A neural mass model for MEG/EEG: : coupling and neuronal dynamics

By Olivier David, Karl J. Friston. NeuroImage, 2003

751 citations recorded by the source corpus.

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 pi 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.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0a34ed16be485865ee1c9f6fcdec2beda0a0ab98

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Citations in the corpus

Structural and functional clusters of complex brain networks (2006, methodology): “The biologically based neural mass models are complicated oscillators describing the mean activities of the neural populations and can reproduce all frequency ranges observed in brain dynamics under different physiological states [36].”
Dynamic causal modelling: A critical review of the biophysical and statistical foundations (2011, methodology): “Simulations and experimental studies in humans and animals have been performed to address the reliability (Schuyler et al. 2009), face validity (e.g. Friston et al. 2003, Stephan et al. 2008b), construct validity (e.g. Penny et al. 2004a) and predictive validity (David et al. 2008; Moran et al.…”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, methodology): “Long-range interactions between two cortical areas (Area 1 driving Area 2) are modeled by excitatory connections between two extended Jansen models (representing each cortical area) (David and Friston, 2003).”
Mechanisms of evoked and induced responses in MEG/EEG (2006, methodology): “Here we model MEG/EEG signals using the Jansen model (Jansen and Rit, 1995), extended to cover neuronal ensembles with different kinetics (David and Friston, 2003) and extrinsic cortico-cortical connections (David et al., in press; Crick and Koch, 1998).”
Functional and Effective Connectivity: A Review (2011, neutral): “The construct validity of these models calls on established electrophysiological phenomena and metrics of coupling (e.g., David and Friston, 2003; David et al., 2004).”
Source connectivity analysis with MEG and EEG (2009, methodology): “DCM is conceptually very different from the methods discussed so far [David and Friston, 2003].”
Population dynamics: Variance and the sigmoid activation function (2008, methodology): “We will start with the following ordinary differential equation (ODE) modelling the dynamics of a single neuron from the neuralmass model for EEG/MEG (David and friston, 2003, 2006a,b; Kiebel et al., 2006; Garrido et al., in press; Moran et al., 2007); for example, the i-th neuron in a population of”
A theory of cortical responses (2005, methodology): “Each region or source is modelled using a neural mass model decribed in David & Friston (2003), based on the model of Jansen & Rit (1995).”
The functional anatomy of the MMN: A DCM study of the roving paradigm (2008, methodology): “DCMs for MEG/EEG use neural mass models (David and Friston, 2003) to explain source activity in terms of the ensemble dynamics of interacting inhibitory and excitatory subpopulations of neurons, based on the model of Jansen and Rit (1995).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “It has also been shown that in addition to alpha activity, this model produces responses similar to evoked potentials (EPs) after an impulse train input (Jansen et al., 1993; David and Friston, 2003; David et al., 2005; Spiegler et al., 2010, 2011); high-alpha and low-beta oscillations when recurren”
Modelling event-related responses in the brain (2005, methodology): “The same changes in connectivity are also responsible for the modulation of long-range synchrony (or any type of interdependence measure) between distant MEG/EEG oscillators (David and Friston, 2003; David et al., 2004a).”
Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization (2006, neutral): “The integration of the differential equations pertaining to each subpopulation can be expressed as a convolution (David and Friston, 2003).”