An optimal strategy for epilepsy surgery: Disruption of the rich-club?

Abstract

Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.

Pathological responses to single‐pulse electrical stimuli in epilepsy: The role of feedforward inhibition

“…Computational modelling of epilepsy surgery has received considerable attention in recent years with promising results (Goodfellow et al, 2016;Hebbink, Meijer, Huiskamp, van Gils, & Leijten, 2017;Jirsa et al, 2017;Lopes et al, 2017;Sinha et al, 2017;Terry, Benjamin, & Richardson, 2012). In this framework, neural masses or similar models are coupled through a patient-specific connectivity.…”

Quantification and selection of ictogenic zones in epilepsy surgery

“…We use the canonical theta model (Ermentrout et al, 1986, Lopes et al, 2017 which has been shown to capture relevant dynamics of neural mass models (Lopes et al, 2017); nodes can display transitions between "normal activity" (stable fixed point) and "epileptiform dynamics" (stable limit cycle) through a saddle node on invariant circle (SNIC) bifurcation. The dynamics of node ( = 1, … , ) is represented by its phase and obeys ̇( ) = (1 − cos ( )) + (1 + cos ( )) ( ) (1) where ( ) is the input current that the node receives from the other nodes in the network,…”

“…(2) the term + is white uncorrelated Gaussian noise with mean = −1.2 and standard deviation 0.6, as used in previous studies (Lopes et al, 2017). is the ( , ) th entry of the adjacency matrix (i.e., the weighted correlation matrix of the functional network), is a global scaling factor that scales the network interactions compared to the noise, and is the stable fixed point of node ( ̇= 0 at = = 0).…”

“…For instance, one could use heuristics that are quick but do not guarantee finding an optimum (Pearl 1984, Branke et al, 2016. For example, previous approaches (Goodfellow et al, 2016, Lopes et al, 2017, Sinha et al, 2017 applied heuristics to attempt to identify a set of nodes that optimally reduces the network ictogenicity. However, the selection of nodes of that set was solely based on the contribution of single nodes to seizure generation.…”

How sensitive is functional connectivity to electrode resampling on intracranial EEG? Implications for personalized network models in drug-resistant epilepsy

“…Prior work has used global and nodal network measures to stratify surgical candidates and to select nodes for resection (Tomlinson, Porter, and Marsh 2017;Sinha et al 2016;Lopes et al 2017;Shah et al 2019) . Here we provide a simple algorithm to augment these methods by determining patient-specific confidence in the robustness of estimated statistics to small perturbations in spatial sampling.…”