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12
A neural mass model for MEG/EEG: coupling and neuronal dynamics
- NeuroImage
, 2003
"... 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 func ..."
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Cited by 81 (21 self)
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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.
Dynamic causal modelling of evoked responses in eeg/meg with lead-field parameterization. Under revision
, 2005
"... Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed mo ..."
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Cited by 48 (18 self)
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Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-Abbreviations: DCM, dynamic causal Model(ing); EEG, electroencephalography; ERF, event-related field; ERP, event-related potential;
Spectral spatiotemporal imaging of cortical oscillations and interactions in
, 2004
"... the human brain ..."
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Neurophenomenology: An introduction for neurophilosophers
- In A. Brook & K. Akins (Eds.), Cognition
, 2005
"... ..."
2.1 Applicants
"... Patterns from EEG: The next step towards neuronal control of motor and communication prostheses. ..."
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Cited by 1 (0 self)
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Patterns from EEG: The next step towards neuronal control of motor and communication prostheses.
Stochastic maximum likelihood mean and cross-spectrum structure estimation: analytic and neuromagnetic Monte Carlo results
, 2004
"... In [1] we proposed to analyze cross-spectrum matrices obtained from electro- or magneto-encephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and se ..."
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Cited by 1 (1 self)
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In [1] we proposed to analyze cross-spectrum matrices obtained from electro- or magneto-encephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and second, by taking the mean of the signals across different trials into account. To obtain estimates we propose a stochastic maximum likelihood (SML) method, and obtain the concentrated likelihood that includes the trial means.
Edited by:
, 2011
"... doi: 10.3389/fnsys.2011.00058 The role of long-range connectivity for the characterization of the functional–anatomical organization of the cortex ..."
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doi: 10.3389/fnsys.2011.00058 The role of long-range connectivity for the characterization of the functional–anatomical organization of the cortex
Index Terms Stochastic maximum likelihood, SML algorithm, concentrated likelihood, singular Hessian
, 2005
"... Maximum likelihood estimation of emitter source parameters in array signal processing for stochastic source signals is well established in the literature. Currently available results in the literature however, have relied on the assumption that the array response matrix has full column rank. In cert ..."
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Maximum likelihood estimation of emitter source parameters in array signal processing for stochastic source signals is well established in the literature. Currently available results in the literature however, have relied on the assumption that the array response matrix has full column rank. In certain models used in for example chemistry, telecommunications, oceanography, and biomedical engineering this is not necessarily the case. In this paper we focus on magnetoencephalography, where the method has been previously proposed in brain function connectivity analysis. Here rank deficiency of the response matrix occurs if spherically symmetric head models for MEG signals are used. In this paper we show that the assumption of a full rank response matrix is unnecessary, which has the advantage that complicating reparameterization is unnecessary. We show that the method of concentrating the likelihood remains valid, including statistical inference from the concentrated likelihood, and generalize a well known algorithm to this case. We exemplify the derived results in simulations.
neuro-magnetic
"... Stochastic maximum likelihood mean and cross-spectrum structure modelling in ..."
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Stochastic maximum likelihood mean and cross-spectrum structure modelling in
neural mass
, 2003
"... of different measures of functional connectivity using a ..."
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