Long-Term Measurement of Impedance in Chronically Implanted Depth and Subdural Electrodes During Responsive Neurostimulation in Humans

Chronic recording and electrochemical performance of Utah microelectrode arrays implanted in rat motor cortex

Multisite implantable electrode arrays serve as a tool to understand cortical network connectivity and plasticity. Furthermore, they enable electrical stimulation to drive plasticity, study motor/sensory mapping, or provide network input for controlling brain-computer interfaces. Neurobehavioral rodent models are prevalent in studies of motor cortex injury and recovery as well as restoration of auditory/visual cues due to their relatively low cost and ease of training. Therefore, it is important to understand the chronic performance of relevant electrode arrays in rodent models. In this report, we evaluate the chronic recording and electrochemical performance of 16-channel Utah electrode arrays, the current state-of-the-art in pre-/clinical cortical recording and stimulation, in rat motor cortex over a period of 6 mo. The single-unit active electrode yield decreased from 52.8 ± 10.0 ( week 1) to 13.4 ± 5.1% ( week 24). Similarly, the total number of single units recorded on all electrodes across all arrays decreased from 106 to 15 over the same time period. Parallel measurements of electrochemical impedance spectra and cathodic charge storage capacity exhibited significant changes in electrochemical characteristics consistent with development of electrolyte leakage pathways over time. Additionally, measurements of maximum cathodal potential excursion indicated that only a relatively small fraction of electrodes (10-35% at 1 and 24 wk postimplantation) were capable of delivering relevant currents (20 µA at 4 nC/ph) without exceeding negative or positive electrochemical potential limits. In total, our findings suggest mainly abiotic failure modes, including mechanical wire breakage as well as degradation of conducting and insulating substrates. NEW & NOTEWORTHY Multisite implantable electrode arrays serve as a tool to record cortical network activity and enable electrical stimulation to drive plasticity or provide network feedback. The use of rodent models in these fields is prevalent. We evaluated chronic recording and electrochemical performance of 16-channel Utah electrode arrays in rat motor cortex over a period of 6 mo. We primarily observed abiotic failure modes suggestive of mechanical wire breakage and/or degradation of insulation.

Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable

Recent work has demonstrated the feasibility of minimally-invasive implantation of electrodes into a cortical blood vessel. However, the effect of the dura and blood vessel on recording signal quality is not understood and may be a critical factor impacting implementation of a closed-loop endovascular neuromodulation system. The present work compares the performance and recording signal quality of a minimally-invasive endovascular neural interface with conventional subdural and epidural interfaces. We compared bandwidth, signal-to-noise ratio, and spatial resolution of recorded cortical signals using subdural, epidural and endovascular arrays four weeks after implantation in sheep. We show that the quality of the signals (bandwidth and signal-to-noise ratio) of the endovascular neural interface is not significantly different from conventional neural sensors. However, the spatial resolution depends on the array location and the frequency of recording. We also show that there is a direct correlation between the signal-noise-ratio and classification accuracy, and that decoding accuracy is comparable between electrode arrays. These results support the consideration for use of an endovascular neural interface in a clinical trial of a novel closed-loop neuromodulation technology.

A forward-looking review of seizure prediction

PURPOSE OF REVIEW

Seizure prediction has made important advances over the last decade, with the recent demonstration that prospective seizure prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trials, with the aim of identifying research goals for the next decade.

RECENT FINDINGS

Unexpected results from these studies, including strong and highly individual relationships between spikes and seizures, diurnal patterns of seizure activity, and the coexistence of different seizure populations within individual patients exhibiting distinctive dynamics, have caused us to re-evaluate many prior assumptions in seizure prediction studies and suggest alternative strategies that could be employed in the search for algorithms providing greater clinical utility. Advances in analytical approaches, particularly deep-learning techniques, harbour great promise and in combination with less-invasive systems with sufficiently power-efficient computational capacity will bring broader clinical application within reach.

SUMMARY

We conclude the review with an exercise in wishful thinking, which asks what the ideal seizure prediction dataset would look like and how these data should be manipulated to maximize benefits for patients. The motivation for structuring the review in this way is to create a forward-looking, optimistic critique of the existing methodologies.

Longitudinal Follow-up of Impedance Drift in Deep Brain Stimulation Cases

Background

Impedance is an integral property of neuromodulation devices that determines the current delivered to brain tissue. Long-term variability in therapeutic impedance following deep brain stimulation (DBS) has not been extensively investigated across different brain targets. The aim was to evaluate DBS impedance drift and variability over an extended postoperative period across common DBS targets.

Methods

Retrospective data from 1,764 electrode leads were included and drawn from 866 DBS patients enrolled in the University of Florida Institutional Review Board-approved INFORM database and analyzed up to 84 months post implantation. An exploratory analysis was conducted to identify trends in impedances using a Mann–Kendall test of trend.

Results

There were 866 patients and 1,764 leads available for analysis. The majority of subjects had Parkinson’s disease (60.7%). The mean age at implantation was 58.7 years old and the mean follow-up time was 36.8 months. There were significant fluctuations in the mean impedance of all electrodes analyzed that largely stabilized by 6 months except for the subthalamic nucleus (STN) target, in which fluctuations persisted throughout the duration of follow-up with a continued downward trend (p < 0.001).

Discussion

The drift in impedance observed primarily within the first 6 months is in keeping with prior studies and is likely due to surgical micro-lesioning effects and brain parenchyma remodeling at the electrode–tissue interface, typically at values approximating 1,000 Ω. The differences in impedance trends over time in the various DBS targets may be due to underlying differences in structure and tissue composition.

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System

BACKGROUND

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs.

METHODS

Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided.

RESULTS

The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%.

CONCLUSION

This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.

Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings

Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.

Changes in the electrocorticogram after implantation of intracranial electrodes in humans: The implant effect

OBJECTIVE

Subacute and long-term electrocorticographic (ECoG) changes in ambulatory patients with depth and cortical strip electrodes were evaluated in order to determine the length of the implant effect.

METHODS

ECoG records were assessed in patients with medically intractable epilepsy who had depth and/or strip leads implanted in order to be treated with brain-responsive stimulation. Changes in total spectral power, band-limited spectral power, and spike rate were assessed.

RESULTS

121 patients participating in trials of the RNS® System had a total of 93994 ECoG records analyzed. Significant changes in total spectral power occurred from the first to second months after implantation, involving 55% of all ECoG channels (68% of strip and 47% of depth lead channels). Significant, but less pronounced, changes continued over the 2nd to 5th post-implant months, after which total power became more stable. Similar patterns of changes were observed within frequency bands and spike rate.

CONCLUSIONS

ECoG spectral power and spike rates are not stable in the first 5 months after implantation, presumably due to neurophysiological and electrode-tissue interface changes.

SIGNIFICANCE

ECoG data collected in the first 5 months after implantation of intracranial electrodes may not be fully representative of chronic cortical electrophysiology.

CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing

Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm 2 and 0.352 mm 2 of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044 mm 2 of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm 2 of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.

Intracranial EEG fluctuates over months after implanting electrodes in human brain

OBJECTIVE

Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings.

APPROACH

Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability.

MAIN RESULTS

A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant.

SIGNIFICANCE

These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

The circadian profile of epilepsy improves seizure forecasting

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

METHODS

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

RESULTS

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

CONCLUSIONS

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Ultrasoft microwire neural electrodes improve chronic tissue integration

Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response. In order to minimize the disparity between the device and the brain, we fabricated novel ultrasoft electrodes consisting of elastomers and conducting polymers with mechanical properties much more similar to those of brain tissue than previous neural implants. In this study, these ultrasoft microelectrodes were inserted and released using a stainless steel shuttle with polyethyleneglycol (PEG) glue. The implanted microwires showed functionality in acute neural stimulation. When implanted for 1 or 8weeks, the novel soft implants demonstrated significantly reduced inflammatory tissue response at week 8 compared to tungsten wires of similar dimension and surface chemistry. Furthermore, a higher degree of cell body distortion was found next to the tungsten implants compared to the polymer implants. Our results support the use of these novel ultrasoft electrodes for long term neural implants.

STATEMENT OF SIGNIFICANCE

One critical challenge to the translation of neural recording/stimulation electrode technology to clinically viable devices for brain computer interface (BCI) or deep brain stimulation (DBS) applications is the chronic degradation of device performance due to the inflammatory tissue reaction. While many hypothesize that soft and flexible devices elicit reduced inflammatory tissue responses, there has yet to be a rigorous comparison between soft and stiff implants. We have developed an ultra-soft microelectrode with Young's modulus lower than 1MPa, closely mimicking the brain tissue modulus. Here, we present a rigorous histological comparison of this novel ultrasoft electrode and conventional stiff electrode with the same size, shape and surface chemistry, implanted in rat brains for 1-week and 8-weeks. Significant improvement was observed for ultrasoft electrodes, including inflammatory tissue reaction, electrode-tissue integration as well as mechanical disturbance to nearby neurons. A full spectrum of new techniques were developed in this study, from insertion shuttle to in situ sectioning of the microelectrode to automated cell shape analysis, all of which should contribute new methods to the field. Finally, we showed the electrical functionality of the ultrasoft electrode, demonstrating the potential of flexible neural implant devices for future research and clinical use.

Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease

OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).

Chronic ambulatory electrocorticography from human speech cortex

Direct intracranial recording of human brain activity is an important approach for deciphering neural mechanisms of cognition. Such recordings, usually made in patients with epilepsy undergoing inpatient monitoring for seizure localization, are limited in duration and depend on patients' tolerance for the challenges associated with recovering from brain surgery. Thus, typical intracranial recordings, similar to most non-invasive approaches in humans, provide snapshots of brain activity in acute, highly constrained settings, limiting opportunities to understand long timescale and natural, real-world phenomena. A new device for treating some forms of drug-resistant epilepsy, the NeuroPace RNS® System, includes a cranially-implanted neurostimulator and intracranial electrodes that continuously monitor brain activity and respond to incipient seizures with electrical counterstimulation. The RNS System can record epileptic brain activity over years, but whether it can record meaningful, behavior-related physiological responses has not been demonstrated. Here, in a human subject with electrodes implanted over high-level speech-auditory cortex (Wernicke's area; posterior superior temporal gyrus), we report that cortical evoked responses to spoken sentences are robust, selective to phonetic features, and stable over nearly 1.5 years. In a second subject with RNS System electrodes implanted over frontal cortex (Broca's area, posterior inferior frontal gyrus), we found that word production during a naming task reliably evokes cortical responses preceding speech onset. The spatiotemporal resolution, high signal-to-noise, and wireless nature of this system's intracranial recordings make it a powerful new approach to investigate the neural correlates of human cognition over long timescales in natural ambulatory settings.

Local entrainment of oscillatory activity induced by direct brain stimulation in humans

In a quest for direct evidence of oscillation entrainment, we analyzed intracerebral electroencephalographic recordings obtained during intracranial electrical stimulation in a cohort of three medication-resistant epilepsy patients tested pre-surgically. Spectral analyses of non-epileptogenic cerebral sites stimulated directly with high frequency electrical bursts yielded episodic local enhancements of frequency-specific rhythmic activity, phase-locked to each individual pulse. These outcomes reveal an entrainment of physiological oscillatory activity within a frequency band dictated by the rhythm of the stimulation source. Our results support future uses of rhythmic stimulation to elucidate the causal contributions of synchrony to specific aspects of human cognition and to further develop the therapeutic manipulation of dysfunctional rhythmic activity subtending the symptoms of some neuropsychiatric conditions.

Temporal behavior of seizures and interictal bursts in prolonged intracranial recordings from epileptic canines

OBJECTIVE

Epilepsy is a chronic disorder, but seizure recordings are usually obtained in the acute setting. The chronic behavior of seizures and the interictal bursts that sometimes initiate them is unknown. We investigate the variability of these electrographic patterns over an extended period of time using chronic intracranial recordings in canine epilepsy.

METHODS

Continuous, yearlong intracranial electroencephalography (iEEG) recordings from four dogs with naturally occurring epilepsy were analyzed for seizures and interictal bursts. Following automated detection and clinician verification of interictal bursts and seizures, temporal trends of seizures, burst count, and burst-burst similarities were determined. One dog developed status epilepticus, the recordings of which were also investigated.

RESULTS

Multiple seizure types, determined by onset channels, were observed in each dog, with significant temporal variation between types. The first 14 days of invasive recording, analogous to the average duration of clinical invasive recordings in humans, did not capture the entirety of seizure types. Seizures typically occurred in clusters, and isolated seizures were rare. The count and dynamics of interictal bursts form distinct groups and do not stabilize until several weeks after implantation.

SIGNIFICANCE

There is significant temporal variability in seizures and interictal bursts after electrode implantation that requires several weeks to reach steady state. These findings, comparable to those reported in humans implanted with the NeuroPace Responsive Neurostimulator System (RNS) device, suggest that transient network changes following electrode implantation may need to be taken into account when interpreting or analyzing iEEG during evaluation for epilepsy surgery. Chronic, ambulatory iEEG may be better suited to accurately map epileptic networks in appropriate individuals.

Responsive Direct Brain Stimulation for Epilepsy

Closed-loop, responsive focal brain stimulation provides a new treatment option for patients with refractory partial onset seizures who are not good candidates for potentially curative epilepsy surgery. The first responsive brain neurostimulator (RNS® System, NeuroPace), provides stimulation directly to the seizure focus when abnormal electrocorticographic is detected. Seizure reductions of 44% at one year increase to 60 to 66% at years 3 to 6 of treatment. There is no negative impact on cognition and mood. Risks are similar to other implanted medical devices and therapeutic stimulation is not perceived.

Chronic impedance spectroscopy of an endovascular stent-electrode array

OBJECTIVE

Recently, we reported a minimally invasive stent-electrode array capable of recording neural signals from within a blood vessel. We now investigate the use of electrochemical impedance spectroscopy (EIS) measurements to infer changes occurring to the electrode-tissue interface from devices implanted in a cohort of sheep for up to 190 days.

APPROACH

In a cohort of 15 sheep, endovascular stent-electrode arrays were implanted in the superior sagittal sinus overlying the motor cortex for up to 190 days. EIS was performed routinely to quantify viable electrodes for up to 91 days. An equivalent circuit model (ECM) was developed from the in vivo measurements to characterize the electrode-tissue interface changes occurring to the electrodes chronically implanted within a blood vessel. Post-mortem histological assessment of stent and electrode incorporation into the wall of the cortical vessels was compared to the electrical impedance measurements.

MAIN RESULTS

EIS could be used to infer electrode viability and was consistent with x-ray analysis performed in vivo, and post-mortem evaluation. Viable electrodes exhibited consistent 1 kHz impedances across the 91 day measurement period, with the peak resistance frequency for the acquired data also stable over time. There was a significant change in 100 Hz phase angles, increasing from -67.8° ± 8.8° at day 0 to -43.8° ± 0.8° at day 91, which was observed to stabilize after eight days. ECM's modeled to the data suggested this change was due to an increase in the capacitance of the electrode-tissue interface. This was supported by histological assessment with >85% of the implanted stent struts covered with neointima and incorporated into the blood vessel within two weeks.

CONCLUSION

This work demonstrated that EIS could be used to determine the viability of electrode implanted chronically within a blood vessel. Impedance measurements alone were not observed to be a useful predictor of alterations occurring at the electrode tissue interface. However, measurement of 100 Hz phase angles was in good agreement with the capacitive changes predicted by the ECM and consistent with suggestions that this represents protein absorption on the electrode surface. 100 Hz phase angles stabilized after 8 days, consistent with histologically assessed samples.

SIGNIFICANCE

These findings demonstrate the potential application of this technology for use as a chronic neural recording system and indicate the importance of conducting EIS as a measure to identify viable electrodes and changes occurring at the electrode-tissue interface.

Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate

OBJECTIVE

Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue.

APPROACH

We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d.

MAIN RESULTS

Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation.

SIGNIFICANCE

These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.

Seizure Prediction: Science Fiction or Soon to Become Reality?

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

A novel seizure detection algorithm informed by hidden Markov model event states

OBJECTIVE

Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model.

APPROACH

We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control.

MAIN RESULTS

Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h(-1)). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)).

SIGNIFICANCE

This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

Critical review of the responsive neurostimulator system for epilepsy

Patients with medically refractory epilepsy have historically had few effective treatment options. Electrical brain stimulation for seizures has been studied for decades and ongoing technological refinements have made possible the development of an implantable electrical brain stimulator. The NeuroPace responsive neurostimulator was recently approved by the FDA for clinical use and the initial reports are encouraging. This device continually monitors brain activity and delivers an electric stimulus when abnormal activity is detected. Early reports of efficacy suggest that the device is well tolerated and offers a reduction in seizure frequency by approximately half at 2 years.

Proceedings of the Seventh International Workshop on Advances in Electrocorticography

The Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.

Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals

People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70-170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.

A programmable high-voltage compliance neural stimulator for deep brain stimulation in vivo

Deep brain stimulation (DBS) is one of the most effective therapies for movement and other disorders. The DBS neurosurgical procedure involves the implantation of a DBS device and a battery-operated neurotransmitter, which delivers electrical impulses to treatment targets through implanted electrodes. The DBS modulates the neuronal activities in the brain nucleus for improving physiological responses as long as an electric discharge above the stimulation threshold can be achieved. In an effort to improve the performance of an implanted DBS device, the device size, implementation cost, and power efficiency are among the most important DBS device design aspects. This study aims to present preliminary research results of an efficient stimulator, with emphasis on conversion efficiency. The prototype stimulator features high-voltage compliance, implemented with only a standard semiconductor process, without the use of extra masks in the foundry through our proposed circuit structure. The results of animal experiments, including evaluation of evoked responses induced by thalamic electrical stimuli with our fabricated chip, were shown to demonstrate the proof of concept of our design.

Electrical control of epilepsy

Epilepsy afflicts approximately 1-2% of the world's population. The mainstay therapy for treating the chronic recurrent seizures that are emblematic of epilepsy are drugs that manipulate levels of neuronal excitability in the brain. However, approximately one-third of all epilepsy patients get little to no clinical relief from this therapeutic regimen. The use of electrical stimulation in many forms to treat drug-refractory epilepsy has grown markedly over the past few decades, with some devices and protocols being increasingly used as standard clinical treatment. This article seeks to review the fundamental modes of applying electrical stimulation-from the noninvasive to the nominally invasive to deep brain stimulation-for the control of seizures in epileptic patients. Therapeutic practices from the commonly deployed clinically to the experimental are discussed to provide an overview of the innovative neural engineering approaches being explored to treat this difficult disease.

Extracting kinetic information from human motor cortical signals

Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60 ± 6%, mean ± SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.

Interventional psychiatry: why now?

Interventional psychiatry offers substantial therapeutic benefits in some neuropsychiatric disorders and enormous potential in treating others. However, as interventional diagnostics and therapeutics require specialized knowledge and skill foreign to many psychiatrists, the emerging subspecialty of interventional psychiatry must be more formally integrated into the continuum of psychiatric training to ensure both safe application and continued growth. By establishing training paradigms for interventional psychiatry, academic medical centers can help fill this knowledge gap. The cultivation of a properly trained cohort of interventional psychiatrists will better meet the challenges of treatment-resistant psychiatric illness through safe and ethical practice, while facilitating a more informed development and integration of novel neuromodulation techniques.

Variation in deep brain stimulation electrode impedance over years following electrode implantation

BACKGROUND

Deep brain stimulation (DBS) electrode impedance is a major determinant of current delivery to target tissues, but long-term variation in impedance has received little attention.

OBJECTIVES

To assess the relationship between electrode impedance and time in a large DBS patient population and characterize the relationship between contact activity and impedance.

METHODS

We collected retrospective impedance and programming data from 128 electrodes in 84 patients with Parkinson's disease, essential tremor or dystonia. Effects of time, contact activity, stimulation voltage and other parameters on impedance were assessed. We also examined impedance changes following contact activation and deactivation.

RESULTS

Impedance decreased by 73 Ω/year (p < 0.001), with 72% of contacts following a downward trend. Impedance was on average 163 Ω lower in active contacts (p < 0.001). Contact activation and inactivation were associated with a more (p < 0.001) and less (p = 0.016) rapid decline in impedance, respectively. Higher stimulation voltages were associated with lower impedance values (p < 0.001). Contact number and electrode model were also significant predictors of impedance.

CONCLUSIONS

Impedance decreases gradually in a stimulation-dependent manner. These trends have implications for long-term programming, the development of a closed-loop DBS device and current understanding of the electrode-tissue interface.

Chronic cortical and electromyographic recordings from a fully implantable device: preclinical experience in a nonhuman primate

OBJECTIVE

Analysis of intra- and perioperatively recorded cortical and basal ganglia local field potentials in human movement disorders has provided great insight into the pathophysiology of diseases such as Parkinson's, dystonia, and essential tremor. However, in order to better understand the network abnormalities and effects of chronic therapeutic stimulation in these disorders, long-term recording from a fully implantable data collection system is needed.

APPROACH

A fully implantable investigational data collection system, the Activa® PC + S neurostimulator (Medtronic, Inc., Minneapolis, MN), has been developed for human use. Here, we tested its utility for extended intracranial recording in the motor system of a nonhuman primate. The system was attached to two quadripolar paddle arrays: one covering sensorimotor cortex, and one covering a proximal forelimb muscle, to study simultaneous cortical field potentials and electromyography during spontaneous transitions from rest to movement.

MAIN RESULTS

Over 24 months of recording, movement-related changes in physiologically relevant frequency bands were readily detected, including beta and gamma signals at approximately 2.5 μV/[Formula: see text] and 0.7 μV/[Formula: see text], respectively. The system architecture allowed for flexible recording configurations and algorithm triggered data recording. In the course of physiological analyses, sensing artifacts were observed (∼1 μVrms stationary tones at fixed frequency), which were mitigated either with post-processing or algorithm design and did not impact the scientific conclusions. Histological examination revealed no underlying tissue damage; however, a fibrous capsule had developed around the paddles, demonstrating a potential mechanism for the observed signal amplitude reduction.

SIGNIFICANCE

This study establishes the usefulness of this system in measuring chronic brain and muscle signals. Use of this system may potentially be valuable in human trials of chronic brain recording in movement disorders, a next step in the design of closed-loop neurostimulation paradigms.

Proceedings of the Fourth International Workshop on Advances in Electrocorticography

The Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11-12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain-machine interface, as well as fresh insights into brain electrical stimulation.