Using the connectome to predict epileptic seizure propagation in the human brain
© Proix and Jirsa 2015
Published: 18 December 2015
Partial seizures in epileptic patients are generated in localized networks, so-called Epileptogenic Zone (EZ), before recruiting other regions, so-called Propagation Zone (PZ) . For drug-resistant patients, surgical resection is sometimes possible. Correctly delineating the extent of the EZ and PZ is critical for a successful surgical resection, in order to remove enough of the epileptogenic tissue to prevent seizures while minimizing the cognitive collateral damages. EZ and PZ extents are evaluated using imaging tools such as M/EEG, MRI, PET and stereotaxic EEG (sEEG). In this modelisation work, we used the large-scale connectome to build a network of neural masses in order to reproduce seizure propagation through the human brain. In particular, we aimed at predicting the propagation of epileptic seizures, i.e. the PZ, using the localization of the EZ.
We preprocessed data obtained from 18 different patients with different types of partial epilepsy. Using MRI and diffusion MRI data, we generated patient-specific connectomes along with cortical surfaces, using different parcellation resolutions. Epileptic dynamics of a single region was based on the Epileptor, a neural mass model able to autonomously generate epileptic seizures . The different regions interacted via a permittivity coupling allowing to reproduce seizures propagation such as observed in sEEG . Using a reduced Epileptor model, we performed a stability analysis at the edge of the seizure onset. We confirmed our results with simulations of the network of Epileptors using The Virtual Brain, a neuroinformatics platform to simulate large-scale dynamics . The analytical prediction of seizure spatial extent correctly reproduces seizure simulations.
In conclusion, our results show that large-scale white matter tracts play an important role in the propagation of epileptic seizures. Better understanding of their exact role can help to significantly improve the success rate of surgical resections for epileptic patients.
This research has been supported by the James S. McDonnell Foundation.
- Spencer SS: Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia. 2002, 43: 219-227.PubMedView ArticleGoogle Scholar
- Jirsa VK, Stacey W, Quilichini P, Ivanov A, Bernard C: On the nature of seizure dynamics. Brain. 2014, 137: 2210-2230.PubMedPubMed CentralView ArticleGoogle Scholar
- Proix T, Bartolomei F, Chauvel P, Bernard C, Jirsa VK: Permittivity coupling across brain regions determines seizure recruitment in partial epilepsy. J Neurosci. 2014, 34 (45): 15009-15021.PubMedView ArticleGoogle Scholar
- Sanz-Leon P, Knock SA, Woodman Domide L, Mersmann J, McIntosh AR, Jirsa VK: The Virtual Brain: a simulator of primate brai network dynamics. Frontiers in Neuroinformatics. 2013, 1-23.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.