A Neural Mass Model to Simulate Different Rhythms in a Cortical Region

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, and respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma () band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of rhythms and to provide better understanding of cortical EEG spectra.

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The Effect of Connectivity on EEG Rhythms, Power Spectral Density and Coherence Among Coupled Neural Populations: Analysis With a Neural Mass Model

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2007.897814 ◽

2008 ◽

Vol 55 (1) ◽

pp. 69-77 ◽

Cited By ~ 26

Author(s):

M. Zavaglia ◽

L. Astolfi ◽

F. Babiloni ◽

M. Ursino

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Neural Mass Model ◽

Eeg Rhythms ◽

Mass Model ◽

Power Spectral ◽

Neural Mass ◽

Neural Populations

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A personalizable autonomous neural mass model of epileptic seizures

10.1101/2021.12.24.474090 ◽

2021 ◽

Author(s):

Edmundo Lopez-Sola ◽

Roser Sanchez-Todo ◽

Èlia Lleal ◽

Elif Köksal-Ersöz ◽

Maxime Yochum ◽

...

Keyword(s):

Low Voltage ◽

Chloride Transport ◽

Pyramidal Cells ◽

Brain Network ◽

Neural Mass Model ◽

Inhibitory Input ◽

Model Parameters ◽

Epileptogenic Zone ◽

Mass Model ◽

Neural Mass

The prospect of personalized computational modeling in neurological disorders, and in particular in epilepsy, is poised to revolutionize the field. Work in the last two decades has demonstrated that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by i) increasing excitation in NMM and ii) heuristically varying network inhibitory coupling parameters or, equivalently, inhibitory synaptic gains. Based on those studies, we provide here a laminar neural mass model capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input onto the pyramidal cell population, the model dynamics are autonomous --- all model parameters are static. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically plausible algorithm for chloride accumulation dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of Cl$^-$ in pyramidal cells, due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals to compare with real recordings performed in epileptic patients, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods using brain network models based on NMMs.

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The generation of rhythms within a cortical region: Analysis of a neural mass model

NeuroImage ◽

10.1016/j.neuroimage.2009.12.084 ◽

2010 ◽

Vol 52 (3) ◽

pp. 1080-1094 ◽

Cited By ~ 61

Author(s):

Mauro Ursino ◽

Filippo Cona ◽

Melissa Zavaglia

Keyword(s):

Neural Mass Model ◽

Cortical Region ◽

Mass Model ◽

Region Analysis ◽

Neural Mass

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The critical role of spiny stellate cells in seizure onset based on dynamic analysis of a neural mass model

10.1101/2021.07.19.452876 ◽

2021 ◽

Author(s):

Saba Tabatabaee ◽

Fariba Bahrami ◽

Mahyar Janahmadi

Keyword(s):

Bifurcation Analysis ◽

Epileptic Seizure ◽

Critical Role ◽

Stellate Cells ◽

Neural Mass Model ◽

Seizure Onset ◽

Mass Model ◽

Neural Mass ◽

The Brain

Increasing evidence has shown that excitatory neurons in the brain play a significant role in seizure generation. However, spiny stellate cells are cortical excitatory non-pyramidal neurons in the brain which their basic role in seizure occurrence is not well understood. In the present research, we study the critical role of spiny stellate cells or the excitatory interneurons (EI), for the first time, in epileptic seizure generation using an extended neural mass model introduced originally by Taylor and colleagues in 2014. Applying bifurcation analysis on this modified model, we investigated the rich dynamics corresponding to the epileptic seizure onset and transition between interictal and ictal states due to the EI. Our results indicate that the transition is described by a supercritical Hopf bifurcation which shapes the preictal activity in the model and suggests why before seizure onset, the amplitude and frequency of neural activities increase gradually. Moreover, we showed that 1) the altered function of GABAergic and glutamatergic receptors of EI can cause seizure, and 2) the pathway between the thalamic relay nucleus and EI facilitates the transition from interictal to the ictal activity by decreasing the preictal period. Thereafter, we considered both sensory and cortical periodic inputs to drive the model responses to various harmonic stimulations. Our results from the bifurcation analysis of the model suggest that the initial stage of the brain might be the main cause for the transition between interictal and ictal states as the stimulus frequency changes. The extended thalamocortical model shows also that the amplitude jump phenomenon and nonlinear resonance behavior result from the preictal stage of the brain. These results can be considered as a step forward to a deeper understanding of the mechanisms underlying the transition from brain normal activities to epileptic activities.

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The multi-frequency EEG rhythms modeling based on two-parameter bifurcation of neural mass model

2014 IEEE International Conference on Mechatronics and Automation ◽

10.1109/icma.2014.6885933 ◽

2014 ◽

Author(s):

Enzeng Dong ◽

Zhihan Liang

Keyword(s):

Neural Mass Model ◽

Eeg Rhythms ◽

Mass Model ◽

Neural Mass ◽

Two Parameter ◽

Parameter Bifurcation

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Realistically Coupled Neural Mass Models Can Generate EEG Rhythms

Neural Computation ◽

10.1162/neco.2007.19.2.478 ◽

2007 ◽

Vol 19 (2) ◽

pp. 478-512 ◽

Cited By ~ 107

Author(s):

Roberto C. Sotero ◽

Nelson J. Trujillo-Barreto ◽

Yasser Iturria-Medina ◽

Felix Carbonell ◽

Juan C. Jimenez

Keyword(s):

Differential Equations ◽

Short Range ◽

Imaging Techniques ◽

Diffusion Imaging ◽

Neural Mass Model ◽

Connectivity Matrix ◽

Eeg Rhythms ◽

Mass Model ◽

Neural Mass ◽

Random Differential Equations

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.

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Interactions between two neural populations: A mechanism of chaos and oscillation in neural mass model

Neurocomputing ◽

10.1016/j.neucom.2010.11.019 ◽

2011 ◽

Vol 74 (6) ◽

pp. 1026-1034 ◽

Cited By ~ 19

Author(s):

Gan Huang ◽

Dingguo Zhang ◽

Jiangjun Meng ◽

Xiangyang Zhu

Keyword(s):

Neural Mass Model ◽

Mass Model ◽

Neural Mass ◽

Neural Populations

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Mean-Field Models for EEG/MEG: From Oscillations to Waves

Brain Topography ◽

10.1007/s10548-021-00842-4 ◽

2021 ◽

Author(s):

Áine Byrne ◽

James Ross ◽

Rachel Nicks ◽

Stephen Coombes

Keyword(s):

Gap Junction ◽

Large Scale ◽

Mean Field ◽

Coarse Grained ◽

Neural Mass Model ◽

Neuron Network ◽

Mass Model ◽

Mean Field Model ◽

Neural Mass ◽

The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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