Mechanically Induced Motor Tremors Disrupt the Perception of Time.

Contemporary research has begun to show a strong relationship between movements and the perception of time. More specifically, concurrent movements serve to both bias and enhance time estimates. To explain these effects, we recently proposed a mechanism by which movements provide a secondary channel for estimating duration that is combined optimally with sensory estimates. However, a critical test of this framework is that by introducing "noise" into movements, sensory estimates of time should similarly become noisier. To accomplish this, we had human participants move a robotic arm while estimating intervals of time in either auditory or visual modalities (n = 24, ea.). Crucially, we introduced an artificial "tremor" in the arm while subjects were moving, that varied across three levels of amplitude (1-3 N) or frequency (4-12 Hz). The results of both experiments revealed that increasing the frequency of the tremor led to noisier estimates of duration. Further, the effect of noise varied with the base precision of the interval, such that a naturally less precise timing (i.e., visual) was more influenced by the tremor than a naturally more precise modality (i.e., auditory). To explain these findings, we fit the data with a recently developed drift-diffusion model of perceptual decision-making, in which the momentary, within-trial variance was allowed to vary across conditions. Here, we found that the model could recapitulate the observed findings, further supporting the theory that movements influence perception directly. Overall, our findings support the proposed framework, and demonstrate the utility of inducing motor noise via artificial tremors.

Exploring White Matter Microstructure with Symptom Severity and Outcomes Following Deep Brain Stimulation in Tremor Syndromes.

Background: Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients. Objectives: We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS. Methods: A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups. Results: DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes. Discussion: Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.

Improved Mathematical Models of Parkinson's Disease with Hopf Bifurcation and Huntington's Disease with Chaos.

Using delay differential equations to study mathematical models of Parkinson's disease and Huntington's disease is important to show how important it is for synchronization between basal ganglia loops to work together. We used the delay circuit RLC (resistor, inductor, capacitor) model to show how the direct pathway and the indirect pathway in the basal ganglia excite and inhibit the motor cortex, respectively. A term has been added to the mathematical model without time delay in the case of the hyperdirect pathway. It is proposed to add a non-linear term to adjust the synchronization. We studied Hopf bifurcation conditions for the proposed models. The desynchronization of response times between the direct pathway and the indirect pathway leads to different symptoms of Parkinson's disease. Tremor appears when the response time in the indirect pathway increases at rest. The simulation confirmed that tremor occurs and the motor cortex is in an inhibited state. The direct pathway can increase the time delay in the dopaminergic pathway, which significantly increases the activity of the motor cortex. The hyperdirect pathway regulates the activity of the motor cortex. The simulation showed bradykinesia occurs when we switch from one movement to another that is less exciting for the motor cortex. A decrease of GABA in the striatum or delayed excitation of the substantia nigra from the subthalamus may be a major cause of Parkinson's disease. An increase in the response time delay in one of the pathways results in the chaotic movement characteristic of Huntington's disease.

Functional correlation between cerebellum and basal ganglia: A parkinsonism model.

INTRODUCTION: The cerebellar response has been studied for years with different models of alteration of other brain structures to understand its complex functioning and its relationship with the rest of the body. Studies in patients with Parkinson's disease (PD) showed that the cerebellar function is modified by deficit of the basal ganglia; which supports the hypothesis that both structures are related anatomically and functionally. METHODS: In our study, the ventrolateral striatum (VLS) of the basal ganglia was altered by an electrolytic lesion, in order to produce a similar jaw frequency of jaw tremor movements presented in parkinsonism, thereafter we analyzed the effect of the lesion on the expression of multiunit activity (MUA) of the cerebellum. RESULTS: We found cerebellar activation during mandibular movements and increment during oral jaw tremor movements. In addition, the amplitude of baseline MUA registered in animals with alteration of the VLS decreased with respect to the intact group. CONCLUSIONS: Accordingly, we conclude that cerebellar changes in MUA may be due to a decrease in the cerebellar inflectional or as a possible compensatory function between cerebellum and basal ganglia.

Tremor Suppression Using Functional Electrical Stimulation.

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

Action tremor features discovery for essential tremor and Parkinson's disease with explainable multilayer BiLSTM.

The tremors of Parkinson's disease (PD) and essential tremor (ET) are known to have overlapping characteristics that make it complicated for clinicians to distinguish them. While deep learning is robust in detecting features unnoticeable to humans, an opaque trained model is impractical in clinical scenarios as coincidental correlations in the training data may be used by the model to make classifications, which may result in misdiagnosis. This work aims to overcome the aforementioned challenge of deep learning models by introducing a multilayer BiLSTM network with explainable AI (XAI) that can better explain tremulous characteristics and quantify the respective discovered important regions in tremor differentiation. The proposed network classifies PD, ET, and normal tremors during drinking actions and derives the contribution from tremor characteristics, (i.e., time, frequency, amplitude, and actions) utilized in the classification task. The analysis shows that the XAI-BiLSTM marks the regions with high tremor amplitude as important in classification, which is verified by a high correlation between relevance distribution and tremor displacement amplitude. The XAI-BiLSTM discovered that the transition phases from arm resting to lifting (during the drinking cycle) is the most important action to classify tremors. Additionally, the XAI-BiLSTM reveals frequency ranges that only contribute to the classification of one tremor class, which may be the potential distinctive feature to overcome the overlapping frequencies problem. By revealing critical timing and frequency patterns unique to PD and ET tremors, this proposed XAI-BiLSTM model enables clinicians to make more informed classifications, potentially reducing misclassification rates and improving treatment outcomes.

Effects of Low-Frequency Deep Brain Stimulation in Bilateral Zona Incerta for a Patient With Tremor and Cerebellar Ataxia.

Background: Whether low-frequency deep brain stimulation (DBS) in the caudal zona incerta (cZi) can improve cerebellar ataxia symptoms remains unexplored. Case Report: We report a 66-year-old man initially diagnosed with essential tremor and subsequently developed cerebellar ataxia after bilateral cZi DBS implantation. We tested the effects of low-frequency DBS stimulations (sham, 10 Hz, 15 Hz, 30 Hz) on ataxia severity. Discussion: Low-frequency cZi DBS improves ataxic speech at 30 Hz, but not at 10 Hz or 15 Hz in this patient. Low-frequency DBS did not improve gait or stance. Therefore, low-frequency stimulation may play a role in treating ataxic speech. Highlights: The finding of this case study suggests that bilateral low-frequency DBS at 30 Hz in the caudal zona incerta has the potential to improve ataxic speech but has limited impact on gait and stance. The involvement of zona incerta in speech warrants further investigation.

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.

Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system's fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

Research on Tremor Suppression Strategies Under a Constant Current Peripheral Electrical Stimulation Device for Parkinson's Disease.

Tremor, a prevalent symptom in Parkinson's patients, is conventionally treated with medications and craniotomy. However, the associated side effects and high surgical costs pose challenges for some individuals. In this study, a lightweight constant current electrical stimulator was developed, which is driven by the FPGA to control the underlying logic and has multiple programmable stimulation parameters. Clinical experiments involving patients with Parkinson's-related resting tremor symptoms were conducted to assess the efficacy of peripheral electrical stimulation. Two Co-contraction Avoidance Stimulation (CAS) strategies targeting nerves and muscles were proposed to reduce tremors. Four Parkinson's disease (PD) patients were recruited to verify the effectiveness of these strategies. Kinematic data recorded by inertial sensors showed that the radial nerve and muscle intervention strategies reduced the average angular velocity amplitude of finger joints during resting tremor by 75.92% and 82.41%, respectively. Notably, under low-frequency pulse stimulation (100 Hz) focused on muscle interference, a low-intensity current of no more than 8 mA maintained a tremor suppression rate of 59.91% even 5 minutes post-stimulation. Based on the experimental results, it is concluded that the constant current electrical stimulator developed in this study can effectively suppress tremor under specific stimulation strategies. These findings have significant implications for the development of lightweight, wearable tremor suppression devices. The stimulator's adaptability, coupled with its precise control parameters, demonstrates promise for advancing non-invasive and cost-effective tremor management in Parkinson's patients.

Pseudo-Orthostatic Tremor in Graves' Disease: A Possible Early Sign of Parkinsonism?

Background: Pseudo-orthostatic tremor is a hyperkinetic movement disorder usually associated with other neurological comorbidities, mainly Parkinson's disease. Case report: A 65-year-old male presented with unsteadiness and leg tremor while standing. Electrophysiological evaluation confirmed the presence of pseudo-orthostatic tremor. Blood test showed an undiagnosed Graves' disease. A complete remission of tremor was achieved with methimazole. Dopamine transporter scintigraphy showed a mild reduction of the striatal binding, bilaterally. Discussion: Graves' disease can be associated with pseudo-orthostatic tremor. Thyroid function should be assessed in patients complaining of unsteadiness. The causative role of hyperthyroidism in determining dopaminergic degeneration and uncovering subclinical parkinsonism warrants further investigations.

Cerebellar nuclei cells produce distinct pathogenic spike signatures in mouse models of ataxia, dystonia, and tremor.

The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.

Intentional movement is fundamental to achieving many goals, whether they are as complicated as driving a car or as routine as feeding ourselves with a spoon. The cerebellum is a key brain area for coordinating such movement. Damage to this region can cause various movement disorders: ataxia (uncoordinated movement); dystonia (uncontrolled muscle contractions); and tremor (involuntary and rhythmic shaking). While abnormal electrical activity in the brain associated with movement disorders has been recorded for decades, previous studies often explored one movement disorder at a time. Therefore, it remained unclear whether the underlying brain activity is similar across movement disorders. Van der Heijden and Brown et al. analyzed recordings of neuron activity in the cerebellum of mice with movement disorders to create an activity profile for each disorder. The researchers then used machine learning to generate a classifier that could separate profiles associated with manifestations of ataxia, dystonia, and tremor based on unique features of their neural activity. The ability of the model to separate the three types of movement disorders indicates that abnormal movements can be distinguished based on neural activity patterns. When additional manifestations of these abnormal movements were considered, multiple mouse models of dystonia and tremor tended to show similar profiles. Ataxia models had several different types of neural activity that were all distinct from the dystonia and tremor profiles. After identifying the activity associated with each movement disorder, Van der Heijden and Brown et al. induced the same activity in the cerebella of healthy mice, which then caused the corresponding abnormal movements. These findings lay an important groundwork for the development of treatments for neurological disorders involving ataxia, dystonia, and tremor. They identify the cerebellum, and specific patterns of activity within it, as potential therapeutic targets. While the different activity profiles of ataxia may require more consideration, the neural activity associated with dystonia and tremor appears to be generalizable across multiple manifestations, suggesting potential treatments could be broadly applicable for these disorders.

Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework.

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

Tremor in Spinocerebellar Ataxia: A Scoping Review.

Background: Spinocerebellar ataxia (SCA) denotes an expanding list of autosomal dominant cerebellar ataxias. Although tremor is an important aspect of the clinical spectrum of the SCAs, its prevalence, phenomenology, and pathophysiology are unknown. Objectives: This review aims to describe the various types of tremors seen in the different SCAs, with a discussion on the pathophysiology of the tremors, and the possible treatment modalities. Methods: The authors conducted a literature search on PubMed using search terms including tremor and the various SCAs. Relevant articles were included in the review after excluding duplicate publications. Results: While action (postural and intention) tremors are most frequently associated with SCA, rest and other rare tremors have also been documented. The prevalence and types of tremors vary among the different SCAs. SCA12, common in certain ethnic populations, presents a unique situation, where the tremor is typically the principal manifestation. Clinical manifestations of SCAs may be confused with essential tremor or Parkinson's disease. The pathophysiology of tremors in SCAs predominantly involves the cerebellum and its networks, especially the cerebello-thalamo-cortical circuit. Additionally, connections with the basal ganglia, and striatal dopaminergic dysfunction may have a role. Medical management of tremor is usually guided by the phenomenology and associated clinical features. Deep brain stimulation surgery may be helpful in treatment-resistant tremors. Conclusions: Tremor is an elemental component of SCAs, with diverse phenomenology, and emphasizes the role of the cerebellum in tremor. Further studies will be useful to delineate the clinical, pathophysiological, and therapeutic aspects of tremor in SCAs.

Probabilistic Tractography-Based Tremor Network Connectivity in Tremor Dominant Parkinson's Disease and Essential Tremor plus.

Background: The basal ganglia-thalamocortical (BGTC) and cerebello-thalamocortical (CTC) networks are implicated in tremor genesis; however, exact contributions across disorders have not been studied. Objective: Evaluate the structural connectivity of BGTC and CTC in tremor-dominant Parkinson's disease (TDPD) and essential tremor plus (ETP) with the aid of probabilistic tractography and graph theory analysis. Methods: Structural connectomes of the BGTC and CTC were generated by probabilistic tractography for TDPD (n = 25), ETP (ET with rest tremor, n = 25), and healthy control (HC, n = 22). The Brain Connectivity Toolbox was used for computing standard topological graph measures of segregation, integration, and centrality. Tremor severity was ascertained using the Fahn-Tolosa-Marin tremor rating scale (FTMRS). Results: There was no difference in total FTMRS scores. Compared with HC, TDPD had a lower global efficiency and characteristic path length. Abnormality in segregation, integration, and centrality of bilateral putamen, globus pallidus externa (GPe), and GP interna (GPi), with reduction of centrality of right caudate and cerebellar lobule 8, was observed. ETP showed reduction in segregation and integration of right GPe and GPi, ventrolateral posterior nucleus, and centrality of right putamen, compared with HC. Differences between TDPD and ETP were a reduction of strength of the right putamen, and lower clustering coefficient, local efficiency, and strength of the left GPi in TDPD. Conclusions: Contrary to expectations, TDPD and ETP may not be significantly different with regard to tremor pathogenesis, with definite overlaps. There may be fundamental similarities in network disruption across different tremor disorders with the same tremor activation patterns, along with disease-specific changes.

Careful Phenotypic Characterization of Tremor Phenomenology in a Patient with Spinocerebellar Ataxia Type 12-Tremor Features Do Not Match Those of Essential Tremor.

Background: The tremor characteristics of patients with spinocerebellar ataxia 12 (SCA12) are often likened to those in patients with essential tremor (ET); however, data are sparse, and videotaped tremor examinations are rare. Case Report: A 37-year-old woman with progressive hand and head tremors underwent genetic testing after conventional diagnostics failed to explain her symptoms. A PPP2R2B variation confirmed spinocerebellar ataxia type 12 (SCA12), a condition not previously considered because classical cerebellar signs were absent. The tremor characteristics of this patient differed in numerous respects from those seen in patients with ET. Discussion: Although often likened to ET, under careful scrutiny, the tremor characteristics observed in this patient with SCA12 were inconsistent with those typically seen in ET. Such discrepancies highlight the necessity of careful phenotyping for tremor disorders, particularly in familial cases. Recognizing the specific tremor phenomenology of SCA12 and distinguishing it from ET is crucial to avoid misdiagnosis and to guide appropriate management and familial counseling. Highlights: This report characterizes in detail an early-stage SCA12 patient initially misdiagnosed as essential tremor, underscoring the importance of nuanced clinical assessment and genetic testing in atypical tremor cases. Similar patients should be meticulously phenotyped to prevent misclassification and enhance our understanding of tremor pathophysiology.

A comparison of quantitative parameters of axial posture and spinal mobility between motor subtypes of Parkinson's disease.

Background and purpose:

Parkinson’s disease (PD) is a heterogeneous neurodegenerative disorder characterized by contradictory clinical outcomes among its several subtypes. The disease can manifest with a tremor-dominant (TD) or a non-tremor-dominant (NTD) phenotype. Although the TD subtype may show a better prognosis, there is limited information on the phenotypic differences regarding the level of axial symptoms. For this reason, in this study it was aimed to make a quantitative comparison of axial posture and spinal mobility between PD with TD and NTD. 

This case-control study was conducted on 94 patients with diagnosed PD. A group diagnosis approach was used in the study, such that the diagnosis of each patient was confirmed, and they were assig-ned to TD and NTD groups by a neurologist expert on movement disorders. Of the patients with PD, 61 were in the TD group, and 33 were in the NTD group. Spinal mouse was used to measure spinal posture and spinal mobility in both sagittal and frontal planes. 

Two groups of 61 patients (25 male + 36 female) with TD-PD (mean age: 64.49±10.37 years) and 33 patients (20 male +13 female) with NTD-PD (mean age: 63.45±9.11 years) were enrolled in the study. There were no significant differences bet­ween the patients with TD and NTD in terms of sagittal and frontal postures (p>0.05). In addition to this, anterior trunk tilt was found to significantly increase as the disease stage advanced in both groups. While the greatest anterior trunk tilt change in the TD-PD group was observed in the 3rd stage, NTD-PD group was in the 2.5th stage. Aside from this, the out­comes of the spinal mobility measurements in the frontal and sagittal planes were similar between the groups (p>0.05).

It is widely acknowledged that many clinical aspects of the TD and NTD forms of PD differ; however, in our study, it was observed that there may be no difference in the axial symptoms of the patients with PD in terms of classification according to tremor dominance.