Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks

Abstract

Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors - changes in brain dynamics predictive of an impending seizure - can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.

Figure 1

Identifying constituents of evolving epileptic networks from which seizure precursors emerge. The strength of coupling (level of synchrony) between pairs of sampled brain areas is estimated in a sliding-window fashion from multichannel iEEG data (Methods). In each window, electrode contacts are associated with nodes and the strength of coupling is associated with the weight of an edge between two nodes in the resulting snapshot network. From the temporal sequence of snapshot networks – evolving epileptic networks – the predictive performance of time varying properties of edges (weight) and nodes (centrality) is assessed using various downstream statistical analyses (Methods). Only if performance exceeds chance level, an edge resp. node is considered as predictive. Eventually, predictive edges and nodes (coloured red; non-predictive constituents are coloured orange) are identified and assigned to predefined functional modules (S: seizure onset zone; purple, N: neighbourhood; brownish, and O: other; greenish, Methods).

Figure 2

Mean numbers of predictive edges and nodes grouped by functional module. Bar graph of the (mean ± standard deviation) number of predictive edges (A) and nodes (B) per patient (pre-ictal periods of 78 seizures from 38 patients). Predictive edges connect (predictive and non-predictive) network nodes (brain regions) within and between functional modules (S SOZ, N neighbourhood, O other). Note that there may be more than one predictive edge and more than one predictive node per pre-ictal period and these edges may connect different nodes. Predictivity of nodes estimated with strength centrality.

Figure 3

Time-varying changes of node centrality in an epileptic network. Exemplary time course of centrality of each node in a patient’s evolving epileptic network derived from multichannel iEEG recorded continuously over more than seven days. Data grouped by functional module (O other, N neighbourhood, S  SOZ). Bolts on top of the plot mark times of seizure onset, and tics on x-axis denote midnight. On average, the most important node belongs to functional module O.

Figure 4

Predictive edges and predictive nodes. Schematics summarizing our findings of the spatial distribution of predictive edges connecting predictive (red) and/or non-predictive nodes (grey) within and between functional modules (O other, N neighbourhood, S SOZ). For the sake of clarity, we do not show non-predictive edges. The table reports the number of network constituents contributing to each case (c1–c5). Percentages refer to the total amount of the respective predictive network constituent. We do not report the number of solitary predictive nodes (24.3% of all predictive nodes).

Figure 5

Distributions of pre-seizure changes in characteristics of predictive edges and nodes. Boxplots of the relative change δ in weights W_e of predictive edges connecting predictive nodes and in centrality values (top, strength centrality C^S) and (bottom, betweenness centrality C^B) of predictive nodes connected by predictive edges (left: both nodes (n_h, n_l) carry predictive information; case c1; right: only one node (n) carries predictive information; case c2). Relative changes are calculated as δ = (M_p − M_i)/M_i, were M_p and M_i denote placeholders for the medians of the respective characteristics from the pre-ictal and inter-ictal periods. Bottom and top of a box are the first and third quartiles, and the (blue) band inside a box is the median of the distribution. The ends of the whiskers represent the interquartile range of the data. Note that the medians of relative change in edge weights for cases c1 and c2 differ only by 0.9% for C^S and by 10.5% for C^B.

Figure 6

Ictogenesis in evolving epileptic networks. Schematics on how, when, and from which brain regions seizure precursors are being generated in evolving large-scale epileptic networks. Predictive edges connecting predictive (red) and/or non-predictive nodes (grey) within and between functional modules O (other; greenish), N (neighbourhood; brownish), and S (SOZ; purple). Non-predictive edges are shown as black dotted lines. The inset exemplifies the rearrangement of the epileptic network’s path structure that results in a formation of a bottleneck.

Figure 7

Different centrality indices identify different nodes as most central. Exemplary weighted network consisting of 15 nodes. Edge weights are colour coded with darker colours representing larger weights. “B” and “S” mark node identified as most central with betweenness centrality and strength centrality, respectively.