Closed-Loop Brain Stimulation for Drug-Resistant Epilepsy: Towards an Evidence-Based Approach to Personalized Medicine

The role of neuromodulation in the management of drug-resistant epilepsy

Drug-resistant epilepsy (DRE) poses significant challenges in terms of effective management and seizure control. Neuromodulation techniques have emerged as promising solutions for individuals who are unresponsive to pharmacological treatments, especially for those who are not good surgical candidates for surgical resection or laser interstitial therapy (LiTT). Currently, there are three neuromodulation techniques that are FDA-approved for the management of DRE. These include vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). Device selection, optimal time, and DBS and RNS target selection can also be challenging. In general, the number and localizability of the epileptic foci, alongside the comorbidities manifested by the patients, substantially influence the selection process. In the past, the general axiom was that DBS and VNS can be used for generalized and localized focal seizures, while RNS is typically reserved for patients with one or two highly localized epileptic foci, especially if they are in eloquent areas of the brain. Nowadays, with the advance in our understanding of thalamic involvement in DRE, RNS is also very effective for general non-focal epilepsy. In this review, we will discuss the underlying mechanisms of action, patient selection criteria, and the evidence supporting the use of each technique. Additionally, we explore emerging technologies and novel approaches in neuromodulation, such as closed-loop systems. Moreover, we examine the challenges and limitations associated with neuromodulation therapies, including adverse effects, complications, and the need for further long-term studies. This comprehensive review aims to provide valuable insights on present and future use of neuromodulation.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Transient targeting of hypothalamic orexin neurons alleviates seizures in a mouse model of epilepsy

Lateral hypothalamic (LH) hypocretin/orexin neurons (HONs) control brain-wide electrical excitation. Abnormally high excitation produces epileptic seizures, which affect millions of people and need better treatments. HON population activity spikes from minute to minute, but the role of this in seizures is unknown. Here, we describe correlative and causal links between HON activity spikes and seizures. Applying temporally-targeted HON recordings and optogenetic silencing to a male mouse model of acute epilepsy, we found that pre-seizure HON activity predicts and controls the electrophysiology and behavioral pathology of subsequent seizures. No such links were detected for HON activity during seizures. Having thus defined the time window where HONs influence seizures, we targeted it with LH deep brain stimulation (DBS), which inhibited HON population activity, and produced seizure protection. Collectively, these results uncover a feature of brain activity linked to seizures, and demonstrate a proof-of-concept treatment that controls this feature and alleviates epilepsy.

Closed-loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes

In patients with drug-resistant epilepsy, electrical stimulation of the brain in response to epileptiform activity can make seizures less frequent and debilitating. This therapy, known as closed-loop responsive neurostimulation (RNS), aims to directly halt seizure activity via targeted stimulation of a burgeoning seizure. Rather than immediately stopping seizures as they start, many RNS implants produce slower, long-lasting changes in brain dynamics that better predict clinical outcomes. Here we hypothesize that stimulation during brain states with less epileptiform activity drives long-term changes that restore healthy brain networks. To test this, we quantified stimulation episodes during low- and high-risk brain states-that is, stimulation during periods with a lower or higher risk of generating epileptiform activity-in a cohort of 40 patients treated with RNS. More frequent stimulation in tonic low-risk states and out of rhythmic high-risk states predicted seizure reduction. Additionally, stimulation events were more likely to be phase-locked to prolonged episodes of abnormal activity for intermediate and poor responders when compared to super-responders, consistent with the hypothesis that improved outcomes are driven by stimulation during low-risk states. These results support the hypothesis that stimulation during low-risk periods might underlie the mechanisms of RNS, suggesting a relationship between temporal patterns of neuromodulation and plasticity that facilitates long-term seizure reduction.

Utility of Chronic Intracranial Electroencephalography in Responsive Neurostimulation Therapy

Responsive neurostimulation (RNS) therapy is an effective treatment for reducing seizures in some patients with focal epilepsy. Utilizing a chronically implanted device, RNS involves monitoring brain activity signals for user-defined patterns of seizure activity and delivering electrical stimulation in response. Devices store chronic data including counts of detected activity patterns and brief recordings of intracranial electroencephalography signals. Data platforms for reviewing stored chronic data retrospectively may be used to evaluate therapy performance and to fine-tune detection and stimulation settings. New frontiers in RNS research can leverage raw chronic data to reverse engineer neurostimulation mechanisms and improve therapy effectiveness.

Seizure forecasting: Bifurcations in the long and winding road

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

Closed-loop neurostimulation via expression of magnetogenetics-sensitive protein in inhibitory neurons leads to reduction of seizure activity in a rat model of epilepsy

On-demand neurostimulation has shown success in epilepsy patients with pharmacoresistant seizures. Seizures produce magnetic fields that can be recorded using magnetoencephalography. We developed a new closed-loop approach to control seizure activity based on magnetogenetics using the electromagnetic perceptive gene (EPG) that encodes a protein that responds to magnetic fields. The EPG transgene was expressed in inhibitory interneurons under the hDlx promoter and kainic acid was used to induce acute seizures. In vivo electrophysiological signals were recorded. We found that hDlx EPG rats exhibited a significant delay in the onset of first seizure (1142.72 ± 186.35 s) compared to controls (644.03 ± 15.06 s) and significantly less seizures (4.11 ± 1.03) compared to controls (8.33 ± 1.58). These preliminary findings suggest that on-demand activation of EPG expressed in inhibitory interneurons suppresses seizure activity, and magnetogenetics via EPG may be an effective strategy to alleviate seizure severity in a closed-loop, and cell-specific fashion.

Laser amygdalohippocampotomy reduces contralateral hippocampal sub-clinical activity in bitemporal epilepsy: A case illustration of responsive neurostimulator ambulatory recordings

Responsive neurostimulation (RNS) is a valuable tool in the diagnosis and treatment of medication refractory epilepsy (MRE) and provides clinicians with better insights into patients' seizure patterns. In this case illustration, we present a patient with bilateral hippocampal RNS for presumed bilateral mesial temporal lobe epilepsy. The patient subsequently underwent a right sided LITT amygdalohippocampotomy based upon chronic RNS data revealing predominance of seizures from that side. Analyzing electrocorticography (ECOG) from the RNS system, we identified the frequency of high amplitude discharges recorded from the left hippocampal lead pre- and post- right LITT amygdalohippocampotomy. A reduction in contralateral interictal epileptiform activity was observed through RNS recordings over a two-year period, suggesting the potential dependency of the contralateral activity on the primary epileptogenic zone. These findings suggest that early targeted surgical resection or laser ablation by leveraging RNS data can potentially impede the progression of dependent epileptiform activity and may aid in preserving neurocognitive networks. RNS recordings are essential in shaping further management decisions for our patient with a presumed bitemporal epilepsy.

The bidirectional relationship between the cerebellum and seizure networks: a double-edged sword

Epilepsy is highly prevalent and notoriously pharmacoresistant. New therapeutic interventions are urgently needed, both for preventing the seizures themselves as well as negative outcomes and comorbidities associated with chronic epilepsy. While the cerebellum is not traditionally associated with epilepsy or seizures, research over the past decade has outlined the cerebellum as a brain region that is uniquely suited for both therapeutic needs. This review discusses our current understanding of the cerebellum as a key node within seizure networks, capable of both attenuating seizures in several animal models, and conversely, prone to altered structure and function in chronic epilepsy. Critical next steps are to advance therapeutic modulation of the cerebellum more towards translation, and to provide a more comprehensive characterization of how the cerebellum is impacted by chronic epilepsy, in order to subvert negative outcomes.

Open Hardware Implementation of Real-Time Phase and Amplitude Estimation for Neurophysiologic Signals

Adaptive deep brain stimulation (aDBS) is a promising concept in the field of DBS that consists of delivering electrical stimulation in response to specific events. Dynamic adaptivity arises when stimulation targets dynamically changing states, which often calls for a reliable and fast causal estimation of the phase and amplitude of the signals. Here, we present an open-hardware implementation that exploits the concepts of resonators and Hilbert filters embedded in an open-hardware platform. To emulate real-world scenarios, we built a hardware setup that included a system to replay and process different types of physiological signals and test the accuracy of the instantaneous phase and amplitude estimates. The results show that the system can provide a precise and reliable estimation of the phase even in the challenging scenario of dealing with high-frequency oscillations (~250 Hz) in real-time. The framework might be adopted in neuromodulation studies to quickly test biomarkers in clinical and preclinical settings, supporting the advancement of aDBS.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management

Epilepsy is a complex neurological disorder affecting millions worldwide, with a substantial number of patients facing drug-resistant epilepsy. This comprehensive review explores innovative therapies for epilepsy management, focusing on their principles, clinical evidence, and potential applications. Traditional antiseizure medications (ASMs) form the cornerstone of epilepsy treatment, but their limitations necessitate alternative approaches. The review delves into cutting-edge therapies such as responsive neurostimulation (RNS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), highlighting their mechanisms of action and promising clinical outcomes. Additionally, the potential of gene therapies and optogenetics in epilepsy research is discussed, revealing groundbreaking findings that shed light on seizure mechanisms. Insights into cannabidiol (CBD) and the ketogenic diet as adjunctive therapies further broaden the spectrum of epilepsy management. Challenges in achieving seizure control with traditional therapies, including treatment resistance and individual variability, are addressed. The importance of staying updated with emerging trends in epilepsy management is emphasized, along with the hope for improved therapeutic options. Future research directions, such as combining therapies, AI applications, and non-invasive optogenetics, hold promise for personalized and effective epilepsy treatment. As the field advances, collaboration among researchers of natural and synthetic biochemistry, clinicians from different streams and various forms of medicine, and patients will drive progress toward better seizure control and a higher quality of life for individuals living with epilepsy.

Utility of hybrid PET/MRI in stereoelectroencephalography guided radiofrequency thermocoagulation in MRI negative epilepsy patients

Introduction

Hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) is a novel advanced non-invasive presurgical examination tool for patients with drug-resistant epilepsy (DRE). This study aims to evaluate the utility of PET/MRI in patients with DRE who undergo stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RFTC).

Methods

This retrospective study included 27 patients with DRE who underwent hybrid PET/MRI and SEEG-guided RFTC. Surgery outcome was assessed using a modified Engel classification, 2 years after RFTC. Potential areas of the seizure onset zone (SOZ) were identified on PET/MRI and confirmed by SEEG.

Results

Fifteen patients (55%) became seizure-free after SEEG-guided RFTC. Engel class II, III, and IV were achieved in six, two, and four patients, respectively at the 2 years follow-up. MRI was negative in 23 patients and structural abnormalities were found in four patients. Hybrid PET/MRI contributed to the identification of new structural or metabolic lesions in 22 patients. Concordant results between PET/MRI and SEEG were found in 19 patients in the identification of SOZ. Among the patients with multifocal onset, seizure-free status was achieved in 50% (6/12).

Conclusion

SEEG-guided RFTC is an effective and safe treatment for drug-resistant epilepsy. Hybrid PET/MRI serves as a useful tool for detecting the potential SOZs in MRI-negative patients and guide the implantation of SEEG electrodes. Patients with multifocal epilepsy may also benefit from this palliative treatment.

The cerebellum's understated role and influences in the epilepsies

Approximately 1 in 26 people will develop epilepsy in their lifetime, but current treatment options leave as many as half of all epilepsy patients with uncontrolled seizures. In addition to the burden of the seizures themselves, chronic epilepsy can be associated with cognitive deficits, structural changes, and devastating negative outcomes such as sudden unexpected death in epilepsy (SUDEP). Thus, major challenges in epilepsy research surround the need to both develop new therapeutic targets for intervention as well as shed light on the mechanisms by which chronic epilepsy can lead to comorbidities and negative outcomes. Despite not being traditionally associated with epilepsy or seizures, the cerebellum has emerged as not only a brain region that can serve as an important target for seizure control, but one that may also be profoundly impacted by chronic epilepsy. Here, we discuss targeting the cerebellum for potential therapeutic intervention and discuss pathway insights gained from recent optogenetic studies. We then review observations of cerebellar alterations during seizures and in chronic epilepsy, as well as the potential for the cerebellum to be a seizure focus. Cerebellar alterations in epilepsy may be critical to patient outcomes, highlighting the need for a more comprehensive understanding and appreciation of the cerebellum in the epilepsies.

Deep Brain Stimulation of the Centromedian Nucleus of the Thalamus for Lennox-Gastaut Syndrome: A Systematic Review and Individual Patient Data Analysis

BACKGROUND

Lennox-Gastaut syndrome (LGS) is a severe childhood-onset pharmacoresistant epilepsy. Deep brain stimulation (DBS) of the centromedian nucleus of the thalamus (CMN) has been utilized.

OBJECTIVE

To conduct a systematic review and individual patient data (IPD) analysis to characterize outcomes of DBS of CMN in LGS.

METHODS

PubMed, Embase, and Scopus were searched per Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Articles were screened by title/abstract then full text. Included articles were reviewed for bibliographic, demographic, and outcome data. IPD were extracted from studies providing IPD for all patients.

RESULTS

Of 72 resultant articles, 10 studies (114 patients) were included. Six of 7 studies reporting the outcome of ≥50% seizure reduction indicated that ≥50% of patients achieved this, with improved functional ability. Seizure freedom rate was generally <10%. Six studies with 47 patients provided IPD. The mean ages at epilepsy onset and CMN DBS were 3.9 ± 4.5 years and 17.4 ± 8.8 years, respectively. Nineteen of 41 (46.3%) patients had positive MRI findings. Seizure types included atypical absence in 39 (83.0%) patients, generalized tonic-clonic in 32 (68.1%), tonic in 22 (46.8%), and atonic in 20 (42.6%). Thirty-eight (80.9%) patients experienced ≥50% reduction in seizure frequency, and only 3 (6.4%) experienced seizure freedom. The mean seizure reduction was 62.9% ± 31.2% overall. Quality of life improved in 30/34 (88.2%) and was unchanged in the remainder (11.8%). The complication rate was 2/41 (4.9%). The mean length of follow-up was 19.8 ± 26.1 months (IQR: 4-18 months).

CONCLUSION

Limited data indicate that DBS of the CMN may be effective and safe for people with LGS.

HH model based smart deep brain stimulator to detect, predict and control epilepsy using machine learning algorithm

BACKGROUND

Epilepsy is the most common neurological disorder in the world. To control epilepsy, deep brain stimulation is one of the widely accepted treatment techniques. However, conventional deep brain stimulation technique provides continuous stimulation without optimizing the stimulation parameters, resulting in adverse side effects and unexpected death. Hence, understanding the dynamic behavior of brain neural networks at a cellular level is required for patient-specific epilepsy treatment. Considering the underlying mechanism of a single neuronal shift in the brain neural network, computational model-based techniques have a new face for healthcare, which aims to develop effective medical devices for preclinical investigations.

NEW METHOD

This paper discusses the design of a Smart Deep Brain Stimulator (SDBS) using the Hodgkin-Huxley (HH) conductance-based cellular model of brain neurons to automatically detect, predict and regulate epilepsy against patient-specific conditions. Epileptic activity is simulated as a spike train of action potential due to sodium and potassium channel conductance variations in the single-neuron HH model. The proposed SDBS consists of three components:- i) seizure detection using bagging and boosting-based ensemble machine learning classifiers, ii) channel conductance prediction using Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) based Deep Neural Network (DNN) for updating model parameters of brain neuron, and iii) model-based intelligent control of epileptic seizure with Nonlinear Autoregressive Moving Average-L2 (NARMA-L2) Controller and Nonlinear Model Predictive Controller (NMPC).

RESULTS

For effective treatment, improving the overall accuracy and efficiency of SDBS is essential. For epilepsy detection, the ensemble bagging machine learning algorithm provides better accuracy of 92.7% compared to the ensemble boosting algorithm. LSTM-RNN deep neural network model with four layers predicts the variations in channel conductance with Root Mean Square Error (RMSE) of 0.00568 and 0.009081 for sodium and potassium channel conductance, respectively. From the closed-loop performances of SDBS with an intelligent control scheme, it is observed that SDBS with NMPC provides efficient and accurate stimulation with minimum energy consumption. From a stability point of view, SDBS with NMPC provides better stability than SDBS with NARMA-L2 Controller.

COMPARISON WITH EXISTING METHOD

The proposed SDBS is designed to generate accurate stimulation pulses for epilepsy patients with specific conditions depending on the neuronal activity of a single neuron. Moreover, it will also adapt to the dynamic condition of epilepsy patients. The existing deep brain stimulator continuously provides stimulation pulses without adapting to the patient's conditions.

CONCLUSION

The proposed SDBS could provide patient-specific treatment based on sodium/potassium channel conductance variations of brain neurons. It will help increase the use of deep brain stimulation techniques and reduce sudden death. Furthermore, the proposed technique will be extended to neural network models with larger neuronal populations to improve the practical feasibility.

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.

Multiple Sources of Fast Traveling Waves during Human Seizures: Resolving a Controversy

During human seizures, organized waves of voltage activity rapidly sweep across the cortex. Two contradictory theories describe the source of these fast traveling waves: either a slowly advancing narrow region of multiunit activity (an ictal wavefront) or a fixed cortical location. Limited observations and different analyses prevent resolution of these incompatible theories. Here we address this disagreement by combining the methods and microelectrode array recordings (N = 11 patients, 2 females, N = 31 seizures) from previous human studies to analyze the traveling wave source. We find, inconsistent with both existing theories, a transient relationship between the ictal wavefront and traveling waves, and multiple stable directions of traveling waves in many seizures. Using a computational model that combines elements of both existing theories, we show that interactions between an ictal wavefront and fixed source reproduce the traveling wave dynamics observed in vivo We conclude that combining both existing theories can generate the diversity of ictal traveling waves.SIGNIFICANCE STATEMENT The source of voltage discharges that propagate across cortex during human seizures remains unknown. Two candidate theories exist, each proposing a different discharge source. Support for each theory consists of observations from a small number of human subject recordings, analyzed with separately developed methods. How the different, limited data and different analysis methods impact the evidence for each theory is unclear. To resolve these differences, we combine the unique, human microelectrode array recordings collected separately for each theory and analyze these combined data with a unified approach. We show that neither existing theory adequately describes the data. We then propose a new theory that unifies existing proposals and successfully reproduces the voltage discharge dynamics observed in vivo.

Responsive Thalamic Neurostimulation: A Systematic Review of a Promising Approach for Refractory Epilepsy

Introduction

Responsive neurostimulation is an evolving therapeutic option for patients with treatment-refractory epilepsy. Open-loop, continuous stimulation of the anterior thalamic nuclei is the only approved modality, yet chronic stimulation rarely induces complete seizure remission and is associated with neuropsychiatric adverse effects. Accounts of off-label responsive stimulation in thalamic nuclei describe significant improvements in patients who have failed multiple drug regimens, vagal nerve stimulation, and other invasive measures. This systematic review surveys the currently available data supporting the use of responsive thalamic neurostimulation in primary and secondary generalized, treatment-refractory epilepsy.

Materials and Methods

A systematic review was performed using the following combination of keywords and controlled vocabulary: (“Seizures”[Mesh] AND “Thalamus”[Mesh] AND “Deep Brain Stimulation”[Mesh]) OR (responsive neurostim* AND (thalamus[MeSH])) OR [responsive neurostimulation AND thalamus AND (epilepsy OR seizures)]. In addition, a search of the publications listed under the PubMed “cited by” tab was performed for all publications that passed title/abstract screening in addition to manually searching their reference lists.

Results

Ten publications were identified describing a total of 29 subjects with a broad range of epilepsy disorders treated with closed-loop thalamic neurostimulation. The median age of subjects was 31 years old (range 10–65 years). Of the 29 subjects, 15 were stimulated in the anterior, 11 in the centromedian, and 3 in the pulvinar nuclei. Excluding 5 subjects who were treated for 1 month or less, median time on stimulation was 19 months (range 2.4–54 months). Of these subjects, 17/24 experienced greater than or equal to 50%, 11/24 least 75%, and 9/24 at least 90% reduction in seizures. Although a minority of patients did not exhibit significant clinical improvement by follow-up, there was a general trend of increasing treatment efficacy with longer periods on closed-loop thalamic stimulation.

Conclusion

The data supporting off-label closed-loop thalamic stimulation for refractory epilepsy is limited to 29 adult and pediatric patients, many of whom experienced significant improvement in seizure duration and frequency. This encouraging progress must be verified in larger studies.

Single-interface bioelectronic medicines - concept, clinical applications and preclinical data

Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.

Responsive neurostimulation of the thalamus improves seizure control in idiopathic generalised epilepsy: initial case series

OBJECTIVES

Up to 40% of patients with idiopathic generalised epilepsy (IGE) are drug resistant and potentially could benefit from intracranial neuromodulation of the seizure circuit. We present outcomes following 2 years of thalamic-responsive neurostimulation for IGE.

METHODS

Four patients with pharmacoresistant epilepsy underwent RNS System implantation in the bilateral centromedian (CM) nucleus region. Electrophysiological data were extracted from the clinical patient data management system and analysed using a specialised platform (BRAINStim). Postoperative visualisation of electrode locations was performed using Lead-DBS. Seizure outcomes were reported using the Engel scale.

RESULTS

Patients experienced a 75%-99% reduction in seizure frequency with decreased seizure duration and severity (Engel class IB, IC, IIA and IIIA), as well as significant improvements in quality of life. Outcomes were durable through at least 2 years of therapy. Detection accuracy for all patients overall decreased over successive programming epochs from a mean of 96.5% to 88.3%. Most electrodes used to deliver stimulation were located in the CM (7/10) followed by the posterior dorsal ventral lateral (2/2), posterior ventral posterior lateral (3/4) and posterior ventral ventral lateral (2/3). In all patients, stimulation varied from 0.2 to 2.0 mA and amplitude only increased over successive epochs. The raw percentage of intracranial electroencephalography recordings with stimulations delivered to electrographic seizures was 24.8%, 1.2%, 7.6% and 8.8%.

CONCLUSION

Closed-loop stimulation of the CM region may provide significant improvement in seizure control and quality of life for patients with drug-resistant IGE. Optimal detection and stimulation locations and parameters remain an active area of investigation for accelerating and fine-tuning clinical responses.

Low Power EEG Data Encoding for Brain Neurostimulation Implants

Neurostimulation devices applied for the treatment of epilepsy that collect, encode, temporarily store, and transfer electroencephalographic (EEG) signals recorded intracranially from epileptic patients, suffer from short battery life spans. The principal goal of this study is to implement strategies for low power consumption rates during the device’s smooth and uninterrupted operation as well as during data transmission. Our approach is organised in three basic levels. The first level regards the initial modelling and creation of the template for the following two stages. The second level regards the development of code for programming integrated circuits and simulation. The third and final stage regards the transmitter’s implementation at the evaluation level. In particular, more than one software and device are involved in this phase, in order to achieve realistic performance. Our research aims to evolve such technologies so that they can transmit wireless data with simultaneous energy efficiency.

Emerging Trends in Neuromodulation for Treatment of Drug-Resistant Epilepsy

Epilepsy is a neurological disorder that affects more than 70 million people globally. A considerable proportion of epilepsy is resistant to anti-epileptic drugs (AED). For patients with drug-resistant epilepsy (DRE), who are not eligible for resective or ablative surgery, neuromodulation has been a palliative option. Since the approval of vagus nerve stimulation (VNS) in 1997, expansion to include other modalities, such as deep brain stimulation (DBS) and responsive neurostimulation (RNS), has led to improved seizure control in this population. In this article, we discuss the current updates and emerging trends on neuromodulation for epilepsy.

An ASIC System for Closed-Loop Blood Pressure Modulation through Right Cervical Vagus Nerve Stimulation

OBJECTIVE

Heart disease is the leading cause of death worldwide. Hypertension is an important precursor and the most common risk factor to heart failure. While some patients can control their high blood pressure with pharmaceuticals, many suffer from resistant hypertension, where antihypertensive medications do not achieve the desired outcome. Electrical stimulation is an emerging therapy to modulate blood pressure and integrating it with closed-loop feedback can improve blood pressure control.

METHODS

We design and fabricate two application-specific integrated circuits (ASICs) for stimulation and pressure sensing using TSMC's 180 nm MS RF G process. We create a closed-loop system by integrating the ASICs with a microscale pressure sensor and a custom-built Python script and test the full system in six Long Evans rats using vagus nerve stimulation.

RESULTS

After calibration and benchtop verification, we prove the functionality of the system in lowering, and maintaining a desired blood pressure in vivo. The system effectively monitors pressure and stimulates when that pressure exceeds the user-determined threshold.

CONCLUSION

By combining this stimulation therapy with a pressure sensor, we present a novel closed-loop, electroceutical system that has the potential to monitor and modulate blood pressure.

SIGNIFICANCE

We present a drug-free, potentially side-effect-free electroceutical therapeutic for managing resistant hypertension.

Enhanced topical corticosteroids delivery to the eye: A trade-off in strategy choice

Topical corticosteroids are the primary treatment of ocular inflammation caused by surgery, injury, or other conditions. Drug pre-corneal residence time, drug water solubility, and drug corneal permeability coefficient are the major factors that determine the ocular drug bioavailability after topical administration. Although growing research successfully enhanced local delivery of corticosteroids utilizing various strategies, rational and dynamic approaches to strategy selection are still lacking. Within this review, an overview of the various strategies as well as their performance in retention, solubility, and permeability coefficient of corticosteroids are provided. On this basis, the tradeoff of strategy selection is discussed, which may shed light on the rational choice and application of ophthalmic delivery enhancement strategies.

Three dimensional microelectrodes enable high signal and spatial resolution for neural seizure recordings in brain slices and freely behaving animals

Neural recordings made to date through various approaches—both in-vitro or in-vivo—lack high spatial resolution and a high signal-to-noise ratio (SNR) required for detailed understanding of brain function, synaptic plasticity, and dysfunction. These shortcomings in turn deter the ability to further design diagnostic, therapeutic strategies and the fabrication of neuro-modulatory devices with various feedback loop systems. We report here on the simulation and fabrication of fully configurable neural micro-electrodes that can be used for both in vitro and in vivo applications, with three-dimensional semi-insulated structures patterned onto custom, fine-pitch, high density arrays. These microelectrodes were interfaced with isolated brain slices as well as implanted in brains of freely behaving rats to demonstrate their ability to maintain a high SNR. Moreover, the electrodes enabled the detection of epileptiform events and high frequency oscillations in an epilepsy model thus offering a diagnostic potential for neurological disorders such as epilepsy. These microelectrodes provide unique opportunities to study brain activity under normal and various pathological conditions, both in-vivo and in in-vitro, thus furthering the ability to develop drug screening and neuromodulation systems that could accurately record and map the activity of large neural networks over an extended time period.

Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects

INTRODUCTION

Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons.

AREAS COVERED

This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development.

EXPERT OPINION

The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.

A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System

This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 time-multiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered auto-ranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180 nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5-200 Hz band of 1.4 μV_rms for a spot noise of about 85 nV /√{Hz}. The system draws 1.5 μW per channel from 1.2 V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware auto-ranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or common-mode and power supply rejection ratios.

Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

Responsive neural stimulation (RNS) is considered a promising neural modulation therapy for refractory epilepsy. Combined stimulation on different targets may hold great promise for improving the efficacy of seizure control since neural activity changed dynamically within associated brain targets in the epileptic network. Three major issues need to be further explored to achieve better efficacy of combined stimulation: (1) which nodes within the epileptogenic network should be chosen as stimulation targets? (2) What stimulus frequency should be delivered to different targets? and (3) Could the efficacy of RNS for seizure control be optimized by combined different stimulation targets together? In our current study, Granger causality (GC) method was applied to analyze epileptogenic networks for finding key targets of RNS. Single target stimulation (100 μA amplitude, 300 μs pulse width, 5s duration, biphasic, charge-balanced) with high frequency (130 Hz, HFS) or low frequency (5 Hz, LFS) was firstly delivered by our lab designed RNS systems to CA3, CA1, subiculum (SUB) of hippocampi, and anterior nucleus of thalamus (ANT). The efficacy of combined stimulation with different groups of frequencies was finally assessed to find out better combined key targets with optimal stimulus frequency. Our results showed that stimulation individually delivered to SUB and CA1 could shorten the average duration of seizures. Different stimulation frequencies impacted the efficacy of seizure control, as HFS delivered to CA1 and LFS delivered to SUB, respectively, were more effective for shortening the average duration of electrographic seizure in Sprague-Dawley rats (n = 3). Moreover, the synchronous stimulation of HFS in CA1 combined with LFS in SUB reduced the duration of discharge significantly in rats (n = 6). The combination of responsive stimulation at different targets may be an inspiration to optimize stimulation therapy for epilepsy.

Personalized brain stimulation for effective neurointervention across participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants-personalized Bayesian optimization (pBO)-that searches available parameter combinations to optimize an intervention as a function of an individual's ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject's baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors

Objective.Closed-loop neuromodulation technology is a rapidly expanding category of therapeutics for a broad range of indications. Development of these innovative neurological devices requires high-throughput systems for closed-loop stimulation of model organisms, while monitoring physiological signals and complex, naturalistic behaviors. To address this need, we developed CLARA, a closed-loop automated reaching apparatus.Approach.Using breakthroughs in computer vision, CLARA integrates fully-automated, markerless kinematic tracking of multiple features to classify animal behavior and precisely deliver neural stimulation based on behavioral outcomes. CLARA is compatible with advanced neurophysiological tools, enabling the testing of neurostimulation devices and identification of novel neurological biomarkers.Results.The CLARA system tracks unconstrained skilled reach behavior in 3D at 150 Hz without physical markers. The system fully automates trial initiation and pellet delivery and is capable of accurately delivering stimulation in response to trial outcome with short latency. Kinematic data from the CLARA system provided novel insights into the dynamics of reach consistency over the course of learning, suggesting that learning selectively improves reach failures but does not alter the kinematics of successful reaches. Additionally, using the closed-loop capabilities of CLARA, we demonstrate that vagus nerve stimulation (VNS) improves skilled reach performance and increases reach trajectory consistency in healthy animals.Significance.The CLARA system is the first mouse behavior apparatus that uses markerless pose tracking to provide real-time closed-loop stimulation in response to the outcome of an unconstrained motor task. Additionally, we demonstrate that the CLARA system was essential for our investigating the role of closed-loop VNS stimulation on motor performance in healthy animals. This approach has high translational relevance for developing neurostimulation technology based on complex human behavior.

Extrapial Hippocampal Resection in Anterior Temporal Lobectomy: Technical Description and Clinical Outcomes in a 62-Patient Case Series

BACKGROUND

Anterior temporal lobectomy (ATL) is the most effective treatment for drug-resistant mesial temporal lobe epilepsy. Extrapial en bloc hippocampal resection facilitates complete removal of the hippocampus. With increasing use of minimally invasive treatments, considering open resection techniques that optimize the integrity of tissue specimens is important both for obtaining the correct histopathological diagnosis and for further study.

OBJECTIVE

To describe the operative strategy and clinical outcomes associated with an extrapial approach to hippocampal resection during ATL.

METHODS

A database of epilepsy surgeries performed by a single surgeon between October 2011 and February 2019 was reviewed to identify all patients who underwent ATL using an extrapial approach to hippocampal resection. To reduce confounding variables for outcome analysis, subjects with prior resections, tumors, and cavernous malformations were excluded. Seizure outcomes were classified using the Engel scale.

RESULTS

The surgical technique is described and illustrated with intraoperative images. A total of 62 patients met inclusion criteria (31 females) for outcome analysis. Patients with most recent follow-up <3 yr (n = 33) and >3 yr (n = 29) exhibited 79% and 52% class I outcomes, respectively. An infarct was observed on postoperative magnetic resonance imaging in 3 patients (1 asymptomatic and 2 temporarily symptomatic). An en bloc specimen in which the subiculum and all hippocampal subfields were preserved was obtained in each case. Examples of innovative research opportunities resulting from this approach are presented.

CONCLUSION

Extrapial resection of the hippocampus can be performed safely with seizure freedom and complication rates at least as good as those reported with the use of subpial techniques.

Recording Strategies for High Channel Count, Densely Spaced Microelectrode Arrays

Neuroscience research into how complex brain functions are implemented at an extra-cellular level requires in vivo neural recording interfaces, including microelectrodes and read-out circuitry, with increased observability and spatial resolution. The trend in neural recording interfaces toward employing high-channel-count probes or 2D microelectrodes arrays with densely spaced recording sites for recording large neuronal populations makes it harder to save on resources. The low-noise, low-power requirement specifications of the analog front-end usually requires large silicon occupation, making the problem even more challenging. One common approach to alleviating this consumption area burden relies on time-division multiplexing techniques in which read-out electronics are shared, either partially or totally, between channels while preserving the spatial and temporal resolution of the recordings. In this approach, shared elements have to operate over a shorter time slot per channel and active area is thus traded off against larger operating frequencies and signal bandwidths. As a result, power consumption is only mildly affected, although other performance metrics such as in-band noise or crosstalk may be degraded, particularly if the whole read-out circuit is multiplexed at the analog front-end input. In this article, we review the different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and suggest strategies for improving performance.

In Vivo Microelectrode Arrays for Detecting Multi-Region Epileptic Activities in the Hippocampus in the Latent Period of Rat Model of Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) is a form of refractory focal epilepsy, which includes a latent period and a chronic period. Microelectrode arrays capable of multi-region detection of neural activities are important for accurately identifying the epileptic focus and pathogenesis mechanism in the latent period of TLE. Here, we fabricated multi-shank MEAs to detect neural activities in the DG, hilus, CA3, and CA1 in the TLE rat model. In the latent period in TLE rats, seizures were induced and changes in neural activities were detected. The results showed that induced seizures spread from the hilus and CA3 to other areas. Furthermore, interneurons in the hilus and CA3 were more excited than principal cells and exhibited rhythmic oscillations at approximately 15 Hz in grand mal seizures. In addition, the power spectral density (PSD) of neural spikes and local field potentials (LFPs) were synchronized in the frequency domain of the alpha band (9–15 Hz) after the induction of seizures. The results suggest that fabricated MEAs have the advantages of simultaneous and precise detection of neural activities in multiple subregions of the hippocampus. Our MEAs promote the study of cellular mechanisms of TLE during the latent period, which provides an important basis for the diagnosis of the lesion focus of TLE.

Responsive neurostimulation for focal motor status epilepticus

No clear evidence‐based treatment paradigm currently exists for refractory and super‐refractory status epilepticus, which can result in significant mortality and morbidity. While patients are typically treated with antiepileptic drugs and anesthetics, neurosurgical neuromodulation techniques can also be considered. We present a novel case in which responsive neurostimulation was used to effectively treat a patient who had developed super‐refractory status epilepticus, later consistent with epilepsia partialis continua, that was refractory to antiepileptic drugs, immunomodulatory therapies, and transcranial magnetic stimulation. This case demonstrates how regional therapy provided by responsive neurostimulation can be effective in treating super‐refractory status epilepticus through neuromodulation of seizure networks.

Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network

Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC). Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72-0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3-8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68-0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8-8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features. Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.

Engineering Minds? Ethical Considerations on Biotechnological Approaches to Mental Health, Well-Being, and Human Flourishing

Our bodies can be designed and modified in accordance with our ideals of health and well-being. These increasingly targeted and personalized interventions will be more effective than current therapies. Here we review technologies to alter mood, and explore the ethics of bioengineering approaches to mental health.

Case Report: Responsive Neurostimulation of the Centromedian Thalamic Nucleus for the Detection and Treatment of Seizures in Pediatric Primary Generalized Epilepsy

Up to 20% of pediatric patients with primary generalized epilepsy (PGE) will not respond effectively to medication for seizure control. Responsive neurostimulation (RNS) is a promising therapy for pediatric patients with drug-resistant epilepsy and has been shown to be an effective therapy for reducing seizure frequency and severity in adult patients. RNS of the centromedian nucleus of the thalamus may help to prevent loss of awareness during seizure activity in PGE patients with absence seizures. Here we present a 16-year-old male, with drug-resistant PGE with absence seizures, characterized by 3 Hz spike-and-slow-wave discharges on EEG, who achieved a 75% reduction in seizure frequency following bilateral RNS of the centromedian nuclei. At 6-months post-implant, this patient reported complete resolution of the baseline daily absence seizure activity, and decrease from 3-4 generalized convulsive seizures per month to 1 per month. RNS recordings showed well-formed 3 Hz spike-wave discharges in bilateral CM nuclei, further supporting the notion that clinically relevant ictal discharges in PGE can be detected in CM. This report demonstrates that CM RNS can detect PGE-related seizures in the CM nucleus and deliver therapeutic stimulation.

Quantifying a frequency modulation response biomarker in responsive neurostimulation

Objective.Responsive neurostimulation (RNS) is an effective treatment for controlling seizures in patients with drug-resistant focal epilepsy who are not suitable candidates for resection surgery. A lack of tools for detecting and characterizing potential response biomarkers, however, contributes to a limited understanding of mechanisms by which RNS improves seizure control. We developed a method to quantify ictal frequency modulation, previously identified as a biomarker of clinical responsiveness to RNS.Approach.Frequency modulation is characterized by shifts in power across spectral bands during ictal events, over several months of neurostimulation. This effect was quantified by partitioning each seizure pattern into segments with distinct spectral content and measuring the extent of change from the baseline distribution of spectral content using the squared earth mover's distance.Main results.We analyzed intracranial electroencephalography data from 13 patients who received RNS therapy, six of whom exhibited frequency modulation on expert evaluation. Patients in the frequency modulation group had, on average, significantly larger and more sustained changes in their squared earth mover's distances (mean = 13.97 × 10^-3± 1.197 × 10^-3). In contrast, those patients without expert-identified frequency modulation exhibited statistically insignificant or negligible distances (mean = 4.994 × 10^-3± 0.732 × 10^-3).Significance.This method is the first step towards a quantitative, feedback-driven system for systematically optimizing RNS stimulation parameters, with an ultimate goal of truly personalized closed-loop therapy for epilepsy.

Responsive Neurostimulation of the Thalamus Improves Seizure Control in Idiopathic Generalized Epilepsy: A Case Report

BACKGROUND AND IMPORTANCE

At least 25% of patients with idiopathic generalized epilepsy do not obtain adequate seizure control with medication. This report describes the first use of responsive neurostimulation (RNS), bilaterally targeting the centromedian/ventrolateral (CM/VL) region in a patient with drug-refractory Jeavons syndrome (eyelid myoclonia with absences).

CLINICAL PRESENTATION

A patient, diagnosed with eyelid myoclonia with absences (EMA) and refractory to medication, was offered RNS treatment in the CM/VL region of the thalamus. Stimulation was triggered by thalamic neural activity having morphological, spectral, and synchronous features that corresponded to 3- to 5-Hz spike-wave discharges recorded on prior scalp electroencephalography.

CONCLUSION

RNS decreased daily absence seizures from a mean of 60 to ≤10 and maintained the patient's level of consciousness during the occurring episodes. This therapy should be evaluated further for its potential to treat patients with pharmaco-refractory generalized epilepsy.

Intelligent automated drug administration and therapy: future of healthcare

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review

Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.

Neural diffusivity and pre-emptive epileptic seizure intervention

The propagation of epileptic seizure activity in the brain is a widespread pathophysiology that, in principle, should yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics. During a seizure, neural activity will deviate from its current dynamical regime to one in which there are significant signal fluctuations. In silico treatments of neural activity are an important tool for the understanding of how the healthy brain can maintain stability, as well as of how pathology can lead to seizures. The hope is that, contained within the mathematical foundations of such treatments, there lie potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one extends the framework to model the ways in which activity propagates along the structural connections of neural systems. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity–i.e., by lowering the rate at which neuronal fluctuations disperse to neighbouring regions. We show, for neural systems prone to epileptic seizures, that such a reduction in diffusivity can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror the activity profile of a pathological region in its functionally connected areas. This counter-intuitive method is intended to be used pre-emptively–i.e., in order to mitigate the effects of the seizure, or ideally even prevent it from occurring in the first place. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network prior to seizure onset. Our hope is that this technique can form the basis for future real-time monitoring and intervention devices that are capable of treating epilepsy in a non-invasive manner.

Epilepsy is a disease that affects over 50 million people worldwide. Current treatments include dangerous surgical procedures in which brain connections are severed, or even in which entire problem brain regions are removed. Pharmaceutical options are available, but only about one third of patients are responsive. However, even in these cases the drugs can cause such severe side effects that the patients sometimes choose to suffer seizures. We are proposing an innovative treatment of epilepsy that could be achieved by using non-invasive electrical stimulation. Specifically, we show that stimulation should be applied in such a way as to mirror the activity in a problem brain region, by targeting its neighbouring areas. This counterintuitive approach is based on a mathematical model in which this mirroring strategy is applied pre-emptively, i.e. long before the seizure has a chance to set in. The hope is that future clinical trials will be able to use this model to lessen the effect of seizures, or even prevent them from occurring in the first place.

Association of Closed-Loop Brain Stimulation Neurophysiological Features With Seizure Control Among Patients With Focal Epilepsy

Importance

A bidirectional brain-computer interface that performs neurostimulation has been shown to improve seizure control in patients with refractory epilepsy, but the therapeutic mechanism is unknown.

Objective

To investigate whether electrographic effects of responsive neurostimulation (RNS), identified in electrocorticographic (ECOG) recordings from the device, are associated with patient outcomes.

Design, Setting, and Participants

Retrospective review of ECOG recordings and accompanying clinical meta-data from 11 consecutive patients with focal epilepsy who were implanted with a neurostimulation system between January 28, 2015, and June 6, 2017, with 22 to 112 weeks of follow-up. Recorded ECOG data were obtained from the manufacturer; additional system-generated meta-data, including recording and detection settings, were collected directly from the manufacturer's management system using an in-house, custom-built platform. Electrographic seizure patterns were identified in RNS recordings and evaluated in the time-frequency domain, which was locked to the onset of the seizure pattern.

Main Outcomes and Measures

Patterns of electrophysiological modulation were identified and then classified according to their latency of onset in relation to triggered stimulation events. Seizure control after RNS implantation was assessed by 3 main variables: mean frequency of seizure occurrence, estimated mean severity of seizures, and mean duration of seizures. Overall seizure outcomes were evaluated by the extended Personal Impact of Epilepsy Scale questionnaires, a patient-reported outcome measure of 3 domains (seizure characteristics, medication adverse effects, and quality of life), with a range of possible scores from 0 to 300 in which lower scores indicate worse status, and the Engel scale, which comprises 4 classes (I-IV) in which lower numbers indicate greater improvement.

Results

Electrocorticographic data from 11 patients (8 female; mean [range] age, 35 [19-65] years; mean [range] duration of epilepsy, 19 [5-37] years) were analyzed. Two main categories of electrophysiological signatures of stimulation-induced modulation of the seizure network were discovered: direct and indirect effects. Direct effects included ictal inhibition and early frequency modulation but were not associated with improved clinical outcomes (odds ratio [OR], 0.67; 95% CI, 0.06-7.35; P > .99). Only indirect effects-those occurring remote from triggered stimulation-were associated with improved clinical outcomes (OR, infinity; 95% CI, -infinity to infinity; P = .02). These indirect effects included spontaneous ictal inhibition, frequency modulation, fragmentation, and ictal duration modulation.

Conclusions and Relevance

These findings suggest that RNS effectiveness may be explained by long-term, stimulation-induced modulation of seizure network activity rather than by direct effects on each detected seizure.

Sleep disruption is not observed with brain-responsive neurostimulation for epilepsy

OBJECTIVE

Neurostimulation devices that deliver electrical impulses to the nervous system are widely used to treat seizures in patients with medically refractory epilepsy, but the effects of these therapies on sleep are incompletely understood. Vagus nerve stimulation can contribute to obstructive sleep apnea, and thalamic deep brain stimulation can cause sleep disruption. A device for brain-responsive neurostimulation (RNS^® System, NeuroPace, Inc) is well tolerated in clinical trials, but potential effects on sleep are unknown.

METHODS

Six adults with medically refractory focal epilepsy treated for at least six months with the RNS System underwent a single night of polysomnography (PSG). RNS System lead locations included mesial temporal and neocortical targets. Sleep stages and arousals were scored according to standard guidelines. Stimulations delivered by the RNS System in response to detections of epileptiform activity were identified by artifacts on scalp electroencephalography.

RESULTS

One subject was excluded for technical reasons related to unreliable identification of stimulation artifact on EEG during PSG. In the remaining five subjects, PSG showed fragmented sleep with frequent arousals. Arousal histograms aligned to stimulations revealed a significant peak in arousals just before stimulation. In one of these subjects, the arousal peak began before stimulation and extended ~1 seconds after stimulation. A peak in arousals occurring only after stimulation was not observed.

SIGNIFICANCE

In this small cohort of patients, brain-responsive neurostimulation does not appear to disrupt sleep. If confirmed in larger studies, this could represent a potential clinical advantage of brain-responsive neurostimulation over other neurostimulation modalities.

Responsive neurostimulation for regional neocortical epilepsy

OBJECTIVE

Surgical resection of seizure-producing brain tissue is a gold standard treatment for drug-resistant focal epilepsy. However, several patient-specific factors can preclude resective surgery, including a spatially extensive ("regional") seizure-onset zone (SOZ). For such patients, responsive neurostimulation (RNS) represents a potential treatment, but its efficacy has not been investigated in this population.

METHODS

We performed a multicenter retrospective cohort study of patients (N = 30) with drug-resistant focal epilepsy and a regional neocortical SOZ delineated by intracranial monitoring who were treated with the RNS System for at least 6 months. RNS System leads were placed at least 1-cm apart over the SOZ, and most patients were treated with a lead-to-lead stimulation pathway. Five patients underwent partial resection of the SOZ concurrent with RNS System implantation. We assessed change in seizure frequency relative to preimplant baseline and evaluated correlation between clinical outcome and stimulation parameters.

RESULTS

Median follow-up duration was 21.5 months (range 6-52). Median reduction in clinical seizure frequency was 75.5% (interquartile range [IQR] 40%-93.9%). There was no significant difference in outcome between patients treated with and without concurrent partial resection. Most patients were treated with low charge densities (1-2.5 µC/cm² ), but charge density, interlead distance, and duration of treatment were not significantly correlated with outcome.

SIGNIFICANCE

RNS is a feasible and effective treatment in patients with drug-resistant regional neocortical seizures. Prospective studies in larger cohorts are necessary to determine optimal lead configuration and stimulation parameters, although our results suggest that lead-to-lead stimulation and low charge density may be effective in some patients.

Seizure control in a neural mass model by an active disturbance rejection approach

A closed-loop neuromodulation automatically adjusts stimuli according to brain response in real time. It is viewed as a promising way to control medically intractable epilepsy. A suitable closed-loop modulation strategy, which is robust enough to unknown nonlinearities, dynamics, and disturbances, is in great need in the clinic. For the specialization of epilepsy, the Jansen’s neural mass model is utilized to simulate the undesired high amplitudes epileptic activities, and active disturbance rejection control is designed to suppress the high amplitudes of epileptiform discharges. With the help of active disturbance rejection control, closed-loop roots of the system are far from the imaginary axis. Time domain response shows that active disturbance rejection control is able to control seizure no matter whether disturbances exist or not. At the same time, frequency domain response presents that enough stability margins and a broader range of tunable controller parameters can be obtained. Stable regions have also been presented to provide guidance to choose the parameters of active disturbance rejection control. Numerical results show that, compared with proportional-integral control, more accurate modulation with less energy can be achieved by active disturbance rejection control. It confirms that the active disturbance rejection control-based neuromodulation solution is able to achieve a desired performance. It is a promising closed-loop neuromodulation strategy in seizure control.