Stimulating Solutions for Intractable Epilepsy

Large Animal Epilepsy Model Platform: Kainic Acid Porcine Model of Mesial Temporal Lobe Epilepsy

Objective

Translational animal models are essential for advancing neuromodulation therapies for drug-resistant epilepsy. Large animal epilepsy models that accommodate implantable neuromodulation devices designed for humans are needed to advance electrical brain stimulation and sensing applicaitons. We establish a kainic acid (KA) porcine model of mesial temporal lobe epilepsy (mTLE) for pre-clinical research and development of novel stimulating therapies.

Methods

We developed a platform integrating MRI-guided stereotactic electrode and chemotoxin delivery with intraoperative and long-term electrophysiology monitoring. Six domestic pigs underwent MRI-guided stereotactic implantation of bilateral hippocampal (HPC) and anterior nucleus of the thalamus (ANT) electrodes, followed by KA infusion into the right HPC. Local field potential (LFP) monitoring was conducted intraoperatively with a bedside workstation and chronically with an implantable neural sensing and recording (INSR) device designed for human use. Cresyl violet staining, commonly used to visualize neurons, was used to assess the neuropathophysiological effect of the KA infusion.

Results

KA infusion led to the emergence of interictal epileptiform discharges (IEDs), acute status epilepticus (SE), and spontaneous seizure activity. Acute IEDs and electrographic SE was observed in all cases, with two pigs euthanized due to uncontrolled seizures. Of the surviving four pigs, spontaneous IEDs were detected in all, and spontaneous seizures within 2 weeks were observed in three. Seizures in freely behaving animals varied from subclinical events to focal and generalized tonic-clonic seizures. Single pulse evoked response potentials (SPEP) measured in two pigs demonstrated functional connectivity between ANT and HPC circuits. Neuronal disorganization and loss, both major components of mTLE neuropathology were observed.

Significance

The KA-induced porcine mTLE model supports the feasibility of testing human neuromodulation devices and provides a translational platform for studying therapeutic electrical sensing and stimulation. In particular, the model should be useful for advancing automated seizure diaries, closed-loop and adaptive therapies targeting Papez circuit nodes.

Towards network-guided neuromodulation for epilepsy

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.