Responsive cortical stimulation for the treatment of epilepsy

xDev: a mixed-signal, software-defined neurotechnology interface platform for accelerated system development

Objective.Advances in electronics and materials science have led to the development of sophisticated components for clinical and research neurotechnology systems. However, instrumentation to easily evaluate how these components function in a complete system does not yet exist. In this work, we set out to design and validate a software-defined mixed-signal routing fabric, 'xDev', that enables neurotechnology system designers to rapidly iterate, evaluate, and deploy advanced multi-component systems.Approach.We developed a set of system requirements for xDev, and implemented a design based on a 16 × 16 analog crosspoint multiplexer. We then tested the impedance and switching characteristics of the design, assessed signal gain and crosstalk attenuation across biological and high-speed digital signaling frequencies, and evaluated the ability of xDev to flexibly reroute microvolt-scale amplitude and high-speed signals. Finally, we conducted an intraoperativein vivodeployment of xDev to rapidly conduct neuromodulation experiments using diverse neurotechnology submodules.Main results.The xDev system impedance matching, crosstalk attenuation, and frequency response characteristics accurately transmitted signals over a broad range of frequencies, encapsulating features typical of biosignals and extending into high-speed digital ranges. Microvolt-scale biosignals and 600 Mbps Ethernet connections were accurately routed through the fabric. These performance characteristics culminated in anin vivodemonstration of the flexibility of the system via implanted spinal electrode arrays in an ovine model.Significance.xDev represents a first-of-its-kind, low-cost, software-defined neurotechnology development accelerator platform. Through the public, open-source distribution of our designs, we lower the obstacles facing the development of future neurotechnology systems.

Neuroprotective Efficacy and Complementary Treatment with Medicinal Herbs: A Comprehensive Review of Recent Therapeutic Approaches in Epilepsy Management

Central Nervous System (CNS) disorders affect millions of people worldwide, with a significant proportion experiencing drug-resistant forms where conventional medications fail to provide adequate seizure control. This abstract delves into recent advancements and innovative therapies aimed at addressing the complex challenge of CNS-related drug-resistant epilepsy (DRE) management. The idea of precision medicine has opened up new avenues for epilepsy treatment. Herbs such as curcumin, ginkgo biloba, panax ginseng, bacopa monnieri, ashwagandha, and rhodiola rosea influence the BDNF pathway through various mechanisms. These include the activation of CREB, inhibition of NF-κB, modulation of neurotransmitters, reduction of oxidative stress, and anti- inflammatory effects. By promoting BDNF expression and activity, these herbs support neuroplasticity, cognitive function, and overall neuronal health. Novel antiepileptic drugs (AEDs) with distinct mechanisms of action demonstrate efficacy in refractory cases where traditional medications falter. Additionally, repurposing existing drugs for antiepileptic purposes presents a cost-effective strategy to broaden therapeutic choices. Cannabidiol (CBD), derived from cannabis herbs, has garnered attention for its anticonvulsant properties, offering a potential adjunctive therapy for refractory seizures. In conclusion, recent advances and innovative therapies represent a multifaceted approach to managing drug-resistant epilepsy. Leveraging precision medicine, neurostimulation technologies, novel pharmaceuticals, and complementary therapies, clinicians can optimize treatment outcomes and improve the life expectancy of patients living with refractory seizures. Genetic testing and biomarker identification now allow for personalized therapeutic approaches tailored to individual patient profiles. Utilizing next-generation sequencing techniques, researchers have elucidated genetic mutations.

Emergent responsive neurostimulation in pediatric super-refractory epilepsia partialis continua

Focal status epilepticus, particularly the motor variant of epilepsia partialis continua (EPC), is a rare condition characterized by near-continuous, chronic focal motor seizures, and associated with poor outcomes. Medications, including anesthetics, are often unsuccessful. Surgical resection can result in motor deficits. We report a medically complex pediatric case of super-refractory EPC that was successfully managed with combined focal resection and responsive neuromodulation. This case introduces neuromodulation as a treatment modality for this challenging condition.

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Multimodal Technologies for Closed-Loop Neural Modulation and Sensing

Existing methods for studying neural circuits and treating neurological disorders are typically based on physical and chemical cues to manipulate and record neural activities. These approaches often involve predefined, rigid, and unchangeable signal patterns, which cannot be adjusted in real time according to the patient's condition or neural activities. With the continuous development of neural interfaces, conducting in vivo research on adaptive and modifiable treatments for neurological diseases and neural circuits is now possible. In this review, current and potential integration of various modalities to achieve precise, closed-loop modulation, and sensing in neural systems are summarized. Advanced materials, devices, or systems that generate or detect electrical, magnetic, optical, acoustic, or chemical signals are highlighted and utilized to interact with neural cells, tissues, and networks for closed-loop interrogation. Further, the significance of developing closed-loop techniques for diagnostics and treatment of neurological disorders such as epilepsy, depression, rehabilitation of spinal cord injury patients, and exploration of brain neural circuit functionality is elaborated.

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Objective

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.

Approach

We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.

Main results

Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.

Significance

Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed?

OBJECTIVES

Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model.

MATERIALS AND METHODS

A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation.

RESULTS

Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements.

CONCLUSION

The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.

NeuroBus - Architecture for an Ultra-Flexible Neural Interface

This article presents the system architecture for an implant concept called NeuroBus. Tiny distributed direct digitizing neural recorder ASICs on an ultra-flexible polyimide substrate are connected in a bus-like structure, allowing short connections between electrode and recording front-end with low wiring effort and high customizability. The small size (344 μm × 294 μm) of the ASICs and the ultraflexible substrate allow a low bending stiffness, enabling the implant to adapt to the curvature of the brain and achieving high structural biocompatibility. We introduce the architecture, the integrated building blocks, and the post-CMOS processes required to realize a NeuroBus, and we characterize the prototyped direct digitizing neural recorder front-end as well as polyimide-based ECoG brain interface. A rodent animal model is further used to validate the joint capability of the recording front-end and thin-film electrode array.

Technical note: preliminary surgical experience with a new implantable epicranial stimulation device for chronic focal cortex stimulation in drug-resistant epilepsy

PURPOSE

This study is to report some preliminary surgical considerations and outcomes after the first implantations of a new and commercially available implantable epicranial stimulation device for focal epilepsy.

METHODS

We retrospectively analyzed data from clinical notes. Outcome parameters were as follows: wound healing, surgery time, and adverse events.

RESULTS

Five patients were included (17-52 y/o; 3 female). Epicranial systems were uneventfully implanted under neuronavigation guidance. Some minor adverse events occurred. Wound healing in primary intention was seen in all patients. Out of these surgeries, certain concepts were developed: Skin incisions had to be significantly larger than expected. S-shaped incisions appeared to be a good choice in typical locations behind the hairline. Preoperative discussions between neurologist and neurosurgeon are mandatory in order to allow for the optimal coverage of the epileptogenic zone with the electrode geometry.

CONCLUSION

In this first small series, we were able to show safe implantation of this new epicranial stimulation device. The use of neuronavigation is strongly recommended. The procedure is simple but not trivial and ideally belongs in the hands of a neurosurgeon.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Myasthenia gravis in a pediatric patient with Lennox-Gastaut syndrome following responsive neurostimulation device implantation: illustrative case

BACKGROUND

Myasthenia gravis (MG) is an autoimmune disorder in which the postsynaptic acetylcholine receptor of the neuromuscular junction is destroyed by autoantibodies. The authors report a case of MG in a pediatric patient who also suffered from Lennox-Gastaut syndrome (LGS) and is one of a limited number of pediatric patients who have undergone placement of a responsive neurostimulation (RNS) device (NeuroPace).

OBSERVATIONS

A 17-year-old female underwent placement of an RNS device for drug-resistant epilepsy in the setting of LGS. Five months after device placement, the patient began experiencing intermittent slurred speech, fatigue, and muscle weakness. Initially, the symptoms were attributed to increased seizure activity and/or medication side effects. However, despite changing medications and RNS settings, no improvements occurred. Her antiacetylcholine receptor antibodies measured 62.50 nmol/L, consistent with a diagnosis of MG. The patient was then prescribed pyridostigmine and underwent a thymectomy, which alleviated most of her symptoms.

LESSONS

The authors share the cautionary tale of a case of MG in a pediatric patient who was treated with RNS for intractable epilepsy associated with LGS. Although slurred speech, fatigue, muscle weakness, and other symptoms might stem from increased seizure activity and/or medication side effects, they could also be due to MG development.

A 4-Channel Neural Stimulation IC Design With Charge Balancing and Multiple Current Output Modes

This article proposes a neural stimulation integrated circuit design with multiple current output modes. In the cathodic stimulation phase and anodic stimulation phase, each output current waveform can be independently selected to either exponential waveform or square wave, so the stimulator holds four stimulation modes. To minimize the headroom voltage of the output stage and enhance the power efficiency of the proposed stimulator, we introduce the exponentially decaying current which is realized by the exponential current generation circuit in this work. It can enhance the longer duration of the stimulation pulse as well. In case the residual charge may cause harm to patients, a charge balancing technique is implemented in this work for all operation modes. The four-channel stimulator IC is implemented in a 180-nm CMOS process, occupying a core area of 1.93 mm2. The measurement results show that the proposed stimulator realized a maximum power efficiency of 91.3% and the maximum stimulation duration is 3 times larger than previous works. Moreover, even in exponential output waveform mode, the maximum residual charge in a single cycle is only 255 pC due to the proposed charge balancing technique. The experiment results based on the PBS solution also show that the stimulator IC can remove residual charges within 60 μs, and the electrode voltage remains stable within a safe range under multicycle stimulation.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Spatiotemporal Expression of RNA-Seq Identified Proteins at the Electrode Interface

Implantation of electrodes in the brain can be used to record from or stimulate neural tissues to treat neurological disease and injury. However, the tissue response to implanted devices can limit their functional longevity. Recent RNA-seq datasets identify hundreds of genes associated with gliosis, neuronal function, myelination, and cellular metabolism that are spatiotemporally expressed in neural tissues following the insertion of microelectrodes. To validate mRNA as a predictor of protein expression, this study evaluates a sub-set of RNA-seq identified proteins (RSIP) at 24-hours, 1-week, and 6-weeks post-implantation using quantitative immunofluorescence methods. This study found that expression of RSIPs associated with glial activation (Glial fibrillary acidic protein (GFAP), Polypyrmidine tract binding protein-1 (Ptbp1)), neuronal structure (Neurofilament heavy chain (Nefh), Proteolipid protein-1 (Plp1), Myelin Basic Protein (MBP)), and iron metabolism (Transferrin (TF), Ferritin heavy chain-1 (Fth1)) reinforce transcriptional data. This study also provides additional context to the cellular distribution of RSIPs using a MATLAB-based approach to quantify immunofluorescence intensity within specific cell types. Ptbp1, TF, and Fth1 were found to be spatiotemporally distributed within neurons, astrocytes, microglia, and oligodendrocytes at the device interface relative to distal and contralateral tissues. The altered distribution of RSIPs relative to distal tissue is largely localized within 100µm of the device injury, which approaches the functional recording range of implanted electrodes. This study provides evidence that RNA-sequencing can be used to predict protein-level changes in cortical tissues and that RSIPs can be further investigated to identify new biomarkers of the tissue response that influence signal quality. STATEMENT OF SIGNIFICANCE: : Microelectrode arrays implanted into the brain are useful tools that can be used to study neuroscience and to treat pathological conditions in a clinical setting. The tissue response to these devices, however, can severely limit their functional longevity. Transcriptomics has deepened the understandings of the tissue response by revealing numerous genes which are differentially expressed following device insertion. This manuscript provides validation for the use of transcriptomics to characterize the tissue response by evaluating a subset of known differentially expressed genes at the protein level around implanted electrodes over time. In additional to validating mRNA-to-protein relationships at the device interface, this study has identified emerging trends in the spatiotemporal distribution of proteins involved with glial activation, neuronal remodeling, and essential iron binding proteins around implanted silicon devices. This study additionally provides a new MATLAB based methodology to quantify protein distribution within discrete cell types at the device interface which may be used as biomarkers for further study or therapeutic intervention in the future.

Reducing the Energy Consumption of sEMG-Based Gesture Recognition at the Edge Using Transformers and Dynamic Inference

Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep learning (DL) models for this task on memory-constrained and battery-operated edge devices, such as wearables, requires a careful optimization process, both at design time, with an appropriate tuning of the DL models' architectures, and at execution time, where the execution of large and computationally complex models should be avoided unless strictly needed. In this work, we pursue both optimization targets, proposing a novel gesture recognition system that improves upon the state-of-the-art models both in terms of accuracy and efficiency. At the level of DL model architecture, we apply for the first time tiny transformer models (which we call bioformers) to sEMG-based gesture recognition. Through an extensive architecture exploration, we show that our most accurate bioformer achieves a higher classification accuracy on the popular Non-Invasive Adaptive hand Prosthetics Database 6 (Ninapro DB6) dataset compared to the state-of-the-art convolutional neural network (CNN) TEMPONet (+3.1%). When deployed on the RISC-V-based low-power system-on-chip (SoC) GAP8, bioformers that outperform TEMPONet in accuracy consume 7.8×-44.5× less energy per inference. At runtime, we propose a three-level dynamic inference approach that combines a shallow classifier, i.e., a random forest (RF) implementing a simple "rest detector" with two bioformers of different accuracy and complexity, which are sequentially applied to each new input, stopping the classification early for "easy" data. With this mechanism, we obtain a flexible inference system, capable of working in many different operating points in terms of accuracy and average energy consumption. On GAP8, we obtain a further 1.03×-1.35× energy reduction compared to static bioformers at iso-accuracy.

Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.

Adaptively Clock-Boosted Auto-Ranging Neural-Interface for Emerging Neuromodulation Applications

Responsive deep brain stimulation (DBS) requires recruiting deep brain structures without affecting the superficial neuronal population. Neurosurgeons widely use implanted electrodes, which are highly localized but invasive, to stimulate the deep brain. Temporally interfering stimulation (TIS) excites the deep brain non-invasively. This neuromodulation technique utilizes two high-frequency sinusoidal electric fields that do not recruit superficial neural structures but have a small frequency differential. The small differential causes a low-frequency interference envelope that stimulates deep regions and is steerable by changing the intensity of the electric fields without physically moving the electrodes. Using TIS as a non-invasive DBS method generates high-frequency stimulation artifacts at recording sites, which may saturate a conventional recording front-end. This paper presents a low-power bidirectional 64-channel CMOS neural-ADC that is immune to artifacts such as those in the TIS techniques or conventional biphasic stimulation. The presented DC-coupled chopped analog front-end leverages delta-spectrum shaping to remove electrode DC offset voltage and maintain the input impedance higher than 250 MΩ, which is sufficient for interfacing with non-invasive scalp electrodes. The AFE operates on the input signal difference to detect large and rapid stimulation artifacts. It incorporates both exponential tracking and boosted-rate sampling to recover within 100 μs. Upon recovery, the neural-ADC range and speed are reduced to achieve noise and power efficiency factors of 2.98 and 10.6, respectively. In vivo recordings from anesthetized mice demonstrate the unique capabilities of the presented architecture in resolving local field potentials from the surface and epidural electrodes.

Gene therapy for epilepsy

On-demand inhibition of neuronal activity reduced spontaneous seizures in mice

Diaphragmatic pacing for the prevention of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy is the leading cause of epilepsy related death. Currently, there are no reliable methods for preventing sudden unexpected death in epilepsy. The precise pathophysiology of sudden unexpected death in epilepsy is unclear; however, convergent lines of evidence suggest that seizure-induced respiratory arrest plays a central role. It is generally agreed that sudden unexpected death in epilepsy could be averted if the patient could be rapidly ventilated following the seizure. The diaphragm is a muscle in the chest which contracts to draw air into the lungs. Diaphragmatic pacing is a surgical intervention which facilitates normal ventilation in situations, such as spinal cord injury and sleep apnoea, in which endogenous respiration would be inadequate or non-existent. In diaphragmatic pacing, electrodes are implanted directly onto diaphragm or adjacent to the phrenic nerves which innervate the diaphragm. These electrodes are then rhythmically stimulated, thereby eliciting contractions of the diaphragm which emulate endogenous breathing. The goal of this study was to test the hypothesis that seizure-induced respiratory arrest and death can be prevented with diaphragmatic pacing. Our approach was to induce respiratory arrest using maximal electroshock seizures in adult, male, C57BL6 mice outfitted with EEG and diaphragmatic electrodes (n = 8 mice). In the experimental group, the diaphragm was stimulated to exogenously induce breathing. In the control group, no stimulation was applied. Breathing and cortical electrographic activity were monitored using whole body plethysmography and EEG, respectively. A majority of the animals that did not receive the diaphragmatic pacing intervention died of seizure-induced respiratory arrest. Conversely, none of the animals that received the diaphragmatic pacing intervention died. Diaphragmatic pacing improved postictal respiratory outcomes (two-way ANOVA, P < 0.001) and reduced the likelyhood of seizure-induced death (Fisher's exact test, P = 0.026). Unexpectedly, diaphragmatic pacing did not instantly restore breathing during the postictal period, potentially indicating peripheral airway occlusion by laryngospasm. All diaphragmatically paced animals breathed at some point during the pacing stimulation. Two animals took their first breath prior to the onset of pacing and some animals had significant apnoeas after the pacing stimulation. Sudden unexpected death in epilepsy results in more years of potential life lost than any other neurological condition with the exception of stroke. By demonstrating that seizure-induced respiratory arrest can be prevented by transient diaphragmatic pacing in animal models we hope to inform the development of closed-loop systems capable of detecting and preventing sudden unexpected death in epilepsy.

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.

The future perspectives of psychiatric neurosurgery

The future of psychiatric neurosurgery can be viewed from two separate perspectives: the immediate future and the distant future. Both show promise, but the treatment strategy for mental diseases and the technology utilized during these separate periods will likely differ dramatically. It can be expected that the initial advancements will be built upon progress of neuroimaging and stereotactic targeting while surgical technology becomes adapted to patient-specific symptomatology and structural/functional imaging parameters. This individualized approach has already begun to show significant promise when applied to deep brain stimulation for treatment-resistant depression and obsessive-compulsive disorder. If effectiveness of these strategies is confirmed by well designed, double-blind, placebo-controlled clinical studies, further technological advances will continue into the distant future, and will likely involve precise neuromodulation at the cellular level, perhaps using wireless technology with or without closed-loop design. This approach, being theoretically less invasive and carrying less risk, may ultimately propel psychiatric neurosurgery to the forefront in the treatment algorithm of mental illness. Despite prominent development of non-invasive therapeutic options, such as stereotactic radiosurgery or transcranial magnetic resonance-guided focused ultrasound, chances are there will still be a need in surgical management of patients with most intractable psychiatric conditions.

The size of via holes influence the amplitude and selectivity of neural signals in Micro-ECoG arrays

Background

Electrocorticography (ECoG) arrays are commonly used to record the brain activity both in animal and human subjects. There is a lack of guidelines in the literature as to how the array geometry, particularly the via holes in the substrate, affects the recorded signals. A finite element (FE) model was developed to simulate the electric field generated by neurons located at different depths in the rat brain cortex and a micro ECoG array (μECoG) was placed on the pia surface for recording the neural signal. The array design chosen was a typical array of 8 × 8 circular (100 μm in diam.) contacts with 500 μm pitch. The size of the via holes between the recording contacts was varied to see the effect.

Results

The results showed that recorded signal amplitudes were reduced if the substrate was smaller than about four times the depth of the neuron in the gray matter. The signal amplitude profiles had dips around the via holes and the amplitudes were also lower at the contact sites as compared to the design without the holes; an effect that increased with the hole size. Another noteworthy result is that the spatial selectivity of the multi-contact recordings could be improved or reduced by the selection of the via hole sizes, and the effect depended on the distance between the neuron pair targeted for selective recording and its depth.

Conclusions

The results suggest that the via-hole size clearly affects the recorded neural signal amplitudes and it can be leveraged as a parameter to reduce the inter-channel correlation and thus maximize the information content of neural signals with μECoG arrays.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and "connectomics" will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model

This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.

Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states

Infraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states.

A multimodal, implantable sensor array and measurement system to investigate the suppression of focal epileptic seizure using hypothermia

Objective.Local cooling of the brain as a therapeutic intervention is a promising alternative for patients with epilepsy who do not respond to medication.In vitroandin vivostudies have demonstrated the seizure-suppressing effect of local cooling in various animal models. In our work, focal brain cooling in a bicuculline induced epilepsy model in rats is demonstrated and evaluated using a multimodal micro-electrocorticography (microECoG) device.Approach.We designed and experimentally tested a novel polyimide-based sensor array capable of recording microECoG and temperature signals concurrently from the cortical surface of rats. The effect of cortical cooling after seizure onset was evaluated using 32 electrophysiological sites and eight temperature sensing elements covering the brain hemisphere, where injection of the epileptic drug was performed. The focal cooling of the cortex right above the injection site was accomplished using a miniaturized Peltier chip combined with a heat pipe to transfer heat. Control of cooling and collection of sensor data was provided by a custom designed Arduino based electronic board. We tested the experimental setup using an agar gel modelin vitro, and thenin vivoin Wistar rats.Main results.Spatial variation of temperature during the Peltier controlled cooling was evaluated through calibrated, on-chip platinum temperature sensors. We found that frequency of epileptic discharges was not substantially reduced by cooling the cortical surface to 30 °C, but was suppressed efficiently at temperature values around 20 °C. The multimodal array revealed that seizure-like ictal events far from the focus and not exposed to high drop in temperature can be also inhibited at an extent like the directly cooled area.Significance.Our results imply that not only the absolute drop in temperature determines the efficacy of seizure suppression, and distant cortical areas not directly cooled can be influenced.

Monitoring the frequency and duration of epileptic seizures: "A journey through time"

Seizure monitoring plays an undeniably important role in diagnosing and managing epileptic seizures. Establishing the frequency and duration of seizures is crucial for assessing the burden of this chronic neurological disease, selecting treatment methods, determining how frequently these methods are applied, and informing short and long-term therapeutic decisions. Over the years, seizure monitoring tools and methods have evolved and become increasingly sophisticated; from home seizure diaries to EEG monitoring to cutting-edge responsive neurostimulation systems. In this article, the various methods of seizure monitoring are reviewed.

The potential of closed-loop endovascular neurostimulation as a viable therapeutic approach for drug-resistant epilepsy: A critical review

Over the last few decades, biomedical implants have successfully delivered therapeutic electrical stimulation to reduce the frequency and severity of seizures in people with drug-resistant epilepsy. However, neurostimulation approaches require invasive surgery to implant stimulating electrodes, and surgical, medical, and hardware complications are not uncommon. An endovascular approach provides a potentially safer and less invasive surgical alternative. This article critically evaluates the feasibility of endovascular closed-loop neuromodulation for the treatment of epilepsy. By reviewing literature that reported the impact of direct electrical stimulation to reduce the frequency of epileptic seizures, we identified clinically validated extracranial, cortical, and deep cortical neural targets. We identified veins in close proximity to these targets and evaluated the potential of delivering an endovascular implant to these veins based on their diameter. We then compared the risks and benefits of existing technology to describe a benchmark of clinical safety and efficacy that would need to be achieved for endovascular neuromodulation to provide therapeutic benefit. For the majority of brain regions that have been clinically demonstrated to reduce seizure occurrence in response to delivered electrical stimulation, vessels of appropriate diameter for delivery of an endovascular electrode to these regions could be achieved. This includes delivery to the vagus nerve via the 13.2 ± 0.9 mm diameter internal jugular vein, the motor cortex via the 6.5 ± 1.7 mm diameter superior sagittal sinus, and the cerebellum via the 7.7 ± 1.4 mm diameter sigmoid sinus or 6.2 ± 1.4 mm diameter transverse sinus. Deep cerebral targets can also be accessed with an endovascular approach, with the 1.9 ± 0.5 mm diameter internal cerebral vein and 1.2-mm-diameter thalamostriate vein lying in close proximity to the anterior and centromedian nuclei of the thalamus, respectively. This work identified numerous veins that are in close proximity to conventional stimulation targets that are of a diameter large enough for delivery and deployment of an endovascular electrode array, supporting future work to assess clinical efficacy and chronic safety of an endovascular approach to deliver therapeutic neurostimulation.

Treating of focal epilepsy: a patent review

Focal epilepsy is one of the most frequent specific type of epilepsies, with 30% treatment-resistant patients. There are several directions researchers can follow to improve existing treatment of focal epilepsy: synthesis of new compounds with anticonvulsant activity, repurposing drugs approved for other indications, finding drugs targeted to specific genetic and biochemical defects that underlie focal epilepsy syndromes, development of viral vectors for specific gene therapy, creation of devices and methods for suppression of seizures by electrostimulation and development of methods to increase safety of epilepsy surgery. Improvement of efficacy and safety of current therapies is necessary, as well as developing targeted treatment of genetic epilepsy syndromes that will not only suppress seizures, but stop further epileptogenesis.

Low-Cutoff Frequency Reduction in Neural Amplifiers: Analysis and Implementation in CMOS 65 nm

Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 μW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by in vitro recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 μW at 1 V power supply. The measurement results show an input-referred noise of 6.1 μV_rms and occupy 0.04 mm² silicon area.

Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface Systems

Miniaturized implantable devices play a crucial role in neural interfaces by monitoring and modulating neural activities on the peripheral and central nervous systems. Research efforts toward a compact wireless closed-loop system stimulating the nerve automatically according to the user's condition have been maintained. These systems have several advantages over open-loop stimulation systems such as reduction in both power consumption and side effects of continuous stimulation. Furthermore, a compact and wireless device consuming low energy alleviates foreign body reactions and risk of frequent surgical operations. Unfortunately, however, the miniaturized closed-loop neural interface system induces several hardware design challenges such as neural activity recording with severe stimulation artifact, real-time stimulation artifact removal, and energy-efficient wireless power delivery. Here, we will review recent approaches toward the miniaturized closed-loop neural interface system with integrated circuit (IC) techniques.

Validation of an EEG seizure detection paradigm optimized for clinical use in a chronically implanted subcutaneous device

BACKGROUND

Many electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need.

METHODS

Ten-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recordings were analyzed by three experienced clinical neurophysiologists. The 201subscalp EEG time series epochs where classified as diagnostic for awake, asleep, or seizure by the clinicians who were blinded to all other information. Seventy of the epochs were classified in this way as representing seizure activity. A subject specific seizure detection algorithm was trained and then evaluated offline for each patient in the data set using the expert consensus classification as the gold standard.

RESULTS

The average seizure detection performance of the algorithm across 21 subjects exceeded 90 % accuracy: 97 % sensitivity, 91 % specificity, and 93 % accuracy. For 19 of 21 patient datasets the algorithm achieved 100 % sensitivity. For 15 of 21 patients, the algorithm achieved 100 % specificity. For 13 of 21 patients the algorithm achieved 100 % accuracy.

COMPARISON

No comparable published methods are available for subgaleal EEG seizure detection.

CONCLUSIONS

These findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.

Predicting task performance from biomarkers of mental fatigue in global brain activity

Objective.

Detection and early prediction of mental fatigue (i.e. shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue.

Approach.

In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two healthy non-human primates (NHP) as they performed a sustained attention task over extended periods of time. We employed a set of spectrotemporal and connectivity biomarkers of the ECoG signals to identify periods of mental fatigue and a gradient boosting classifier to predict performance, up to several seconds prior to the behavioral response.

Main results.

Wavelet entropy and the instantaneous amplitude and frequency were among the best single features across sessions in both NHPs. The classification performance using higher order spectral-temporal (HOST) features was significantly higher than that of conventional spectral power features in both NHPs. Across the 99 sessions analyzed, average F1 scores of 77.5%±8.2% and 91.2%±3.6%, and accuracy of 79.5%±8.9% and 87.6%±3.9 % for the classifier were obtained for each animal, respectively.

Significance.

Our results here demonstrate the feasibility of predicting performance and detecting periods of mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies.

A sparse multiscale nonlinear autoregressive model for seizure prediction

Objectives.Accurate seizure prediction is highly desirable for medical interventions such as responsive electrical stimulation. We aim to develop a classification model that can predict seizures by identifying preictal states, i.e. the precursor of a seizure, based on multi-channel intracranial electroencephalography (iEEG) signals.Approach.A two-level sparse multiscale classification model was developed to classify interictal and preictal states from iEEG data. In the first level, short time-scale linear dynamical features were extracted as autoregressive (AR) model coefficients; arbitrary (usually long) time-scale linear and nonlinear dynamical features were extracted as Laguerre-Volterra AR model coefficients; root-mean-square error of model prediction was used as a feature representing model unpredictability. In the second level, all features were fed into a sparse classifier to discriminate the iEEG data between interictal and preictal states.Main results. The two-level model can accurately classify seizure states using iEEG data recorded from ten canine and human subjects. Adding arbitrary (usually long) time-scale and nonlinear features significantly improves model performance compared with the conventional AR modeling approach. There is a high degree of variability in the types of features contributing to seizure prediction across different subjects.Significance. This study suggests that seizure generation may involve distinct linear/nonlinear dynamical processes caused by different underlying neurobiological mechanisms. It is necessary to build patient-specific classification models with a wide range of dynamical features.

NeuroDAC: an open-source arbitrary biosignal waveform generator

Objective.Researchers are developing biomedical devices with embedded closed-loop algorithms for providing advanced adaptive therapies. As these devices become more capable and algorithms become more complex, tasked with integrating and interpreting multi-channel, multi-modal electrophysiological signals, there is a need for flexible bench-top testing and prototyping. We present a methodology for leveraging off-the-shelf audio equipment to construct a biosignal waveform generator capable of streaming pre-recorded biosignals from a host computer. By re-playing known, well-characterized, but physiologically relevant real-world biosignals into a device under test, researchers can evaluate their systems without the need for expensivein vivoexperiments.Approach.An open-source design based on the proposed methodology is described and validated, the NeuroDAC. NeuroDAC allows for 8 independent channels of biosignal playback using a simple, custom designed attenuation and buffering circuit. Applications can communicate with the device over a USB interface using standard audio drivers. On-board analog amplitude adjustment is used to maximize the dynamic range for a given signal and can be independently tuned for each channel.Main results.Low noise component selection yields a no-signal noise floor of just 5.35 ± 0.063. NeuroDAC's frequency response is characterized with a high pass -3 dB rolloff at 0.57 Hz, and is capable of accurately reproducing a wide assortment of biosignals ranging from EMG, EEG, and ECG to extracellularly recorded neural activity. We also present an application example using the device to test embedded algorithms on a closed-loop neural modulation device, the Medtronic RC+S.Significance.By making the design of NeuroDAC open-source we aim to present an accessible tool for rapidly prototyping new biomedical devices and algorithms than can be easily modified based on individual testing needs.ClinicalTrials.gov Identifiers: NCT04281134, NCT03437928, NCT03582891.

A Rational Approach to Understanding and Evaluating Responsive Neurostimulation

Closed-loop brain stimulation is increasingly used in level 4 epilepsy centers without an understanding of how the device behaves on a daily basis. This lack of insight is a barrier to improving closed-loop therapy and ultimately understanding why some patients never achieve seizure reduction. We aimed to quantify the accuracy of closed-loop seizure detection and stimulation on the RNS device through extrapolating information derived from manually reviewed ECoG recordings and comprehensive device logging information. RNS System event logging data were obtained, reviewed, and analyzed using a custom-built software package. A weighted-means methodology was developed to adjust for bias and incompleteness in event logs and evaluated using Bland–Altman plots and Wilcoxon signed-rank tests to compare adjusted and non-weighted (standard method) results. Twelve patients implanted for a mean of 21.5 (interquartile range 13.5–31) months were reviewed. The mean seizure frequency reduction post-RNS implantation was 40.1% (interquartile range 0–96.2%). Three primary levels of event logging granularity were identified (ECoG recordings: 3.0% complete (interquartile range 0.3–1.8%); Event Lists: 72.9% complete (interquartile range 44.7–99.8%); Activity Logs: 100% complete; completeness measured with respect to Activity Logs). Bland–Altman interpretation confirmed non-equivalence with unpredictable differences in both magnitude and direction. Wilcoxon signed rank tests demonstrated significant (p < 10⁻⁶) differences in accuracy, sensitivity, and specificity at >5% absolute mean difference for extrapolated versus standard results. Device behavior logged by the RNS System should be used in conjunction with careful review of stored ECoG data to extrapolate metrics for detector performance and stimulation.

Safety of responsive neurostimulation in pediatric patients with medically refractory epilepsy

OBJECTIVE

Approximately 75% of pediatric patients who suffer from epilepsy are successfully treated with antiepileptic drugs, while the disease is drug resistant in the remaining patients, who continue to have seizures. Patients with drug-resistant epilepsy (DRE) may have options to undergo invasive treatment such as resection, laser ablation of the epileptogenic focus, or vagus nerve stimulation. To date, treatment with responsive neurostimulation (RNS) has not been sufficiently studied in the pediatric population because the FDA has not approved the RNS device for patients younger than 18 years of age. Here, the authors sought to investigate the safety of RNS in pediatric patients.

METHODS

The authors performed a retrospective single-center study of consecutive patients with DRE who had undergone RNS system implantation from September 2015 to December 2019. Patients were followed up postoperatively to evaluate seizure freedom and complications.

RESULTS

Of the 27 patients studied, 3 developed infections and were treated with antibiotics. Of these 3 patients, one required partial removal and salvaging of a functioning system, and one required complete removal of the RNS device. No other complications, such as intracranial hemorrhage, stroke, or device malfunction, were seen. The average follow-up period was 22 months. All patients showed improvement in seizure frequency.

CONCLUSIONS

The authors demonstrated the safety and efficacy of RNS in pediatric patients, with infections being the main complication.

ABBREVIATIONS

DBS = deep brain stimulation; DRE = drug-resistant epilepsy; MDC = multidisciplinary conference; MER = microelectrode recording; MSHS = Mount Sinai Health System; RNS = responsive neurostimulation; SEEG = stereo-EEG; VNS = vagus nerve stimulation.

Different modalities of invasive neurostimulation for epilepsy

Epilepsy affects 1% of the general population, about one-third of which is pharmacologically resistant. Uncontrolled seizures are associated with an increased risk of traumatic injury and sudden unexpected death of epilepsy. There is a considerable psychological and financial burden on caregivers of patients with epilepsy, particularly among pediatric patients. Epilepsy surgery, when indicated, is the most promising cure for epilepsy. However, when surgery is contraindicated or refused by the patient, neurostimulation is an alternative palliative approach, albeit with a lower chance of entirely curing patients of seizures. There are many options for neurostimulation. The three most commonly used invasive neurostimulation procedures that consistently show evidence of being safe and efficacious are vagal nerve stimulation, responsive neuro stimulation, or anterior thalamic nucleus deep brain stimulation. The goal of this review is to summarize the current evidence supporting the use of these three techniques, which are approved by most regulatory bodies, and discuss different factors that may enable epilepsy surgeons to choose the most appropriate modality for each patient.

Robotic Orthogonal Implantation of Responsive Neurostimulation (RNS) Depth Electrodes in the Mesial Temporal Lobe: Case Series

BACKGROUND

Responsive neurostimulation (RNS) is a closed-loop neurostimulation modality for treating intractable epilepsy in patients who are not candidates for resection. In the past, implantation of depth electrodes was done through a transoccipital approach that transverses the hippocampus. There have been no descriptions of orthogonal approaches to RNS electrode placement.

OBJECTIVE

To describe our initial experience with placing RNS depth electrodes using an orthogonal approach to target the short axis of the mesial temporal lobe.

METHODS

Presurgical work-up included magnetic resonance imaging, video electroencephalography, and neuropsychological testing. During the procedure, patients were placed with their heads in a neutral position. Electrodes were placed via stereotactic robotic assistance using a unilateral orthogonal approach targeting the amygdala or hippocampus. Patients who underwent RNS electrode implantation via orthogonal approach were identified. Multiple variables were collected, including age, disease onset, complications, follow-up, semiology, and seizure reduction.

RESULTS

There were 8 patients who underwent RNS electrode placement with orthogonal approach. The mean age and follow-up were 44.8 and 1.2 yr, respectively. There were 4 patients with at least 1-yr follow-up. Of them, 1 was seizure free and 2 experienced over 50% reduction in seizures. There were no complications associated with electrode implantation.

CONCLUSION

The initial experience using an orthogonal approach for depth electrode placement for RNS implantation was described. The potential advantages may include better safety, accuracy, and positioning in comparison to a transoccipital approach.

Virtual Cortical Stimulation Mapping of Epilepsy Networks to Localize the Epileptogenic Zone

Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at three different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an virtual CSM by applying impulse perturbations to each electrode contact in the LTVN model and then measuring the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs before, during, and after seizures. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.

A 0.0023 mm 2/ch. Delta-Encoded, Time-Division Multiplexed Mixed-Signal ECoG Recording Architecture With Stimulus Artifact Suppression

This article demonstrates a scalable, time-division multiplexed biopotential recording front-end capable of real-time differential- and common-mode artifact suppression. A delta-encoded recording architecture exploits the power spectral density (PSD) characteristics of Electrocorticography (ECoG) recordings, combining an 8-bit ADC, and an 8-bit DAC to achieve 14 bits of dynamic range. The flexibility of the digital feedback architecture is leveraged to time-division multiplex 64 differential input channels onto a shared mixed-signal front-end, reducing channel area by 2x compared to the state-of-the-art. The feedback DAC used for delta-encoding also serves to cancel differential artifacts with an off-chip adaptive loop. Analysis of this architecture and measured silicon performance of a 65 nm CMOS test-chip implementation, both on the bench and in-vivo, are included with this paper.

NanoPt-A Nanostructured Electrode Coating for Neural Recording and Microstimulation

Bioelectronic devices, interfacing neural tissue for therapeutic, diagnostic, or rehabilitation purposes, rely on small electrode contacts in order to achieve highly sophisticated communication at the neural interface. Reliable recording and safe stimulation with small electrodes, however, are limited when conventional electrode metallizations are used, demanding the development of new materials to enable future progress within bioelectronics. In this study, we present a versatile process for the realization of nanostructured platinum (nanoPt) coatings with a high electrochemically active surface area, showing promising biocompatibility and providing low impedance, high charge injection capacity, and outstanding long-term stability both for recording and stimulation. The proposed electrochemical fabrication process offers exceptional control over the nanoPt deposition, allowing the realization of specific coating morphologies such as small grains, pyramids, or nanoflakes, and can moreover be scaled up to wafer level or batch fabrication under economic process conditions. The suitability of nanoPt as a coating for neural interfaces is here demonstrated, in vitro and in vivo, revealing superior stimulation performance under chronic conditions. Thus, nanoPt offers promising qualities as an advanced neural interface coating which moreover extends to the numerous application fields where a large (electro)chemically active surface area contributes to increased efficiency.

Model-based design for seizure control by stimulation

OBJECTIVE

Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs.

APPROACH

Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy.

MAIN RESULTS

Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes.

SIGNIFICANCE

Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.

Toward guiding principles for the design of biologically-integrated electrodes for the central nervous system

Innovation in electrode design has produced a myriad of new and creative strategies for interfacing the nervous system with softer, less invasive, more broadly distributed sites with high spatial resolution. However, despite rapid growth in the use of implanted electrode arrays in research and clinical applications, there are no broadly accepted guiding principles for the design of biocompatible chronic recording interfaces in the central nervous system (CNS). Studies suggest that the architecture and flexibility of devices play important roles in determining effective tissue integration: device feature dimensions (varying from 'sub'- to 'supra'-cellular scales, <10 µm to  >100 µm), Young's modulus, and bending modulus have all been identified as key features of design. However, critical knowledge gaps remain in the field with respect to the underlying motivation for these designs: (1) a systematic study of the relationship between device design features (materials, architecture, flexibility), biointegration, and signal quality needs to be performed, including controls for interaction effects between design features, (2) benchmarks for success need to be determined (biological integration, recording performance, longevity, stability), and (3) user results, particularly those that champion a specific design or electrode modification, need to be replicated across laboratories. Finally, the ancillary effects of factors such as tethering, site impedance and insertion method need to be considered. Here, we briefly review observations to-date of device design effects on tissue integration and performance, and then highlight the need for comprehensive and systematic testing of these effects moving forward.

Association of Seizure Spread With Surgical Failure in Epilepsy

Importance

Seizures recur in as many as half of patients who undergo surgery for drug-resistant temporal lobe epilepsy (TLE). Understanding why TLE is resistant to surgery in some patients may reveal insights into epileptogenic networks and direct new therapies to improve outcomes.

Objective

To characterize features of surgically refractory TLE.

Design, Setting, and Participants

Medical records from a comprehensive epilepsy center were retrospectively reviewed for 131 patients who received a standard anteromedial temporal resection by a single surgeon from January 1, 2000, to December 31, 2015. Thirteen patients were excluded for having less than 1 year of follow-up. Patients at the highest risk for seizure recurrence were identified. Intracranial electroencephalogram (iEEG) analyses generated 3-dimensional seizure spread representations and quantified rapid seizure spread. The final analyses of seizure outcome and follow-up data were performed in June 2017.

Main Outcomes and Measures

The Engel class seizure outcome following surgery was evaluated for all patients, defining seizure recurrence as Engel class II or greater. Intracranial recordings of neocortical grids/strips and depth electrodes were analyzed visually for seizure spread. Fast β power was projected onto reconstructions of patients' brain magnetic resonance imaging scans to visualize spread patterns and was quantified to compare power within vs outside resective margins.

Results

Of 118 patients with 1 year of follow-up or more (mean [SD], 6.5 [4.6] years), 66 (55.9%) were women and 52 (44.1%) were men (median age, 39 years [range, 4-66 years]). The cumulative probability of continuous Engel class I seizure freedom since surgery at postoperative year 10 and afterward was 65.6%, with 92% of recurrences in years 1 to 3. Multivariable statistical analyses found that the selection for iEEG study was the most reliable predictor of seizure recurrence, with a mixed-effects model estimating that the Engel score in the iEEG cohort was higher by a mean (SD) of 1.1 (0.33) (P = .001). In patients with iEEG results, rapid seizure spread in less than 10 seconds was associated with recurrence (hazard ratio, 5.99; 95% CI, 1.7-21.1; P < .01). In the first 10 seconds of seizures, fast β power activity outside the resective margins in the lateral temporal cortex was significantly greater in patients whose seizures recurred compared with patients who were seizure-free (mean [SEM], 137.5% [16.8%] vs 93.4% [4.6%]; P < .05).

Conclusions and significance

Rapid seizure spread outside anteromedial temporal resection resective margins plays a significant role in the surgical failure of drug-resistant TLE. Seizure control after epilepsy surgery might be improved by investigating areas of early spread as candidates for resection or neuromodulation.

Neurostimulation in the treatment of refractory and super-refractory status epilepticus

Status epilepticus (SE) is a life-threatening condition with a mortality of up to 60% in the advanced and comatose forms of SE. In one out of five adults, first and second line fails to control epileptic activity, leading to refractory status epilepticus (RSE) and in around 3% to super-refractory status epilepticus (SRSE), where SE continues despite anesthetic treatment for 24 h or more. In this rare but devastating condition, innovative and safe treatments are needed. In a recent review on the use of vagal nerve stimulation in RSE and SRSE, a 74% response rate for abrogation of SE was reported. Here, we review the currently available evidence supporting the use of neurostimulation, including vagal nerve stimulation, direct cortical stimulation, transcranial magnetic stimulation, electroconvulsive therapy, and deep brain stimulation in RSE and SRSE. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".