Realistic alpha oscillations and transient responses in a cortical microcircuit model

Neural-mass modeling of neural population data (EEG, ECoG, or LFPs) has shown promise both in elucidating the neural processes underlying cortical rhythms and changes in brain state, as well as offering a framework for testing the interplay between these rhythms and information processing. Models of cortical alpha rhythms (8 - 12 Hz) and their impact in visual sensory processing have been at the forefront of this effort, with the Jansen-Rit being one of the more popular models in this domain. The Jansen-Rit model, however, fails in reproducing key physiological observations including the level of inputs that cortical neurons receive and their responses to visual transients. To address these issues we generated a neural mass model that complies better with synaptic mediated dynamics, intrinsic alpha behavior, and produces realistic responses. The model is robust to many changes in parameter values but critically depends on the ratio of excitation to inhibition, producing response transients whose features are dependent on this ratio and alpha phase and power. The model is sufficiently flexible so as to be able to easily replicate the range of low frequency oscillations observed in different studies. Consistent with experimental observations, we find phase-dependent response dynamics to both visual and electrical stimulation using this model. The model suggests that stimulation facilitates alpha at particular phases and suppresses it in others due to a phase dependent lag in inhibitory responses. Hence, the model generates insight into the physiological parameters responsible for intrinsic oscillations and testable hypotheses regarding the interactions between visual and electrical stimulation on those oscillations.

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A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

Journal of Neural Engineering ◽

10.1088/1741-2552/aae136 ◽

2018 ◽

Vol 15 (6) ◽

pp. 066012 ◽

Cited By ~ 2

Author(s):

Ishita Basu ◽

Britni Crocker ◽

Kara Farnes ◽

Madeline M Robertson ◽

Angelique C Paulk ◽

...

Keyword(s):

Electrical Stimulation ◽

Neural Mass Model ◽

Evoked Responses ◽

Mass Model ◽

Primate Brain ◽

Neural Mass ◽

Human Primate

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On the Validity of Neural Mass Models

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2020.581040 ◽

2021 ◽

Vol 14 ◽

Author(s):

Nicolás Deschle ◽

Juan Ignacio Gossn ◽

Prejaas Tewarie ◽

Björn Schelter ◽

Andreas Daffertshofer

Keyword(s):

Mean Field ◽

Low Frequency ◽

Inhibitory Neuron ◽

Inhibitory Neurons ◽

Neural Population ◽

Mass Model ◽

Time Scale Separation ◽

Neural Mass ◽

The Mean ◽

Parameter Ranges

Modeling the dynamics of neural masses is a common approach in the study of neural populations. Various models have been proven useful to describe a plenitude of empirical observations including self-sustained local oscillations and patterns of distant synchronization. We discuss the extent to which mass models really resemble the mean dynamics of a neural population. In particular, we question the validity of neural mass models if the population under study comprises a mixture of excitatory and inhibitory neurons that are densely (inter-)connected. Starting from a network of noisy leaky integrate-and-fire neurons, we formulated two different population dynamics that both fall into the category of seminal Freeman neural mass models. The derivations contained several mean-field assumptions and time scale separation(s) between membrane and synapse dynamics. Our comparison of these neural mass models with the averaged dynamics of the population reveals bounds in the fraction of excitatory/inhibitory neuron as well as overall network degree for a mass model to provide adequate estimates. For substantial parameter ranges, our models fail to mimic the neural network's dynamics proper, be that in de-synchronized or in (high-frequency) synchronized states. Only around the onset of low-frequency synchronization our models provide proper estimates of the mean potential dynamics. While this shows their potential for, e.g., studying resting state dynamics obtained by encephalography with focus on the transition region, we must accept that predicting the more general dynamic outcome of a neural network via its mass dynamics requires great care.

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Bifurcation Analysis of Jansen's Neural Mass Model

Neural Computation ◽

10.1162/neco.2006.18.12.3052 ◽

2006 ◽

Vol 18 (12) ◽

pp. 3052-3068 ◽

Cited By ~ 98

Author(s):

François Grimbert ◽

Olivier Faugeras

Keyword(s):

Bifurcation Analysis ◽

Cortical Neurons ◽

Input Parameter ◽

Nonlinear Dynamical System ◽

Epileptic Activity ◽

Neural Mass Model ◽

Mass Model ◽

Nonlinear Dynamical ◽

Neural Mass ◽

Model Features

We present a mathematical model of a neural mass developed by a number of people, including Lopes da Silva and Jansen. This model features three interacting populations of cortical neurons and is described by a six-dimensional nonlinear dynamical system. We address some aspects of its behavior through a bifurcation analysis with respect to the input parameter of the system. This leads to a compact description of the oscillatory behaviors observed in Jansen and Rit (1995) (alpha activity) and Wendling, Bellanger, Bartolomei, and Chauvel (2000) (spike-like epileptic activity). In the case of small or slow variation of the input, the model can even be described as a binary unit. Again using the bifurcation framework, we discuss the influence of other parameters of the system on the behavior of the neural mass model.

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Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

Frontiers in Systems Neuroscience ◽

10.3389/fnsys.2021.675272 ◽

2021 ◽

Vol 15 ◽

Author(s):

Moritz Gerster ◽

Halgurd Taher ◽

Antonín Škoch ◽

Jaroslav Hlinka ◽

Maxime Guye ◽

...

Keyword(s):

Local Network ◽

Neural Mass Model ◽

Neural Population ◽

Patient Specific ◽

Epileptogenic Zone ◽

Mass Model ◽

Mean Field Equations ◽

Neural Mass ◽

Epileptic Patients ◽

Emergent Dynamics

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

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Causal Role of Thalamic Interneurons in Brain State Transitions: A Study Using a Neural Mass Model Implementing Synaptic Kinetics

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2016.00115 ◽

2016 ◽

Vol 10 ◽

Cited By ~ 9

Author(s):

Basabdatta Sen Bhattacharya ◽

Thomas P. Bond ◽

Louise O'Hare ◽

Daniel Turner ◽

Simon J. Durrant

Keyword(s):

Causal Role ◽

Neural Mass Model ◽

State Transitions ◽

Brain State ◽

Mass Model ◽

Neural Mass

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Cross frequency coupling in next generation inhibitory neural mass models

10.1101/745828 ◽

2019 ◽

Cited By ~ 1

Author(s):

Andrea Ceni ◽

Simona Olmi ◽

Alessandro Torcini ◽

David Angulo-Garcia

Keyword(s):

Information Processing ◽

Cognitive Processes ◽

Neural Mass Model ◽

Neural Population ◽

Mass Model ◽

Collective Behaviors ◽

Neural Mass ◽

Frequency Coupling ◽

Cross Frequency Coupling ◽

The Brain

Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master-slave configuration with different synaptic time scales can display various collective dynamical regimes: namely, damped oscillations towards a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled the two inhibitory populations can exhibit different types of θ-γ cross-frequency couplings (CFCs): namely, phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in presence of a external θ forcing, reminiscent of the type of modulation induced in Hippocampal and Cortex circuits via optogenetic drive.In healthy conditions, the brain’s activity reveals a series of intermingled oscillations, generated by large ensembles of neurons, which provide a functional substrate for information processing. How single neuron properties influence neuronal population dynamics is an unsolved question, whose solution could help in the understanding of the emergent collective behaviors arising during cognitive processes. Here we consider a neural mass model, which reproduces exactly the macroscopic activity of a network of spiking neurons. This mean-field model is employed to shade some light on an important and ubiquitous neural mechanism underlying information processing in the brain: the θ-γ cross-frequency coupling. In particular, we will explore in detail the conditions under which two coupled inhibitory neural populations can generate these functionally relevant coupled rhythms.

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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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