Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation
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, mathematica...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Systems Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnsys.2021.675272/full |
| _version_ | 1818725358457847808 |
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| author | Moritz Gerster Halgurd Taher Antonín Škoch Antonín Škoch Jaroslav Hlinka Jaroslav Hlinka Maxime Guye Maxime Guye Fabrice Bartolomei Viktor Jirsa Anna Zakharova Simona Olmi Simona Olmi |
| author_facet | Moritz Gerster Halgurd Taher Antonín Škoch Antonín Škoch Jaroslav Hlinka Jaroslav Hlinka Maxime Guye Maxime Guye Fabrice Bartolomei Viktor Jirsa Anna Zakharova Simona Olmi Simona Olmi |
| author_sort | Moritz Gerster |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T21:41:02Z |
| format | Article |
| id | doaj.art-daf217036e094e129b1fd5ee3c82accc |
| institution | Directory Open Access Journal |
| issn | 1662-5137 |
| language | English |
| last_indexed | 2024-12-17T21:41:02Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Systems Neuroscience |
| spelling | doaj.art-daf217036e094e129b1fd5ee3c82accc2022-12-21T21:31:37ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372021-09-011510.3389/fnsys.2021.675272675272Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure PropagationMoritz Gerster0Halgurd Taher1Antonín Škoch2Antonín Škoch3Jaroslav Hlinka4Jaroslav Hlinka5Maxime Guye6Maxime Guye7Fabrice Bartolomei8Viktor Jirsa9Anna Zakharova10Simona Olmi11Simona Olmi12Institut für Theoretische Physik, Technische Universität Berlin, Berlin, GermanyInria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, FranceNational Institute of Mental Health, Klecany, CzechiaMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, CzechiaNational Institute of Mental Health, Klecany, CzechiaInstitute of Computer Science of the Czech Academy of Sciences, Prague, CzechiaFaculté de Médecine de la Timone, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, Aix-Marseille Université, Marseille, FranceAssistance Publique -Hôpitaux de Marseille, Hôpital de la Timone, Pôle d'Imagerie, Marseille, FranceAssistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, FranceAix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMRS 1106, Marseille, FranceInstitut für Theoretische Physik, Technische Universität Berlin, Berlin, GermanyInria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France0Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, ItalyDynamics 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.https://www.frontiersin.org/articles/10.3389/fnsys.2021.675272/fullneural mass modelquadratic integrate-and-fire neuronpatient-specific brain network modelepileptic seizure-like eventtopological network measure |
| spellingShingle | Moritz Gerster Halgurd Taher Antonín Škoch Antonín Škoch Jaroslav Hlinka Jaroslav Hlinka Maxime Guye Maxime Guye Fabrice Bartolomei Viktor Jirsa Anna Zakharova Simona Olmi Simona Olmi Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation Frontiers in Systems Neuroscience neural mass model quadratic integrate-and-fire neuron patient-specific brain network model epileptic seizure-like event topological network measure |
| title | Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation |
| title_full | Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation |
| title_fullStr | Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation |
| title_full_unstemmed | Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation |
| title_short | Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation |
| title_sort | patient specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation |
| topic | neural mass model quadratic integrate-and-fire neuron patient-specific brain network model epileptic seizure-like event topological network measure |
| url | https://www.frontiersin.org/articles/10.3389/fnsys.2021.675272/full |
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