Review articleFunctional localization of brain sources using EEG and/or MEG data: volume conductor and source models
Introduction
The electrical activity of the brain, that is, the electroencephalogram (EEG), either the ongoing activity or the changes of activity related to a given sensory or motor event — the event-related potentials — gives us the possibility of studying brain functions with a high time resolution, although with a relatively modest spatial resolution. The latter, however, has been improved more recently with the development of the magnetoencephalogram, or MEG, and of more sophisticated source imaging techniques. These new methods allow an analysis of the dynamics of brain activities not only of global brain functions, such as sleep and arousal, but also of cognitive processes, such as perception, motor preparation and higher cognitive functions. Furthermore, these methods are essential for the characterization of pathophysiological processes, particularly with a paroxysmal character, such as epilepsy.
The EEG consists essentially of the summed electrical activity of populations of neurons, with a contribution of glial cells. Considering that the neurons are excitable cells with characteristic intrinsic electrical properties and that the inter-neuronal communication is essentially mediated by electrochemical processes at synapses, it follows that these cells can produce electrical and magnetic fields that may be recorded at a distance from the sources. Thus, these fields may be recorded at short distance from the sources, called the local EEG or local field potentials, or from the cortical surface (the electrocorticogram) or even from the scalp, that is, the EEG in the most common sense. The associated MEG is recorded usually by way of sensors placed at a short distance around the scalp [1].
In order to understand how the electrical and magnetic signals of the brain are generated, it is necessary to examine how the activity of assemblies of neurons is organized both in time and in space, and which biophysical laws govern the generation of extracellular field potentials or magnetic fields.
Section snippets
The generation of extracellular fields: the importance of spatial and temporal properties. Models of neural sources and dynamics
It is generally assumed that the neuronal events that cause the generation of electric and/or magnetic fields in a neural mass consist of ionic currents that have mainly postsynaptic sources. For these fields to be measurable at a distance from the sources, it is important that the underlying neuronal currents are well organized both in space and time. The ionic currents in the brain obey Maxwell's and Ohm's laws. More extensive treatments of the basic biophysical issues can be found in the
Models of the volume conductor with layers of different conductivities
A basic problem in EEG/MEG is how to estimate the neuronal sources corresponding to a certain distribution of electrical potentials or of magnetic fields recorded at the scalp. This is called the inverse problem of EEG/MEG. It is an ill-posed problem that has no unique solution. Therefore, one must assume specific models of the sources and of the volume conductor in order to estimate approximate solutions. The simplest source model is the equivalent current dipole. However, it should not be
Functional localization of brain oscillatory activities: EEG/MEG and functional MRI
A basic question in EEG/MEG studies is whether the main rhythmic activities, alpha and mu rhythms on the one hand, and sleep spindles on the other, are generated in distinct or overlapping cortical areas. In order to solve this question, advanced spatiotemporal analysis methods are necessary. The recent development of a new algorithm [8] with the aim of estimating sources of large data sets, as is the case with this kind of signals, led us to investigate this issue, namely, whether generators
Functional localization of epileptiform transients: contrast between EEG and MEG
Source localization of spontaneously occurring interictal epileptiform transients often becomes a tedious exercise, involving tens or hundreds of individual source solutions of an equal number of paroxysms per data set. Moreover, the daily practice in source localization of individual paroxysms frequently yields solutions too dispersed within the brain to be plausible. In addition, solutions may be identified to arise from plainly impossible sources, for example, bone tissue, ventricles, or
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