Literature DB >> 11310163

Investigation of brain dynamics in Parkinson's disease by methods derived from nonlinear dynamics.

V Müller1, W Lutzenberger, F Pulvermüller, B Mohr, N Birbaumer.   

Abstract

EEGs were recorded from patients in early stages of Parkinson's disease (17 patients, 9 females) and healthy controls (12 subjects, 8 females) during rest and during execution/imagining of a complex motor task. The prediction that Parkinson's disease patients compared to controls would show more complex brain dynamics during performance of a complex motor task and imagination of the movements was confirmed by methods derived from nonlinear dynamics. In the resting state, analysis of correlation dimension of EEG time series revealed only slight topographical differences between the groups. During performance of a complex motor task, however, data from Parkinson's disease patients showed higher dimensionality than data from controls, indicating more complex EEG time series. The same difference was found when subjects did not perform any motor movements but imagined the complex movements they had just performed. The data are consistent with the hypothesis that the disturbances in Parkinson's disease result in the recruitment of superfluous cortical networks due to failed inhibition of alternative motor programs in the striatum and thus increase the complexity of cortical representation in motor conditions.

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Year:  2001        PMID: 11310163     DOI: 10.1007/s002210000638

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  13 in total

1.  Effects of hydration and hyperventilation on cortical complexity.

Authors:  Viktor Müller; Niels Birbaumer; Hubert Preissl; Christoph Braun; Gottfried Mayer-Kress; Florian Lang
Journal:  Exp Brain Res       Date:  2003-04-16       Impact factor: 1.972

2.  Genetic influences on dynamic complexity of brain oscillations.

Authors:  Andrey P Anokhin; Viktor Müller; Ulman Lindenberger; Andrew C Heath; Erin Myers
Journal:  Neurosci Lett       Date:  2006-01-27       Impact factor: 3.046

3.  Complexity of resting-state EEG activity in the patients with early-stage Parkinson's disease.

Authors:  Guo-Sheng Yi; Jiang Wang; Bin Deng; Xi-Le Wei
Journal:  Cogn Neurodyn       Date:  2016-10-20       Impact factor: 5.082

4.  Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy.

Authors:  Chiara Pappalettera; Francesca Miraglia; Maria Cotelli; Paolo Maria Rossini; Fabrizio Vecchio
Journal:  Geroscience       Date:  2022-03-28       Impact factor: 7.581

5.  Investigation of EEG abnormalities in the early stage of Parkinson's disease.

Authors:  Chun-Xiao Han; Jiang Wang; Guo-Sheng Yi; Yan-Qiu Che
Journal:  Cogn Neurodyn       Date:  2013-02-10       Impact factor: 5.082

6.  Fractal Dimension Analysis of Transient Visual Evoked Potentials: Optimisation and Applications.

Authors:  Mei Ying Boon; Bruce Ian Henry; Byoung Sun Chu; Nour Basahi; Catherine May Suttle; Chi Luu; Harry Leung; Stephen Hing
Journal:  PLoS One       Date:  2016-09-06       Impact factor: 3.240

7.  What brain signals are suitable for feedback control of deep brain stimulation in Parkinson's disease?

Authors:  Simon Little; Peter Brown
Journal:  Ann N Y Acad Sci       Date:  2012-07-25       Impact factor: 5.691

Review 8.  Non-Linear EMG Parameters for Differential and Early Diagnostics of Parkinson's Disease.

Authors:  Alexander Y Meigal; Saara M Rissanen; Mika P Tarvainen; Olavi Airaksinen; Markku Kankaanpää; Pasi A Karjalainen
Journal:  Front Neurol       Date:  2013-09-17       Impact factor: 4.003

9.  Non-linear dynamical classification of short time series of the rössler system in high noise regimes.

Authors:  Claudia Lainscsek; Jonathan Weyhenmeyer; Manuel E Hernandez; Howard Poizner; Terrence J Sejnowski
Journal:  Front Neurol       Date:  2013-11-12       Impact factor: 4.003

10.  Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals.

Authors:  Claudia Lainscsek; Manuel E Hernandez; Jonathan Weyhenmeyer; Terrence J Sejnowski; Howard Poizner
Journal:  Front Neurol       Date:  2013-12-11       Impact factor: 4.003

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