Tremor-correlated cortical activity in essential tremor

Hellwig, Bernhard; Häußler, SM; Schelter, Björn; Lauk, M.; Guschlbauer, B.; Timmer, Jens; Ch, Lücking

doi:10.1016/s0140-6736(00)04044-7

Cited by 177 publications

(132 citation statements)

References 25 publications

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“…the thalamus) the cerebellum and the brainstem in magnetoencephalographic (MEG) studies (Gross et al, 2001, Gross et al, 2002, Timmermann et al, 2003b, Timmermann et al, 2003a, Sudmeyer et al, 2006 as well as thalamus in a recent EEG study (Moeller et al, 2011 (in press)). However, the interaction between the different network components cannot be resolved by DICS alone (Gross et al, 2002, Hellwig et al, 2000, Hellwig et al, 2001, Hellwig et al, 2003, Schack et al, 2003, Tass et al, 1998, Volkmann et al, 1996.…”

Section: Source Analysesmentioning

confidence: 99%

Neuronal Networks in West Syndrome as Revealed by Source Analysis and Renormalized Partial Directed Coherence

et al. 2012

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Section: Source Analysesmentioning

confidence: 99%

Neuronal Networks in West Syndrome as Revealed by Source Analysis and Renormalized Partial Directed Coherence

et al. 2012

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“…Local field potential (LFP) recordings of thalamic ventralis intermedius (Vim) nucleus show strong linear correlation with the contralateral EMG during tremor (Marsden et al, 2000). Besides this, there is evidence suggesting the sensorimotor cortex is part of this central tremor related oscillatory network with significant coupling in some cases between the primary motor cortex (M1) and the contralateral tremorogenic EMG (Hellwig et al, 2000, Hellwig et al, 2001, Hellwig et al, 2003, Govindan et al, 2006, Schnitzler et al, 2009, Muthuraman et al, 2012. However, this was not a universal finding and negative results were also reported (Halliday et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis

Sarrigiannis

Billings

et al. 2016

Neuroscience

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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 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. AbstractThere is increasing evidence to suggest that essential tremor has a central origin. Different structures appear to be part of the central tremorogenic network, including the motor cortex, the thalamus and the cerebellum. Some studies using EEG and MEG show linear association in the tremor frequency between the motor cortex and the contralateral tremor EMG.Additionally, high thalamomuscular coherence is found with the use of thalamic local field potential (LFP) recordings and tremulous EMG in patients undergoing surgery for deep brain stimulation (DBS). Despite a well-established reciprocal anatomical connection between thalamus and cortex, the functional association between the two structures during "tremor- Highlights• We use a nonlinear autoregressive exogenous model to de-codify corticothalamic communication• We reveal nonlinear interactions between motor cortex and thalamus in essential tremor• We define corticothalamic interactions during tremor active and rest states• There are state specific frequencies and time lags in corticothalamic interrelationships

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“…Segments without clear tremor in the EMG were excluded, leading to 58 segments, 30 of which were from ET, 28 from PT patients. For more details of the patients and selection criteria, see [12,13]. The data are available for download [14].…”

Section: Methodsmentioning

confidence: 99%

Spectral and Higher-Order-Spectral Analysis of Tremor Time Series

Mader

Klatt²,

Amtage³

et al. 2014

Clin Exp Pharmacol

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Objectives: The aim of this study was to investigate linear and nonlinear properties of tremor time series. To this end, we applied linear (second order) and nonlinear (higher order) spectral and cross-spectral analysis to 58 Electroencephalographic (EEG) and Electromyographic (EMG) tremor recordings of seven essential and five Parkinsonian Tremor patients.Methods: Second and third order spectral analysis was performed on two types of data. First, data was simulated from a model mimicking the nonlinear properties of the tremor time series. Limitations of linear second order spectral analysis are illustrated in those simulations. Those limitations can be overcome by nonlinear third order spectral analysis. Second, tremor recordings from the trembling hand and contralateral motor area of the brain of the tremor patients were analyzed both by second and third order spectral analysis.Results: Linear spectral analysis suggested nonlinearity of dynamics and interactions of processes measured by EEG and EMG. Applying bispectral analysis those nonlinearities were investigated. We found that a measure for nonlinearity based on bispectra was significant for most EMG recordings, as well as for the interaction of EEG and EMG. Conclusions:Linear spectral analysis is a powerful tool when assessing spectral properties of time series. However, linear techniques fail to reveal nonlinear couplings. In the application of nonlinear spectral analysis to tremor time series, we showed that the dynamics of the hand muscle as well as the interaction of hand muscle and brain are governed by nonlinearities. Keywords:Higher order spectral analysis; Nonlinearity; Bispectrum; Tremor ObjectiveA powerful tool for the reconstruction of process properties from measured data is spectral analysis [1]. Univariate spectral analysis quantifies the frequency content of each process. Bivariate spectral analysis quantifies the interaction of two or more processes, revealing functional connectivity [2]. Mostly, linear spectral analysis is applied, specifically the auto-spectrum in the univariate case and the coherence in the bivariate case [3][4][5]. An auto-spectral peak reveals that a process oscillates at the frequency at which the peak occurs. Peaks in the coherence spectrum reveal a linear interaction of two processes at the peak frequency. Strictly speaking, linear spectral analysis is restricted to revealing linear properties of a process. Nonlinear properties of the processes, such as quadratic phase coupling, must be investigated by nonlinear spectral analysis. As we show, higher harmonic spectral peaks cannot be distinguished from peaks which occur due to an independent sub-process by linear analysis. Thus independent sub-processes may be erroneously postulated, when truly nonlinear couplings are present.A solution to this has been provided in the 1960s in terms of the extension of linear or second order spectral analysis to higher orders [1,6]. The latter enables quantification of higher order properties. Third order spectral analysis...

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Tremor-correlated cortical activity in essential tremor

Cited by 177 publications

References 25 publications

Neuronal Networks in West Syndrome as Revealed by Source Analysis and Renormalized Partial Directed Coherence

Neuronal Networks in West Syndrome as Revealed by Source Analysis and Renormalized Partial Directed Coherence

Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis

Spectral and Higher-Order-Spectral Analysis of Tremor Time Series

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