Steering Toward Normative Wide-Dynamic-Range Neuron Activity in Nerve-Injured Rats With Closed-Loop Peripheral Nerve Stimulation.

OBJECTIVES: Chronic pain is primarily treated with pharmaceuticals, but the effects remain unsatisfactory. A promising alternative therapy is peripheral nerve stimulation (PNS), but it has been associated with suboptimal efficacy because its modulation mechanisms are not clear and the current therapies are primarily open loop (ie, manually adjusting the stimulation parameters). In this study, we developed a proof-of-concept computational modeling as the first step toward implementing closed-loop PNS in future biological studies. When developing new pain therapies, a useful pain biomarker is the wide-dynamic-range (WDR) neuron activity in the dorsal horn. In healthy animals, the WDR neuron activity occurs in a stereotyped manner; however, this response profile can vary widely after nerve injury to create a chronic pain condition. We hypothesized that if injury-induced changes of neuronal response can be normalized to resemble those of a healthy condition, the pathological aspects of pain may be treated while maintaining protective physiological nociception. MATERIALS AND METHODS: Using an in vivo electrophysiology data set of WDR neuron recordings obtained in nerve-injured rats and naïve rats, we constructed sets of linear phenomenologic models of WDR firing rate during windup stimulation for both conditions. Then, we applied robust control systems techniques to identify a closed-loop PNS controller, which can drive the dynamics of WDR neuron response in neuropathic pain model into ranges associated with normal physiological pain. RESULTS: The sets of identified linear models can accurately predict, in silico, nonlinear neural responses to electrical stimulation of the peripheral nerve. In addition, we showed that continuous closed-loop control of PNS can be used to normalize WDR neuron firing responses in three injured cases. CONCLUSIONS: In this proof-of-concept study, we show how tractable, linear mathematical models of pain-related neurotransmission can be used to inform the development of closed-loop PNS. This new application of robust control to neurotechnology may also be expanded and applied across other neuromodulation applications.

Temporal and morphological characteristics of high-frequency oscillations in an acute in vivo model of epilepsy.

Approximately 30% of patients with epilepsy do not respond to anti-epileptogenic drugs. Surgical removal of the epileptogenic zone (EZ), the brain regions where the seizures originate and spread, can be a possible therapy for these patients, but localizing the EZ is challenging due to a variety of clinical factors. High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the EZ, but it is currently unknown whether HFO rates and HFO morphology modulate as pathological brain networks evolve in a way that gives rise to seizures. To address this question, we assessed the temporal evolution of the duration of HFO events, amplitude of HFO events, and rates of HFOs per minute. HFO events were quantified using the 4AP in vivo rodent model of epilepsy, inducing seizures in two different brain areas. We found that the duration and amplitude of HFO events were significantly increased for the cortex model when compared to the hippocampus model. Additionally, the duration and amplitude increased significantly between baseline and pre-ictal HFOs in both models. On the other hand, the two models did not display a consistent increasing or decreasing trend in amplitude, duration or rate when comparing ictal and postictal intervals. Clinical Relevance- We assessed the amplitude, duration, and rate of HFOs in two acute in vivo rodent models of epilepsy. The significant modulation of HFO morphology from baseline to pre-ictal periods suggests that these features may be a robust biomarker for pathological tissue involved in epileptogenesis. Moreover, the differences in HFO morphology observed between cortex and hippocampus animal models possibly indicate that different structural network characteristics of the EZ cause this modulation. In all, we found that HFO features modulate significantly with the onset of seizures, further highlighting the need to consider of HFO morphology in EZ-localizing studies.

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. METHODS: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations. RESULTS: Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection. CONCLUSIONS: Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Network Fragility for Seizure Genesis in an Acute in vivo Model of Epilepsy.

Epilepsy affects over 50 million people worldwide and 30% of patients' seizures are medically refractory. The process of localizing and removing the epileptogenic zone is error-prone and ill-posed in part because we do not understand how epilepsy manifests. 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. If the fragility of the cortical network could be computed over a period in which seizure genesis occurs, then it might elucidate network mechanisms correlated to the epileptogenic zone. In this study, we used local field potentials (LFP) from neocortex by implementing an acute model of epilepsy in mice. These recordings were used to develop a dynamical network model that quantifies the fragility of the nodes from LFP epochs of baseline activity, preictal and ictal states. Fragility was quantified by the generation of a linear time-varying model to which we then applied a perturbation to determine the sensitivity of nodes in the network. Spatiotemporal fragility maps showed clear quantifiable changes in the epileptogenic network's properties throughout different states of seizure genesis. We quantified this difference over a baseline, preictal and ictal periods to show that network fragility is modulated in the manifestation of epilepsy.

Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy.

Epilepsy affects over 70 million people worldwide and 30% of patients' seizures cannot be controlled with medications, motivating the development of alternative therapies such as electrical stimulation. Current stimulation strategies attempt to stop seizures after they start, but none aim to prevent seizures altogether. Preventing seizures requires knowing when the brain is entering a preictal state (i.e., approaching seizure onset). Here we show that such preictal activity can be detected by an informative neural signal that progressively and monotonically changes as the brain approaches a seizure event. Specifically, we use local field potentials (LFP) from a rat model of epilepsy to develop an innovative measure of signal novelty relative to nonseizure activity, that shows the presence of progressive neural dynamics in an ultra broad band (4 Hz - 5 kHz). The measure is extracted from functional connectivity features computed from the LFPs which are used as an input to a one-class Support Vector Machine (SVM). The SVM outputs a scalar signal which quantifies how novel the current activity looks relative to baseline (non-seizure) activity and shows a progression towards seizure onset minutes ahead of time. The use of ultra broad band multivariate features into the SVM results in a novelty signal that has a significantly higher slope in the progression to seizure onset when compared to using power in conventional frequency bands (4 - 500 Hz) on individual channels as input features to the SVM. Functional connectivity in conjunction with the SVM is a strategy that generates a new measurement of novelty that can be used by closed-loop systems for seizure forecasting and prevention.

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model.

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, and then stimulating with an exogenous current to stabilize the network. In this study, a non-linear 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 was to measure spike train observations from this epileptic network and then apply a feedback controller that (i) detects when the network goes unstable, and then (ii) applies a state-feedback gain control input to the network to stabilize it. 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 using 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 bringing 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 control suppressed seizures within 2 s after onset.

Instability detector of a fragile neural network: application to seizure detection in epilepsy.

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. Therefore, one method for detecting seizures is to detect when the neuronal network has gone unstable. This is important for implementing a closed-loop therapy to suppress seizures. In this paper, we consider a widely used nonlinear stochastic model of a neuronal network, and assume that spiking dynamics during non-seizure periods correspond to certain synaptic connections that render its fixed point stable. We then apply a minimum energy perturbation theory we recently developed for networks to determine the changes in the most fragile node's synaptic connections that make the same fixed point unstable (our model during seizure). Then a detector is designed as follows. First a 2-state HMM is constructed (stable=state 1 and unstable=state 2) with fixed state transition probabilities, where the output observation is the firing rate of the most fragile node in the network. The output density functions are assumed to be Gaussian with parameters computed using maximum likelihood estimation on data generated from the nonlinear network model in each state. Then, to detect a transition from stable to unstable, spiking activity is simulated in all nodes from the nonlinear model. The detector first measures the firing rate of the fragile node, and computes the derivative of the cumulative likelihood ratio of the observed firing rate from the HMM's output distributions. When the derivative exceeds a certain threshold, a transition to the unstable state is detected. Various thresholds were tested when firing rate was computed by averaging over a different number of windows of different lengths. High performance was achieved and a tradeoff was found between the accuracy of the detector and the detection delay.