Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Abstract

Previous research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

RETRACTED: EEG Signal Connectivity for Characterizing Interictal Activity in Patients With Mesial Temporal Lobe Epilepsy

“…We also found a decrease in strength ( Figure 7 ) and an increase in eigenvector centrality ( Figure 10 ) for before and mid classes compared with far for motifs in the beta band. A 2020 scientific report from Fruengel and colleagues ( 33 ) analyzes these graph metrics on a group of 38 patients with intracranial EEG in multiday recordings. Though their patients' epilepsies were inhomogeneous, they used a different connectivity function, and their work was specifically focused on preseizure networks (instead of IEDs), some of the scenarios hypothesized there might apply to our research.…”

Impact of Transcutaneous Auricular Vagus Nerve Stimulation on Large-Scale Functional Brain Networks: From Local to Global

“…This inconsistency, however, can be resolved when considering the following model of a stimulation-induced stretching and compression of the network (see Figure 4 ; cf. Fruengel et al, 2020 ), which may be due to some nonlinear mechanism. The stimulation-induced increase of global clustering coefficient and decrease of average shortest path length points to an, on average, global compression of the evolving functional brain network.…”

“…We derived these phase time series adaptively with the Hilbert transform from the respective EEG time series (Osterhage et al, 2007 ). Non-overlapping windows (with index w ) had a duration of 20 s (5,120 data points), which represents a compromise between the required statistical accuracy for the calculation of r nm and approximate stationarity within a window length (Osterhage et al, 2007 ; Kuhnert et al, 2013 ; Fruengel et al, 2020 ). The synchronization index was repeatedly shown to serve as an indicator for the strength of interactions in functional brain networks and is confined to the unit interval: r nm = 1 indicates fully phase-synchronized brain regions and r nm = 0 indexes no phase synchronization.…”

“…Betweenness centrality is a shortest-path-based centrality index, which requires the definition of “length” of a path between pairs of vertices or between pairs of edges. Following Lehnertz et al ( 2014 ) (and references therein), we related the length of the shortest path between pairs of vertices/edges to the sum of the inverse weights of edges along this path. For a pair being connected to a same vertex/edge, we set the length to zero.…”

“…Studies on taVNS show widespread activity in expected vagal projections areas, including NTS, locus coeruleus, hypothalamus, thalamus, amygdala, hippocampus, as well as the prefrontal cortex and other widespread areas (Yap et al, 2020 ), though interpretation is difficult due to different study protocols and investigated subjects. Given these observations, we hypothesized that the impact of the global, apparently unspecific VNS-mediated activation of the brain can be suitably assessed with an analysis approach which makes use of EEG-derived, evolving functional brain networks (Bullmore and Sporns, 2009 ; Lehnertz et al, 2014 ). In a previous study (von Wrede et al, 2021 ), we could demonstrate that short-term taVNS has a topology-modifying, robustness- and stability-enhancing immediate effect on such brain networks derived from subjects with focal epilepsy.…”