Source-sink connectivity: a novel interictal EEG marker for seizure localization

Abstract

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes (‘sources’) and the inhibited nodes themselves (‘sinks’). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians’ predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Individualized epidemic spreading models predict epilepsy surgery outcomes: a pseudo-prospective study

“…Our findings here did not depend on the presence of iEEG data, and even iEEG data were available we only included a low-resolution description of them. IEEG data does provide the most detailed information of epileptogenic activity, and is it often the most valuable tool to identify the epileptogenic zone or predict surginal outcome for patients with complicated ethiology [22,25,27,[33][34][35][36]. In fact, for the modeling cohort we found the best classification results using the goodness-of-fit of the ESSES-predicted seizure propagation patterns to the iEEG seizures, in agreement with previous studies [27].…”

“…Importantly, the global parameters of ESSES were defined at the population level, and the model was individualized for each patient via patient-specific MEG networks, which characterized the local spreading probabilities. As a consequence, ESSES can be extended to patients without iEEG recordings, in contrast to previous modeling studies, which typically required the existence of patient-specific iEEG data to individualize the model for each patient [22,25,27,[33][34][35][36]. IEEG allows for a highly resolved description of seizure dynamics, but its spatial sampling is sparse and it is highly invasive.…”

Non-invasive mapping of epileptogenic networks predicts surgical outcome

“… 70 , 71 Our findings may also be considered in the context of patient-specific dynamical network models based on the principle of ‘sources’ and ‘sinks’. 72 Future studies may estimate source–sink metrics from VSs time series extracted non-invasively from HD-EEG and MEG data to also validate their role as a non-invasive interictal biomarker of the EZ. Finally, our tool has the ability to reconstruct brain signals with similar FC features with those obtained invasively through iEEG.…”

Network alterations in temporal lobe epilepsy during non-rapid eye movement sleep and wakefulness

“…According to the Interictal Suppression Hypothesis, seizures are prevented during interictal activity by successful inhibition of the epileptogenic zone (EZ) (Johnson et al, 2023). To this regard, it was shown that regions belonging to the EZ were indeed those with the highest inward connections (sinks) (Gunnarsdottir et al, 2021; Johnson et al, 2023), likely reflecting inhibitory inputs from regions external to the EZ, that could be replaced by outward connections supporting seizure spread once the seizure begins (Narasimhan et al, 2020). High functional segregation of the EZ was also shown at rest (Bandt et al, 2014; Johnson et al, 2023; Schevon et al, 2012).…”