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.

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

“…In particular, neural field models (Amari, 1978;Jirsa and Haken, 1996;Nunez, 1974Robinson, 1997Wilson-Cowan, 1974) have a spatial symmetry in their connectivity, which is always reflected in the symmetry of the resulting neural source activations, even though it may be significantly less apparent (if at all) in the EEG and MEG space. This led to the conclusion that the integration of tractographic data is imperative for future large-scale brain modeling attempts ( Jirsa et al, 2002), since the symmetry of the connectivity will constrain the solutions of the neural sources.…”

Natural world physical, brain operational, and mind phenomenal space–time

“…The local field potentials (or the "wave packets" as Freeman [97] names them), which are the unified mean-field potentials of neuronal assemblies generated by the synchronized activity of thousands of neurons in the extracellular space of the cortical sheet, are understood to generate the EEG [97,132]. The neuronal cell membranes, being good electrical insulators, guide the flow of both intracellular and extracellular currents and, thus, result in a current flow perpendicular to the cortical surface due to the perpendicular alignment and elongated shape of pyramidal neurons [194]. The neuronal assemble average of these currents results in the primary current density with the same waveform and mean frequency over the entire neuronal assembly [195].…”

Stabilization of bimanual coordination due to active interhemispheric inhibition: a dynamical account

“…Emanating from large ensembles of mutually interacting neurons, averaging methods like mean-field approaches result in weakly nonlinear mappings from action potentials to dendritic currents. Since the latter may be considered as generators of extracellular fields, ensemble or field-theoretical descriptions of the neocortex yield the macroscopic dynamics of distinct cortical areas as studied encephalographicallysee, e.g., Frank et al (2000) and Jirsa et al (2002) for recent discussions and explicit derivations. To capture the main aspects of pertinent data, however, we here abstain from such ensemble or field-theoretical approaches but simply assume that pulse rates and/or dendritic currents have oscillatory properties.…”

“…Based on previous investigations of patterns of brain activity during the performance of unimanual and bimanual isofrequency tasks (Kelso et al 1992;Wallenstein et al 1995;Mayville et al 2001) and corresponding modeling work (e.g., Frank et al 2000;Jirsa et al 2002), it may be expected that the spatiotemporal brain activity patterns during multifrequency tasks can be described in terms of a few dynamical processes and their couplings. These couplings, which are brought about via inhibitory and excitatory inter-and intrahemispheric connections, are of essential importance because they may invoke entrained (i.e., coherent) activity distributions, both within and across hemispheres, as well as spontaneous transitions between such distributions.…”