Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model - Ehrens, Daniel; Sritharan, Duluxan; Sarma, Sridevi V. (Frontiers in Neuroscience, 2015)
It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable from spike train measurements, and then stimulating with an exogenous current to stabilize the network. In his study, a nonlinear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study is to measure spike train observations from this epileptic network and then apply a feedback controller that will (i) detect when the network goes unstable, and then (ii) apply a state-feedback gain control input to the network to re-stabilize the network. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node to bring the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback controlled suppressed seizures within 2 seconds after onset.