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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy.

  • Courtiol, Julie1
  • Guye, Maxime2, 3
  • Bartolomei, Fabrice1, 4
  • Petkoski, Spase5
  • Jirsa, Viktor K5
  • 1 Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France. , (France)
  • 2 Aix-Marseille Univ, CNRS, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM), 13005 Marseille, France. , (France)
  • 3 Assistance Publique-Hôpitaux de Marseille, Hôpital de La Timone, CEMEREM, Pôle d'Imagerie Médicale, CHU, 13005 Marseille, France. , (France)
  • 4 Assistance Publique-Hôpitaux de Marseille, Hôpital de La Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France. , (France)
  • 5 Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France [email protected] [email protected] , (France)
Published Article
Journal of Neuroscience
Society for Neuroscience
Publication Date
Jul 15, 2020
DOI: 10.1523/JNEUROSCI.0905-19.2020
PMID: 32513827


Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas.SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC. Copyright © 2020 the authors.

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