Simulated Electrocortical Activity at Microscopic, Mesoscopic, and Global Scales

Wright, J. J.; Rennie, Chris; Lees, G.J.; Robinson, P. A.; Bourke, Paul; Chapman, Clare; Gordon, Evian; Rowe, Donald L.

doi:10.1038/sj.npp.1300138

Cited by 36 publications

(14 citation statements)

References 71 publications

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“…As we pointed in the Introduction Section 1, evidence from neurophysiology studies, neuroanatomy, biophysics, and neuroimaging suggests that cortical activation is a distributed spatiotemporal dynamic process (Bullock et al, 1995; Destexhe et al, 1999; Leopold et al, 2003; Nunez, 1995; Jirsa et al, 2002; Wright et al, 2004; Robinson et al, 2005; David et al, 2005; Sotero et al, 2007; Kim and Robinson, 2007; Izhikevich and Edelman, 2008; Gross et al, 2001; Raichle et al, 2001; Gusnard and Raichle, 2001; Fox et al, 2005; Fox and Raichle, 2007). Because spatiotemporal dynamics of this kind are fundamental to brain physiology, inverse solutions could be greatly improved by incorporating models that approximate these dynamics.…”

Section: Methodsmentioning

confidence: 93%

“…Similarly, non-invasive fMRI and PET studies have shown that brain activation is temporally coherent in a spatially distributed network (Raichle et al, 2001; Gusnard and Raichle, 2001; Fox et al, 2005; Fox and Raichle, 2007). On the modeling side, biophysical spatiotemporal dynamic models of neuronal networks have been able to simulate electromagnetic scalp signals similar to those seen in recordings during normal and disease states (Jirsa et al, 2002; Wright et al, 2004; Robinson et al, 2005; David et al, 2005; Sotero et al, 2007; Kim and Robinson, 2007; Izhikevich and Edelman, 2008; Gross et al, 2001). We incorporated these insights to probabilistically model cortical activation as a distributed spatiotemporal dynamic process, and used this model as the basis for an inverse solution.…”

Section: Discussionmentioning

confidence: 99%

“…These local spatial interactions are supported neuroanatomically by axonal collateral projections from pyramidal cells that spread laterally at distances of up to 10 mm (Nunez, 1995). Biophysical spatiotemporal dynamic models of neuronal networks at various levels of abstraction have been effective in reproducing properties of electromagnetic scalp recordings seen during both normal and disease states (Jirsa et al, 2002; Wright et al, 2004; Robinson et al, 2005; David et al, 2005; Sotero et al, 2007; Kim and Robinson, 2007; Izhikevich and Edelman, 2008; Gross et al, 2001). Furthermore, fMRI and PET studies have shown temporally coherent fluctuation in activation within widely distributed cortical networks during resting-state and experimentally administered task periods (Raichle et al, 2001; Gusnard and Raichle, 2001; Fox et al, 2005; Fox and Raichle, 2007).…”

Section: Introductionmentioning

confidence: 99%

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A spatiotemporal dynamic distributed solution to the MEG inverse problem

et al. 2012

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MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods.

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Section: Methodsmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A spatiotemporal dynamic distributed solution to the MEG inverse problem

et al. 2012

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“…As alternatives to neural network models, lumped neural models and neural field equations have been expressed in many forms (e.g., Wilson and Cowan, 1973; Freeman, 1975; Haken, 1996; Amari, 1977; Nunez, 1981; van Rotterdam et al, 1982; Jirsa and Haken, 1996; Robinson et al, 2001; Wright et al, 2003; beim Graben, 2008; Bressloff, 2012). These offer means of approximating the properties of ensembles of cells on a larger scale then neural networks per se .…”

Section: Description Of Modelmentioning

confidence: 99%

On the dynamics of cortical development: synchrony and synaptic self-organization

Wright

Bourke

2013

Front. Comput. Neurosci.

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We describe a model for cortical development that resolves long-standing difficulties of earlier models. It is proposed that, during embryonic development, synchronous firing of neurons and their competition for limited metabolic resources leads to selection of an array of neurons with ultra-small-world characteristics. Consequently, in the visual cortex, macrocolumns linked by superficial patchy connections emerge in anatomically realistic patterns, with an ante-natal arrangement which projects signals from the surrounding cortex onto each macrocolumn in a form analogous to the projection of a Euclidean plane onto a Möbius strip. This configuration reproduces typical cortical response maps, and simulations of signal flow explain cortical responses to moving lines as functions of stimulus velocity, length, and orientation. With the introduction of direct visual inputs, under the operation of Hebbian learning, development of mature selective response “tuning” to stimuli of given orientation, spatial frequency, and temporal frequency would then take place, overwriting the earlier ante-natal configuration. The model is provisionally extended to hierarchical interactions of the visual cortex with higher centers, and a general principle for cortical processing of spatio-temporal images is sketched.

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“…An alternative approach to simulating individual neuronal activity has been to simulate the activity in an ensemble of neurons. An example of this is the continuum cortex model, developed by Wright et al [15]–[18], which has been used to simulate ensemble activity at different scales [17]. Existing continuum cortex models do not take into account the laminar architecture of the cerebral cortex.…”

Section: Introductionmentioning

confidence: 99%

The Laminar Cortex Model: A New Continuum Cortex Model Incorporating Laminar Architecture

Vegh²,

Reutens³

2012

PLoS Comput Biol

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Local field potentials (LFPs) are widely used to study the function of local networks in the brain. They are also closely correlated with the blood-oxygen-level-dependent signal, the predominant contrast mechanism in functional magnetic resonance imaging. We developed a new laminar cortex model (LCM) to simulate the amplitude and frequency of LFPs. Our model combines the laminar architecture of the cerebral cortex and multiple continuum models to simulate the collective activity of cortical neurons. The five cortical layers (layer I, II/III, IV, V, and VI) are simulated as separate continuum models between which there are synaptic connections. The LCM was used to simulate the dynamics of the visual cortex under different conditions of visual stimulation. LFPs are reported for two kinds of visual stimulation: general visual stimulation and intermittent light stimulation. The power spectra of LFPs were calculated and compared with existing empirical data. The LCM was able to produce spontaneous LFPs exhibiting frequency-inverse (1/ƒ) power spectrum behaviour. Laminar profiles of current source density showed similarities to experimental data. General stimulation enhanced the oscillation of LFPs corresponding to gamma frequencies. During simulated intermittent light stimulation, the LCM captured the fundamental as well as high order harmonics as previously reported. The power spectrum expected with a reduction in layer IV neurons, often observed with focal cortical dysplasias associated with epilepsy was also simulated.

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Simulated Electrocortical Activity at Microscopic, Mesoscopic, and Global Scales

Cited by 36 publications

References 71 publications

A spatiotemporal dynamic distributed solution to the MEG inverse problem

A spatiotemporal dynamic distributed solution to the MEG inverse problem

On the dynamics of cortical development: synchrony and synaptic self-organization

The Laminar Cortex Model: A New Continuum Cortex Model Incorporating Laminar Architecture

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