High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients

Abstract

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

“…Our approach builds on our previous work with intracranial EEG, where we show that patients generally have better seizure outcomes when high-strength nodes are surgically removed (27)(28)(29)(30). Other groups have reported similar findings from intracranial EEG (31)(32)(33)(34)(35)(36)(37). With MEG data, Nissen et al (9) showed in a cohort of 22 patients, that interictal source localized network hubs overlapped with the resection in seizure-free patients only.…”

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

“…We calculated functional connectivity using a pipeline that we have previously described and validated 10,14,16 . We used intracranial EEG stored on ieeg.org, a cloud-based platform for electrophysiologic data 24,25 .…”

“…We randomly selected an interictal segment of > 2 minutes for each patient that occurs at least 1 hour away from clinically-annotated seizures; the segment length is justified by prior evidence that even short interictal segments accurately capture critical network patterns in epilepsy 29 . We divided the interictal epoch into one-second intervals ( Figure 1A) and for each window calculated connectivity using coherence in the beta (15-40 Hz) band, which we have previously shown to be effective at detecting abnormal connectivity 10 . We computed the mean of each edge over time to obtain a single adjacency matrix for each patient ( Figure 1B).…”

“…Such approaches use interictal [10][11][12][13] or ictal recordings [14][15][16] and are derived from ECoG 10,11,14 or SEEG 17,18 to generate networks. These networks are analyzed using graph theory 10,14,19 or neural mass models, which simulate seizure-like activity and probe the effects of different surgical interventions 11,13 . Intracranial EEG network models are also used to study networks activated during normal brain function, for example in recent studies probing cognition 20 and attention 21 .…”

“…A variety of network methods are being explored to localize the epileptogenic zone from intracranial EEG data. Such approaches use interictal 10–13 or ictal recordings 14–16 and are derived from ECoG 10,11,14 or SEEG 17,18 to generate networks. These networks are analyzed using graph theory 10,14,19 or neural mass models, which simulate seizure-like activity and probe the effects of different surgical interventions 11,13 .…”