The role of epidemic spreading in seizure dynamics and epilepsy surgery

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but one in three patients continue to have seizures one year after surgery. In order to improve the chances of good outcomes, computational models of seizure dynamics are being integrated into surgical planning to simulate the effects of the planned surgeries. These modelling frameworks require several conceptual and methodological choices, as well as large amounts of patient-specific data, which hinders their clinical applicability. To address this problem, we considered the patient-specific brain network, derived from magnetoencephalography (MEG) recordings, and a simple epidemic spreading model as the dynamical basis for seizure propagation. This simple model was enough to reproduce the seizure propagation patterns derived from stereo-tactical electroencephalography recordings (SEEG) of all considered patients ( N = 15), when the patients’ resected areas (RA) were used as the origin of epidemic spreading. The model yielded a more accurate fit for the seizure-free (SF, N = 11) than the non-SF (NSF) group and, even though the difference between the groups was not significant, the goodness-of-fit distinguished NSF from SF patients with an area under the curve AUC = 84.1%. We also explored the definition of a population model that combined data from different patients to fit the model parameters but was still individualized by considering the patient-specific MEG network. Even though the goodness-of-fit decreased compared to the individualized models, the difference between the SF and NSF groups held, and in fact became stronger and significant ( p = 0.023), and the group classification also improved slightly (AUC= 88.6%). Therefore, combining data from different patients may pave the way not only to generalize this framework to patients without SEEG recordings, but also to reduce the risk of over-fitting and improve the stability of the models. Finally, we considered the individualized models to derive alternative hypothesis of the seizure onset zones and to test the surgical strategy in silico for each patient. We found that RA regions were on average more likely to originate the seizures, but that alternative explanations were possible. Virtual resections of the RA when considering these alternative seeds significantly reduced seizure propagation, and to a greater extend for SF than NSF patients (although the difference was not significant). Overall, our findings indicate that spreading models based on the patient-specific MEG network can be used to predict surgical outcomes, with better fit results and greater reduction on seizure spreading linked to higher likelihood of seizure freedom after surgery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Ana P. Millan and Ida A. Nissen were supported by ZonMw and the Dutch Epilepsy Foundation, project number 95105006. The funding sources had no role in study design, data collection and analysis, interpretation of results, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All patients gave written informed consent and the study was performed in accordance with the Declaration of Helsinki and approved by the VUmc Medical Ethics Committee. No rules or procedures were imposed other than routine clinical care. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data used for this manuscript are not publicly available because the patients did not consent for the sharing of their clinically obtained data. Requests to access to the datasets should be directed to the corresponding author. All user-developed codes are available from the corresponding author upon reasonable request.