Dynamic functional networks in human seizure activity

Abstract

Event Abstract Back to Event Dynamic functional networks in human seizure activity Mark Kramer1*, Uri Eden1, Eric Kolaczyk1 and Sydney Cash2 1 Boston University, United States 2 Harvard Medical School, United States Epilepsy, the condition of recurrent unprovoked seizures, is the world's most prominent serious brain disorder, affecting some 50 million people worldwide. For an estimated 30% of these patients, seizures remain poorly controlled despite maximal medical management. Moreover, control of epilepsy through medication and surgery often results in significant, sometimes debilitating, side effects. Advancing the therapeutic management of epilepsy requires a detailed understanding of the focal initiation and subsequent spread of the seizure over a network of interconnected brain regions. The human brain is naturally conceived as a network capable of generating rhythmic fluctuations of coordinated neuronal activity. This “brain network” consists of two fundamental components: nodes (e.g., a cortical column) and edges (e.g., synaptic connections) that connect node pairs. In neuroscience, brain networks are typically divided into two categories: persistent structural networks based on anatomical connections between brain regions, and transient functional networks representing the coupling between dynamic activity recorded from separate brain areas. Ongoing research suggests common topological properties emerge in these brain networks, including small-worldness, heirarchal organization, and the presence of densly connected hubs. In epilepsy - perhaps best characterized as a disease of brain rhythms - the relationship between dynamic functional networks and the pathological brain rhythms of this disease remains unknown. Here we present a general paradigm for the inference of dynamic functional networks from time series data, and apply this paradigm to the anaysis of multielectrode invasive voltage recordings made directly from cortex and deep brain regions of human subjects during seizure. We first describe the implementation of this paradigm for a computationally efficient choice of coupling measure and consider the robustness of this measure for simulated data sets. We then apply this procedure to the analysis of electrocorticogram (ECoG) data recorded from a population of human subjects during seizure. We investiagate how the dynamic functional networks evolve during seizure, and propose three new insights: 1) At the spatial scale of ECoG recordings, brain regions decouple during seizure, 2) Network topologies coalesce at seizure onset and just before termination, while fragmenting during seizure, and 3) Similar functional network topologies appear from seizure-to-seizure. To address the relationship between dynamic networks and the rhythmic neuronal activity characteristic of the seizure, we develop a simulation study of network dynamics. At each node, we implement a mean-field model of cortical activity capable of producing the rhythmic fluctuations of seizure. We then connect these nodes with topolgical structures consistent with the functional networks observed in the clinical data. In this way, we examine how the changing network connectivity (i.e., dynamics of networks) affects the neuronal rhythms produced at each node (i.e., dynamics on networks). Combined, a deeper understanding of the network dynamics and neuronal mechanisms of the seizure promises to provide new insights, and perhaps theraputic strategies, for the treatment of epilepsy. Keywords: computational neuroscience, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Computational neuroscience Citation: Kramer M, Eden U, Kolaczyk E and Cash S (2011). Dynamic functional networks in human seizure activity. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00132 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: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Mark Kramer, Boston University, Boston, United States, mak@bu.edu 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 Mark Kramer Uri Eden Eric Kolaczyk Sydney Cash Google Mark Kramer Uri Eden Eric Kolaczyk Sydney Cash Google Scholar Mark Kramer Uri Eden Eric Kolaczyk Sydney Cash PubMed Mark Kramer Uri Eden Eric Kolaczyk Sydney Cash 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.