Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Abstract

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

“…Using the iEEG data for the modeling cohort patients, we fit γ and ρ to reproduce the iEEG-recorded seizure propagation patterns with the ESSES-modeled seizures, setting the resection area was used as the epidemic seed (see 5.5 and Supp. section 5.2 for more details) [30,32]. The degree of similarity between the ESSES and iEEG seizures was measured with the goodness-of-fit C(ρ, γ) as defined in eq.…”

“…Although such models ignore the underlying bio-physical processes that lead to seizure generation and propagation, they do describe the basic rules that govern spreading processes. In previous studies [30,32], we found that epidemic spreading models could reproduce stereotypical patterns of seizure propagation as recorded via invasive electroencephalography (iEEG) recordings . Moreover, once fitted with patient-specific data, ESSES could identify alternative resection strategies, either of smaller size or at a different location than the actual surgery [24,30].…”

“…In previous studies [30,32], we found that epidemic spreading models could reproduce stereotypical patterns of seizure propagation as recorded via invasive electroencephalography (iEEG) recordings . Moreover, once fitted with patient-specific data, ESSES could identify alternative resection strategies, either of smaller size or at a different location than the actual surgery [24,30]. In a more recent study [32], we showed that the goodness-of-fit of the ESSES seizures and those recorded via iEEG predicted surgery outcome -with an area under the curve of 88.6% -indicating that ESSES not only reproduces the basic aspects of seizure propagation, but it also captures the differences, either in the location of the resection area relative to the EZ, or intrinsically in the iEEG or MEG data, between patients with good and bad outcome.…”

“…Following the same rationale, we developed a computational model of seizure propagation and epilepsy surgery based on epidemic spreading dynamics and patient-specific MEG brain connectivity [30], to which we refer here as the Epidemic Spreading Seizure and Epilepsy Surgery model (ESSES). Epidemic models describe the spread of an infectious agent through a network.…”

“…Epidemic models describe the spread of an infectious agent through a network. Epidemic processes on fixed networks have a rich mathematical history [31] with a plethora of models that can be exploited for epilepsy surgery optimization [24,30]. Although such models ignore the underlying bio-physical processes that lead to seizure generation and propagation, they do describe the basic rules that govern spreading processes.…”