Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns

Jansen, Ben H.; Rit, Vincent G.

doi:10.1007/bf00199471

Cited by 957 publications

(1,075 citation statements)

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“…We use a system of two coupled Jansen-Rit neural mass models [19,20] driven by independent realizations of white noise. A scheme of inter-and intra-columns connectivity of this system is presented in Fig.…”

Section: Coupled Neural Mass Modelmentioning

confidence: 99%

Collective excitability in a mesoscopic neuronal model of epileptic activity

2018

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Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista [Physical Review E]. URL d'aquest document a UPCommons E-prints:http://hdl.handle.net/2117/113120Article publicat / Published paper: Maciej J., Pons, A.J. and García-Ojalvo, J. (2018) The brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we study the joint dynamics of two cortical columns described by Jansen-Rit neural mass models, and show that coupling between the columns gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.

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Section: Coupled Neural Mass Modelmentioning

confidence: 99%

Collective excitability in a mesoscopic neuronal model of epileptic activity

2018

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“…This is an important advance over conventional analyses of evoked responses, because it places natural constraints on the inversion; namely, activity in one source has to be caused by activity in another. DCMs for MEG/EEG use neural mass models (6) to explain source activity in terms of the ensemble dynamics of the interacting inhibitory and excitatory subpopulations of neurons, based on the model of Jansen and Rit (32). This model emulates the activity of a source by using three neural subpopulations, each assigned to one of three cortical layers; an excitatory subpopulation in the granular layer, an inhibitory subpopulation in the supragranular layer, and a population of deep pyramidal cells in the infragranular layer.…”

Section: Dynamic Causal Modelingmentioning

confidence: 99%

Evoked brain responses are generated by feedback loops

Garrido

Kilner

Kiebel

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

252

215

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Neuronal responses to stimuli, measured electrophysiologically, unfold over several hundred milliseconds. Typically, they show characteristic waveforms with early and late components. It is thought that early or exogenous components reflect a perturbation of neuronal dynamics by sensory input bottom-up processing. Conversely, later, endogenous components have been ascribed to recurrent dynamics among hierarchically disposed cortical processing levels, top-down effects. Here, we show that evoked brain responses are generated by recurrent dynamics in cortical networks, and late components of event-related responses are mediated by backward connections. This evidence is furnished by dynamic causal modeling of mismatch responses, elicited in an oddball paradigm. We used the evidence for models with and without backward connections to assess their likelihood as a function of peristimulus time and show that backward connections are necessary to explain late components. Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time. These results link a generic feature of brain responses to changes in the sensorium and a key architectural component of functional anatomy; namely, backward connections are necessary for recurrent interactions among levels of cortical hierarchies. This is the theoretical cornerstone of most modern theories of perceptual inference and learning.connectivity ͉ dynamic causal modeling ͉ EEG ͉ predictive coding ͉ top-down E vent-related potentials (ERPs) or event-related fields (ERFs) in electroencephalography (EEG) and magnetoencephalography (MEG) are among the mainstays of noninvasive neuroscience. Typically, the response evoked by a stimulus evolves in a systematic way, showing a series of waves or components. Many of these components are elicited so reliably that they are studied in their own right. These include very early sensory-evoked potentials, observed within a few milliseconds; early cortical responses such as the N1 and P2 components; and later components expressed several hundred milliseconds afterward. Broadly speaking, ERP components can be divided into early and late (1). Early-or short-latency stimulus-dependent (exogenous) components reflect the integrity of primary afferent pathways. Late stimulus-independent (endogenous) components entail long-latency (Ͼ100 ms) responses thought to reflect cognitive processes (1, 2). Early components have been associated with exogenous bottom-up stimulus-bound effects, whereas late components have been ascribed to endogenous dynamics involving top-down influences. Indeed, the amplitude and latency of early (e.g., P1 and N1) and late (e.g., N2pc) components have been used as explicit indices of bottom-up and top-down processing, respectively (3). Here, we demonstrate that late components are mediated by recurrent interactions among remote cortical regions; specifically, we show that late components rest on backward extrinsic corticocortical c...

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“…15 and 16) The model here was extended from a description by (Larter and Speelman 1999) in Breakspear et al (2003a). An alternative neural mass model is the Jansen model (Jansen and Rit 1995) as recently further elaborated by . As with the spiking neural ensembles, the present goal of these neural mass models is to provide plausible examples of interesting and computationally relevant dynamics.…”

Section: Appendix B: Neural Mass Of Cortical Columnsmentioning

confidence: 99%

Kinetic Models of Brain Activity

Breakspear

Knock

2008

Brain Imaging and Behavior

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Brain imaging sciences, like neurosciences in general, have predominantly been an empirical endeavour. This paper argues that the maturation of "kinetic models" of large-scale neuronal activity will provide a unifying theory to underpin brain imaging sciences. In particular, this framework will provide a means of unifying data from different imaging modalities, afford a direct link with cognitive theories of brain function, equip researchers with novel data analysis methodologies and underpin a dialogue in which theoretical formalisms are iteratively refined or refuted through empirical studies. Three steps are crucial to this endeavour: (1) The extension of models of spiking neural ensembles (where the states of all neurons are specified) to statistical models of neural "masses" (where only a few moments of the distribution of states are specified); (2) The refinement of "forward models", such as neurovascular coupling, which map neuronal states to observables; and (3) A theory which links the distribution of neuronal states to cognitive operations, hence informing cognitive neuroscience experiments. We provide illustrative examples of kinetic models of neuronal dynamics at the mesoscopic scale, focusing on the manner by which sensory inputs modify the expression of ongoing "background" activity. The paper concludes with an overview of some of the cutting edge developments in kinetic models of brain activity.

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Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns

Cited by 957 publications

References 20 publications

Collective excitability in a mesoscopic neuronal model of epileptic activity

Collective excitability in a mesoscopic neuronal model of epileptic activity

Evoked brain responses are generated by feedback loops

Kinetic Models of Brain Activity

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