Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Whole-head MEG-systems and modern spatial-filter-based analysis tools recently provided new possibilities to analyze non-invasively cerebral networks of human tremor syndromes. We compared tremor syndromes in Parkinsonian patients with a typical resting tremor as well as in patients with hepatic encephalopathy (HE) with a postural tremor called "mini-asterixis". In 6 patients with idiopathic Parkinson's disease (PD) we found strong coherence between the electromyography (EMG) of forearm muscles and activity in the contralateral primary motor cortex (M1) at tremor frequency but also at double tremor frequency. Furthermore, significant coherences were observed between M1 and medial wall areas (CMA/SMA), lateral premotor cortex, diencephalon, SII cortex, posterior parietal cortex and the contralateral cerebellum at tremor and, stronger, at double tremor frequency. In contrast, in 6 patients with "mini-asterixis" and HE due to chronic liver cirrhosis excessive corticomuscular coherence occurred at the individual tremor frequency between EMG and M1 activity. Interestingly, thalamus-M1 coupling was significantly altered towards lower frequencies matching the individual frequency of the mini-asterixis. Cerebro-muscular or cerebro-cerebral coupling at double tremor frequency was not observed. Therefore, "mini-asterixis" reflects most likely a pathologically decelerated and augmented synchronized rhythmical motor cortical output. This could be due to functional alterations in the M1-basal-ganglia-thalamo-cortical loops in severe HE. In summary, tremor syndromes in PD as well as in patients with HE and "mini-asterixis" are characterized by pathological oscillatory activity within cerebral networks of motor areas. However, the present study shows different mechanisms of tremor generation in PD and HE patients.
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PMID:Pathological oscillatory coupling within the human motor system in different tremor syndromes as revealed by magnetoencephalography. 1601 24

Synchronization of neuronal activity within and across distributed brain regions is a fundamental property of cortical and subcortical networks and serves a variety of functions including motor and cognitive processes. Data will be reviewed here from cross-sectional EEG and MEG studies to suggest that Parkinson's disease is characterized by changing patterns of disturbed neural synchrony that appear to be dependent on the stage of disease. Some of these alterations in neural synchrony may directly account for a number of disease-related impairments in motor and cognitive functions. Future longitudinal studies are required to fully understand the disturbances of functional brain networks in Parkinson's disease and how they evolve throughout the course of the disease.
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PMID:Stage-dependent patterns of disturbed neural synchrony in Parkinson's disease. 1826 80

Resting tremor in idiopathic Parkinson's disease (PD) is associated with an oscillatory network comprising cortical as well as subcortical brain areas. To shed light on the effect of levodopa on these network interactions, we investigated 10 patients with tremor-dominant PD and reanalyzed data in 11 healthy volunteers mimicking PD resting tremor. To this end, we recorded surface electromyograms of forearm muscles and neuromagnetic activity using a 122-channel whole-head magnetometer (MEG). Measurements were performed after overnight withdrawal of levodopa (OFF) and 30 min after oral application of fast-acting levodopa (ON). During OFF, patients showed the typical antagonistic resting tremor. Using the analysis tool Dynamic Imaging of Coherent Sources, we identified the oscillatory network associated with tremor comprising contralateral primary sensorimotor cortex (S1/M1), supplementary motor area (SMA), contralateral premotor cortex (PMC), thalamus, secondary somatosensory cortex (S2), posterior parietal cortex (PPC), and ipsilateral cerebellum oscillating at 8 to 10 Hz. After intake of levodopa, we found a significant decrease of cerebro-cerebral coupling between thalamus and motor cortical areas. Similarly, in healthy controls mimicking resting tremor, we found a significant decrease of functional interaction within a thalamus-premotor-motor network during rest. However, in patients with PD, decrease of functional interaction between thalamus and PMC was significantly stronger when compared with healthy controls. These data support the hypothesis that (1) in patients with PD the basal ganglia and motor cortical structures become more closely entrained and (2) levodopa is associated with normalization of the functional interaction between thalamus and motor cortical areas.
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PMID:Levodopa affects functional brain networks in Parkinsonian resting tremor. 1882 37

Parkinson's disease (PD) related dementia (PDD) develops in up to 60% of patients, but the pathophysiology is far from being elucidated. Abnormalities of resting state functional connectivity have been reported in Alzheimer's disease (AD). The present study was performed to determine whether PDD is likewise characterized by changes in resting state functional connectivity. MEG recordings were obtained in 13 demented and 13 non-demented PD patients. The synchronization likelihood (SL) was calculated within and between cortical areas in six frequency bands. Compared to non-demented PD, PDD was characterized by lower fronto-temporal SL in the alpha range, lower intertemporal SL in delta, theta and alpha1 bands as well as decreased centro-parietal gamma band synchronization. In addition, higher parieto-occipital synchronization in the alpha2 and beta bands was found in PDD. The observed changes in functional connectivity are reminiscent of changes in AD, and may reflect reduced cholinergic activity and/or loss of cortico-cortical anatomical connections in PDD.
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PMID:MEG resting state functional connectivity in Parkinson's disease related dementia. 1898 41

The terms "oscillations" or "oscillatory activity" are frequently used not only to define the rhythmic fluctuations of the postsynaptic potentials of a neuronal group (local field potentials) or a cortical region (EEG, MEG), but also to indicate the rhythmic discharge pattern of action potentials from a neuron or a small group of neurons. Oscillatory activity makes possible the synchronization of different neuronal groups from nearby or distant cortical regions that participate in the same motor, sensory or cognitive task. The presence of oscillatory activity is usually associated to the existence of synchronization, but both phenomena are not necessarily always equivalent. Abnormalities of oscillatory activities or synchronization within or between different brain structures have been described in several neurological and psychiatric diseases; these abnormalities might play a relevant pathophysiological role in Parkinson's disease (and other movement disorders), schizophrenia or epilepsy. This review discusses all these aspects, with emphasis on their potential role both as a basic mechanism in brain function and as a pathophysiological substrate for some of the symptoms and signs observed in several diseases.
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PMID:[Brain oscillations: pathophysiological and potentially therapeutic role in some neurological and psychiatric diseases]. 2009 85

Neuroimaging has in recent years greatly contributed to our understanding of a wide range of aspects of central neurological diseases. These include the classification and localization of disease (e.g., in headache), the understanding of pathology (e.g., in Parkinson's disease), mechanisms of reorganization (e.g., in stroke), and the subclinical progress of disease (e.g., in degenerative diseases). Apart form presurgical mapping, clinical applications of fMRI are limited. However, functional imaging enables the formulation of neurobiological hypotheses that can be tested clinically and is suited to test classical clinical hypotheses about how the brain works. Understanding the mechanisms and the site of pathology, e.g., in cluster headaches, will lead and has led to new therapeutic strategies. New methodological developments for neuroscientific applications are aimed at the integration of functional and morphological connectivity through a combination of magnetic resonance techniques (fMRI, DTI) and electrophysiological (EEG, MEG) recordings. In addition to stimulus-dependent activations, resting state activity has found increasing interest, for example, in sleep research and various psychiatric diseases (e.g., schizophrenia, borderline).
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PMID:[Neuroimaging in medicine]. 2070 Jul 79

Parkinson's disease (PD) related dementia (PDD) develops in up to 80% of PD patients. The present study was performed to further unravel the underlying pathophysiological mechanisms by applying a new analysis approach that uses an atlas-based MEG beamformer to provide a detailed anatomical mapping of cortical rhythms and functional interactions. Importantly, we used the phase lag index (PLI) as a measure of functional connectivity to avoid any biases due to effects of volume conduction. MEG recordings were obtained in 13 PDD and 13 non-demented PD patients. Beamforming was used to estimate spectral power and PLI in delta, theta, alpha, beta and gamma frequency bands. Compared to PD patients, PDD patients had more delta and theta power in parieto-occipital and fronto-parietal areas, respectively. The PDD patients had less alpha and beta power in parieto-temporo-occipital and frontal areas, respectively. Compared to PD patients, PDD patients had lower mean PLI values in the delta and alpha bands in fronto-temporal and parieto-temporo-occipital areas, respectively. In addition, in PDD patients connectivity between pairs of regions of interest (Brodmann areas) was stronger in the theta band and weaker in the delta, alpha and beta bands. The added value of the present results over previous studies analysing frequency-specific changes in neuronal activity in PD patients, is the anatomical framework in which we demonstrated a slowing in neuronal activity and a reduction in functional connectivity in PD related dementia. Moreover, this study shows a widespread reduction in functional connectivity between different regions in PDD.
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PMID:A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming. 2417 62

In recent years there has been a shift in focus from the study of local, mostly task-related activation to the exploration of the organization and functioning of large-scale structural and functional complex brain networks. Progress in the interdisciplinary field of modern network science has introduced many new concepts, analytical tools and models which allow a systematic interpretation of multivariate data obtained from structural and functional MRI, EEG and MEG. However, progress in this field has been hampered by the absence of a simple, unbiased method to represent the essential features of brain networks, and to compare these across different conditions, behavioural states and neuropsychiatric/neurological diseases. One promising solution to this problem is to represent brain networks by a minimum spanning tree (MST), a unique acyclic subgraph that connects all nodes and maximizes a property of interest such as synchronization between brain areas. We explain how the global and local properties of an MST can be characterized. We then review early and more recent applications of the MST to EEG and MEG in epilepsy, development, schizophrenia, brain tumours, multiple sclerosis and Parkinson's disease, and show how MST characterization performs compared to more conventional graph analysis. Finally, we illustrate how MST characterization allows representation of observed brain networks in a space of all possible tree configurations and discuss how this may simplify the construction of simple generative models of normal and abnormal brain network organization.
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PMID:The trees and the forest: Characterization of complex brain networks with minimum spanning trees. 2472

Increased beta (13-30 Hz) oscillatory synchrony in basal ganglia-cortical circuits is a physiological characteristic of Parkinson's disease (PD). While the function of the beta rhythm is unknown, there is evidence that its modulation serves a predictive role, in preparation of future actions. We investigate the relation between predictive beta modulation and entrainment of brain oscillations in a task inviting behavioral entrainment by a regular task structure. MEG was recorded during a serial choice response task, in a group of 12 PD patients and 12 control subjects. In one condition, the reaction stimuli allowed for temporal preparation only (random condition), while in a second condition (predictable condition) the reaction stimuli allowed both temporal and effector preparation. Reaction times were identical between groups, and both groups benefited equally from the known effector side in the predictable condition. Analysis of oscillatory activity, by contrast, revealed marked differences between groups. In patients, the proportion of preparatory beta power desynchronization preceding the reaction stimuli was significantly smaller than in controls, while the proportion of beta desynchronization following the events was larger. In addition to this shift from prospective to reactive modulation of beta-band oscillations, patients showed a trend to reduced motor cortical pre-stimulus delta phase synchronization, and later gamma power synchronization than controls. Delta phase synchronization was, furthermore, significantly correlated with predictive beta desynchronization, supporting the relevance of hierarchical coupling between oscillations of different frequencies for the analysis of oscillatory changes in PD. Together, these features of task-related oscillatory activity indicate that entrainment fails to engender the same predictive mode of motor activation in PD patients as in healthy controls.
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PMID:A shift from prospective to reactive modulation of beta-band oscillations in Parkinson's disease. 2496 69

Oscillatory activity in the beta range, in human primary motor cortex (M1), shows interesting dynamics that are tied to behaviour and change systematically in disease. To investigate the pathophysiology underlying these changes, we must first understand how changes in beta activity are caused in healthy subjects. We therefore adapted a canonical (repeatable) microcircuit model used in dynamic causal modelling (DCM) previously used to model induced responses in visual cortex. We adapted this model to accommodate cytoarchitectural differences between visual and motor cortex. Using biologically plausible connections, we used Bayesian model selection to identify the best model of measured MEG data from 11 young healthy participants, performing a simple handgrip task. We found that the canonical M1 model had substantially more model evidence than the generic canonical microcircuit model when explaining measured MEG data. The canonical M1 model reproduced measured dynamics in humans at rest, in a manner consistent with equivalent studies performed in mice. Furthermore, the changes in excitability (self-inhibition) necessary to explain beta suppression during handgrip were consistent with the attenuation of sensory precision implied by predictive coding. These results establish the face validity of a model that can be used to explore the laminar interactions that underlie beta-oscillatory dynamics in humans in vivo. Our canonical M1 model may be useful for characterising the synaptic mechanisms that mediate pathophysiological beta dynamics associated with movement disorders, such as stroke or Parkinson's disease.
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PMID:Computational modelling of movement-related beta-oscillatory dynamics in human motor cortex. 2695 10


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