Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution

Abstract

In ∼20 million people with drug-resistant epilepsy, focal seizures originating in dysfunctional brain networks will often evolve and spread to surrounding tissue, disrupting function in otherwise normal brain regions. To identify network control mechanisms that regulate seizure spread, we developed a novel tool for pinpointing brain regions that facilitate synchronization in the epileptic network. Our method measures the impact of virtually resecting putative control regions on synchronization in a validated model of the human epileptic network. By applying our technique to time-varying functional networks, we identified brain regions whose topological role is to synchronize or desynchronize the epileptic network. Our results suggest that greater antagonistic push-pull interaction between synchronizing and desynchronizing brain regions better constrains seizure spread. These methods, while applied here to epilepsy, are generalizable to other brain networks and have wide applicability in isolating and mapping functional drivers of brain dynamics in health and disease.

Keywords: epileptic network; network neuroscience; push-pull control; seizure spread; synchronizability.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1. Hypothesized Mechanism of Seizure Regulation

(A) We created functional networks from intracranial electrophysiology of patients with neocortical epilepsy. Each sensor is a network node, and weighted functional connectivity between sensors, or magnitude coherence, is a network connection. (B) Diagram demonstrates push-pull control, where opposing synchronizing and desynchronizing forces (nodes) shifts overall network synchronizability. (C) Schematic of the epileptic network composed of a seizure-generating system and a hypothesized regulatory system that controls the spread of pathologic seizure activity. (D) Example partial seizure that remains focal: the seizure begins at a single node and evolves to and persists within a focal area. (E) Example partial seizure that generalizes to surrounding tissue: the seizure begins at two nodes and evolves to the broader network. We hypothesize that these two types of dynamics are determined by differences in the regulatory system.

Figure 2. Differential Pre-Seizure Synchronizability Predicts Seizure Spread

(A) Time-dependent synchronizability captures the potential for seizure spread through high-γ functional networks. The distributed network describes seizures with secondary generalization (N=16), the focal network describes seizures without secondary generalization (N=18). Analyzed epileptic events spanned the clinically-defined seizure and period of time equal in duration to the seizure, immediately preceding seizure-onset. Events were time-normalized with each pre-seizure and seizure period divided into 5 equally-spaced time bins (10 bins per event). Synchronizability was averaged within each bin. Synchronizability was significantly greater in the distributed network than in the focal network prior to seizure onset (functional data analysis, ppre-seizure = 1–56 * 10−2pseizure = 5–01 * 10−2). Thick lines represent mean, shaded area represents standard error around mean, p-values are obtained via the statistical technique known as functional data analysis (FDA) where event labels (two seizure types) were permuted uniformly at random (see Experimental Procedures): *p < 0.05. (B) Relationship between synchronizability and log-scaled dispersion of node strengths in high-γ functional networks across all distributed and focal events. Each point represents average synchronizability and dispersion of average node strengths from a single time-window (N=3560). Greater synchronizability was strongly related to greater network heterogeneity, or lower node strength dispersion (Pearson correlation coefficient; r = −0.964, p < 10−16). (C) Schematic demonstrating that the distributed network has greater synchronizability and more homogeneous topology than the focal network. Seizures may spread more easily in the distributed network due to more homogeneous connectivity between network nodes.

Figure 3. Virtual Cortical Resection Localizes Network Controllers

(A) Effect of node removal on network synchronizability (control centrality) in a toy network. Highlighted node removals resulting in increased synchronizability (desynchronizing node; green) or decreased synchronizability (synchronizing nodes; purple and orange). The strongest desynchronizing node increased synchronizability by 5.8% and was present in the network periphery, while the strongest synchronizing nodes decreased synchronizability by 27.2% and 16.1% and were located in the network core. The magnitude and direction of change upon removing a node is called its control centrality. (B) Virtual cortical resection applied to example distributed ((C) and focal) high-γ epileptic network event in a pre-seizure (left) and associated seizure (right) epoch yields a time-varying control centrality for each node. Network nodes are ordered by increasing mean control centrality during the epoch. We assigned each node as a desynchronizing, synchronizing, or bulk controller type using a null distribution of control centrality, constructed by randomly permuting functional connection strength 100 times for each network time window and applying virtual cortical resection to every node from every rewiring permutation. Nodes with mean control centrality in the upper or lower-tail of the null distribution (p < 0.05) were assigned as desynchronizing (red) or synchronizing (purple) nodes, respectively, otherwise, nodes with mean control centrality within the null distribution (range highlighted in gray) were assigned to the bulk (black). Error bars represent standard error of control centrality computed over time windows during the pre-seizure or seizure epoch.

Figure 4. Desynchronizing and Synchronizing Control Differentiate Seizure Type

(A) Distribution of average magnitude control centrality for each controller type across the focal (N=18) and distributed (N=16) networks during the pre-seizure epoch. Magnitude control centrality for each event was averaged across regions of same controller type and across time windows. Desynchronizing regions are stronger in the focal network than in the distributed network, pre-seizure (Wilcoxon rank-sum; z = 2.86, p = 4.18 * 10−3). Synchronizing regions are stronger in the focal network than in the distributed network, pre-seizure (Wilcoxon rank-sum; z = 2.00, p = 4.54 * 10−2). Bulk regions have similar strength in the focal and distributed networks, pre-seizure. (B) Distribution of average magnitude control centrality for each controller type across the focal (N=18) and distributed (N=16) networks during the seizure epoch. Magnitude control centrality for each event was averaged across nodes of same controller type and across time windows. Desynchronizing regions are stronger in the focal network than in the distributed network, during the seizure (Wilcoxon rank-sum; z = 2.97, p = 3.00 * 10−3). Synchronizing regions are stronger in the focal network than in distributed network, during the seizure (Wilcoxon rank-sum; z = 2.10, p = 3.53 * 10−2). Bulk regions have similar strength in the focal and distributed networks, during the seizure. *p < 0.05, **p < 0.01.

Figure 5. Regional Control Centrality Differentiates Seizure Type

(A) Distribution of average magnitude control centrality for each controller type across the focal (N=18) and distributed (N=16) networks during the pre-seizure epoch amongst seizure-onset (left) and surrounding (right) regions. Magnitude control centrality was averaged across regions of same controller type, location (seizure-onset or surround), and across time windows. No significant difference in desynchronizing, synchronizing, or bulk magnitude control was observed between seizure-onset regions of the focal and distributed networks, pre-seizure. Surrounding regions exhibit greater desynchronizing control in the focal network than in distributed network, pre-seizure (Wilcoxon rank-sum; z = 2.73, p = 6.42 * 10−3). Surrounding regions exhibit greater synchronizing control in the focal network than in the distributed network, pre-seizure (Wilcoxon rank-sum; z = 2.00, p = 4.54 * 10−2). No significant difference in bulk magnitude control was observed between surrounding regions of the focal and distributed networks, pre-seizure. (B) Distribution of average magnitude control centrality for each controller type across the focal (N=18) and distributed (N=16) networks during the seizure epoch amongst seizure-onset (left) and surrounding (right) regions. Magnitude control centrality was averaged across nodes of same controller type, location (seizure-onset or surround), and across time windows. No significant difference in desynchronizing, synchronizing, or bulk magnitude control was observed between seizure-onset regions of the focal and distributed networks, during the seizure. Surrounding regions exhibit greater desynchronizing control in the focal networks than in the distributed networks, during the seizure (Wilcoxon rank-sum; z = 3.07, p = 2.13 * 10−3). Surrounding regions exhibit greater synchronizing control in the focal network than in the distributed network, during the seizure (Wilcoxon rank-sum; z = 2.59, p = 9.67 * 10−3). No significant difference in bulk magnitude control was observed between surrounding regions of focal and distributed events, during the seizure. *p < 0.05, **p < 0.01.