Event-related desynchronization/synchronization in the putamen. An SEEG case study

Sochůrková, Daniela; Rektor, Ivan

doi:10.1007/s00221-003-1371-2

Cited by 51 publications

(39 citation statements)

References 11 publications

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“…These tremor-like movements often interrupt the enhanced beta oscillation episodes before each movement onset. This observation is consistent with evidence that desynchronization of beta frequency oscillations occurs in both non-Parkinson's and Parkinson's disease patients before movement, although the latency to premovement desynchronization is delayed in Parkinson's disease patients in the absence of L-dopa (21)(22)(23).…”

Section: Increase In Striatal Cholinergic Activity Leads To Increase supporting

confidence: 90%

Striatal origin of the pathologic beta oscillations in Parkinson's disease

McCarthy

Moore-Kochlacs²,

Gu³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

293

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Enhanced oscillations at beta frequencies (8-30 Hz) are a signature neural dynamic pathology in the basal ganglia and cortex of Parkinson's disease patients. The mechanisms underlying these pathological beta oscillations remain elusive. Here, using mathematical models, we find that robust beta oscillations can emerge from inhibitory interactions between striatal medium spiny neurons. The interaction of the synaptic GABAa currents and the intrinsic membrane M-current promotes population oscillations in the beta frequency range. Increased levels of cholinergic drive, a condition relevant to the parkinsonian striatum, lead to enhanced beta oscillations in the striatal model. We show experimentally that direct infusion of the cholinergic agonist carbachol into the striatum, but not into the neighboring cortex, of the awake, normal rodent induces prominent beta frequency oscillations in the local field potential. These results provide evidence for amplification of normal striatal network dynamics as a mechanism responsible for the enhanced beta frequency oscillations in Parkinson's disease.computational model | acetylcholine | muscarinic | mouse E nhanced beta frequency oscillations are correlated with bradykinesia, a disabling movement abnormality in Parkinson's disease patients (1). Improvement of bradykinesia correlates with a decrease in the enhanced beta frequency oscillations in the subthalamic nucleus (STN) and cortex of Parkinson's disease patients (2). However, the function of the beta oscillations in parkinsonian pathology remains elusive, and the source of these oscillations is unknown. Two predominating theories exist about the origin of these enhanced beta rhythms. One hypothesis proposes that oscillations arise from the interaction of the STN and the external segment of the globus pallidus (GPe); this is known as the STN/GPe pacemaker hypothesis (3). Evidence for the STN/ GPe pacemaker hypothesis comes from a study showing that GPe and STN are able to generate synchronized oscillatory bursting activity in the range of 0.4-1.8 Hz in organotypic cultures of cortex-striatum-STN-GPe (4). A second theory of beta generation in Parkinson's disease entails cortical patterning of the STN (3). This theory derives partly from studies on anesthetized rats showing that, with dopamine depletion, oscillatory spiking activity in STN and GPe is correlated with and largely dependent on cortical slow-wave (∼1 Hz) activity (5). For the cortical patterning, but not the STN/GPe hypothesis, there is experimental evidence for intrinsic production of the beta rhythm (6).Degeneration of dopaminergic neurons that project to the striatum is a hallmark of Parkinson's disease pathology (7), but the striatum has been largely ignored as a possible source of beta frequency rhythms in Parkinson's disease. There are several reasons for ignoring the striatum. The striatum consists of an almost entirely inhibitory network of cells (99.7% are GABAergic and 0.3% are cholinergic in the rat neostriatum) (8). Furthermore, the predominant striat...

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Section: Increase In Striatal Cholinergic Activity Leads To Increase supporting

confidence: 90%

Striatal origin of the pathologic beta oscillations in Parkinson's disease

McCarthy

Moore-Kochlacs²,

Gu³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

293

View full text Add to dashboard Cite

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“…The latter technique, however, fails to capture any activity that is not specifically phase locked to the stimulus. This includes the modulation of ongoing activity, and it is becoming increasingly apparent that the basal ganglia, like the cerebral cortex, are continuously involved in synchronised oscillatory activity Levy et al, 2002a,b;Marsden et al, 2001;Priori et al, 2002;Sochurkova and Rektor, 2003;Williams et al, 2002). Synchronisation occurs in several frequency bands, partly dictated by the level of dopaminergic stimulation.…”

Section: Introductionmentioning

confidence: 97%

Online prediction of self-paced hand-movements from subthalamic activity using neural networks in Parkinson’s disease

Loukas

Brown

2004

Journal of Neuroscience Methods

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“…However, because EEG records potentials from the scalp where intervening tissues may blur the neuronal signals from the cortex, those features are evident only after averaging across a large number of trials. (Toro et al 1994b;Sochurkova and Rektor 2003;Bai et al 2005). For single trial EEG, signals relevant to movement are usually very small compared with ongoing background activity.…”

Section: Introductionmentioning

confidence: 99%

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

Bai

Lin

Vorbach

et al. 2007

Clinical Neurophysiology

120

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Objectives-To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).Methods-Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. NIH Public AccessResults-The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention.Conclusions-Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.Significance-Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

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Event-related desynchronization/synchronization in the putamen. An SEEG case study

Cited by 51 publications

References 11 publications

Striatal origin of the pathologic beta oscillations in Parkinson's disease

Striatal origin of the pathologic beta oscillations in Parkinson's disease

Online prediction of self-paced hand-movements from subthalamic activity using neural networks in Parkinson’s disease

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

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