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

Simona Olmi,Maxime Guye,Anna Zakharova,Halgurd Taher,Viktor Jirsa,Moritz Gerster,Fabrice Bartolomei,Antonín Škoch,Jaroslav Hlinka

doi:10.3389/fnsys.2021.675272

Abstract

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.

Highlights

Epilepsy is a chronic neurological disorder characterized by the occurrence and recurrence of seizures and represents the third most common neurological disorder affecting more than 50 million people worldwide (World Health Organization, 2005)
To exploit the predictive power of personalized brain network models, we have implemented, on subject-specific connectomes, a next-generation neural mass model that, differently from the previous applied heuristic mean-field models (Proix et al, 2017, 2018; Olmi et al, 2019), is exactly derived from an infinite size network of quadratic integrate-and-fire neurons (Montbrió et al, 2015), that represent the normal form of Hodgkin’s class I excitable membranes (Ermentrout and Kopell, 1986). This generation neural mass model can describe the variation of synchrony within a neuronal population, which is believed to underlie the decrease or increase of power seen in given EEG frequency bands while allowing for a more direct comparison with the results of electrophysiological experiments like local field potential, EEG, and event-related potentials (ERPs), thanks to its ability to capture the macroscopic evolution of the mean membrane potential
The single-population firing rate (Equation 4) shows, in the absence of forcing, only fixed points as attractors. As it will become clear a stable node and a stable focus are observable, separated by a bistability region between a high- and a low-activity state, whose boundaries are the locus of a saddle-node bifurcation

Summary

Introduction

Epilepsy is a chronic neurological disorder characterized by the occurrence and recurrence of seizures and represents the third most common neurological disorder affecting more than 50 million people worldwide (World Health Organization, 2005). Anti-epileptic drugs are the first line of treatment for epilepsy, and they provide sufficient seizure control in around two-thirds of cases (Kwan and Brodie, 2000). A possible treatment is the surgical resection of the brain tissue responsible for the generation of seizures. Epilepsy surgery is preceded by a qualitative assessment of different brain imaging modalities to identify the brain tissue responsible for seizure generation, i.e., the epileptogenic zone (EZ) (Rosenow and Lüders, 2001), which in general represents a localized region or a network where seizures arise, before recruiting secondary networks, called the propagation zone (PZ) (Talairach and Bancaud, 1966; Bartolomei et al, 2001; Spencer, 2002; Richardson, 2012). Outcomes are positive whenever the patient has become seizure-free after surgical operation

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