An Apple a Day to Keep the Parkinson's Disease Doctor Away?

It is challenging to reliably assess the motor features of Parkinson's disease in real-time. This has motivated the search for new digital outcomes that can objectively and remotely measure the severity of parkinsonian motor impairments over an extended period of time. The United States Food and Drug Administration (FDA) has recently granted a 510(k) clearance to the Rune Labs Kinematics System, an ambulatory, smartwatch-based monitoring system to remotely track tremor and dyskinesias in persons with Parkinson's disease. The FDA clearance means that this new digital approach can be regarded as being safe for use in daily practice, with acceptable correlations to clinically based measures. However, the immediate implications for clinicians are limited, because it remains to be demonstrated whether the digital signals correlate well to clinically meaningful outcomes at patient level. The impact on research is also restricted for now, as more validation studies are needed before this new digital approach can be used as primary or secondary endpoint in clinical trials. ANN NEUROL 2023;93:681-685.

Reversible encephalitis-like episodes in fragile X-associated tremor/ataxia syndrome: a case report.

BACKGROUND: Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder caused by CGG repeat expansion of FMR1 gene. Both FXTAS and neuronal intranuclear inclusion disease (NIID) belong to polyglycine diseases and present similar clinical, radiological, and pathological features, making it difficult to distinguish these diseases. Reversible encephalitis-like attacks are often observed in NIID. It is unclear whether they are presented in FXTAS and can be used for differential diagnosis of NIID and FXTAS. CASE PRESENTATION: A 63-year-old Chinese male with late-onset gait disturbance, cognitive decline, and reversible attacks of fever, consciousness impairment, dizziness, vomiting, and urinary incontinence underwent neurological assessment and examinations, including laboratory tests, electroencephalogram test, imaging, skin biopsy, and genetic test. Brain MRI showed T2 hyperintensities in middle cerebellar peduncle and cerebrum, in addition to cerebellar atrophy and DWI hyperintensities along the corticomedullary junction. Lesions in the brainstem were observed. Skin biopsy showed p62-positive intranuclear inclusions. The possibilities of hypoglycemia, lactic acidosis, epileptic seizures, and cerebrovascular attacks were excluded. Genetic analysis revealed CGG repeat expansion in FMR1 gene, and the number of repeats was 111. The patient was finally diagnosed as FXTAS. He received supportive treatment as well as symptomatic treatment during hospitalization. His encephalitic symptoms were completely relieved within one week. CONCLUSIONS: This is a detailed report of a case of FXTAS with reversible encephalitis-like episodes. This report provides new information for the possible and rare features of FXTAS, highlighting that encephalitis-like episodes are common in polyglycine diseases and unable to be used for differential diagnosis.

Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review.

Tremor, bradykinesia, and rigidity are incapacitating motor symptoms that can be suppressed with stereotactic neurosurgical treatment like deep brain stimulation (DBS) and ablative surgery (e.g., thalamotomy, pallidotomy). Traditionally, clinicians rely on clinical rating scales for intraoperative evaluation of these motor symptoms during awake stereotactic neurosurgery. However, these clinical scales have a relatively high inter-rater variability and rely on experienced raters. Therefore, objective registration (e.g., using movement sensors) is a reasonable extension for intraoperative assessment of tremor, bradykinesia, and rigidity. The main goal of this scoping review is to provide an overview of electronic motor measurements during awake stereotactic neurosurgery. The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), articles were screened for relevance. Hundred-and-three articles were subject to detailed screening. Key clinical and technical information was extracted. The inclusion criteria encompassed use of electronic motor measurements during stereotactic neurosurgery performed under local anesthesia. Twenty-three articles were included. These studies had various objectives, including correlating sensor-based outcome measures to clinical scores, identifying optimal DBS electrode positions, and translating clinical assessments to objective assessments. The studies were highly heterogeneous in device choice, sensor location, measurement protocol, design, outcome measures, and data analysis. This review shows that intraoperative quantification of motor symptoms is still limited by variable signal analysis techniques and lacking standardized measurement protocols. However, electronic motor measurements can complement visual evaluations and provide objective confirmation of correct placement of the DBS electrode and/or lesioning. On the long term, this might benefit patient outcomes and provide reliable outcome measures in scientific research.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology.

BACKGROUND: Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS: This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS: Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.

To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson's disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD.

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center.

Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Symptomatic Palatal Tremors in a Rare Treatable Neurometabolic Disorder: A Case Report.

BACKGROUND: Palatal tremor (PT) is an involuntary, rhythmic and oscillatory movement of the soft palate. Two types of PTs-essential palatal tremor (EPT) and symptomatic palatal tremor (SPT) are described. SPT is caused by a lesion in the triangle of Guillain and Mollaret which is formed by the ipsilateral red nucleus, ipsilateral inferior olivary nucleus and contralateral dentate nuclei. EPT reveals no underlying structural pathology. We describe two consecutive patients with PT-a rare clinical sign in a rare distinct clinical entity cerebrotendinous xanthomatosis (CTX) which is an autosomal recessive neurometabolic disorder characterized by a myriad of neurological signs of progressive ataxia, parkinsonian features pyramidal signs, epilepsy; peripheral neuropathy, and nonneurological features of early cataract, xanthomas and infantile-onset diarrhea. These cases emphasize the expanding phenotype of CTX featuring PTs and suggest that this clinical sign is underdiagnosed in CTX.

[Additional functional symptoms in Parkinson's disease and tremor syndromes]. / Zusätzliche funktionelle Symptome bei Parkinson und Tremorsyndromen.

Patients with Parkinson's Disease or a tremor syndrome may present with additional functional movement disorders. The differential diagnosis is particularly difficult. In some cases, functional symptoms occur either before the manifestation of the organic disease or can emerge as an additional symptom after Parkinson's disease or tremor became apparent. In patients with Parkinson's disease the prevalence for additional functional symptoms is 7 %. In the case that patients with Parkinson's diseases have one side that is more severely affected, additional functional motor symptoms such as functional rest tremor also occur on that same, predominantly affected side. Functional gait disorders occur frequently. Clinically, patients appear notably slow in automatized, daily tasks. Their speech is more whispering than hypophonic, bradykinesia during finger tapping manifest without a decrement. The Dopamintransporterszintigraphy (123) I FP-CIT SPECT; DaTSCANTM) may be helpful to differentiate between functional Parkinsonism and Parkinson's disease. Functional tremor in patients with an organic tremor syndrome is diagnosed with the same distraction techniques as in solely functional tremor. This includes cognitive, motor, and suggestive distraction maneuvers. In some cases, additional neurophysiological investigations such as accelerometry are useful for the differential diagnosis. It is most important to identify patients with additional functional symptoms in non-functional movement disorders, because the therapeutic approach differs and a multi professional team is required to initiate effective treatment strategies.

Flapping Tremor: Unraveling Asterixis-A Narrative Review.

Asterixis is a subtype of negative myoclonus characterized by brief, arrhythmic lapses of sustained posture due to involuntary pauses in muscle contraction. We performed a narrative review to characterize further asterixis regarding nomenclature, historical aspects, etiology, pathophysiology, classification, diagnosis, and treatment. Asterixis has been classically used as a synonym for negative myoclonus across the literature and in previous articles. However, it is important to distinguish asterixis from other subtypes of negative myoclonus, for example, epileptic negative myoclonus, because management could change. Asterixis is not specific to any pathophysiological process, but it is more commonly reported in hepatic encephalopathy, renal and respiratory failure, cerebrovascular diseases, as well as associated with drugs that could potentially lead to hyperammonemia, such as valproic acid, carbamazepine, and phenytoin. Asterixis is usually asymptomatic and not spontaneously reported by patients. This highlights the importance of actively searching for this sign in the physical exam of encephalopathic patients because it could indicate an underlying toxic or metabolic cause. Asterixis is usually reversible upon treatment of the underlying cause.

Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders.

Patients with movement disorders such as Parkinson's disease (PD) living in remote and underserved areas often have limited access to specialized healthcare, while the feasibility and reliability of the video-based examination remains unclear. The aim of this narrative review is to examine which parts of remote neurological assessment are feasible and reliable in movement disorders. Clinical studies have demonstrated that most parts of the video-based neurological examination are feasible, even in the absence of a third party, including stance and gait-if an assistive device is not required-bradykinesia, tremor, dystonia, some ocular mobility parts, coordination, and gross muscle power and sensation assessment. Technical issues (video quality, internet connection, camera placement) might affect bradykinesia and tremor evaluation, especially in mild cases, possibly due to their rhythmic nature. Rigidity, postural instability and deep tendon reflexes cannot be remotely performed unless a trained healthcare professional is present. A modified version of incomplete Unified Parkinson's Disease Rating Scale (UPDRS)-III and a related equation lacking rigidity and pull testing items can reliably predict total UPDRS-III. UPDRS-II, -IV, Timed "Up and Go", and non-motor and quality of life scales can be administered remotely, while the remote Movement Disorder Society (MDS)-UPDRS-III requires further investigation. In conclusion, most parts of neurological examination can be performed virtually in PD, except for rigidity and postural instability, while technical issues might affect the assessment of mild bradykinesia and tremor. The combined use of wearable devices may at least partially compensate for these challenges in the future.

The Emergence and Progression of Motor Dysfunction in Individuals at Risk of Parkinson's Disease.

BACKGROUND: PREDICT-PD is a United Kingdom population-based study aiming to stratify individuals for future Parkinson's disease (PD) using a risk algorithm. METHODS: A randomly selected, representative sample of participants in PREDICT-PD were examined using several motor assessments, including the motor section of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-III, at baseline (2012) and after an average of 6 years of follow-up. We checked for new PD diagnoses in participants seen at baseline and examined the association between risk scores and incident sub-threshold parkinsonism, motor decline (increasing ≥5 points in MDS-UPDRS-III) and single motor domains in the MDS-UPDRS-III. We replicated analyses in two independent datasets (Bruneck and Parkinson's Progression Markers Initiative [PPMI]). RESULTS: After 6 years of follow-up, the PREDICT-PD higher-risk group (n = 33) had a greater motor decline compared with the lower-risk group (n = 95) (30% vs. 12.5%, P = 0.031). Two participants (both considered higher risk at baseline) were given a diagnosis of PD during follow-up, with motor signs emerging between 2 and 5 years before diagnosis. A meta-analysis of data from PREDICT-PD, Bruneck, and PPMI showed an association between PD risk estimates and incident sub-threshold parkinsonism (odds ratio [OR], 2.01 [95% confidence interval (CI), 1.55-2.61]), as well as new onset bradykinesia (OR, 1.69 [95% CI, 1.33-2.16]) and action tremor (OR, 1.61 [95% CI, 1.30-1.98]). CONCLUSIONS: Risk estimates using the PREDICT-PD algorithm were associated with the occurrence of sub-threshold parkinsonism, including bradykinesia and action tremor. The algorithm could also identify individuals whose motor examination experience a decline over time. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Using Movement Disorder Society Unified Parkinson's Disease Rating Scale Parts 2 and 3 Simultaneously: Combining the Patient Voice with Clinician Ratings.

BACKGROUND: Regulatory recommendations favor outcomes combining objective and patient input. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the most commonly used scale in Parkinson's disease (PD), includes patient and investigator ratings in distinct parts, but original clinimetric analyses failed to confirm the validity of combining parts by simple summing. OBJECTIVES: The aim was to develop clinimetrically valid constructs for combining patient-reported Part 2 and investigator-rated Part 3 MDS-UPDRS scores. METHODS: Using 7888 MDS-UPDRS scores, we assessed construct validity of combined Part 2 and Part 3 items using exploratory factor analysis (EFA) and graded item response theory (IRT) with threshold criteria: comparative fit index ≥0.9 (EFA) and discrimination parameters ≥0.65 (IRT). RESULTS: The direct sum of Parts 2 + 3 failed to meet the threshold for a valid outcome of PD severity (comparative fit index, CFI = 0.855). However, a two-domain construct combining item scores for tremor and non-tremor domains from Parts 2 and 3 confirmed validity, meeting both EFA and IRT criteria as distinct but correlated indices of disease severity (CFI = 0.923; discrimination mean 2.197 ± 0.480 [tremor] and 1.737 ± 0.344 [non-tremor] domains). CONCLUSIONS: The sum of Parts 2 + 3 is not clinimetrically sound. However, considering tremor and non-tremor items of both Parts 2 and 3 as two outcomes results in a valid summary of PD motor severity that leverages simultaneous patient- and investigator-derived measures. This analytic application addresses regulatory prioritizations and retains the well-validated MDS-UPDRS items. In future interventional trials, we suggest that tremor and non-tremor components of PD motor severity from Parts 2 + 3 be monitored and analyzed to accurately detect objective changes that integrate the patient's voice. © 2023 International Parkinson and Movement Disorder Society.

Machine Learning in Tremor Analysis: Critique and Directions.

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Longitudinal study of clinical and neurophysiological features in essential tremor.

BACKGROUND AND PURPOSE: Essential tremor (ET) is a common and heterogeneous disorder characterized by postural/kinetic tremor of the upper limbs and other body segments and by non-motor symptoms, including cognitive and psychiatric abnormalities. Only a limited number of longitudinal studies have comprehensively and simultaneously investigated motor and non-motor symptom progression in ET. Possible soft signs that configure the ET-plus diagnosis are also under-investigated in follow-up studies. We aimed to longitudinally investigate the progression of ET manifestations by means of clinical and neurophysiological evaluation. METHODS: Thirty-seven ET patients underwent evaluation at baseline (T0) and at follow-up (T1; mean interval ± SD = 39.89 ± 9.83 months). The assessment included the clinical and kinematic evaluation of tremor and voluntary movement execution, as well as the investigation of cognitive and psychiatric disorders. RESULTS: A higher percentage of patients showed tremor in multiple body segments and rest tremor at T1 as compared to T0 (all p-values < 0.01). At T1, the kinematic analysis revealed reduced finger-tapping movement amplitude and velocity as compared to T0 (both p-values < 0.001). The prevalence of cognitive and psychiatric disorders did not change between T0 and T1. Female sex, absence of family history, and rest tremor at baseline were identified as predictive factors of worse disease progression. CONCLUSIONS: ET progression is characterized by the spread of tremor in multiple body segments and by the emergence of soft signs. We also identified possible predictors of disease worsening. The results contribute to a better understanding of ET classification and pathophysiology.

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.

Parkinson's disease tremor prediction using EEG data analysis-A preliminary and feasibility study.

PURPOSE: Tremor is one of the hallmarks of Parkinson's disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series. METHODS: A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions. RESULTS: Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%. CONCLUSION: This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications.

Gait video-based prediction of unified Parkinson's disease rating scale score: a retrospective study.

BACKGROUND: The diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model. METHODS: We retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit. RESULTS: In the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms. CONCLUSIONS: Despite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.

The effect of tremor on disability assessment in chronic inflammatory demyelinating polyradiculoneuropathy.

Tremor in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is common, often unresponsive to treatment, and may contribute to disability. We aim to investigate whether tremor is associated with disability as measured in daily practice and clinical trials, independent of other impairments. We included 76 CIDP patients in this cross-sectional study. We assessed tremor with the Tremor Research Group essential tremor rating assessment scale (TETRAS) and the Fahn-Tolosa-Marin clinical rating scale (FTM). Disability was measured with the inflammatory Rasch-built overall disability scale (I-RODS) and the adjusted Inflammatory Neuropathy Cause and Treatment disability scale (INCAT-DS, categorized separately in arm score, or total score). Impairments including strength, sensory impairment, and fatigue were measured using specific impairment scales. We tested whether "the presence of a clinically relevant tremor" (based on TETRAS and FTM) or "tremor severity" (FTM part B sum score) was associated with disability scores (I-RODS, INCAT-DS total score, and INCAT-DS arm score), independent of the impairment scores, using multivariate regression. Both "the presence of a clinically relevant tremor" and "tremor severity" were significantly associated with disability measured by the INCAT-DS (arm score and total score), but not the I-RODS, independent of strength, sensory impairment, and fatigue. The explained variances were low. Clinically relevant tremor can (partly) explain disability in CIDP, as measured with the INCAT-DS, independent of muscle strength, sensory deficits, and fatigue. To assess disease activity in CIDP patients with tremor, both impairment and disability outcomes should be assessed, as disability is caused partly by tremor while the effect of immunotherapy on tremor seems limited.

Towards a Modular Pathological Tremor Simulation System Based on the Stewart Platform.

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson's disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients' wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson's disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices.

Accelerometric Classification of Resting and Postural Tremor Amplitude.

Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson's Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen's weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.