title: Spatiotemporal forward solution of the EEG and MEG using network modeling.
abstract: Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.
authors.affiliation: Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton 33431, USA. jirsa@walt.ccs.fau.edu
authors.forename: Viktor K
authors.initials: VK
authors.lastname: Jirsa
authors.forename: Kelly J
authors.initials: KJ
authors.lastname: Jantzen
authors.forename: Armin
authors.initials: A
authors.lastname: Fuchs
authors.forename: J A Scott
authors.initials: JA
authors.lastname: Kelso
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D004574:Electromagnetic Fields; D005071:Evoked Potentials; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*