adults aged > 50 years in the Community Medicine Research Center of Keelung Chang Gung Memorial Hospital during 2017–2020. Regression models analyzed mediation effects of physical activity between subjective and objective cognition. The self-reported AD8 questionnaire assessed SCCs. The SCD group, defined by AD8 score ≥ 2, further underwent diffusion MRI scans. Those who agreed to record actigraphy also wore the SOMNOwatch™ for 72 h. Spearman's correlation coefficients evaluated the associations of diffusion indices with physical activity and cognitive performance.

Results: In 95 cognitively normal older adults, the AD8 score and the Montreal Cognitive Assessment (MoCA) score were mediated partially by the metabolic equivalent of the International Physical Activity Questionnaire-Short Form (IPAQ-SF MET) and fully by the sarcopenia score SARC-F. That is, the relation between SCCs and poorer cognitive performance was mediated by physical inactivity. The DTI analysis of 31 SCD participants found that the MoCA score correlated with mean diffusivity at bilateral inferior cerebellar peduncles and the pyramids segment of right corticospinal tract [p < 0.05, false discovery rate (FDR) corrected]. The IPAQ-SF MET was associated with fractional anisotropy (FA) at the right posterior corona radiata (PCR) (p < 0.05, FDR corrected). In 15 SCD participants who completed actigraphy recording, the patterns of physical activity in terms of intradaily variability and interdaily stability highly correlated with FA of bilateral PCR and left superior corona radiata (p < 0.05, FDR corrected).

Conclusions: This study addressed the role of physical activity in preclinical dementia. Physical inactivity mediated the relation between higher SCCs and poorer cognitive performance. The degeneration of specific white matter tracts underlay the co-development process of physical-cognitive decline in SCD.

Wearable Devices for Assessment of Tremor

Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

The Pathophysiology of Dystonic Tremors and Comparison With Essential Tremor

There are two types of dystonic tremor syndromes (DTS), dystonic tremor (DT) and tremor associated with dystonia (TAWD), and neither is understood. DTS likely share some mechanisms with nontremulous dystonia, and there may also be overlaps with essential tremor (ET). We studied 21 ET (8 females, 13 males) and 22 DTS human patients (10 females, 12 males), including 13 human patients with DT (writer's cramp with writing tremor) and 9 human patients with tremor associated with dystonia (TAWD; cervical dystonia with hand tremor). Tremors were analyzed using accelerometry and surface EMG of the antagonist pairs of arm muscles during posture, simple kinetic movement, and writing. Cerebellar inhibition was performed to assess cerebello-thalamo-cortical involvement. DT exhibited higher variability of peak frequency and greater instability of tremor burst intervals over time (higher tremor stability index) than ET or TAWD regardless of tasks. Intermuscular coherence magnitude between the antagonist pairs increased during the writing task in DT, but not ET or TAWD. ET and TAWD exhibited different phase relationships of the temporal fluctuations of voluntary movement and tremor in the kinetic condition. A linear discriminant classifier based on these tremor parameters was able to distinguish the three groups with a classification accuracy of 95.1%. Cerebellar inhibition was significantly reduced in DT, but not in TAWD, compared with ET and healthy controls. Our study shows that the two DTS are distinct entities with DT closer to nontremorous dystonia and TAWD closer to ET.SIGNIFICANCE STATEMENT This study provides novel findings about characteristics and pathophysiology of the two different types of dystonic tremor syndromes compared with essential tremor. Patients with DTS are classified into DT who have dystonia and tremor in the same area, and tremor associated with dystonia (TAWD) who have dystonia and tremor elsewhere. Our results showed that DT exhibits increased tremor variability, instability, and intermuscular coherence, and decreased cerebello-thalamo-cortical inhibition compared with TAWD. Our study shows that DT and TAWD are distinct phenotypes, and that the physiological characteristics of DT are more similar to nontremorous dystonia, and TAWD is closer to ET.

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

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

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

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

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

Network-level connectivity is a critical feature distinguishing dystonic tremor and essential tremor

Dystonia is a movement disorder characterized by involuntary muscle co-contractions that give rise to disabling movements and postures. A recent expert consensus labelled the incidence of tremor as a core feature of dystonia that can affect body regions both symptomatic and asymptomatic to dystonic features. We are only beginning to understand the neural network-level signatures that relate to clinical features of dystonic tremor. At the same time, clinical features of dystonic tremor can resemble that of essential tremor and present a diagnostic confound for clinicians. Here, we examined network-level functional activation and connectivity in patients with dystonic tremor and essential tremor. The dystonic tremor group included primarily cervical dystonia patients with dystonic head tremor and the majority had additional upper-limb tremor. The experimental paradigm included a precision grip-force task wherein online visual feedback related to force was manipulated across high and low spatial feedback levels. Prior work using this paradigm in essential tremor patients produced exacerbation of grip-force tremor and associated changes in functional activation. As such, we directly compared the effect of visual feedback on grip-force tremor and associated functional network-level activation and connectivity between dystonic tremor and essential tremor patient cohorts to better understand disease-specific mechanisms. Increased visual feedback similarly exacerbated force tremor during the grip-force task in dystonic tremor and essential tremor cohorts. Patients with dystonic tremor and essential tremor were characterized by distinct functional activation abnormalities in cortical regions but not in the cerebellum. We examined seed-based functional connectivity from the sensorimotor cortex, globus pallidus internus, ventral intermediate thalamic nucleus, and dentate nucleus, and observed abnormal functional connectivity networks in dystonic tremor and essential tremor groups relative to controls. However, the effects were far more widespread in the dystonic tremor group as changes in functional connectivity were revealed across cortical, subcortical, and cerebellar regions independent of the seed location. A unique pattern for dystonic tremor included widespread reductions in functional connectivity compared to essential tremor within higher-level cortical, basal ganglia, and cerebellar regions. Importantly, a receiver operating characteristic determined that functional connectivity z-scores were able to classify dystonic tremor and essential tremor with 89% area under the curve, whereas combining functional connectivity with force tremor yielded 94%. These findings point to network-level connectivity as an important feature that differs substantially between dystonic tremor and essential tremor and should be further explored in implementing appropriate diagnostic and therapeutic strategies.

Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease

BACKGROUND

Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases.

OBJECTIVE

To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)].

METHODS

We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDS-UPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal- Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time.

RESULTS

Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and Pull Test.

CONCLUSIONS

In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.

Essential tremor: New advances

Background

Essential Tremor (ET) is one of the most common movement disorders but many controversies still exist in regards to its definition and pathophysiology. In view of the recent published criteria by the Tremor Task Force of the International Parkinson's and Movement Disorders Society (IPMDS), we intended to analyze if this has changed our view of ET and if new developments have arisen since.

Methods

A Medline search for English-written articles was done on June 15, 2019 using the keyword "Essential Tremor". Publications from November 2017 (publication date of the new tremor classification) were taken into account. Reviews, letters and original studies relevant to the subject were selected and reviewed according to the following themes: clinical characteristics, epidemiology, genetics, pathology, biomarkers and treatment.

Results

Out of 132 publications the most relevant articles were selected and reviewed (total of 65 articles). The great majority of these studies focused on surgical treatments (new targets, new technologies) while relatively few articles addressed epidemiology, pathology and pathophysiology.

Conclusions

The use of the new classification is not commonly used still, excepting more recent studies on therapeutics. This is in keeping with diverse opinions and criticisms reported by the IPMDS task force members themselves. One important change has been validating ET as a heterogeneous condition and defining the ET-plus category. We propose a further sub-group classification derived from the new definition of ET-plus.