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Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson's disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

Implantable devices for controlling medically intractable seizures nondestructively are rapidly advancing. These offer reversible, potentially, restorative options beyond traditional, surgical procedures, which rely, largely on resection or ablation of selected brain sites. Several lines of, investigation aimed at improving efficacy of these devices are discussed, ranging from identifying novel subcortical, white matter, or cell-type specific targets to engineering advances for adaptive techniques based- on continuous, dynamic system analysis.

Spinal cord injury (SCI) induces haemodynamic instability that threatens survival1–3, impairs neurological recovery4,5, increases the risk of cardiovascular disease6,7, and reduces quality of life8,9. Haemodynamic instability in this context is due to the interruption of supraspinal efferent commands to sympathetic circuits located in the spinal cord10, which prevents the natural baroreflex from controlling these circuits to adjust peripheral vascular resistance. Epidural electrical stimulation (EES) of the spinal cord has been shown to compensate for interrupted supraspinal commands to motor circuits below the injury11, and restored walking after paralysis12. Here, we leveraged these concepts to develop EES protocols that restored haemodynamic stability after SCI. We established a preclinical model that enabled us to dissect the topology and dynamics of the sympathetic circuits, and to understand how EES can engage these circuits. We incorporated these spatial and temporal features into stimulation protocols to conceive a clinical-grade biomimetic haemodynamic regulator that operates in a closed loop. This ‘neuroprosthetic baroreflex’ controlled haemodynamics for extended periods of time in rodents, non-human primates and humans, after both acute and chronic SCI. We will now conduct clinical trials to turn the neuroprosthetic baroreflex into a commonly available therapy for people with SCI. An epidural spinal cord stimulation system regulates blood pressure in the acute and chronic phases of spinal cord injury.

Brain stimulation therapies have been established as effective treatments for Parkinson’s disease, essential tremor, and epilepsy, as well as having high diagnostic and therapeutic potential in a wide range of neurological and psychiatric conditions. Novel interventions such as extended reality (XR), video games and exergames that can improve physiological and cognitive functioning are also emerging as targets for therapeutic and rehabilitative treatments. Previous studies have proposed specific applications involving non-invasive brain stimulation (NIBS) and virtual environments, but to date these have been uni-directional and restricted to specific applications or proprietary hardware. Here, we describe technology integration methods that enable invasive and non-invasive brain stimulation devices to interface with a cross-platform game engine and development platform for creating bi-directional brain-computer interfaces (BCI) and XR-based interventions. Furthermore, we present a highly-modifiable software framework and methods for integrating deep brain stimulation (DBS) in 2D, 3D, virtual and mixed reality applications, as well as extensible applications for BCI integration in wireless systems. The source code and integrated brain stimulation applications are available online at https: //github.com/oxfordbioelectronics/brain-stim-game

BackgroundImplantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.MethodsTwo participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.ResultsUnsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%–100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.ConclusionWe describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (infradian) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and infradian cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and infradian IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and infradian (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

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

The total economic cost of neurological disorders exceeds £100 billion per annum in the United Kingdom alone, yet pharmaceutical companies continue to cut investments due to failed clinical studies and risk [1]. These challenges motivate an alternative to solely pharmacological treatments. The emerging field of bioelectronics suggests a novel alternative to pharmaceutical intervention that uses electronic hardware to directly stimulate the nervous system with physiologically inspired electrical signals [2]. Given the processing capability of electronics and precise targeting of electrodes, the potential advantages of bioelectronics include specificity in the time, method, and location of treatment, with the ability to iteratively refine and update therapy algorithms in software [3]. A primary disadvantage of the current systems is invasiveness due to surgical implantation of the device.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments.

Quadri-pulse stimulation (QPS), a type of repetitive transcranial magnetic stimulation (rTMS), can induce a considerable aftereffect on cortical synapses. Human experiments have shown that the type of effect on synaptic efficiency (in terms of potentiation or depression) depends on the time interval between pulses. The maturation of biophysically-based models, which describe the physiological properties of plasticity mathematically, offers a beneficial framework to explore induced plasticity for new stimulation protocols. To model the QPS paradigm, a phenomenological model based on the knowledge of spike timing-dependent plasticity (STDP) mechanisms of synaptic plasticity was utilized where the cortex builds upon the platform of neuronal population modeling. Induced cortical plasticity was modeled for both conventional monophasic pulses and unidirectional pulses generated by the cTMS device, in a total of 117 different scenarios. For the conventional monophasic stimuli, the results of the predictive model broadly follow what is typically seen in human experiments. Unidirectional pulses can produce a similar range of plasticity. Additionally, changing the pulse width had a considerable effect on the plasticity (approximately 20% increase). As the width of the positive phase increases, the size of the potentiation will also increase. The proposed model can generate predictions to guide future plasticity experiments. Estimating the plasticity and optimizing the rTMS protocols might effectively improve the safety implications of TMS experiments by reducing the number of delivered pulses to participants. Finding the optimal stimulation protocol with the maximum potentiation/depression can lead to the design of a new TMS pulse generator device with targeted hardware and control algorithms.

Sleep disturbances are among the most common nonmotor complications of Parkinson's disease (PD), can present in prodromal stages, and progress with advancing disease. In addition to being a symptom of neurodegeneration, sleep disturbances may also contribute to disease progression. Currently, limited options exist to modulate sleep disturbances in PD. Studying the neurophysiological changes that affect sleep in PD at the cortical and subcortical level may yield new insights into mechanisms for reversal of sleep disruption. In this article, we review cortical and subcortical recording studies of sleep in PD with a particular focus on dissecting reported electrophysiological changes. These studies show that slow-wave sleep and rapid eye movement sleep are both notably disrupted in PD. We further explore the impact of these electrophysiological changes and discuss the potential for targeting sleep via stimulation therapy to modify PD-related motor and nonmotor symptoms. © 2021 International Parkinson and Movement Disorder Society.

BackgroundBrain sensing devices are approved today for Parkinson’s, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability.ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity.MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.aResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination.ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for “closed-loop” algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.HighlightsChronic embedded brain sensing promises algorithm-based neurostimulationAlgorithms for closed-loop stimulation can be impaired by artifactsThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivityECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systemsImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifactsCranial-mounted implants are relatively immune to artifacts

Background Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. Conclusions Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for “closed-loop” algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration., Highlights • Chronic embedded brain sensing promises algorithm-based neurostimulation, but algorithms can be impaired by artifacts. • Implant location can have relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity. • ECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems. • Implanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts. • Cranial-mounted implants are relatively immune to artifacts.

The motor threshold measurement is a standard in preintervention probing in TMS experiments. We aim to predict the motor threshold for near-rectangular stimuli to efficiently determine the motor threshold size before any experiments take place. Estimating the behavior of large-scale networks requires dynamically accurate and efficient modeling. We utilized a Hodgkin–Huxley (HH) type model to evaluate motor threshold values and computationally validated its function with known true threshold data from 50 participants trials from state-of-the-art published datasets. For monophasic, bidirectional, and unidirectional rectangular stimuli in posterior-anterior or anterior-posterior directions as generated by the cTMS device, computational modeling of the HH model captured the experimentally measured population-averaged motor threshold values at high precision (maximum error ≤ 8%). The convergence of our biophysically based modeling study with experimental data in humans reveals that the effect of the stimulus shape is strongly correlated with the activation kinetics of the voltage-gated ion channels. The proposed method can reliably predict motor threshold size using the conductance-based neuronal models and could therefore be embedded in new generation neurostimulators. Advancements in neural modeling will make it possible to enhance treatment procedures by reducing the number of delivered magnetic stimuli to participants.

Deep brain stimulation (DBS) is an approved therapy for the treatment of medically refractory and severe movement disorders. However, most existing neurostimulators can only apply continuous stimulation [open-loop DBS (OL-DBS)], ignoring patient behavior and environmental factors, which consequently leads to an inefficient therapy, thus limiting the therapeutic window. Here, we established the feasibility of a self-adjusting therapeutic DBS [closed-loop DBS (CL-DBS)], fully embedded in a chronic investigational neurostimulator (Activa PC + S), for three patients affected by essential tremor (ET) enrolled in a longitudinal (6 months) within-subject crossover protocol (DBS OFF, OL-DBS, and CL-DBS). Most patients with ET experience involuntary limb tremor during goal-directed movements, but not during rest. Hence, the proposed CL-DBS paradigm explored the efficacy of modulating the stimulation amplitude based on patient-specific motor behavior, suppressing the pathological tremor on-demand based on a cortical electrode detecting upper limb motor activity. Here, we demonstrated how the proposed stimulation paradigm was able to achieve clinical efficacy and tremor suppression comparable with OL-DBS in a range of movements (cup reaching, proximal and distal posture, water pouring, and writing) while having a consistent reduction in energy delivery. The proposed paradigm is an important step toward a behaviorally modulated fully embedded DBS system, capable of delivering stimulation only when needed, and potentially mitigating pitfalls of OL-DBS, such as DBS-induced side effects and premature device replacement.

Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of “scientific platforms,” from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.

Advances in ambulatory intracranial EEG devices have enabled objective analyses of circadian and multiday seizure periodicities, and seizure clusters in humans. This study characterizes circadian and multiday seizure periodicities, and seizure clusters in dogs with naturally occurring focal epilepsy, and considers the implications of an animal model for the study of seizure risk patterns, seizure forecasting and personalized treatment protocols. In this retrospective cohort study, 16 dogs were continuously monitored with ambulatory intracranial EEG devices designed for humans. Detailed medication records were kept for all dogs. Seizure periodicity was evaluated with circular statistics methods. Circular non-uniformity was assessed for circadian, 7-day and approximately monthly periods. The Rayleigh test was used to assess statistical significance, with correction for multiple comparisons. Seizure clusters were evaluated with Fano factor (index of dispersion) measurements, and compared to a Poisson distribution. Relationships between interseizure interval (ISI) and seizure duration were evaluated. Six dogs met the inclusion criteria of having at least 30 seizures and were monitored for an average of 65 weeks. Three dogs had seizures with circadian seizure periodicity, one dog had a 7-day periodicity, and two dogs had approximately monthly periodicity. Four dogs had seizure clusters and significantly elevated Fano factor values. There were subject-specific differences in the dynamics of ISI and seizure durations, both within and between lead and clustered seizure categories. Our findings show that seizure timing in dogs with naturally occurring epilepsy is not random, and that circadian and multiday seizure periodicities, and seizure clusters are common. Circadian, 7-day, and monthly seizure periodicities occur independent of antiseizure medication dosing, and these patterns likely reflect endogenous rhythms of seizure risk., Abbreviated summary Dogs can accommodate ambulatory intracranial EEG devices designed for humans. Gregg et al. report that endogenous circadian and multiday seizure periodicities, and seizure clusters are common in dogs with naturally occurring epilepsy. Canine epilepsy can advance the development of seizure forecasting, personalized treatment protocols, and chronotherapy., Graphical Abstract Graphical Abstract

The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.

In this study, we present a temporal interference (TI) concept to achieve focal and steerable stimulation in the targeted brain area through transcranial magnetic stimulation (TMS). This method works by inducing two high-frequency electric fields with a slight frequency difference via two independent coils. The intrinsic nonlinear nature of the nerve membrane, which acts as a low-pass filter, does not allow the nerve to engage at high frequencies. Instead, neurons at the intersection of two electric fields can follow the frequency difference of the two fields. For 3D MRI-derived head models, the finite element method is used to compute the electric field induced by the time-varying magnetic field along with the electric field penetration depth and the activated volume for the specific coil parameters. A deeper stimulation with an acceptable spatial spread can be obtained by controlling the intersection of the fields by finding the optimal position and orientation of the two coils. Moreover, by changing the voltage ratio of the coils, and not their mechanical orientation, the intended area can be dynamically driven. The computational results show that the TI technique is an efficient approach to resolve the electric field depth-focality trade-off, which can be a reasonable alternative to complex coil designs. The system proposed in this paper shows a great promise for a more dynamic and focused magnetic stimulation.

OBJECTIVE: A transcranial magnetic stimulation system with programmable stimulus pulses and patterns is presented. The stimulus pulses of the implemented system expand beyond conventional damped cosine or near-rectangular pulses and approach an arbitrary waveform. METHODS: The desired stimulus waveform shape is defined as a reference signal. This signal controls the semiconductor switches of an H-bridge inverter to generate a high-power imitation of the reference. The design uses a new paradigm for TMS, applying pulse-width modulation with a non-resonant, high-frequency switching architecture to synthesize waveforms that leverages the low-pass filtering properties of neuronal cells. The modulation technique enables control of the waveform, frequency, pattern, and intensity of the stimulus. RESULTS: A system prototype was developed to demonstrate the technique. The experimental measurements demonstrate that the system is capable of generating stimuli up to 4 kHz with peak voltage and current values of ±1000 V and ±3600 A, respectively. The maximum transferred energy measured in the experimental validation was 100.4 Joules. To characterize repetitive TMS modalities, the efficiency of generating consecutive pulse triplets and quadruplets with interstimulus intervals of 1 ms was tested and verified. CONCLUSION: The implemented TMS device can generate consecutive rectangular pulses with a predetermined time interval, widths and polarities, enables the synthesis of a wide range of magnetic stimuli. SIGNIFICANCE: New waveforms promise functional advantages over the waveforms generated by current-generation TMS systems for clinical neuroscience research.

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the ‘Picostim DyNeuMo Mk-1’ (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However, closed-loop DBS based on LFPs requires simultaneous recording and stimulating, which remains a challenge due to persistent stimulation artefacts that distort underlying LFP biomarkers. Here we first investigate the nature of the stimulation-induced artefacts and review several techniques that have been proposed to deal with them. Then we propose a new method to synchronize the sampling clock with the stimulation pulse so that the stimulation artefacts are never sampled, while at the same time the Nyquist-Shannon theorem is satisfied for uninterrupted LFP recording. Test results show that this method achieves true uninterrupted artefact-free LFP recording over a wide frequency band and for a wide range of stimulation frequencies.Clinical relevance-The method proposed here provides continuous and artefact-free recording of LFPs close to the stimulation target, and thereby facilitates the implementation of more advanced closed-loop DBS using LFPs as feedback.

Neuromodulation therapies offer a unique opportunity for translating brain-computer interface (BCI) technologies into a clinical setting. Several diseases such as Parkinson's disease are effectively treated by invasive device stimulation therapies, and the addition of sensing and algorithm technology is an obvious evolutionary expansion of capabilities. In addition, this infrastructure might enable a roadmap of novel BCI technologies. While the initial applications are focused on epilepsy and movement disorders, the technology is potentially transferable to a broader base of disorders, including stroke and rehabilitation. The ultimate potential of BCI technology will be determined by forthcoming chronic evaluation in multiple neurologic disorders.

Invasive neural recording in humans shows promise for understanding the circuit basis of brain disorders. Most recordings have been done for short durations from externalized brain leads in hospital settings, or from first-generation implantable sensing devices that offer only intermittent brief streaming of time series data. Here we report the first human use of an implantable neural interface for wireless multichannel streaming of field potentials over long periods, with and without simultaneous therapeutic neurostimulation, untethered to receiving devices. Four Parkinson’s disease patients streamed bilateral 4-channel motor cortical and basal ganglia field potentials at home for over 500 hours, paired with wearable monitors that behaviorally categorize states of inadequate or excessive movement. Motor state during normal home activities was efficiently decoded using either supervised learning or unsupervised clustering algorithms. This platform supports adaptive deep brain stimulation, and may be widely applicable to brain disorders treatable by invasive neuromodulation.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson’s disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months., Brain Implants integrated with Local and Distributed Computing Devices provide a seamless interface between patients and physicians, and real-time intracranial EEG can be used to classify brain state (wake/sleep, pre-seizure, seizure), implement control policies for electrical stimulation, and track patient health. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Neuromodulation therapies offer a unique opportunity for translating brain-computer interface (BCI) technologies into a clinical setting. Several diseases such as Parkinson's disease are effectively treated by invasive device stimulation therapies, and the addition of sensing and algorithm technology is an obvious evolutionary expansion of capabilities. In addition, this infrastructure might enable a roadmap of novel BCI technologies. While the initial applications are focused on epilepsy and movement disorders, the technology is potentially transferable to a broader base of disorders, including stroke and rehabilitation. The ultimate potential of BCI technology will be determined by forthcoming chronic evaluation in multiple neurologic disorders.

Background It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5–500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.

Deep brain stimulation (DBS) is an effective treatment for common movement disorders and has been used to modulate neural activity through delivery of electrical stimulation to key brain structures. The long-term efficacy of stimulation in treating disorders, such as Parkinson’s disease and essential tremor, has encouraged its application to a wide range of neurological and psychiatric conditions. Nevertheless, adoption of DBS remains limited, even in Parkinson’s disease. Recent failed clinical trials of DBS in major depression, and modest treatment outcomes in dementia and epilepsy, are spurring further development. These improvements focus on interaction with disease circuits through complementary, spatially and temporally specific approaches. Spatial specificity is promoted by the use of segmented electrodes and field steering, and temporal specificity involves the delivery of patterned stimulation, mostly controlled through disease-related feedback. Underpinning these developments are new insights into brain structure–function relationships and aberrant circuit dynamics, including new methods with which to assess and refine the clinical effects of stimulation.

Synchronized oscillations are a ubiquitous feature of neuronal circuits and can modulate online information transfer and plasticity between brain areas. The disruption of these oscillatory processes is associated with the symptoms of several brain disorders. While conventional therapeutic high-frequency deep brain stimulation can perturb neuronal oscillations, manipulating the timing of oscillatory activity between areas more precisely could provide a more efficient and effective method of modulating these activities. Here we describe a prototype circuit for synchronizing the clocks between an active implantable and an external sensing and stimulation system that could be used to achieve this goal. Our specific focus is on synchronizing the systems for paired-associative stimulation. The ability to repetitively drive two brain regions with a fixed latency has specific implications for neural plasticity. Furthermore, the general concept can be applied for many potential applications involving distributed neural interfaces.

This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain–machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

Objective: Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform. Approach: We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System. Main Results: The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03/hr. Significance: We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

International audience; Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain-computer interfaces have directly linked cortical activity to electrical stimulation of muscles, and have thus restored grasping abilities after hand paralysis. Theoretically, this strategy could also restore control over leg muscle activity for walking. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges. Recently, it was shown in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion. Here we interface leg motor cortex activity with epidural electrical stimulation protocols to establish a brain-spine interface that alleviated gait deficits after a spinal cord injury in non-human primates. Rhesus monkeys (Macaca mulatta) were implanted with an intracortical microelectrode array in the leg area of the motor cortex and with a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain-spine interface in intact (uninjured) monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain-spine interface restored weight-bearing locomotion of the paralysed leg on a treadmill and overground. The implantable components integrated in the brain-spine interface have all been approved for investigational applications in similar human research, suggesting a practical translational pathway for proof-of-concept studies in people with spinal cord injury.

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. METHODS: We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. RESULTS: In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p

The Cleveland Neural Engineering Workshop (NEW) is a biennial meeting started in 2011 as an “unconference” to bring together leaders in the neural engineering and related fields. Since the first iteration of the meeting, NEW has evolved from “just getting together” to a more important purpose of creating, reviewing, and promoting a uniform strategic roadmap for the field. The purpose of this short report, as well as the companion 2015 and 2017 reports, is to provide a historical record of this meeting and the evolution of the roadmap. These reports more importantly establish a baseline for the next meeting to be held in June, 2019. The second Neural Engineering Workshop (NEW) was held in June 2013. The two-day workshop was hosted by the Cleveland Advanced Platform for Technology National Veterans Affairs Center, the Functional Electrical Stimulation National Veterans Affairs Center, and the Case Western Reserve University in Cleveland, Ohio. Participants identified seven areas of future focus in the field of neural engineering: active communications with users, advocacy (regulatory), network building (clinical practice), case studies (clinical and technical), early industrial feedback, value chain resources, engagement, and advocacy (funding). This proceedings document summarizes the meeting outcome.

Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the SummitTM RC+S “neural coprocessor,” that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. The paper describes the updated system architecture for the SummitTM RC+S system, the five custom integrated circuits required for bidirectional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked ActivaTM PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally-occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols.

While deep brain stimulation (DBS) has been used for decades to treat neurological movement disorders, the reliance on tonic stimulation may be limiting the therapy from reaching its maximum potential. Ongoing research into adaptive stimulation that adjusts therapy in response to sensed biomarkers of symptom or patient state has started to show potential benefits of closed-loop DBS therapies. However, much of the previous work on the adaptive DBS relied on systems or algorithms for acute implementation, rather than “embedded” algorithms for out of the clinic use. In this paper, we discuss and demonstrate the capabilities for a next generation investigational human-use implantable research platform for adaptive DBS studies that has expanded embedded algorithm capabilities to enable new adaptive DBS studies in the less controlled environments of a patient's daily life.

Deep Brain Stimulation (DBS) is an effective method for treating a variety of disorders. It may be possible to improve the therapy by providing a dynamic therapy that changes based on the needs of the patient. However, these dynamic closed-loop methods require implanted devices to effectively sense biomarkers of patient state while therapy is being delivered. In this paper we review improvements made in neural sensing during stimulation for a new implantable research tool, the Summit RC+S Investigational System. We then demonstrate the effectiveness of the Summit RC+S sensing components in both saline and an ovine model in both static and dynamic stimulation settings. The results of these tests demonstrate the ability of the Summit System to effectively sense neural signals for closed-loop DBS research purposes.