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• W2314971264 abstract "Event Abstract Back to Event A descriptive model of the dynamics of rhythmic brain activity Lauri Parkkonen1*, Pavan Ramkumar1 and Riitta Hari1 1 Helsinki University of Technology, Finland Spontaneous brain rhythms recorded by EEG or MEG are known to reflect the functional state of sensory and motor systems: for example, the posterior alpha activity around 10 Hz suppresses when the visual system is engaged, and the central mu-rhythm (10- and 20-Hz components) reacts to tactile stimulation and to planning and execution of motor acts. Although the qualitative behavior of these rhythms—a stimulus- or task-related suppression followed by a rebound—is well-known, there have been no models to quantitatively account for their dynamics yet such models would, e.g., facilitate the identification of the cortical generators and potentially contribute to our understanding of the neural mechanisms underlying these rhythms. Here, we constructed a model of the dynamics of brain rhythms. Due to the post-stimulation rebound of the rhythm amplitude, the generative process is clearly non-linear, and we thus applied a generalized convolution model based on a Volterra expansion to predict the envelope of the rhythm given the time course of the stimulus. We derived the model parameters from a series of MEG experiments where eight healthy human subjects received 11-min sequences of tactile and visual stimuli of varying durations. We estimated the envelopes of the 7–13 and 17–23-Hz bands by a Hilbert transform of the bandpass-filtered continuous MEG data. We employed a Laguerre basis function set for the Volterra expansion, and found the coefficients by a least-squares fit, separately for each rhythm and its components. This model, which we call the oscillatory response function (ORF), is analogous to the canonical hemodynamic response function (HRF) commonly employed in BOLD fMRI analysis. To test the applicability of the ORF model, we used it for predicting the modulation of magnetoencephalographic alpha- and mu-rhythms, recorded independently from the above model-training data, to tactile and visual stimuli of 1 and 6 s in duration. We found that the ORF comprising the 1st and 2nd order Volterra kernels successfully predicted the dynamics of the rhythms and clearly outperformed the 1st order (linear) model. In addition, minimum norm-based MEG source localization using the ORF-predicted envelopes as regressors in a general linear model of the MEG data yielded plausible distributions of cortical generator sites of these rhythms. In conclusion, the non-linear ORF model captures the essential temporal dynamics of brain rhythms and thus enables these complex responses to be fully exploited. In particular, ORFs may facilitate the analysis of single-trial oscillatory responses, characterized by a low signal-to-noise ratio, by allowing information to be integrated over the whole response. In addition, ORFs may also be applied to studying the oscillatory responses to more natural stimuli with graded and overlapping sensory input. This methodological advance is also relevant in the context of the increasingly common combination of MEG/EEG and fMRI studies, where electrophysiological and hemodynamic signatures of brain activity should be analyzed under a common framework. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Poster Presentation Topic: Electrophysiology Citation: Parkkonen L, Ramkumar P and Hari R (2008). A descriptive model of the dynamics of rhythmic brain activity. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.104 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Lauri Parkkonen, Helsinki University of Technology, Helsinki, Finland, lauri@neuro.hut.fi Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lauri Parkkonen Pavan Ramkumar Riitta Hari Google Lauri Parkkonen Pavan Ramkumar Riitta Hari Google Scholar Lauri Parkkonen Pavan Ramkumar Riitta Hari PubMed Lauri Parkkonen Pavan Ramkumar Riitta Hari Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
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• W2314971264 title "A descriptive model of the dynamics of rhythmic brain activity" @default.
• W2314971264 doi "https://doi.org/10.3389/conf.neuro.11.2008.01.104" @default.
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