Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology

Abstract

Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment

Biomarkers of seizure response to vagus nerve stimulation: A scoping review

“…Interestingly, one commonality in reported biomarkers of VNS response was that more normative neurophysiological states are more likely to result in good response to VNS. In EEG studies, decreased segregation of frequency bands was reported in VNS nonresponders, whereas responders more closely resembled healthy controls, suggesting a decrease in the robustness and specialization of global brain networks in nonresponders 20 . Similarly, DTI microstructure more closely resembling normal controls was associated with greater responsiveness in a pediatric cohort undergoing VNS 9 .…”

“…In EEG studies, decreased segregation of frequency bands was reported in VNS nonresponders, whereas responders more closely resembled healthy controls, suggesting a decrease in the robustness and specialization of global brain networks in nonresponders. 20 Similarly, DTI microstructure more closely resembling normal controls was associated with greater responsiveness in a pediatric cohort undergoing VNS. 9 These findings are consistent with previous work showing that greater deviations from normative brain network structure portends worse outcomes.…”

“…9,18 Two studies explored global measures of network topology as biomarkers of VNS response. 20,21 Babajani-Feremi et al estimated preoperative resting-state MEG functional connectivity by using the phase-locking value in theta, alpha, and beta frequency bands and calculating the following global graph measures: (1) modularity, representing the robustness of a segregated network; (2) transitivity, representing connectivity strength of network nodes to their neighbors; and (3) characteristic path length (CPL), representing functional integration as a measure of the average shortest path length between all pairs of nodes within a network. 20 They found greater modularity and lesser transitivity in theta, alpha, and…”

“…beta frequency bands in VNS responders compared to nonresponders, but no difference in CPL. 20 Fraschini et al estimated preoperative and postoperative EEG functional connectivity by using the phase-lag index and calculating the following minimum spanning tree (MST) measures: (1) diameter, representing the largest distance between any pair of nodes in a network; (2) eccentricity, representing the largest distance between a node and any other node in the network; (3) normalized leaf number, representing the number of nodes with a degree of "1" divided by the total number of nodes in the network, where degree refers to the number of edges the node has to other nodes; and (4) hierarchy, representing a balance in hub nodes of the network. 21 They observed decreased MST diameter and eccentricity and increased MST leaf number and hierarchy in the theta frequency band of VNS responders compared to nonresponders.…”

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

“…The antiepileptic effect of VNS could be related to a diffuse cortical modulation of synchronization, as suggested in previous studies with animal models (Chase et al, 1967;Magnes et al, 1961;Zanchetti et al, 1952), electroencephalography (EEG) (Bodin et al, 2015), stereoelectroencephalography (SEEG) (Bartolomei et al, 2016), magnetoencephalography (MEG) (Babajani-Feremi et al, 2018;Mithani et al, 2019), or imaging evaluations in humans (Ibrahim et al, 2017;Mithani et al, 2019). Combining diffusion tensor imaging and resting-state MEG recordings in responder (R) and nonresponder (NR) patients, a multimodal connectomic prediction algorithm for VNS has been proposed (Mithani et al, 2019).…”