Nonlinear EEG analysis based on a neural mass model

Valdés, P.; Jiménez, Juan Carlos; Riera, Jorge J.; Biscay, Rolando J.; Ozaki, Tohru

doi:10.1007/s004220050572

Cited by 131 publications

(98 citation statements)

References 27 publications

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“…We have shown that neural mass models (David and Friston, 2003;Jansen and Rit, 1995;Lopes da Silva et al, 1997;Nunez, 1974;Rennie et al, 2002;Robinson et al, 2001;Stam et al, 1999;Suffczynski et al, 2001;Valdes et al, 1999;Wendling et al, 2002) can reproduce a large variety of MEG/EEG signal characteristics. The potential advantage they afford, in comparison to standard data analysis, is their ability to pinpoint specific neuronal mechanisms underlying normal or pathological activity.…”

Section: Resultsmentioning

confidence: 99%

“…This means that MEG/EEG observation models rely upon simplifying assumptions and empirical priors (David and Friston, 2003;Freeman, 1978;Lopes da Silva et al, 1974;Robinson et al, 2001;Stam et al, 1999;Valdes et al, 1999;Van Rotterdam et al, 1982;Wendling et al, 2000). The primary aim of this paper is to describe a candidate forward model and establish its face validity.…”

Section: Neural Mass Modelsmentioning

confidence: 99%

“…The key advantage of this approach is that the parameters of the model, which are estimated, have a direct physiological interpretation. Several studies have shown that it is possible to estimate the parameters of observation models using real MEG/EEG data, in the case of both rhythmic activity (Valdes et al, 1999) and ERPs (Rennie et al, 2002). An important issue, in this context, is the ability to constrain, or regularise, the inverse problem that dynamic forward models like this pose.…”

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confidence: 99%

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Modelling event-related responses in the brain

2005

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The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magneto-encephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural mass model of hierarchically arranged areas using three kinds of inter-area connections (forward, backward and lateral). We investigated how responses, at each level of a cortical hierarchy, depended on the strength of connections or coupling. Our strategy was to systematically add connections and examine the responses of each successive architecture. We did this in the context of deterministic responses and then with stochastic spontaneous activity. Our aim was to show, in a simple way, how event-related dynamics depend on extrinsic connectivity. To emphasise the importance of nonlinear interactions, we tried to disambiguate the components of event-related potentials (ERPs) or event-related fields (ERFs) that can be explained by a linear superposition of trial-specific responses and those engendered nonlinearly (e.g., by phase-resetting). Our key conclusions were; (i) when forward connections, mediating bottom-up or extrinsic inputs, are sufficiently strong, nonlinear mechanisms cause a saturation of excitatory interneuron responses. This endows the system with an inherent stability that precludes nondissipative population dynamics. (ii) The duration of evoked transients increases with the hierarchical depth or level of processing. (iii) When backward connections are added, evoked transients become more protracted, exhibiting damped oscillations. These are formally identical to late or endogenous components seen empirically. This suggests that late components are mediated by reentrant dynamics within cortical hierarchies. (iv) Bilateral connections produce similar effects to backward connections but can also mediate zero-lag phase-locking among areas. (v) Finally, with spontaneous activity, ERPs/ERFs can arise from two distinct mechanisms: For low levels of (stimulus related and ongoing) activity, the systems response conforms to a quasi-linear superposition of separable responses to the fixed and stochastic inputs. This is consistent with classical assumptions that motivate trial averaging to suppress spontaneous activity and disclose the ERP/ ERF. However, when activity is sufficiently high, there are nonlinear interactions between the fixed and stochastic inputs. This interaction is expressed as a phase-resetting and represents a qualitatively different explanation for the ERP/ERF. D 2004 Elsevier Inc. All rights reserved.Keywords: Electroencephalography; Magnetoencephalography; Neural networks; Nonlinear dynamics; Causal modelling IntroductionClassical event-related potentials (ERPs) and event-related fields (ERFs) have been used for decades as putative electrophysiological correlates of perceptual and cognitive operations. However, the exact neurobiological mechanisms underlying their generation are largely unk...

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Section: Resultsmentioning

confidence: 99%

Section: Neural Mass Modelsmentioning

confidence: 99%

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confidence: 99%

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Modelling event-related responses in the brain

2005

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“…However, this approach does not account for noise on the states (i.e., random fluctuations in neuronal activity). There has already been much progress in the solution of stochastic differential equations entailed by stochastic DCMs, particularly in the context of neural mass models (see Valdes et al, 1999;Sotero et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Dynamic causal modelling for fMRI: A two-state model

2008

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Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. These models distinguish between a neuronal level, which models neuronal interactions among regions, and an observation level, which models the hemodynamic responses each region. The original DCM formulation considered only one neuronal state per region. In this work, we adopt a more plausible and less constrained neuronal model, using two neuronal states (populations) per region. Critically, this gives us an explicit model of intrinsic (between-population) connectivity within a region. In addition, by using positivity constraints, the model conforms to the organization of real cortical hierarchies, whose extrinsic connections are excitatory (glutamatergic). By incorporating two populations within each region we can model selective changes in both extrinsic and intrinsic connectivity.Using synthetic data, we show that the two-state model is internal consistent and identifiable. We then apply the model to real data, explicitly modelling intrinsic connections. Using model comparison, we found that the two-state model is better than the single-state model. Furthermore, using the two-state model we find that it is possible to disambiguate between subtle changes in coupling; we were able to show that attentional gain, in the context of visual motion processing, is accounted for sufficiently by an increased sensitivity of excitatory populations of neurons in V5, to forward afferents from earlier visual areas.

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“…The first work (to the authors best knowledge) describing data-driven mesoscopic neural modeling used a neural mass model to fit EEG data (Valdes et al, 1999). This approach was extended to coupled neural masses through a Bayesian estimation scheme called dynamic causal modeling (DCM) (David and Friston, 2003).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal multi-resolution approximation of the Amari type neural field model

Aram

Freestone

Dewar³

et al. 2013

NeuroImage

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Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.

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Nonlinear EEG analysis based on a neural mass model

Cited by 131 publications

References 27 publications

Modelling event-related responses in the brain

Modelling event-related responses in the brain

Dynamic causal modelling for fMRI: A two-state model

Spatiotemporal multi-resolution approximation of the Amari type neural field model

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