Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

Abstract

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

A Stable EEG Epilepsy Network Spans From Infraslow to Ripple and From Interictal to Ictus

“…29 An inhibitory ictal penumbra was further defined by Schevon et al 30 in the present era. More recently, findings analogous to our own have been described for the preictal period using the graph theory measure in-degree 28 and for the interictal period using a number of measures, especially partial FIG. 4.…”

“…29 An inhibitory ictal penumbra was further defined by Schevon et al 30 in the present era. More recently, findings analogous to our own have been described for the preictal period using the graph theory measure in-degree 28 and for the interictal period using a number of measures, especially partial directed coherence at alpha frequencies. 27 Our causal flow data consolidates and extends the findings that inhibition is ubiquitous across different phases of epilepsy and extends from ripple to infraslow frequencies.…”

“…26 However, it did not improve on the yield of causal flow in the present small study or in further unpublished work. Yang and Madsen 28 reported that a wide range of algorithms derived from a somewhat different form of Granger analysis had for the most part similar yields across 25 surgical patients. In-degree performed best in that study; however, causal inflow was not compared.…”

“…The minority of analyses that yielded less significant rankings are not necessarily devoid of localizing activity. Such activity might simply be less prominent, fluctuate over time and state, be potentially identifiable by a larger amount of data 26,28 or a different computational index, 28 or be revealed by crossfrequency correlations. 10 However, the purpose of this study was not to exhaust all possible calculations but to represent the epilepsy network as broadly as possibly using a single, relatively straightforward measure.…”