Closed-loop neurostimulation: the clinical experience

A Flexible, Implantable, Bioelectronic Electroporation Device for Targeted Ablation of Seizure Foci in the Mouse Brain

The primary method of treatment for patients suffering from drug-resistant focal-onset epilepsy is resective surgery, which adversely impacts neurocognitive function. Radio frequency (RF) ablation and laser ablation are the methods with the most promise, achieving seizure-free rates similar to resection but with less negative impact on neurocognitive function. However, there remains a number of concerns and open technical questions about these two methods of thermal ablation, with the primary ones: (1) heating; (2) hemorrhage and bleeding; and (3) poor directionality. Irreversible electroporation (IRE) is a proven method of focal ablation, which circumvents all three of the primary concerns regarding focal RF and laser ablation. Here, we demonstrate the in vivo application of a flexible implant with organic electrodes for focal ablation of epilepsy foci using high-frequency IRE (H-FIRE) in mice. Our results show that local, targeted ablation is possible in the close neighborhood of the electrode, paving the way for the clinical application in the treatment of focal epilepsy.

Vagus nerve stimulation and gut microbiota interactions: A novel therapeutic avenue for neuropsychiatric disorders

The rising prevalence of treatment-resistant neuropsychiatric disorders underscores the need for innovative and effective treatment strategies. The gut microbiota (GM) plays a pivotal role in the progression of these diseases, influencing the brain and mental health through the gut-brain axis (GBA). The vagus nerve plays a significant role in the GBA, making it a key area of focus for potential novel therapeutic interventions. Vagus nerve stimulation (VNS) was introduced and approved as a treatment for refractory forms of some neuropsychological disorders, such as depression and epilepsy. Considering its impact on several brain regions that play a vital part in mood, motivation, affection, and cognitive function, the VNS has shown significant therapeutic potential for treating a variety of neuropsychiatric disorders. Using VNS to target the bidirectional communication pathways linking the GM and the VN could present an exciting and novel approach to treating neuropsychological disorders. Imbalances in the GM, such as dysbiosis, can impair the communication pathways between the gut and the brain, contributing to the development of neuropsychological disorders. VNS shows potential for modulating these interconnected systems, helping to restore balance. Interestingly, the composition of the GM may also influence the effectiveness of VNS, as it has the potential to modify the brain's response to this therapeutic approach. This study provides a comprehensive analysis of a relatively unexplored but noteworthy interaction between VNS and GM in the treatment of neuropsychiatric disorders. In addition, we discussed the mechanisms, therapeutic potential, and clinical implications of VNS on the GBA across neuropsychiatric disorders.

Neurostimulation on lumbosacral nerves as a new treatment for spinal cord injury impairments and its impact on cortical activity: a narrative review

Spinal cord injury (SCI) can cause significant motor, sensory, and autonomic dysfunction by disrupting neural connections. As a result, it is a global health challenge that requires innovative interventions to improve outcomes. This review assesses the wide-ranging impacts of SCI and focuses on the laparoscopic implantation of neuroprosthesis (LION) as an emerging and promising rehabilitation technique. The LION technique involves the surgical implantation of electrodes on lumbosacral nerves to stimulate paralyzed muscles. Recent findings have demonstrated significant improvements in mobility, sexual function, and bladder/bowel control in chronic SCI patients following LION therapy. This manuscript revisits the potential physiological mechanisms underlying these results, including neuroplasticity and modulation of autonomic activity. Additionally, we discuss potential future applications and amendments of LION therapy. This study emphasizes the potential of neuromodulation as a complementary approach to traditional rehabilitation, that can provide a beacon of hope for improving functionality and quality of life for individuals with SCI.

Efficacy observation of combined transcutaneous vagus nerve stimulation and transcranial direct current stimulation on gait in 169 subacute stroke patients

OBJECTIVE

To investigate the combined effect of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation on improving lower limb function in stroke patients.

DESIGN

Randomized controlled trial.

SUBJECTS/PATIENTS

Subacute stroke patients.

METHODS

169 post-stroke hemiplegia patients were randomly divided into 4 groups (control, transcranial direct current stimulation, transcutaneous auricular vagus nerve stimulation, and transcutaneous auricular vagus nerve stimulation combined with transcranial direct current stimulation) and evaluated using the Fugl-Meyer Assessment-Lower Extremity (FMA-LL), Timed Up-and-Go (TUG) test, Modified Barthel Index (MBI), Berg Balance Scale (BBS), gait parameters, and surface electromyography (sEMG).

RESULTS

Significant improvements in FMA-LL, MBI, BBS, TUG, gait parameters, and sEMG were noted in the intervention groups compared with the control, with the transcutaneous auricular vagus nerve stimulation combined with transcranial direct current stimulation group showing the most pronounced improvements. Differences in some outcomes were also notable between the transcutaneous auricular vagus nerve stimulation and transcranial direct current stimulation groups.

CONCLUSION

The combination of transcutaneous auricular vagus nerve stimulation and transcranial direct current stimulation effectively enhances gait, balance, and daily living activities in subacute stroke patients. These benefits are likely due to transcutaneous auricular vagus nerve stimulation activating the solitary and trigeminal nuclei and transcranial direct current stimulation stimulating the motor cortex. Wearable gait analysis systems and electromyography are valuable in clinical gait assessment for these patients.

Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.

Role of Neurosurgical Interventions in the Treatment of Movement Disorders Like Parkinson's Disease, Dystonia, and Tourette Syndrome

This article provides an overview of neurosurgical therapies for movement disorders (MDs), including Tourette syndrome, dystonia, Parkinson's disease (PD), and others. It focuses on the benefits of these treatments and suggests directions for further research. A total of 10 years' worth of English-language PubMed articles were combed through, with an emphasis on studies conducted in North America. To manage MDs like Parkinson's disease and Tourette syndrome, the results suggest that non-invasive neuromodulation techniques, closed-loop deep brain stimulation (DBS), and other advanced therapies may become the treatment of choice in the future. Research on dystonia is being focused on improving treatment methods by investigating new areas of the brain that might be stimulated through neurosurgery and looking at gene therapy. Modern technological developments, such as non-invasive neuromodulation procedures and improved imaging, provide promising substitutes for traditional surgical approaches. This study highlights the need for continuous clinical trials for better outcomes, which is why research and development in this area must continue.

Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

AN ONLINE SPIKE DETECTION AND MONITORING FRAMEWORK IN IEEG RECORDED USING BRAIN INTERCHANGE DEVICE

In this study, we developed and validated an online analysis framework in MATLAB Simulink for recording and analysis of intracranial electroencephalography (iEEG). This framework aims to detect interictal spikes in patients with epilepsy as the data is being recorded. An online spike detection was performed over 10-minute interictal iEEG data recorded with Brain Interchange CorTec in three human subjects. A pool of detected spikes is then broadcasted using User Datagram Protocol (UDP) to an external graphical user interface for further post-processing and visualization. The real-time spike detector demonstrated a 99% similarity index with the previously published offline detector, identifying interictal spikes. Furthermore, our findings indicated that channels with highest spike rates, captured with Brain Interchange CorTec, were in the epileptogenic focus. By enabling the detection of interictal spikes in an online fashion, this work provides early feedback on the probable seizure onset zone (SOZ) and suggests a promising direction for enhancing SOZ localization accuracy to clinicians, which is crucial for the surgical treatment of epilepsy.

A Polymer Thick Film on an Organic Substrate Grid Electrode and an Open-Source Recording System for UHF MRI: An Imaging Study

Electrocorticography (ECoG) is a critical tool in preclinical neuroscience research for studying global network activity. However, integrating ECoG with functional magnetic resonance imaging (fMRI) has posed challenges, due to metal electrode interference with imaging quality and heating around the metallic electrodes. Here, we introduce recent advancements in ECoG grid development that utilize a polymer-thick film on an organic substrate (PTFOS). PTFOS offers notable advantages over traditional ECoG grids. Firstly, it significantly reduces imaging artifacts, ensuring minimal interference with MR image quality when overlaying brain tissue with PTFOS grids. Secondly, during a 30-min fMRI acquisition, the temperature increase associated with PTFOS grids is remarkably low, measuring only 0.4 °C. These findings suggest that utilizing ECoG with PTFOS grids has the potential to enhance the safety and efficacy of neurosurgical procedures. By providing clearer imaging results and mitigating risk factors such as excessive heating during MRI scans, PTFOS-based ECoG grids represent a promising advancement in neurosurgical technology. Furthermore, we describe a cutting-edge open-source system designed for simultaneous electrophysiology and fMRI. This system stands out due to its exceptionally low input noise levels (<0.6 V peak-to-peak), robust electromagnetic compatibility (it is suitable for use in MRI environments up to 9.4 teslas), and the inclusion of user-programmable real-time signal-processing capabilities. The open-platform software is a key feature, enabling researchers to swiftly implement and customize real-time signal-processing algorithms to meet specific experimental needs. This innovative system has been successfully utilized in several rodent EEG/fMRI studies, particularly at magnetic field strengths of 4.7 and 9.4 teslas, focusing on the somatosensory system. These studies have allowed for detailed observation of neural activity and responses within this sensory system, providing insights that are critical for advancing our understanding of neurophysiological processes. The versatility and high performance of our system make it an invaluable tool for researchers aiming to integrate and analyze complex datasets from advanced imaging and electrophysiological recordings, ultimately enhancing the depth and scope of neuroscience research.

Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation

Closed-loop neural stimulation has been an effective treatment for epilepsy patients. Currently, most closed-loop neural stimulation strategies are designed based on accurate neural models. However, the uncertainty and complexity of the neural system make it difficult to build an accurate neural model, which poses a significant challenge to the design of the controller. This paper proposes an Adaptive Fuzzy Iterative Learning Control (AFILC) framework for closed-loop neural stimulation, which can realize neuromodulation with no model or model uncertainty. Recognizing the periodic characteristics of neural stimulation and neuronal firing, Iterative Learning Control (ILC) is employed as the primary controller. Furthermore, a fuzzy optimization module is established to update the internal parameters of the ILC controller in real-time. This module enhances the anti-interference ability of the control system and reduces the influence of initial controller parameters on the control process. The efficacy of this strategy is evaluated using a neural computational model. The simulation results validate the capability of the AFILC strategy to suppress epileptic states. Compared with ILC-based closed-loop neurostimulation schemes, the AFILC-based neurostimulation strategy has faster convergence speed and stronger anti-interference ability. Moreover, the control algorithm is implemented based on a digital signal processor, and the hardware-in-the-loop experimental platform is implemented. The experimental results show that the control method has good control performance and computational efficiency, which provides the possibility for future application in clinical research.

A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems

This study presents a new data acquisition Framework for synchronous dual Brain Interchange (BIC) systems recording. The setup expands the capacity for data recording by offering access to up to 64 channels. The environment utilizes our Simulink model, incorporating functionalities for synchronization using a master clock and email-based status updates. We evaluated the framework in the lab simulations, and we observed a 38 ms post-synchronization delay between the systems. We also demonstrated that this error can be minimized to as low as 5 ms through adjustments in the master clock resolution and data buffer size. We estimated units' sampling frequency with high accuracy to avoid desynchronization. We evaluated the setup on the intracranial EEG (iEEG) recording simultaneously with the clinical system and performed spike detection on the post-synchronized iEEG. We observed over 95% similarity rate between the dual BIC and clinical system. Additionally, we explored the optimal configuration for ground and reference connections between systems to achieve the highest signal quality, along with investigating the implications of frequency interference in dual-system operations.

Non-Invasive Brain Sensing Technologies for Modulation of Neurological Disorders

The non-invasive brain sensing modulation technology field is experiencing rapid development, with new techniques constantly emerging. This study delves into the field of non-invasive brain neuromodulation, a safer and potentially effective approach for treating a spectrum of neurological and psychiatric disorders. Unlike traditional deep brain stimulation (DBS) surgery, non-invasive techniques employ ultrasound, electrical currents, and electromagnetic field stimulation to stimulate the brain from outside the skull, thereby eliminating surgery risks and enhancing patient comfort. This study explores the mechanisms of various modalities, including transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), highlighting their potential to address chronic pain, anxiety, Parkinson's disease, and depression. We also probe into the concept of closed-loop neuromodulation, which personalizes stimulation based on real-time brain activity. While we acknowledge the limitations of current technologies, our study concludes by proposing future research avenues to advance this rapidly evolving field with its immense potential to revolutionize neurological and psychiatric care and lay the foundation for the continuing advancement of innovative non-invasive brain sensing technologies.

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.

Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology

BACKGROUND

Little research exists on extending ex-vivo systems to large animal nerves, and to the best of our knowledge, there has yet to be a study comparing these against in-vivo data. This paper details the first ex-vivo system for large animal peripheral nerves to be compared with in-vivo results.

NEW METHOD

Detailed ex-vivo and in-vivo closed-loop neuromodulation experiments were conducted on pig ulnar nerves. Temperatures from 20 °C to 37 °C were evaluated for the ex-vivo system. The data were analysed in the time and velocity domains, and a regression analysis established how evoked compound action potential amplitude and modal conduction velocity (CV) varied with temperature and time after explantation.

MAIN RESULTS

Pig ulnar nerves were sustained ex-vivo up to 5 h post-explantation. CV distributions of ex-vivo and in-vivo data were compared, showing closer correspondence at 37 °C. Regression analysis results also demonstrated that modal CV and time since explantation were negatively correlated, whereas modal CV and temperature were positively correlated.

COMPARISON WITH EXISTING METHODS

Previous ex-vivo systems were primarily aimed at small animal nerves, and we are not aware of an ex-vivo system to be directly compared with in-vivo data. This new approach provides a route to understand how ex-vivo systems for large animal nerves can be developed and compared with in-vivo data.

CONCLUSION

The proposed ex-vivo system results were compared with those seen in-vivo, providing new insights into large animal nerve activity post-explantation. Such a system is crucial for complementing in-vivo experiments, maximising collected experimental data, and accelerating neural interface development.

A High Precision, Wide Dynamic Range Closed-Loop Neuromodulation IC With Rapid Stimulation Artifact Recovery

This article presents a high precision, wide dynamic range (DR) closed-loop neuromodulation (CLNM) system that can completely reject stimulation artifacts (SA) and achieve rapid SA recovery. In the recorder, a novel SA quick-blanking scheme is proposed for rail-to-rail SA rejection while minimizing SA recovery time. Besides, a new analog front-end (AFE) architecture based on a frequency-shaping (FS) technique is developed to extend DR intrinsically. In the stimulator, a stimulation driver implemented with a proposed redundant crossfire (RXF) technique is incorporated to improve the effective resolution of the stimulation current. The designed CLNM system is implemented in a 180 nm Bipolar-CMOS-DMOS (BCD) process. Measurement results show that the system is capable of tolerating rail-to-rail (5 V) SA and reducing the SA recovery time from 12 ms to 0.15 ms. The FS recorder extends the DR at low frequencies (LF) to 17.5 bits to enhance tolerance to LF interferences. The proposed stimulator adopting the 4-way RXF topology improves the effective resolution to 12.75 bits without consuming much extra area and power. Animal experiments demonstrate that the designed system can acquire high-fidelity neural signals immediately after stimulation onsets, thus supporting concurrent recording and stimulation.

Neurostimulation in the patient with chronic pain: forecasting the future with data from the present - data-driven analysis or just dreams?

Chronic pain involves a structured and individualized development of neurophysiological and biological responses. The final expression in each patient correlates with diverse expressions of mediators and activations of different transmission and modulation pathways, as well as alterations in the structure and function of the brain, all of which develop according to the pain phenotype. Still today, the selection process for the ideal candidate for spinal cord stimulation (SCS) is based on results from test and functional variables analysis as well as pain evaluation. In addition to the difficulties in the initial selection of patients and the predictive analysis of the test phase, which undoubtedly impact on the results in the middle and long term, the rate of explants is one of the most important concerns, in the analysis of suitability of implanted candidates. A potential for useful integration of genome analysis and lymphocyte expression in the daily practice of neurostimulation, for pain management is presented. Structural and functional quantitative information provided by imaging biomarkers will allow establishing a clinical decision support system that improve the effectiveness of the SCS implantation, optimizing human, economic and psychological resources. A correct programming of the neurostimulator, as well as other factors associated with the choice of leads and their position in the epidural space, are the critical factors for the effectiveness of the therapy. Using a model of SCS based on mathematical methods and computational simulation, the effect of different factors of influence on clinical practice studied, as several configurations of electrodes, position of these, and programming of polarities, in order to draw conclusions of clinical utility in neuroestimulation therapy.

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.

Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange

Neuromodulation through implantable pulse generators (IPGs) represents an important treatment approach for neurological disorders. While the field has observed the success of state-of-the-art interventions, such as deep brain stimulation (DBS) or responsive neurostimulation (RNS), implantable systems face various technical challenges, including the restriction of recording from a limited number of brain sites, power management, and limited external access to the assessed neural data in a continuous fashion. To the best of our knowledge, for the first time in this study, we investigated the feasibility of recording human intracranial EEG (iEEG) using a benchtop version of the Brain Interchange (BIC) unit of CorTec, which is a portable, wireless, and externally powered implant with sensing and stimulation capabilities. We developed a MATLAB/SIMULINK-based rapid prototyping environment and a graphical user interface (GUI) to acquire and visualize the iEEG captured from all 32 channels of the BIC unit. We recorded prolonged iEEG (~ 24 h) from three human subjects with externalized depth leads using the BIC and commercially available clinical amplifiers simultaneously in the epilepsy monitoring unit (EMU). The iEEG signal quality of both streams was compared, and the results demonstrated a comparable power spectral density (PSD) in all the systems in the low-frequency band (< 80 Hz). However, notable differences were primarily observed above 100 Hz, where the clinical amplifiers were associated with lower noise floor (BIC-17 dB vs. clinical amplifiers <  - 25 dB). We employed an established spike detector to assess and compare the spike rates in each iEEG stream. We observed over 90% conformity between the spikes rates and their spatial distribution captured with BIC and clinical systems. Additionally, we quantified the packet loss characteristic in the iEEG signal during the wireless data transfer and conducted a series of simulations to compare the performance of different interpolation methods for recovering the missing packets in signals at different frequency bands. We noted that simple linear interpolation has the potential to recover the signal and reduce the noise floor with modest packet loss levels reaching up to 10%. Overall, our results indicate that while tethered clinical amplifiers exhibited noticeably better noise floor above 80 Hz, epileptic spikes can still be detected successfully in the iEEG recorded with the externally powered wireless BIC unit opening the road for future closed-loop neuromodulation applications with continuous access to brain activity.

Safety & feasibility of responsive neurostimulation in children with refractory epilepsy: A single-center experience

OBJECTIVES

Responsive neurostimulation (RNS) is a relatively recent addition to the epilepsy surgery armory, gaining FDA approval in 2013 for use in adults with intractable focal epilepsy. Data for the use of RNS system in patients less than 18 years of age is limited. We aim to determine the safety and feasibility of RNS in children with refractory epilepsy.

METHODS

A retrospective chart review was conducted for all patients who underwent RNS implantation at an urban tertiary children's hospital. Demographics of the patients were obtained, including age at the time of implant, MRI findings, seizure onset zone identification, and RNS targets.

RESULTS

Out of a fourteen patient cohort, one patient had a post-operative complication of infection at surgical site requiring explantation. Thirteen out of 14 patients had immediate post-operative head imaging that was negative for hemorrhage, infarction, or skull fracture; one patient did not undergo head imaging. No patients reported a worsening clinical seizure frequency at a 6-month follow up visit. In the subset of patients who were implanted with RNS and did not undergo concurrent resections, there was a statistically significant reduction in the average number of long episodes at the most recent visit when compared to the 1-month post-operative visit (p = 0.0268).

CONCLUSIONS

RNS is a feasible and safe option for children as young as six years with refractory epilepsy when appropriate seizure focus identification has been performed with stereo CT and stereo EEG evaluations, and can be used in conjunction with other surgical epilepsy treatment modalities. Two canister RNS placement is achievable for patients with a broad epileptogenic network or multifocal seizure onset zones.

First Report on Real-World Outcomes with Evoked Compound Action Potential (ECAP)-Controlled Closed-Loop Spinal Cord Stimulation for Treatment of Chronic Pain

INTRODUCTION

A novel closed-loop spinal cord stimulation (SCS) system has recently been approved for use which records evoked compound action potentials (ECAPs) from the spinal cord and utilizes these recordings to automatically adjust the stimulation strength in real time. It automatically compensates for fluctuations in distance between the epidural leads and the spinal cord by maintaining the neural response (ECAP) at a determined target level. This data collection was principally designed to evaluate the performance of this first closed-loop SCS system in a 'real-world' setting under normal conditions of use in a single European center.

METHODS

In this prospective, single-center observational data collection, 22 patients were recruited at the outpatient pain clinic of the St. Antonius Hospital. All candidates were suffering from chronic pain in the trunk and/or limbs due to PSPS type 2 (persistent spinal pain syndrome). As standard of care, follow-up visits were completed at 3 months, 6 months, and 12 months post-device activation. Patient-reported outcome data (pain intensity, patient satisfaction) and electrophysiological and device data (ECAP amplitude, conduction velocity, current output, pulse width, frequency, usage), and patient interaction with their controller were collected at baseline and during standard of care follow-up visits.

RESULTS

Significant decreases in pain intensity for overall back or leg pain scores (verbal numerical rating score = VNRS) were observed between baseline [mean ± SEM (standard error of the mean); n = 22; 8.4 ± 0.2)], 3 months (n = 12; 1.9 ± 0.5), 6 months (n = 16; 2.6 ± 0.5), and 12 months (n = 20; 2.0 ± 0.5), with 85.0% of the patients being satisfied at 12 months. Additionally, no significant differences in average pain relief at 3 months and 12 months between the real-world data (77.2%; 76.8%) and the AVALON (71.2%; 73.6%) and EVOKE (78.1%; 76.7%) studies were observed.

CONCLUSIONS

These initial 'real-world' data on ECAP-controlled, closed-loop SCS in a real-world clinical setting appear to be promising, as they provide novel insights of the beneficial effect of ECAP-controlled, closed-loop SCS in a real-world setting. The presented results demonstrate a noteworthy maintenance of pain relief over 12 months and corroborate the outcomes observed in the AVALON prospective, multicenter, single-arm study and the EVOKE double-blind, multicenter, randomized controlled trial.

TRIAL REGISTRATION

The data collection is registered on the International Clinical Trials Registry Platform (Trial NL7889).

Beyond Tissue replacement: The Emerging role of smart implants in healthcare

Smart implants are increasingly used to treat various diseases, track patient status, and restore tissue and organ function. These devices support internal organs, actively stimulate nerves, and monitor essential functions. With continuous monitoring or stimulation, patient observation quality and subsequent treatment can be improved. Additionally, using biodegradable and entirely excreted implant materials eliminates the need for surgical removal, providing a patient-friendly solution. In this review, we classify smart implants and discuss the latest prototypes, materials, and technologies employed in their creation. Our focus lies in exploring medical devices beyond replacing an organ or tissue and incorporating new functionality through sensors and electronic circuits. We also examine the advantages, opportunities, and challenges of creating implantable devices that preserve all critical functions. By presenting an in-depth overview of the current state-of-the-art smart implants, we shed light on persistent issues and limitations while discussing potential avenues for future advancements in materials used for these devices.

Communication Protocols Integrating Wearables, Ingestibles, and Implantables for Closed-Loop Therapies

Body-conformal sensors and tissue interfacing robotic therapeutics enable the real-time monitoring and treatment of diabetes, wound healing, and other critical conditions. By integrating sensors and drug delivery devices, scientists and engineers have developed closed-loop drug delivery systems with on-demand therapeutic capabilities to provide just-in-time treatments that correspond to chemical, electrical, and physical signals of a target morbidity. To enable closed-loop functionality in vivo, engineers utilize various low-power means of communication that reduce the size of implants by orders of magnitude, increase device lifetime from hours to months, and ensure the secure high-speed transfer of data. In this review, we highlight how communication protocols used to integrate sensors and drug delivery devices, such as radio frequency communication (e.g., Bluetooth, near-field communication), in-body communication, and ultrasound, enable improved treatment outcomes.

A review of neurophysiological effects and efficiency of waveform parameters in deep brain stimulation

Neurostimulation has diverse clinical applications and potential as a treatment for medically refractory movement disorders, epilepsy, and other neurological disorders. However, the parameters used to program electrodes-polarity, pulse width, amplitude, and frequency-and how they are adjusted have remained largely untouched since the 1970 s. This review summarizes the state-of-the-art in Deep Brain Stimulation (DBS) and highlights the need for further research to uncover the physiological mechanisms of neurostimulation. We focus on studies that reveal the potential for clinicians to use waveform parameters to selectively stimulate neural tissue for therapeutic benefit, while avoiding activating tissue associated with adverse effects. DBS uses cathodic monophasic rectangular pulses with passive recharging in clinical practice to treat neurological conditions such as Parkinson's Disease. However, research has shown that stimulation efficiency can be improved, and side effects reduced, through modulating parameters and adding novel waveform properties. These developments can prolong implantable pulse generator lifespan, reducing costs and surgery-associated risks. Waveform parameters can stimulate neurons based on axon orientation and intrinsic structural properties, providing clinicians with more precise targeting of neural pathways. These findings could expand the spectrum of diseases treatable with neuromodulation and improve patient outcomes.

Chronic vagus nerve stimulation in patients with heart failure: challenge or failed translation?

Autonomic imbalance between the sympathetic and parasympathetic nervous systems contributes to the progression of chronic heart failure (HF). Preclinical studies have demonstrated that various neuromodulation strategies may exert beneficial cardioprotective effects in preclinical models of HF. Based on these encouraging experimental data, vagus nerve stimulation (VNS) has been assessed in patients with HF with a reduced ejection fraction. Nevertheless, the main trials conducted thus far have yielded conflicting findings, questioning the clinical efficacy of VNS in this context. This review will therefore focus on the role of the autonomic nervous system in HF pathophysiology and VNS therapy, highlighting the potential reasons behind the discrepancy between preclinical and clinical studies.

Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study

Mental disorders are among the top most demanding challenges in world-wide health. A large number of mental disorders exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g., pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological α- and γ-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.

Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy

OBJECTIVE

We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state.

METHODS

We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC).

RESULTS

Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs.

CONCLUSIONS

Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs.

SIGNIFICANCE

Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.

Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation

The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves instantaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system's transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.

Importance of timing optimization for closed-loop applications of vagus nerve stimulation

In recent decades, vagus nerve stimulation (VNS) therapy has become widely used for clinical applications including epilepsy, depression, and enhancing the effects of rehabilitation. However, several questions remain regarding optimization of this therapy to maximize clinical outcomes. Although stimulation parameters such as pulse width, amplitude, and frequency are well studied, the timing of stimulation delivery both acutely (with respect to disease events) and chronically (over the timeline of a disease's progression) has generally received less attention. Leveraging such information would provide a framework for the implementation of next generation closed-loop VNS therapies. In this mini-review, we summarize a number of VNS therapies and discuss (1) general timing considerations for these applications and (2) open questions that could lead to further therapy optimization.

Neuromodulation in Children with Drug-Resistant Epilepsy

The introduction of neuromodulation was a revolutionary advancement in the antiseizure armamentarium for refractory epilepsy. The basic principle of neuromodulation is to deliver an electrical stimulation to the desired neuronal site to modify the neuronal functions not only at the site of delivery but also at distant sites by complex neuronal processes like disrupting the neuronal circuitry and amplifying the functions of marginally functional neurons. The modality is considered open-loop when electrical stimulation is provided at a set time interval or closed-loop when delivered in response to an incipient seizure. Neuromodulation in individuals older than 18 years with epilepsy has proven efficacious and safe. The use of neuromodulation is extended off-label to pediatric patients with epilepsy and the results are promising. Vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS) are Food and Drug Administration-approved therapeutic techniques. The VNS provides retrograde signaling to the central nervous system, whereas DBS and RNS are more target specific in the central nervous system. While DBS is open-loop and approved for stimulation of the anterior nucleus of the thalamus, the RNS is closed-loop and can stimulate any cortical or subcortical structure. We will review different modalities and their clinical efficacy in individuals with epilepsy, with a focus on pediatric patients.

A computational roadmap to electronic drugs

A growing number of complex neurostimulation strategies promise symptom relief and functional recovery for several neurological, psychiatric, and even multi-organ disorders. Although pharmacological interventions are currently the mainstay of treatment, neurostimulation offers a potentially effective and safe alternative, capable of providing rapid adjustment to short-term variation and long-term decline of physiological functions. However, rapid advances made by clinical studies have often preceded the fundamental understanding of mechanisms underlying the interactions between stimulation and the nervous system. In turn, therapy design and verification are largely driven by clinical-empirical evidence. Even with titanic efforts and budgets, it is infeasible to comprehensively explore the multi-dimensional optimization space of neurostimulation through empirical research alone, especially since anatomical structures and thus outcomes vary dramatically between patients. Instead, we believe that the future of neurostimulation strongly depends on personalizable computational tools, i.e. Digital Neuro Twins (DNTs) to efficiently identify effective and safe stimulation parameters. DNTs have the potential to accelerate scientific discovery and hypothesis-driven engineering, and aid as a critical regulatory and clinical decision support tool. We outline here how DNTs will pave the way toward effective, cost-, time-, and risk-limited electronic drugs with a broad application bandwidth.

Spinal cord stimulation: Beyond pain management

INTRODUCTION

The gate control theory of pain was the starting point of the development of spinal cord stimulation (SCS). We describe the indications for the treatment in pain management and other uses not related to pain.

DEVELOPMENT

There are currently several paradigms for SCS: tonic, burst, and high frequency. The main difference lies in the presence of paraesthesias. SCS is most beneficial for treating neuropathic pain. Patients with failed back surgery syndrome show the best response rates, although a considerable reduction in pain is also observed in patients with complex regional pain syndrome, diabetic neuropathy, radiculopathy, and low back pain without previous surgery. Phantom pain or pain related to cardiovascular or peripheral vascular disease may improve, although there is a lack of robust evidence supporting generalisation of its use. SCS also improves cancer-related pain, although research on this issue is scarce. Non-pain-related indications for SCS are movement disorders, spasticity, and sequelae of spinal cord injury. The main limiting factors for the use of SCS are mechanical complications and the cost of the treatment.

CONCLUSION

In its 50-year history, SCS has progressed enormously. The perfection of hardware and software may improve its effectiveness and reduce the rate of complications. Indications for SCS could include other diseases, and its use could be expanded, if the costs of the technology are reduced.

A PVT-Robust AFE-Embedded Error-Feedback Noise-Shaping SAR ADC With Chopper-Based Passive High-Pass IIR Filtering for Direct Neural Recording

This paper presents a PVT-robust error-feedback (EF) noise-shaping SAR (NS-SAR) ADC for direct neural-signal recording. For closed-loop bidirectional neural interfaces enabling the next generation neurological devices, a wide-dynamic-range neural recording circuit is required to accommodate stimulation artifacts. A recording structure using an NS-SAR ADC can be a good candidate because the high resolution and wide dynamic range can be obtained with a low oversampling ratio and power consumption. However, NS-SAR ADCs require an additional gain stage to obtain a well-shaped noise transfer function (NTF), and a dynamic amplifier is often used as the gain stage to minimize power overhead at the cost of vulnerability to PVT variations. To overcome this limitation, the proposed work reutilizes the capacitive-feedback amplifier, which is the analog front-end of the neural recording circuit, as a PVT-robust gain stage to achieve a reliable NS performance. In addition, a new chopper-based implementation of a passive high-pass IIR filter is proposed, achieving an improved NTF compared to prior EF NS-SAR ADCs. Fabricated in a 180-nm CMOS process, the proposed NS-SAR ADC consumes 4.3-μW power and achieves a signal-to-noise-and-distortion ratio (SNDR) of 71.7 dB and 82.7 dB for a bandwidth of 5 kHz and 300 Hz, resulting in a Schreier figure of merit (FOM) of 162.4 dB and 162.1 dB, respectively. Direct neural recording using the proposed NS-SAR ADC is demonstrated successfully in vivo, and also its tolerance against stimulation artifacts is validated in vitro.

In vivo closed-loop control of a locust's leg using nerve stimulation

Activity of an innervated tissue can be modulated based on an acquired biomarker through feedback loops. How to convert this biomarker into a meaningful stimulation pattern is still a topic of intensive research. In this article, we present a simple closed-loop mechanism to control the mean angle of a locust’s leg in real time by modulating the frequency of the stimulation on its extensor motor nerve. The nerve is interfaced with a custom-designed cuff electrode and the feedback loop is implemented online with a proportional control algorithm, which runs solely on a microcontroller without the need of an external computer. The results show that the system can be controlled with a single-input, single-output feedback loop. The model described in this article can serve as a primer for young researchers to learn about neural control in biological systems before applying these concepts in advanced systems. We expect that the approach can be advanced to achieve control over more complex movements by increasing the number of recorded biomarkers and selective stimulation units.

Closed-Loop Vagus Nerve Stimulation for the Treatment of Cardiovascular Diseases: State of the Art and Future Directions

The autonomic nervous system exerts a fine beat-to-beat regulation of cardiovascular functions and is consequently involved in the onset and progression of many cardiovascular diseases (CVDs). Selective neuromodulation of the brain-heart axis with advanced neurotechnologies is an emerging approach to corroborate CVDs treatment when classical pharmacological agents show limited effectiveness. The vagus nerve is a major component of the cardiac neuroaxis, and vagus nerve stimulation (VNS) is a promising application to restore autonomic function under various pathological conditions. VNS has led to encouraging results in animal models of CVDs, but its translation to clinical practice has not been equally successful, calling for more investigation to optimize this technique. Herein we reviewed the state of the art of VNS for CVDs and discuss avenues for therapeutic optimization. Firstly, we provided a succinct description of cardiac vagal innervation anatomy and physiology and principles of VNS. Then, we examined the main clinical applications of VNS in CVDs and the related open challenges. Finally, we presented preclinical studies that aim at overcoming VNS limitations through optimization of anatomical targets, development of novel neural interface technologies, and design of efficient VNS closed-loop protocols.

A framework for the design of a closed-loop gastric pacemaker for treating conduction block

BACKGROUND AND OBJECTIVE

Gastrointestinal (GI) motility disorders can be significantly detrimental to the quality of life. Pacing, or long pulse gastric electrical stimulation, is a potential treatment option for treating GI motility disorders by modulating the slow wave activity. Open-loop pacing of the GI tract is the current standard for modulating dysrhythmic patterns, but it is known to be suboptimal and inefficient. Recent work on sensing intracellular potentials and pacing accordingly in a closed-loop has been shown to be effective at modulating dysrhythmic patterns. However, capturing intracellular potentials in an in-vivo setting is not viable. Therefore a closed-loop gastric electrical stimulation that can sense extracellular potentials and pace accordingly to modulate dysrhythmic patterns is required. This paper presents a closed-loop Gastric Electrical Stimulator (GES) design framework, which comprises of extracellular potential generation, sensing, and closed-loop actuation.

METHODS

This work leverages a pre-existing high-fidelity two-dimensional Interstitial Cells of Cajal (ICC) network modeling framework to mimic several normal and dysrhythmic patterns observed in experimental recordings of patients suffering from GI tract diseases. The activation patterns of the of the ICC network are captured by an extracellular potential generation model and is integrated with the GES in a closed-loop to validate the efficacy of the developed pacing algorithms. The proposed GES pacing algorithms extend existing offline filtering and activation detection methods to process the sensed extracellular potentials in real time. The GES detects bradygastric rhythms based on the sensed extracellular potentials and actuates the ICC network via pacing to rectify dysrhythmic patterns.

RESULTS

The proposed GES model is able to sense and process the generated noisy extracellular potentials, detect the bradygastric patterns, and modulate the slow wave activities to normal propagation effectively.

CONCLUSIONS

A closed-loop GES design, which can be applied in an experimental and clinical setting is developed and validated through the ICC network model. The proposed GES model has the ability to modulate a variety of bradygastric patterns, including conduction block effectively in a closed-loop.

Sensory Feedback for Upper-Limb Prostheses: Opportunities and Barriers

The addition of sensory feedback to upper-limb prostheses has been shown to improve control, increase embodiment, and reduce phantom limb pain. However, most commercial prostheses do not incorporate sensory feedback due to several factors. This paper focuses on the major challenges of a lack of deep understanding of user needs, the unavailability of tailored, realistic outcome measures and the segregation between research on control and sensory feedback. The use of methods such as the Person-Based Approach and co-creation can improve the design and testing process. Stronger collaboration between researchers can integrate different prostheses research areas to accelerate the translation process.

Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine

Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system.

Physiologically informed neuromodulation

The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.

Recent advances in recording and modulation technologies for next-generation neural interfaces

Along with the advancement in neural engineering techniques, unprecedented progress in the development of neural interfaces has been made over the past few decades. However, despite these achievements, there is still room for further improvements especially toward the possibility of monitoring and modulating neural activities with high resolution and specificity in our daily lives. In an effort of taking a step toward the next-generation neural interfaces, we want to highlight the recent progress in neural technologies. We will cover a wide scope of such developments ranging from novel platforms for highly specific recording and modulation to system integration for practical applications of novel interfaces.

Combined neuromodulatory approaches in the central nervous system for treatment of spinal cord injury

PURPOSE OF REVIEW

To report progress in neuromodulation following spinal cord injury (SCI) using combined brain and spinal neuromodulation.Neuromodulation refers to alterations in neuronal activity for therapeutic purposes. Beneficial effects are established in disease states such as Parkinson's Disease (PD), chronic pain, epilepsy, and SCI. The repertoire of neuromodulation and bioelectric medicine is rapidly expanding. After SCI, cohort studies have reported the benefits of epidural stimulation (ES) combined with training. Recently, we have explored combining ES with deep brain stimulation (DBS) to increase activation of descending motor systems to address limitations of ES in severe SCI. In this review, we describe the types of applied neuromodulation that could be combined in SCI to amplify efficacy to enable movement. These include ES, mesencephalic locomotor region (MLR) - DBS, noninvasive transcutaneous stimulation, transcranial magnetic stimulation, paired-pulse paradigms, and neuromodulatory drugs. We examine immediate and longer-term effects and what is known about: (1) induced neuroplastic changes, (2) potential safety concerns; (3) relevant outcome measures; (4) optimization of stimulation; (5) therapeutic limitations and prospects to overcome these.

RECENT FINDINGS

DBS of the mesencephalic locomotor region is emerging as a potential clinical target to amplify supraspinal command circuits for locomotion.

SUMMARY

Combinations of neuromodulatory methods may have additive value for restoration of function after spinal cord injury.

A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.

Neuromodulation in Pediatric Epilepsy

Neuromodulation alters neuronal activity with electrical impulses delivered to the targeted neurologic sites. The various neuromodulation options available today for epilepsy management have proven efficacy primarily in adult trials. These include open-loop stimulation with invasive vagus nerve stimulation and deep brain stimulation, as well as closed-loop responsive neurostimulation. The use of neurostimulation therapy to treat intractable epilepsy in children is growing. This article reviews the literature, historical background, and current principles in pediatric patients.

Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals

Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals.Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees.Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles.Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.

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.

Human Body Rhythms in the Development of Non-Invasive Methods of Closed-Loop Adaptive Neurostimulation

The creation and improvement of non-invasive closed-loop brain stimulation technologies represent an exciting and rapidly expanding field of neuroscience. To identify the appropriate way to close the feedback loop in adaptive neurostimulation procedures, it was previously proposed to use on-line automatic sensory stimulation with the parameters modulated by the patient's own rhythmical processes, such as respiratory rate, heart rate, and electroencephalogram (EEG) rhythms. The current paper aims to analyze several recent studies demonstrating further development in this line of research. The advantages of using automatic closed-loop feedback from human endogenous rhythms in non-invasive adaptive neurostimulation procedures have been demonstrated for relaxation assistance, for the correction of stress-induced functional disturbances, for anxiety management, and for the cognitive rehabilitation of an individual. Several distinctive features of the approach are noted to delineate its further development.