Comparison between an exact and a heuristic neural mass model with second order synapses

Clusella, Pau; Ersöz, Elif Köksal; García-Ojalvo, Jordi; Ruffini, Giulio

doi:10.1101/2022.06.15.496262

Cited by 3 publications

(3 citation statements)

References 95 publications

(143 reference statements)

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“…Work along these lines has been carried out in [68, 48], where the effects of electric fields are analyzed at the single neuron level. The emergence of exact mean field theories deriving neural mass models from microscale ones, the construction of detailed population models, as well as experimental work can all help shed some light on the proper rescaling of the coupling parameter in our models [69].…”

Section: Discussionmentioning

confidence: 99%

A personalizable autonomous neural mass model of epileptic seizures

Lopez-Sola¹,

Sanchez-Todo²,

Lleal³

et al. 2021

Preprint

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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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Section: Discussionmentioning

confidence: 99%

A personalizable autonomous neural mass model of epileptic seizures

Lopez-Sola¹,

Sanchez-Todo²,

Lleal³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years there has been a growing use of the next generation population modeling framework in a variety of different neuroscience contexts, including abnormal beta rebound in schizophrenia [47], beta bursts seen in single trial data [48], the generation of gamma oscillations [49] and thetanested gamma oscillations [50], syllable segregation in speech comprehension [51], the generation of functional connectivity in whole brain networks [48,52], seizure propagation [53], crossfrequnecy coupling [54] and communication through coherence [55], working memory (with the inclusion of short-term synaptic plasticity) [56], non-invasive transcranial brain stimulation [57], and neurodegeneration [58].…”

Section: Extensions and Applicationsmentioning

confidence: 99%

Next generation neural population models

Coombes

2023

Front. Appl. Math. Stat.

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Low-dimensional neural mass models are often invoked to model the coarse-grained activity of large populations of neurons and synapses and have been used to help understand the coordination of large scale brain rhythms. However, they are phenomenological in nature and, although motivated by neurobiological considerations, the absence of a direct link to an underlying biophysical reality is a weakness that means they may not be best suited to capturing some of the rich behaviors seen in real neuronal tissue. In this perspective article I discuss a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for synaptic interactions. This has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of population synchrony. This next generation neural mass model is ideally suited to understanding the patterns of brain activity that are ubiquitously seen in neuroimaging recordings. Here I review the mean-field equations, the way in which population synchrony, firing rate, and average voltage are intertwined, together with their application in large scale brain modeling. As well as natural extensions of this new approach to modeling the dynamics of neuronal populations I discuss some of the open mathematical challenges in developing a statistical neurodynamics that can generalize the one discussed here.

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“…Work along these lines has been carried out in [48,68], where the effects of electric fields are analyzed at the single neuron level. The emergence of exact mean field theories deriving neural mass models from microscale ones, the construction of detailed population models, as well as experimental work can all help shed some light on the proper rescaling of the coupling parameter in our models [69].…”

Section: The Model Can Be Used To Represent the Effects Of Brain Stim...mentioning

confidence: 99%

A personalizable autonomous neural mass model of epileptic seizures

Lopez-Sola¹,

Sanchez-Todo²,

Lleal³

et al. 2022

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

Work in the last two decades has shown 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 increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM 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 into the pyramidal cell population, the model dynamics are autonomous. 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 motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, 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 based on NMMs.

show abstract

Comparison between an exact and a heuristic neural mass model with second order synapses

Cited by 3 publications

References 95 publications

A personalizable autonomous neural mass model of epileptic seizures

A personalizable autonomous neural mass model of epileptic seizures

Next generation neural population models

A personalizable autonomous neural mass model of epileptic seizures

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