High‐frequency brain networks undergo modular breakdown during epileptic seizures

Abstract

SUMMARYObjective: Cortical high-frequency oscillations (HFOs; 100-500 Hz) play a critical role in the pathogenesis of epilepsy; however, whether they represent a true epileptogenic process remains largely unknown. HFOs have been recorded in the human cortex but their network dynamics during the transitional period from interictal to ictal phase remain largely unknown. We sought to determine the high-frequency network dynamics of these oscillations in patients with epilepsy who were undergoing intracranial electroencephalographic recording for seizure localization. Methods: We applied a graph theoretical analysis framework to high-resolution intracranial electroencephalographic recordings of 24 interictal and 24 seizure periods to identify the spatiotemporal evolution of community structure of high-frequency cortical networks at rest and during multiple seizure episodes in patients with intractable epilepsy. Results: Cortical networks at all examined frequencies showed temporally stable community architecture in all 24 interictal periods. During seizure periods, high-frequency networks showed a significant breakdown of their community structure, which was characterized by the emergence of numerous small nodal communities, not limited to seizure foci and encompassing the entire recorded network. Such network disorganization was observed on average 225 s before the electrographic seizure onset and extended on average 190 s after termination of the seizure. Gamma networks were characterized by stable community dynamics during resting and seizure periods. Significance: Our findings suggest that the modular breakdown of high-frequency cortical networks represents a distinct functional pathology that underlies epileptogenesis and corresponds to a cortical state of highest propensity to generate seizures.

Multifrequency Dynamics of Cortical Neuromagnetic Activity Underlying Seizure Termination in Absence Epilepsy

“…Previous reports have demonstrated that source localization in high-frequency bands was more focal and stable than that in low-frequency bands ( Miao et al, 2014b ; Tang et al, 2016 ; Wu et al, 2017b ). Furthermore, a study found that HFOs could precede the onset of a seizure and persist after the postictal period ( Fuertinger et al, 2016 ). These related studies suggested the existence of different cellular and network mechanisms between HFOs and other neural activity ( Draguhn et al, 1998 ; Traub et al, 2003 ; Roopun et al, 2010 ).…”

Identifying the Epileptogenic Zone With the Relative Strength of High-Frequency Oscillation: A Stereoelectroencephalography Study

“…The results enhanced the clinical application value of HFO analysis in patients with drugresistant epilepsy and provided a potential marker for guiding epilepsy surgery. The detection of HFOs has been widely studied in intracranial EEG studies but rarely in SEEG studies (Chassoux, 2003;Amiri et al, 2016;Ferrari-Marinho et al, 2016;Fuertinger et al, 2016). Although both subdural and stereotactic electrode recordings can be used to identify the EZ, they exhibit some differences.…”

Stability of Network Communities as a Function of Task Complexity

“…Wang, Sporns, & Burkhalter, 2012), task production induces a behaviorally driven reconfiguration of the functional architecture of the brain on a global scale (Fuertinger et al, 2015; Fuertinger et al, 2016; Stanley, Dagenbach, Lyday, Burdette, & Laurienti, 2014) and may therefore be more sensitive to inter-subject variability. Thus, the observed high within-group stability of resting-state functional partitions as compared to task-related modular layouts might be a consequence of the underlying neuroanatomical architecture.…”

“…The mathematical concept of a graph, which represents a collection of nodes and edges, proved to be particularly well suited to comprehensively assess the whole-brain functional connectome (Bullmore & Sporns, 2009; De Vico Fallani, Richiardi, Chavez, & Achard, 2014). The analysis of the community architecture within these graphs revealed a number of previously unknown brain activity patterns, such as the context-dependent global re-organization of brain activity not necessarily restricted to underlying white matter connections (Andric & Hasson, 2015), the predictive power of nodal assignments to functional modules for learning performance (Bassett et al, 2011), estimation of cognitive performance based on the modular organization of a cognitive control network at rest (Stevens, Tappon, Garg, & Fair, 2012), and modular disorganization and a breakdown of normal network architecture preceding an epileptic seizure (Fuertinger, Simonyan, Sperling, Sharan, & Hamzei-Sichani, 2016). The inherent structure of these functional patterns most likely would have remained undetected without using the theoretical underpinning of graph partitions.…”