Neural Population Responses

These processes are the basis of information processing by neural networks. The patterns of connectivity between cells define which cells can influence which other cells and, thus, define the architecture of information processing. Spikes signal the generation of a response by a single neuron and trigger the transmission of that information to the next cell in the chain. However, most neurons are interconnected in such a way that a spike in a single presynaptic neuron is not sufficient to elicit a response. Given the transient nature of postsynaptic responses, the efficacy of excitation within any particular neuron is critically dependent on the detailed temporal dynamics of activity within the population of neurons that converge on it.

Macroscopic electrophysiological responses depend on the activation of a significant population of neurons activating in concert. This is primarily a consequence of the distance separating the neuronal current generators and the electrodes or magnetic field detectors at the head surface. Electric and magnetic fields drop off

Figure 1 The physical basis of neural electromagnetic source localization. (a) Intracellular currents in tangential neural processes (parallel to the head surface) give rise to extracellular volume return currents. These currents interact with the head volume conductivity to set up a potential distribution, with surface extrema aligned with the current. (b) A detectable magnetic field is associated with the intracellular current. Extracellular currents tend to cancel in a spherical conducting volume. The extrema of the observed magnetic field distribution straddle and are orthogonal to the source current element. (c) An array of electrodes or SQUID-based magnetic field detectors are positioned on or over the surface of the head. Potential and field distributions consistent with one or more simple dipole-like sources are often observed. (d) Source localization based on a time-varying set of equivalent current dipoles. A simple source model is fit to the observed field distribution using nonlinear optimization techniques. In this figure, the uncertainty of source localization due to noise was estimated using Monte Carlo techniques, and a 3D histogram of dipole location was constructed.

Figure 1 The physical basis of neural electromagnetic source localization. (a) Intracellular currents in tangential neural processes (parallel to the head surface) give rise to extracellular volume return currents. These currents interact with the head volume conductivity to set up a potential distribution, with surface extrema aligned with the current. (b) A detectable magnetic field is associated with the intracellular current. Extracellular currents tend to cancel in a spherical conducting volume. The extrema of the observed magnetic field distribution straddle and are orthogonal to the source current element. (c) An array of electrodes or SQUID-based magnetic field detectors are positioned on or over the surface of the head. Potential and field distributions consistent with one or more simple dipole-like sources are often observed. (d) Source localization based on a time-varying set of equivalent current dipoles. A simple source model is fit to the observed field distribution using nonlinear optimization techniques. In this figure, the uncertainty of source localization due to noise was estimated using Monte Carlo techniques, and a 3D histogram of dipole location was constructed.

rapidly with distance. EEG is further compromised by the insulating properties of the skull, which attenuate and diffuse the potential distributions that can be measured at the surface of the brain. Magnetic measurements are much less sensitive to the conductivity properties of the head, but the sensitivity of the method is reduced by the use of gradiometer sensors. Measurement of field gradients makes MEG less sensitive to interference from environmental influences, such as electric equipment, passing vehicles, or moving metal objects such as gurneys, at the expense of absolute sensitivity to neural responses.

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