RT Journal Article
SR Electronic
T1 Patient-specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.15.426839
DO 10.1101/2021.01.15.426839
A1 Moritz Gerster
A1 Halgurd Taher
A1 Antonín Škoch
A1 Jaroslav Hlinka
A1 Maxime Guye
A1 Fabrice Bartolomei
A1 Viktor Jirsa
A1 Anna Zakharova
A1 Simona Olmi
YR 2021
UL http://biorxiv.org/content/early/2021/03/30/2021.01.15.426839.abstract
AB 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 the mathematical modelling with structural information from non-invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test 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 the example of diffusion-weighted magnetic resonance imaging (MRI) 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.Competing Interest StatementThe authors have declared no competing interest.