Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP)

Abstract

Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone (EZ) with the aim of effectively resecting it. However, in a considerable number of patients, EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications. There is a clear need for developing alternative approaches to induce seizure relief, but minimal impact on normal brain functions. Here, we develop a personalized in-silico network approach, that suggests effective and safe surgical interventions for each patient. Based on the clinically identified EZ, we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation. We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes, and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain (TVB), in order to evaluate effectiveness and safety of the target zones (TZs). In particular, we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network. We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread, but only a small subset of them proves safe. The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies.

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

“…The demonstration of a sizeable contribution of cerebro-cerebellar connectivity to global brain dynamics suggests that TVB has the potential to reveal the impact of specific subnetworks on global brain dynamics and to understand the interaction of multiple brain areas ( Aerts et al, 2018 ; Zimmermann et al, 2018 ; An et al, 2019 ). A further improvement to TVB performance could be achieved by using multimodal datasets merging MRI, EEG, MEG, positron emission tomography (PET) data ( Schirner et al, 2018 ; Stefanovski et al, 2019 ; Triebkorn et al, 2020 ).…”

“…A few studies have demonstrated the potential of TVB to investigate physiological brain states not only in healthy subjects ( Schirner et al, 2018 ) but also in neurological diseases. For example, in tumors and epilepsy TVB has been used to predict the surgical outcome, while in Alzheimer’s disease has been used to predict neurodegenerative progression ( Aerts et al, 2018 , 2020 ; Zimmermann et al, 2018 ; An et al, 2019 ).…”

Artificial Intelligence and Computational Approaches for Epilepsy

“… 89 – 90 The VEP model was constructed based on Epileptor, 103 , 104 a phenomenological neural mass model replicating seizure characteristics, and patient-specific features including brain connectivity and magnetic resonance imaging lesions, and fit and validated using the individual sEEG recordings. 98 , 99 Based on this personalized modeling approach, Jirsa’s group has been conducting successive studies in various directions for clinical applications, such as inference of the epileptogenicity of the brain regions, 102 analysis of the seizure propagation characteristics, 99 – 101 and prognostic prediction of the surgical Intervention. 100 , 101 …”

“… 98 , 99 Based on this personalized modeling approach, Jirsa’s group has been conducting successive studies in various directions for clinical applications, such as inference of the epileptogenicity of the brain regions, 102 analysis of the seizure propagation characteristics, 99 – 101 and prognostic prediction of the surgical Intervention. 100 , 101 …”

“…Some other researchers have performed simulations to derive the surgical target sites that are minimally invasive and effective in suppressing seizures, by applying an in-silico surgical approach to the personalized model, i.e., by investigating the degree of seizure propagation in pre- and post-surgical conditions. 100 , 101 An et al 101 have proposed a strategy to evaluate the safety of the target sites in order not to cause impairment of normal brain functions, in which they operationalized the safety by the concept of preservation of signal transmission capacity of the brain network, by comparing the simulation-induced response network characteristics in pre- and post-surgical conditions. These research outcomes have shown the possibility that the personalized brain network model could support clinical decision in determining the target site for surgery, and Jirsa’s group are currently applying this modeling approach to large-scale clinical trials in order to evaluate its effectiveness.…”