Virtual cortical resection reveals push-pull network control preceding seizure evolution

Abstract

For ≈20 million people with drug-resistant epilepsy, recurring, spontaneous seizures have a devastating impact on daily life. The efficacy of surgical treatment for controlling seizures is hindered by a poor understanding of how some seizures spread to and synchronize surrounding tissue while others remain focal. To pinpoint network regions that regulate seizure evolution, we present a novel method to assess changes in synchronizability in response to virtually lesioning cortical areas in a validated computational network model. In human patients implanted with electrocorticographic sensors, we apply our virtual cortical resection technique to time-varying functional networks and identify control regions that synchronize or desynchronize cortical areas using an antagonistic push-pull control scheme to raise or lower synchronizability. Our results suggest that synchronizability before seizures predicts seizure evolution: in focal seizures, the strongest controllers are located outside seizure-generating areas. These methods,while applied here to epilepsy, are generalizable to other brain networks, and have wide applicability in isolating and mapping functional drivers of brain dynamics in health and disease. dynamic network neuroscience -epileptic network -synchronizability -push-pull control -diffusion -seizure evolution

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

“…Khambhati et al proposed a push-pull antagonism in regulating seizure evolution by constructing the functional connectivity network in the high gamma band. 6 Although inspired by this seminal work on a push-pull control mechanism, our study is advanced in 2 main aspects. First, we established a directional connectivity network instead of a functional connectivity network.…”

“…4 For example, focal seizures do not always remain in the seizure onset zone (SOZ), often starting in the SOZ and subsequently generalizing to the surrounding cortex (regions outside SOZ [NonSOZ]) in focal epilepsy patients, suggesting the importance of a more distributed epileptic network in seizure propagation. [5][6][7] Conventionally, seizure propagation is coordinated by different neuronal oscillations such as low-frequency activity (LFA; <30Hz), high-frequency activity (HFA; >30Hz), or low-to-high cross-frequency coupling (CFC). [6][7][8] Different neuronal oscillations and their interactions have been suggested to play critical roles in epileptic seizures and could potentially serve as reliable markers for epileptogenicity and epileptic zones.…”

“…[5][6][7] Conventionally, seizure propagation is coordinated by different neuronal oscillations such as low-frequency activity (LFA; <30Hz), high-frequency activity (HFA; >30Hz), or low-to-high cross-frequency coupling (CFC). [6][7][8] Different neuronal oscillations and their interactions have been suggested to play critical roles in epileptic seizures and could potentially serve as reliable markers for epileptogenicity and epileptic zones. [9][10][11] Understanding the network dynamics of seizure evolution is crucial to identifying the functional architecture of each patient's epileptic network, which in turn could lead to improved surgical outcomes.…”

Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI

“…Previous research in network neuroscience (Bassett and Sporns, 2017) has focused on mapping structural and functional "connectomes" in healthy adults by rigorously characterizing connectivity between regions of the cerebral cortex based on multi-modal neuroimaging (Fornito, Zalesky, & Breakspear, 2013Hagmann et al, 2007;Zuo et al, 2012). Such work to establish valid normative data has spawned an entire field of research mapping network changes across variables such as intelligence, gender, and age, as well as in various neurological and psychiatric diseases (Fair et al, 2009;Khambhati, Davis, Lucas, Litt, & Bassett, 2016;Li et al, 2009;Lynall et al, 2010;Supekar et al, 2008). In this study we extend this approach to map the MTL connectome, which should lead to new insights into brain-behavior relationships, particularly given the integral role of the MTL in cognition and the increasing availability of high resolution MRI datasets.…”

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients

“…Our study is not the first where virtual resection protocols or seizure abatement strategies have been evaluated in silico. In the former context and despite methodological shortcomings (see introduction), virtual resections have been tested based on standard functional networks in a very recent study [Khambhati et al, 2016]. Moreover, two studies combined phenomenological models of neuronal population dynamics with either anatomical [Hutchings et al, 2015] or EEG derived functional network connectivity [Sinha et al, 2014].…”