1. Patient-specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation
- Author
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Moritz Gerster, Halgurd Taher, Antonín Škoch, Jaroslav Hlinka, Maxime Guye, Fabrice Bartolomei, Viktor Jirsa, Anna Zakharova, Simona Olmi, Institute für Theoretische Physik [Berlin], Technische Universität Berlin (TU), Mathématiques pour les Neurosciences (MATHNEURO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), National Institute of Mental Health [Topolova] (NUDZ), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Consiglio Nazionale delle Ricerche (CNR), Deutscher Akademischer Austauschdienst (DAAD, German Academic Exchange Service), Project No. 57445304, PPP Frankreich Phase I, PHC PROCOPE 2019-Numéro de project: 42511TA, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project No. 163436311-SFB 910, Ministry of Health Czech Republic - DRO 2021 ('National Institute of Mental Health - NIMH, IN: 00023752'), Czech Science Foundation project No. 21-32608S, Ministry of Health, Czech Republic - DRO 2021 ('Institute for Clinical and Experimental Medicine - IKEM, IN: 00023001'), Technical University of Berlin / Technische Universität Berlin (TU), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), and OLMI, Simona
- Subjects
[SDV.MHEP.CHI] Life Sciences [q-bio]/Human health and pathology/Surgery ,quadratic integrate-and-fire neuron ,Computer science ,[SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,Electroencephalography ,epileptic seizure-like event ,Epilepsy ,[NLIN.NLIN-AO] Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO] ,0302 clinical medicine ,Neural mass models ,ComputingMilieux_MISCELLANEOUS ,Original Research ,media_common ,0303 health sciences ,[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology ,topological network measure ,medicine.diagnostic_test ,Artificial neural network ,Cognition ,patient-specific brain network models ,Epileptogenic zone ,Connectome ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,Cognitive Neuroscience ,media_common.quotation_subject ,Neuroscience (miscellaneous) ,[SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery ,patient-specific brain network model ,Stereoelectroencephalography ,Cellular and Molecular Neuroscience ,03 medical and health sciences ,Developmental Neuroscience ,Neuroimaging ,medicine ,ddc:530 ,[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn] ,[NLIN.NLIN-AO]Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO] ,neural mass model ,Multistability ,030304 developmental biology ,[SCCO.NEUR]Cognitive science/Neuroscience ,[SCCO.NEUR] Cognitive science/Neuroscience ,medicine.disease ,[PHYS.COND.CM-DS-NN] Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn] ,[SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,Epileptic seizure ,Consciousness ,Neuroscience ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology ,030217 neurology & neurosurgery ,Diffusion MRI - 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 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.
- Published
- 2021
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