Quantification and Selection of Ictogenic Zones in Epilepsy Surgery

Abstract

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.

A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG

“…To date, the BNI framework has proved to be valuable for epilepsy diagnosis using scalp EEG in IGE ( Schmidt et al, 2014 , Schmidt et al, 2016 , Petkov et al, 2014 ), assessment of epilepsy surgery using intracranial EEG in focal epilepsy ( Goodfellow et al, 2016 , Laiou et al, 2019 , Lopes et al, 2020 , Lopes et al, 2018 , Lopes et al, 2017 ), and epilepsy classification using scalp EEG ( Lopes et al, 2019 ). Here we extended previous results, testing whether the concept of BNI could differentiate people with JME from age and gender matched healthy controls using resting-state MEG data.…”

A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG

“…To date, the BNI framework has proved to be valuable for the diagnosis of IGE using scalp EEG (Schmidt et al, 2014, 2016; Petkov et al, 2014), assessment of epilepsy surgery using intracranial EEG (Goodfellow et al, 2016; Lopes et al, 2017, 2018, 2020; Laiou et al, 2019), and epilepsy classification using scalp EEG (Lopes et al, 2019). Here we aimed to test whether the concept of BNI could differentiate people with JME from age and gender matched healthy controls using resting-state MEG data.…”

“…Larsson and Kostov (2005) showed that there is a shift in the peak of the alpha power towards lower frequencies in interictal EEG from people with both focal and generalized epilepsy. More recently, Abela et al (2019) found that a slower alpha rhythm may be an indicator of seizure liability. Other studies have used graph theory to test whether functional networks derived from interictal EEG differ from EEG obtained from healthy controls.…”

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

“…Several such models appeared in past years with the aim to explore the possibilities of surgical interventions and to predict their outcome [ 11 , 14 – 17 ]. Importantly, unlike the models based on the functional connectivity networks derived from interictal and/or ictal intracranial EEG recordings [ 18 – 22 ], the models based on the structural connectivity are not spatially restricted to the implanted brain regions and can simulate the whole brain dynamics. In some of the models, the heterogenity of the node behavior is caused only by the underlying connectivity [ 18 , 19 ].…”