Epilepsy: responsive neurostimulation-modulating the epileptic brain

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy

Soft bioelectronic technologies for neuroengineering have shown remarkable progress, which include novel soft material technologies and device design strategies. Such technological advances that are initiated from fundamental brain science are applied to clinical neuroscience and provided meaningful promises for significant improvement in the diagnosis efficiency and therapeutic efficacy of various brain diseases recently. System-level integration strategies in consideration of specific disease circumstances can enhance treatment effects further. Here, recent advances in soft implantable bioelectronics for neuroengineering, focusing on materials and device designs optimized for the treatment of intracranial disease environments, are reviewed. Various types of soft bioelectronics for neuroengineering are categorized and exemplified first, and then details for the sensing and stimulating device components are explained. Next, application examples of soft implantable bioelectronics to clinical neuroscience, particularly focusing on the treatment of brain tumor and epilepsy are reviewed. Finally, an ideal system of soft intracranial bioelectronics such as closed-loop-type fully-integrated systems is presented, and the remaining challenges for their clinical translation are discussed.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study

Introduction

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

Methods

Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

Results

We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

Discussion

Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States

OBJECTIVE

Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications.

APPROACH

We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity.

MAIN RESULTS

The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms.

SIGNIFICANCE

Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.

Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis

Mesial temporal lobe epilepsy (MTLE) is the most common partial complex epilepsy in adults and the most unresponsive to medications. Electrical deep brain stimulation (DBS) of the hippocampus has proved effective in controlling seizures in epileptic rodents and in drug-refractory MTLE patients. However, current DBS paradigms implement arbitrary fixed-frequency or patterned stimuli, disregarding the temporal profile of brain electrical activity. The latter, herein included hippocampal spontaneous firing, has been shown to follow lognormal temporal dynamics. Here, we present a novel paradigm to devise DBS protocols based on stimulation patterns fashioned as a surrogate brain signal. We focus on the interictal activity originating in the hippocampal subfield CA3, which has been shown to be anti-ictogenic. Using 4-aminopyridine-treated hippocampus-cortex slices coupled to microelectrode array, we pursue three specific aims: (1) address whether lognormal temporal dynamics can describe the CA3-driven interictal pattern, (2) explore the possibility of restoring the non-seizing state by mimicking the temporal dynamics of this anti-ictogenic pattern with electrical stimulation, and (3) compare the performance of the CA3-surrogate against periodic stimulation. We show that the CA3-driven interictal activity follows lognormal temporal dynamics. Further, electrical stimulation fashioned as a surrogate interictal pattern exhibits similar efficacy but uses less pulses than periodic stimulation. Our results support the possibility of mimicking the temporal dynamics of relevant brain signals as a straightforward DBS strategy to ameliorate drug-refractory epilepsy. Further, they herald a paradigm shift in neuromodulation, wherein a compromised brain signal can be recreated by the appropriate stimuli distribution to bypass trial-and-error studies and attain physiologically meaningful DBS operating modes.

Deep brain stimulation in the medial septum attenuates temporal lobe epilepsy via entrainment of hippocampal theta rhythm

Aims

Temporal lobe epilepsy (TLE), often associated with cognitive impairment, is one of the most common types of medically refractory epilepsy. Deep brain stimulation (DBS) shows considerable promise for the treatment of TLE. However, the optimal stimulation targets and parameters of DBS to control seizures and related cognitive impairment are still not fully illustrated.

Methods

In the present study, we evaluated the therapeutic potential of DBS in the medial septum (MS) on seizures and cognitive function in mouse acute and chronic epilepsy models.

Results

We found that DBS in the MS alleviated the severity of seizure activities in both kainic acid‐induced acute seizure model and hippocampal‐kindled epilepsy model. DBS showed antiseizure effects with a wide window of effective stimulation frequencies. The antiseizure effects of DBS were mediated by the hippocampal theta rhythm, as atropine, which reversed the DBS‐induced augmentation of the hippocampal theta oscillation, abolished the antiseizure effects of DBS. Further, in the kainic acid‐induced chronic TLE model, DBS in the MS not only reduced spontaneous seizures, but also improved behavioral performance in novel object recognition.

Conclusion

DBS in the MS is a promising approach to attenuate TLE probably through entrainment of the hippocampal theta rhythm, which may be therapeutically significant for refractory TLE treatment.

Electrical cortical stimulations modulate spike and post-spike slow-related high-frequency activities in human epileptic foci

OBJECTIVE

Using interictal epileptiform discharges (IEDs), consisting of spikes and post-spike slow waves (PSSs), and IED-related high-frequency activities (HFAs), we elucidated inhibitory effects of electrical cortical stimulation (ECS) on human epileptic foci.

METHODS

We recruited 8 patients with intractable focal epilepsy, and 50-Hz ECS was applied to the seizure-onset zone (SOZ) and non-SOZ. Before (5-min) and after (20-min) ECS, we evaluated the number of IED, the amplitudes of spikes and PSSs, spike-related HFA power, and PSS-related low gamma (30-50 Hz) activities.

RESULTS

SOZ stimulation significantly decreased the number of IEDs and amplitude of spikes. Spike-related HFA power values in fast ripple (200-300 Hz) and ripple (80-150 Hz) bands were significantly suppressed only by SOZ stimulation in 4 and 3 patients, respectively. Among 4 patients with discrete PSSs, the amplitude ratio of spike/PSS decreased and the PSS-related low gamma activity power increased significantly in 2 patients and marginally in 1 patient.

CONCLUSIONS

ECS potentially modulates cortical excitability by reducing excitation and increasing inhibition, and monitoring IED-related HFAs may help achieve the optimal effects of ECS.

SIGNIFICANCE

IED and IED-related HFAs are dynamic, potential surrogate markers for epileptic excitability during the interictal period.

Seizures and Epilepsy

Epilepsy affects 65 million people worldwide, and is a leading neurologic cause of loss of quality-adjusted life years. The diagnosis of seizures and epilepsy often depends on a careful history, and is supported with electroencephalogram and imaging. First-line treatment of epilepsy includes medical management. Antiepileptic drugs must be chosen with the patient's particular comorbidities in mind. Drug-resistant epilepsy cases should be referred to an epilepsy specialist and may be evaluated for additional medications, epilepsy surgery, neurostimulation, or dietary therapy. When caring for women, providers must take into account needs for contraception or pregnancy safety where applicable.

Epidural focal brain cooling abolishes neocortical seizures in cats and non-human primates

Focal brain cooling (FBC) is under investigation in preclinical trials of intractable epilepsy (IE), including status epilepticus (SE). This method has been studied in rodents as a possible treatment for epileptic disorders, but more evidence from large animal studies is required. To provide evidence that FBC is a safe and effective therapy for IE, we investigated if FBC using a titanium cooling plate can reduce or terminate focal neocortical seizures without having a significant impact on brain tissue. Two cats and two macaque monkeys were chronically implanted with an epidural FBC device over the somatosensory and motor cortex. Penicillin G was delivered via the intracranial cannula for induction of local seizures. Repetitive FBC was performed using a cooling device implanted for a medium-term period (FBC for 30min at least twice every week; 3 months total) in three of the four animals. The animals exhibited seizures with repetitive epileptiform discharges (EDs) after administration of penicillin G, and these discharges decreased at less than 20°C cooling with no adverse histological effects. The results of this study suggest that epidural FBC is a safe and effective potential treatment for IE and SE.

Restoring Conscious Arousal During Focal Limbic Seizures with Deep Brain Stimulation

Impaired consciousness occurs suddenly and unpredictably in people with epilepsy, markedly worsening quality of life and increasing risk of mortality. Focal seizures with impaired consciousness are the most common form of epilepsy and are refractory to all current medical and surgical therapies in about one-sixth of cases. Restoring consciousness during and following seizures would be potentially transformative for these individuals. Here, we investigate deep brain stimulation to improve level of conscious arousal in a rat model of focal limbic seizures. We found that dual-site stimulation of the central lateral nucleus of the intralaminar thalamus (CL) and the pontine nucleus oralis (PnO) bilaterally during focal limbic seizures restored normal-appearing cortical electrophysiology and markedly improved behavioral arousal. In contrast, single-site bilateral stimulation of CL or PnO alone was insufficient to achieve the same result. These findings support the "network inhibition hypothesis" that focal limbic seizures impair consciousness through widespread inhibition of subcortical arousal. Driving subcortical arousal function would be a novel therapeutic approach to some forms of refractory epilepsy and may be compatible with devices already in use for responsive neurostimulation. Multisite deep brain stimulation of subcortical arousal structures may benefit not only patients with epilepsy but also those with other disorders of consciousness.

Hippocampal closed-loop modeling and implications for seizure stimulation design

OBJECTIVE

Traditional hippocampal modeling has focused on the series of feedforward synapses known as the trisynaptic pathway. However, feedback connections from CA1 back to the hippocampus through the entorhinal cortex (EC) actually make the hippocampus a closed-loop system. By constructing a functional closed-loop model of the hippocampus, one may learn how both physiological and epileptic oscillations emerge and design efficient neurostimulation patterns to abate such oscillations.

APPROACH

Point process input-output models where estimated from recorded rodent hippocampal data to describe the nonlinear dynamical transformation from CA3 → CA1, via the schaffer-collateral synapse, and CA1 → CA3 via the EC. Each Volterra-like subsystem was composed of linear dynamics (principal dynamic modes) followed by static nonlinearities. The two subsystems were then wired together to produce the full closed-loop model of the hippocampus.

MAIN RESULTS

Closed-loop connectivity was found to be necessary for the emergence of theta resonances as seen in recorded data, thus validating the model. The model was then used to identify frequency parameters for the design of neurostimulation patterns to abate seizures.

SIGNIFICANCE

Deep-brain stimulation (DBS) is a new and promising therapy for intractable seizures. Currently, there is no efficient way to determine optimal frequency parameters for DBS, or even whether periodic or broadband stimuli are optimal. Data-based computational models have the potential to be used as a testbed for designing optimal DBS patterns for individual patients. However, in order for these models to be successful they must incorporate the complex closed-loop structure of the seizure focus. This study serves as a proof-of-concept of using such models to design efficient personalized DBS patterns for epilepsy.

Neurostimulation, neuromodulation, and the treatment of epilepsies

Introduction.Neurostimulation and neuromodulation are techniques that may be able to affect the course of epilepsy. In the last 20 years, since the approval of VNS, we have observed a surge of studies assessing the potential of other devices and techniques for the treatment of pharmacoresistant epilepsies including deep brain stimulation (DBS), responsive neurostimulation (RNS), trigeminal nerve stimulation (TNS), transcranial direct current stimulation (tDCS), and repetitive transcranial magnetic stimulation (rTMS). Are these devices and techniques simply another treatment option that can be offered to patients with epilepsy or do they offer specific advantages when compared to the standard antiepileptic drugs (AEDs)?

Aim.The aim of this review is to present the neurostimulation and neuromodulation devices and techniques that are now in use, or at least available for testing and to discuss the science behind them, their applications, efficacy, potential risks vs. benefits and, above all, how to navigate the choices so clinicians are able to provide their patients with the best possible option for the treatment of epilepsy.

Material and methods.We analyzed PubMed and MEDLINE databases to select the most salient and recent (up to November 2014) publications on each treatment device. In addition to these searches bibliographies of selected articles were hand-searched for possible sources.

Discussion and conclusions.Great progress in neurostimulation and neuromodulation has been made over the last two decades with 2 devices (VNS, RNS) approved for the treatment of epilepsy in the US and three (DBS in addition to VNS and RNS) in Europe. The future of neuromodulation/neurostimulation is exciting – various studies and efforts are underway and will provide us with more data in the future. There appears to be one clear advantage of these treatments/devices over the AEDs that is consistently noted – routinely observed is continuous improvement in seizure control over time. This is something that the AEDs have thus far failed to deliver.

On the proper selection of preictal period for seizure prediction

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.

Electric stimulation of the tuberomamillary nucleus affects epileptic activity and sleep-wake cycle in a genetic absence epilepsy model

Deep brain stimulation (DBS) is a promising approach for epilepsy treatment, but the optimal targets and parameters of stimulation are yet to be investigated. Tuberomamillary nucleus (TMN) is involved in EEG desynchronization-one of the proposed mechanisms for DBS action. We studied whether TMN stimulation could interfere with epileptic spike-wave discharges (SWDs) in WAG/Rij rats with inherited absence epilepsy and whether such stimulation would affect sleep-wake cycle. EEG and video registration were used to determine SWD occurrence and stages of sleep and wake during three-hours recording sessions. Stimulation (100Hz) was applied in two modes: closed-loop (with previously determined interruption threshold intensity) or open-loop mode (with 50% or 70% threshold intensity). Closed-loop stimulation successfully interrupted SWDs but elevated their number by 148 ± 54% compared to baseline. It was accompanied by increase in number of episodes but not total duration of both active and passive wakefulness. Open-loop stimulation with amplitude 50% threshold did not change measured parameters, though 70% threshold stimulation reduced SWDs number by 40 ± 9%, significantly raised the amount of active wakefulness and decreased the amount of both slow-wave and rapid eye movement sleep. These results suggest that the TMN is unfavorable as a target for DBS as its stimulation may cause alterations in sleep-wake cycle. A careful choosing of parameters and control of sleep-wake activity is necessary when applying DBS in epilepsy.