A personalizable autonomous neural mass model of epileptic seizures

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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Parameter inference on brain network models with unknown node dynamics and spatial heterogeneity

10.1101/2021.09.01.458521 ◽

2021 ◽

Author(s):

Viktor Sip ◽

Spase Petkoski ◽

Meysam Hashemi ◽

Viktor K Jirsa

Keyword(s):

Synthetic Data ◽

Brain Network ◽

Training Data ◽

Neural Mass Model ◽

Brain Dynamics ◽

Model Parameters ◽

Special Focus ◽

Mass Model ◽

Whole Brain ◽

Neural Mass

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.

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Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Frontiers in Applied Mathematics and Statistics ◽

10.3389/fams.2018.00046 ◽

2018 ◽

Vol 4 ◽

Author(s):

Lara Escuain-Poole ◽

Jordi Garcia-Ojalvo ◽

Antonio J. Pons

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Neural Mass Model ◽

Model Parameters ◽

Mass Model ◽

Neural Mass

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Neurophysiological Analysis of the Genesis Mechanism of EEG During the Interictal and Ictal Periods Using a Multiple Neural Masses Model

International Journal of Neural Systems ◽

10.1142/s0129065717500277 ◽

2017 ◽

Vol 28 (01) ◽

pp. 1750027 ◽

Cited By ~ 2

Author(s):

Zhen Ma

Keyword(s):

Matching Pursuit ◽

Neural Mass Model ◽

Model Parameters ◽

Eeg Signals ◽

Neural Signals ◽

Mass Model ◽

Neurophysiological Mechanism ◽

Norm Minimization ◽

Neural Mass ◽

Inhibitory Strength

Electroencephalography (EEG) is an important method to investigate the neurophysiological mechanism underlying epileptogenesis to identify new therapies for the treatment of epilepsy. The neurophysiologically based neural mass model (NMM) can build a bridge between signal processing and neurophysiology, which can be used as a platform to explore the neurophysiological mechanism of epileptogenesis. Most EEG signals cannot be regarded as the outputs of a single NMM with identical model parameters. The outputs of NMM are simple because the diversity of neural signals in the same NMM is ignored. To improve the simulation of EEG signals, a multiple NMM is proposed, the output of which is the linear combination of the outputs of all NMMs. The NMM number is not fixed and is minimized under the premise of guaranteeing the fitting effect. Orthogonal matching pursuit is used to solve a constrained [Formula: see text] norm minimization problem for NMM number and the strength of every NMM. The results showed that the NMM number was significantly lower during the ictal period than during the interictal period, and the strength of major NMMs increased. This indicates that neural masses fuse into fewer larger neural masses with greater strength. The distribution of excitatory and inhibitory strength during the ictal and interictal periods was similar, whereas the excitation/inhibition ratio was higher during the ictal period than during the interictal period.

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

Computational Intelligence and Neuroscience ◽

10.1155/2010/456140 ◽

2010 ◽

Vol 2010 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

M. Zavaglia ◽

F. Cona ◽

M. Ursino

Keyword(s):

Long Range ◽

Critical Role ◽

Experimental Studies ◽

Pyramidal Cells ◽

Neural Mass Model ◽

Cortical Region ◽

Eeg Rhythms ◽

Mass Model ◽

Neural Mass ◽

Neural Populations

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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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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Extracranial estimation of neural mass model parameters using the Unscented Kalman Filter

10.1101/171207 ◽

2017 ◽

Author(s):

Lara Escuain-Poole ◽

Jordi Garcia-Ojalvo ◽

Antonio J. Pons

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Neural Mass Model ◽

Model Parameters ◽

Mass Model ◽

Imperfect Data ◽

Non Invasive ◽

Neural Mass ◽

Eeg Recordings ◽

The Brain

AbstractData assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary’s model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method.Author SummaryTo completely understand brain function, we will need to integrate experimental information into a consistent theoretical framework. Invasive techniques as EcoG recordings, together with models that describe the brain at the mesoscale, provide valuable information about the brain state and its dynamical evolution when combined with techniques coming from control theory, such as the Kalman filter. This method, which is specifically designed to deal with systems with noisy or imperfect data, combines experimental data with theoretical models assuming Bayesian inference. So far, implementations of the Kalman filter have not been suited for non-invasive measures like EEG. Here we attempt to overcome this situation by introducing a model of the head that allows to transfer the intracranial signals produced by a mesoscopic model to the scalp in the form of EEG recordings. Our results show the advantages of using multichannel EEG recordings, which are extended in space and allow to discriminate signals produced by the interaction of coupled columns. The extension of the Kalman method presented here can be expected to expand the applicability of the technique to all situations where EEG recordings are used, including the routine monitoring of illnesses or rehabilitation tasks, brain-computer interface protocols, and transcranial stimulation.

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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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