Tremor pathophysiology:lessons from neuroimaging

Contains fulltext : 226036.pdf (Publisher’s version ) (Closed access)PURPOSE OF REVIEW: We discuss the latest neuroimaging studies investigating the pathophysiology of Parkinson's tremor, essential tremor, dystonic tremor and Holmes tremor. RECENT FINDINGS: Parkinson's tremor is associated with increased activity in the cerebello-thalamo-cortical circuit, with interindividual differences depending on the clinical dopamine response of the tremor. Although dopamine-resistant Parkinson's tremor arises from a larger contribution of the (dopamine-insensitive) cerebellum, dopamine-responsive tremor may be explained by thalamic dopamine depletion. In essential tremor, deep brain stimulation normalizes cerebellar overactivity, which fits with the cerebellar oscillator hypothesis. On the other hand, disconnection of the dentate nucleus and abnormal white matter microstructural integrity support a decoupling of the cerebellum in essential tremor. In dystonic tremor, there is evidence for involvement of both cerebellum and basal ganglia, although this may depend on the clinical phenotype. Finally, in Holmes tremor, different causal lesions map to a common network consisting of the red nucleus, internal globus pallidus, thalamus, cerebellum and pontomedullary junction. SUMMARY: The pathophysiology of all investigated tremors involves the cerebello-thalamo-cortical pathway, and clinical and pathophysiological features overlap among tremor disorders. We draw the outlines of a hypothetical pathophysiological axis, which may be used besides clinical features and cause in future tremor classifications

Myoclonus-dystonia : distinctive motor and non-motor phenotype from other dystonia syndromes

Background: myoclonus-dystonia (M-D) due to a pathogenic variant of SGCE is an autosomal dominant inherited movement disorder. Apart from motor symptoms, psychiatric disorders are highly prevalent in patients with MD. Previous studies suggest, but never tested directly, that the type of psychiatric disorder differs between dystonia syndromes, probably related to disease specific pathology. Little is known about other non-motor symptoms (NMS) in M.D. Here, we systematically study NMS in M-D in direct comparison to other types of dystonia and healthy controls. Methods: Standardized questionnaires were used to assess type and severity of psychiatric co-morbidity, sleep problems, fatigue and quality of life. Results of M-D patients with a pathogenic variant of SGCE were compared to results of idiopathic cervical dystonia (CD) patients, dopa-responsive dystonia (DRD) patients with a pathogenic variant of GCH1 and controls. Results: We included 164 participants: 41 M-D, 51 CD, 19 DRD patients, 53 controls. Dystonia patients (M-D, CD and DRD) had an increased prevalence of psychiatric disorders compared to controls (56-74% vs. 29%). In M-D we found a significantly increased prevalence of obsessive-compulsive disorder (OCD) and psychosis compared to CD and DRD. All dystonia patients had more sleep problems (49-68% vs. 36%) and fatigue (42-73% vs. 15%) than controls. Compared to other dystonia subtypes, M-D patients reported less excessive daytime sleepiness and fatigue. Conclusion: Psychiatric comorbidity is frequent in all dystonia types, but OCD and psychosis are more common in M-D patients. Further research is necessary to elucidate underlying pathways

Altered brain connectivity in hyperkinetic movement disorders:A review of resting-state fMRI

BACKGROUND: Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive. OBJECTIVES: Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures. METHODS: A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes. RESULTS: Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described. CONCLUSION: Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes

Systematic clinical approach for diagnosing upper limb tremor

Tremor is the most common movement disorder worldwide, but diagnosis is challenging. In 2018, the task force on tremor of the International Parkinson and Movement Disorder Society published a consensus statement that proposes a tremor classification along two independent axes: a clinical tremor syndrome and its underlying aetiology. In line with this statement, we here propose a stepwise diagnostic approach that leads to the correct clinical and aetiological classification of upper limb tremor. We also describe the typical clinical signs of each clinical tremor syndrome. A key feature of our algorithm is the distinction between isolated and combined tremor syndromes, in which tremor is accompanied by bradykinesia, cerebellar signs, dystonia, peripheral neuropathy or brainstem signs. This distinction subsequently informs the selection of appropriate diagnostic tests, such as neurophysiology, laboratory testing, structural and dopaminergic imaging and genetic testing. We highlight treatable metabolic causes of tremor, as well as drugs and toxins that can provoke tremor. The stepwise approach facilitates appropriate diagnostic testing and avoids unnecessary investigations. We expect that the approach offered in this article will reduce diagnostic uncertainty and increase the diagnostic yield in patients with tremor

Next move in movement disorders (NEMO):Developing a computer-aided classification tool for hyperkinetic movement disorders

Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process. Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision. Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases

The chronnectome as a model for Charcot's 'dynamic lesion' in functional movement disorders

This exploratory study set out to investigate dynamic functional connectivity (dFC) in patients with jerky and tremulous functional movement disorders (JT-FMD). The focus in this work is on dynamic brain states, which represent distinct dFC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 17 patients with JT-FMD and 17 healthy controls (HC). Symptom severity was measured using the Clinical Global Impression-Severity scale. Depression and anxiety were measured using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), respectively. Independent component analysis was used to extract functional brain components. After computing dFC, dynamic brain states were determined for every subject using k-means clustering. Compared to HC, patients with JT-FMD spent more time in a state that was characterized predominantly by increasing medial prefrontal, and decreasing posterior midline connectivity over time. They also tended to visit this state more frequently. In addition, patients with JT-FMD transitioned significantly more often between different states compared to HC, and incorporated a state with decreasing medial prefrontal, and increasing posterior midline connectivity in their attractor, i.e., the cyclic patterns of state transitions. Altogether, this is the first study that demonstrates altered functional brain network dynamics in JT-FMD that may support concepts of increased self-reflective processes and impaired sense of agency as driving factors in FMD

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

Background: Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive. Objectives: Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures. Methods: A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes. Results: Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described. Conclusion: Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes