A neural mass model for MEG/EEG: coupling and neuronal dynamics (2003)
| Venue: | NeuroImage |
| Citations: | 81 - 21 self |
BibTeX
@MISC{David03aneural,
author = {Olivier David and Karl J. Friston},
title = { A neural mass model for MEG/EEG: coupling and neuronal dynamics},
year = {2003}
}
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Abstract
Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumped-parameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phase-locked activity, with a phase shift of 0 or π when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate event-related transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.
Keyphrases
neuronal dynamic meg eeg neural mass model meg eeg signal propagation delay robust neural correlate average evoked response systematic change inhibitory interaction population kinetics perceptual process neuronal interaction alpha rhythm classical nonlinear lumped-parameter model rhythm emerge statistical modelling various index nonlinear coupling oscillatory regime coupled cortical area induces phase-locked activity whole spectrum meg eeg spectrum functional significance main finding lope da silva cellular level phase shift distinct frequency band complex dynamic construct validity critical determinant population dynamic meg eeg oscillation generate event-related transient frequency-specific change basis function
