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

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.

Human brain structural connectivity matrices–ready for modelling

“…[35][36][37][38][39] ) or custom-written code should not pose a challenge for modellers. Apart from conducting novel analyses, we encourage researchers to try to replicate and extend any of the results that have been reported by the use of (a subset of) the current dataset 11,15,16 , or other results in the literature that used structural connectivity data derived with the same 30 or other methodologies to test the robustness of the previously reported analyses and simulation results.…”

“…The importance and richness of this research area were soon recognized 7 and motivated a stream of further efforts 8 , and the modelling of static functional connectivity has further extended into the effort to reliably explain three aspects of brain activity dynamics: the spatial properties, temporal dynamics, and spectral features 9 . Modelling functional connectivity of a healthy brain is, however, not the only use of brain structural connectivity data-it has been increasingly used also for modelling of brain disease dynamics, including epilepsy 10,11 , as well as to study deeper characteristics of the structural connectivity itself 12 . This enterprise is thus a promising and growing area of research, calling for the utilization of publicly shared data at a level accessible to data scientists and, generally, researchers across disciplines.…”

“…Finally, we also share basic demographic and clinical data of the subjects (gender, age, handedness, education, weight, height, BMI) and the list of AAL ROIs in the order used in the matrices. The data correspond to a control healthy subject cohort from a study on early changes in schizophrenia 14 , and a subset of the connectivity matrices has already been used for previous modelling studies that aimed at modelling chimera states 15 , unihemispheric sleep 16 , or epileptic dynamics 11,17 .…”

Shot noise in next-generation neural mass models

“…A distinctive feature of these models is their capability to account for the degree of synchrony in neuronal populations. Next-generation models were proved useful in a number of contexts including the modeling of β and γ oscillations [40][41][42][43][44], working memory [45,46], whole-brain simulations [47,48], etc. Being exact in the thermodynamic limit, neural mass models are considered as a good proxy of finite neuronal populations of sufficiently large size.…”

“…System (21) completely describes the macroscopic dynamics of population (1) in the thermodynamic limit. This very system and its modifications are widely used as "next-generation" neural mass models [39,40,46,47,[50][51][52][53][54].…”