Neuronal dynamics and cortical oscillations

Neuronal dynamics and cortical oscillations

Journal of Physiology - Paris 99 (2006) 1–2 www.elsevier.com/locate/jphysparis Foreword Neuronal dynamics and cortical oscillations This issue of t...

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Journal of Physiology - Paris 99 (2006) 1–2 www.elsevier.com/locate/jphysparis

Foreword

Neuronal dynamics and cortical oscillations

This issue of the Journal of Physiology (Paris) contains a selection of 9 papers presented at the 2nd Workshop ‘‘Neuronal Dynamics and Cortical Oscillations’’, which was held in Jena, Germany, on September 15, 2004. The first workshop in this series, entitled ‘‘Cortical activation and deactivation non-linear analysis of their dynamics’’ was held 1999 during the 4. Hans-BergerCongress and provided a review of the state of the art in non-linear EEG/MEG property analysis. The second workshop now provided a comprehensive and comprehensible overview on neuronal dynamics and cortical oscillations including the latest developments this new and highly innovative field. Cortical Oscillations—measured by means of EEG or MEG—yield information about dynamic processes in the brain. In this context, one of the basic hypotheses is that the temporal coordination of distributed activity in the brain is realized by oscillatory processes in neuronal populations. Thus, the investigation of information transfer and generation of concepts in the brain requires the analysis of cortical oscillations and their coupling. There exist a variety of methods for this purpose, from timefrequency analysis of evoked and induced activity to coherence analysis, analysis of phase coupling, and modeling with nonlinear systems of differential equations. The papers in this issue reflect the variety of the methods mentioned above. While the first papers employ coherence analysis, the paper by Gratkowski et al. introduces a matching pursuit time-frequency analysis to MEG data, and the last two papers by Leistritz et al. and Liske et al. use linear and nonlinear systems of differential equations, respectively. For the papers applying coherence analysis it is worth noting that they very well reflect the general current shift in paradigm from sensor coherence towards source coherence. The first paper by Schnitzler et al. reviews the synchronization mechanisms involved in the control of precise slow finger movements both under normal conditions and in patients with parkinsonian resting tremor, as well as in patients with high-grade hepatic encephalopathy. The

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second paper by Pollok et al. gives a review of the differences between evoked responses allowing for detailed insights into the time course of brain activation and frequency analysis enabling the detection of brain networks and therefore shedding light on how brain areas interact with each other. The next paper by Butz et al. applies the recently introduced method of Dynamic Imaging of Coherent Sources to the analysis of MEG data of patients suffering from writerÕs cramp and demonstrates coherence between cortical sources and muscles. In the fourth paper Kessler et al. investigate long-range inter-area phase synchronization, that mediate communication within the attentional network. The fifth paper by Lehmann et al. puts two time frames of brain information processing in contrast to each other. The first one is in the order of a few milliseconds, describes a very basic level of processes, and is analyzed with source coherence based on LORETA modeling. The second time frame is in the range of tens to about one hundred milliseconds and indicates the concept that complete brain functions of higher order such as a momentary thought might be incorporated in temporal chunks of processing as quasi-stable brain state. Schelter et al., the sixth paper, analyze graphical models applying partial coherence and partial directed coherence for neural oscillators, which allow for deeper insights into multivariate systems. In the seventh paper Gratkowski et al. introduce time-frequency filtering of MEG signals with Matching Pursuit enabling the identification, extraction and description of signal components as well as the identification and removal of artifact components. Leistritz et al. analyze in the next paper methods for parameter identification in oscillatory networks, where they utilize a system of coupled oscillators In order to model cortical 600 Hz activity. In the ninth paper Liske et al. employ nonlinear differential equations to model the thalamo-reticular network. The workshop was held within the frame of the annual conference of the German Society for Clinical Neurophysiology (DGKN2004), which opened this

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Foreword / Journal of Physiology - Paris 99 (2006) 1–2

emerging field to a large number of interested participants in that area. The generous timing of all sessions of the workshop allowed for both in depth discussions of methodological topics, as well as general discussion on applications in clinical neurophysiology. We thank the organizers of the DGKN for providing the excellent conditions for our workshop. We would like to thank all workshop contributors, particularly those who contributed to this issue, and reviewers for their work. Our special thanks goes to Prof. Gabriel Curio and Dr. Thomas Kno¨sche who both gave valuable scientific input for workshop organization and to Dr. Ralph Huonker who created the cover figure. We thank Dr. Yves Fre´gnac for making this special issue possible and Kirsty Grant for the helpful guidance through the editorial process.

Jens Haueisen Department of Neurology, Biomagnetic Centre Friedrich Schiller University Jena Germany Tel.: +49 3641 9325770; fax: +49 3641 9325772 E-mail address: [email protected] Herbert Witte Institute of Medical Statistics, Computer Science and Documentation, Friedrich Schiller University Jena Germany Otto W. Witte Department of Neurology Friedrich Schiller University Jena Germany