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

Abstract

Summary In ~20 million people with drug-resistant epilepsy, focal seizures originating in dysfunctional brain networks will often evolve and spread to surrounding tissue, disrupting function in otherwise normal brain regions. To identify network control mechanisms that regulate seizure spread, we developed a novel tool for pinpointing brain regions that facilitate synchronization in the epileptic network. Our method measures the impact of virtually resecting putative control regions on synchronization in a validated model of the human epileptic network. By applying our technique to time-varying, functional networks, we identified brain regions whose topological role is synchronize or desynchronize the epileptic network. Our results suggest that greater antagonistic, push-pull interaction between synchronizing and desynchronizing brain regions better constrains seizure spread. 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.

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

“…In the epileptic network, ictal subgraphs of the cluster periphery may be more likely to facilitate dynamical transitions between clusters of different subgraph topologies than interictal subgraphs. Furthermore, our finding that subgraphs of seizures with pronounced spatial spread (CP + GTC) lie closer to their cluster periphery than focal seizures (CP) may contribute to global properties of network topology that have been used to predict seizure type in prior work (Khambhati et al, 2016). Neurophysiologically, the epileptic network demonstrates a weakened regulatory, push-pull control in constraining CP + GTC seizures (Khambhati et al, 2016) and might contribute to the ability of CP + GTC subgraphs to more flexibly transition between subgraph clusters than CP subgraphs.…”

Network neuroscience

“…Network control combines estimates of network connectivity with models of system dynamics to predict where in the system one should inject energy to push the system toward a desired target state or target dynamics 112 . Such a theory offers the fundamental backdrop against which to better understand cognitive control 113 , optimize stimulation for neurological disorders 114 , maintain and control levels of anesthesia 115 , and inform surgical or stimulation-based interventions such as in the case of epilepsy 116 (Fig. 6d,e).…”

“…Reproduced from ref. 116, Elsevier. ( e ) Exogenously controlling the dynamics in either of these zones using stimulation requires careful computational models of control mechanisms, optimal stimulation intensities and optimal targets of that stimulation as a function of time.…”

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

“…In recent applications to brain networks, these models have been exercised in the context of static network representations in both health [111] and disease [112], and in both humans and non-human animals [113]. Extending these tools into the temporal domain is a particularly exciting prospect which could offer fundamental insights into the mechanisms of network reconfiguration, and alterations in those mechanisms that may accompany normative neurodevelopment [114], healthy aging [115], or aberrant dynamics in neurological disease [116-118] or psychiatric disorders [107,119,120] that impact on learning. Classical network models are summarized in Box 3.…”