Network dynamics of the brain and influence of the epileptic seizure onset zone

Abstract

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.focal epilepsy | seizure localization | network analysis | eigenvector centrality | ECoG signals E pilepsy affects over 60 million people worldwide, and approximately 40% of patients have drug-resistant epilepsy (DRE) with recurrent seizures that are not controlled by available medications (1-3). It is now routine to consider drugresistant partial epilepsy patients, who represent the largest cohort of patients with uncontrolled seizures, for possible resective seizure surgery (4). Successful seizure surgery is predicated upon the ability to localize the seizure onset zone. Although some patients (e.g., those with mesial temporal sclerosis or lesional epilepsy) can proceed to surgery following scalp recordings of seizures delineating a seizure onset zone (5), a significant number of patients have seizures that are challenging to localize with scalp ictal (i.e., seizure) recordings. In this case, ictal recordings using intracranial electrodes (e.g., subdural strips, grids, or depth electrode arrays) are necessary. The purpose of these intracranial recording arrays is to provide information about seizure onset and propagation, representing spatiotemporal changes in cerebral function.Using intracranial electrocorticographic (ECoG) recordings taken over several days to capture ictal events, clinicians visually inspect the ECoG recordings at the onset of the seizures an...

Multimodal investigation of epileptic networks

“…In addition, insular contacts were characterized by an increase in outgoing connections at seizure initiation, followed by disconnection. These results are in agreement with those reported by Burns et al (2014), which suggests that an IF state is present in patients with ICE. This interpretation is supported by the fact that most of our ICE cases were seizure free after insulectomy.…”

“…When present, the IF state occurred at seizure initiation and lasted for about the first half of seizures. Importantly, patients for whom IF state could be detected had significantly better surgical outcome than patients for whom IF state was not detected (Burns et al, 2014). This important study shows that the epileptic zones that specifically detach from the EN at seizure initiation are good candidates for surgical resection.…”

Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution

“…The prospect of patient-centric algorithms that modulate brain state to abort seizures is exciting to clinicians and researchers alike (Afshar et al, 2013; Stacey & Litt, 2008; Stanslaski et al, 2012). However, the best targets for chronic devices remain elusive, partly because functional brain networks, including the epileptic network, reorganize dynamically (Bassett et al, 2011; Bassett et al, 2006; Burns et al, 2014; Khambhati et al, 2015; Rummel et al, 2013). Such reorganization appears to follow a specific progression through network states unique to the patient's seizures (Burns et al, 2014; Khambhati et al, 2015; Wulsin et al, 2013).…”

“…However, the best targets for chronic devices remain elusive, partly because functional brain networks, including the epileptic network, reorganize dynamically (Bassett et al, 2011; Bassett et al, 2006; Burns et al, 2014; Khambhati et al, 2015; Rummel et al, 2013). Such reorganization appears to follow a specific progression through network states unique to the patient's seizures (Burns et al, 2014; Khambhati et al, 2015; Wulsin et al, 2013). The mechanisms that drive seizures through network states can inform neural control paradigms that aim to stop or contain propagation of seizure activity.…”

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

“…MEG has been widely applied for studying epilepsy disorders, particularly the localization of pathological brain activity or lesions in candidates for epilepsy surgery [15][16][17]. In addition, the sensitivity of MEG has been investigated for spike detection that depends on two sensor types (magnetometer and gradiometer) in patients with epileptic foci in the mesial temporal lobe [18].…”