Mood and quality of life in patients treated with brain-responsive neurostimulation: The value of earlier intervention.

OBJECTIVE: To establish whether earlier treatment using direct brain-responsive neurostimulation for medically intractable focal-onset seizures is associated with better mood and Quality of Life (QoL) compared to later treatment intervention. METHODS: Data were retrospectively analyzed from prospective clinical trials of a direct brain-responsive neurostimulator (RNS® System) for treatment of adults with medically intractable focal-onset epilepsy. Participants completed the Quality of Life in Epilepsy Inventory (QOLIE-31) yearly through 9 years of follow-up and the Beck Depression Inventory-II (BDI-II) through 2 years of follow-up. Changes in each assessment after treatment with responsive neurostimulation were calculated for patients who began treatment within 10 years of seizure onset (early) and those who began treatment 20 years or more after seizure onset (late). RESULTS: The median duration of epilepsy was 18.3 years at enrollment. At 9 years, both the early (N = 51) and late (N = 109) treatment groups experienced similar and significant reductions in the frequency of disabling seizures (73.4% and 77.8%, respectively). The early treatment patients had significant improvements in QoL and mood. However, the late treatment patients not only failed to show these improvements but also declined in the emotional QoL subscale. CONCLUSIONS: Patients treated with brain-responsive neurostimulation earlier in the course of their epilepsy show significant improvements in multiple domains of QoL and mood that are not observed in patients treated later in the course of their epilepsy despite similar efficacy in seizure reduction. Even with similar and substantial reductions in seizure frequency, the comorbidities of uncontrolled epilepsy may be less responsive to treatment when too many years have passed. The results of this study suggest that, as with resective and ablative surgery, treatment with brain-responsive neurostimulation should be delivered as early as possible in the course of medically resistant epilepsy to maximize the opportunity for improvements in mood and QoL.

The RNS System: brain-responsive neurostimulation for the treatment of epilepsy.

Introduction: Epilepsy affects more than 1% of the US population, and over 30% of adults with epilepsy do not respond to antiseizure medications without life-impacting medication-related side effects. Resection of the seizure focus is not an option for many patients because it would cause unacceptable neurological or cognitive harm. For these patients, neuromodulation has emerged as a nondestructive, effective, and safe alternative. The NeuroPace® RNS® System, the only brain-responsive neurostimulation device, records neural activity from leads placed at one or two seizure foci. When the neurostimulator detects epileptiform activity, as defined for each patient by his or her physician, brief pulses of electrical stimulation are delivered to normalize the activity.Areas covered: This review describes the RNS System, the results of multi-year clinical trials, and the research discoveries enabled by the chronic ambulatory brain data collected by the RNS System.Expert commentary: Brain-responsive neurostimulation could potentially be used to treat any episodic neurological disorder that's accompanied by a neurophysiological biomarker of severity. Combining advanced machine learning approaches with the chronic ambulatory brain data collected by the RNS System could eventually enable automatic fine-tuning of detection and stimulation for each patient, creating a general-purpose neurotechnological platform for precision medicine.

Brain-responsive corticothalamic stimulation in the centromedian nucleus for the treatment of regional neocortical epilepsy.

OBJECTIVE: The aim of the study was to determine if corticothalamic responsive stimulation targeting the centromedian nucleus of the thalamus (CMT) is a potential treatment for neocortical epilepsies with regional onsets. METHODS: We assessed efficacy and safety of CMT and neocortical responsive stimulation, detection, and stimulation programming, methods for implantation, and location and patterns of electrographic seizure onset and spread in 7 patients with medically intractable focal seizures with a regional neocortical onset. RESULTS: The median follow-up duration was 17 months (average: 17 months, range: 8-28 months). The median % reduction in disabling seizures (excludes auras) in the 7 patients was 88% (mean: 80%, range: 55-100%). The median % reduction in all seizure types (disabling + auras) was 73% (mean: 67%, range: 15-94%). There were no adverse events related to implantation of the responsive neurostimulator and leads or related to the delivery of responsive stimulation. Stimulation-related contralateral paresthesias were addressed by adjusting stimulation parameters in the clinic during stimulation testing. Electrographic seizures were detected in the CMT and neocortex in all seven patients. Four patients had simultaneous or near simultaneous seizure onsets in the neocortex and CMT and three had onsets in the neocortex with spread to the CMT. CONCLUSION: In this small series of patients with medically intractable focal seizures and regional neocortical onset, responsive neurostimulation to the neocortex and CMT improved seizure control and was well tolerated. SIGNIFICANCE: Responsive corticothalamic neurostimulation of the CMT and neocortex is a potential treatment for patients with regional neocortical epilepsies.

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

OBJECTIVE: Neurostimulation devices that deliver electrical impulses to the nervous system are widely used to treat seizures in patients with medically refractory epilepsy, but the effects of these therapies on sleep are incompletely understood. Vagus nerve stimulation can contribute to obstructive sleep apnea, and thalamic deep brain stimulation can cause sleep disruption. A device for brain-responsive neurostimulation (RNS® System, NeuroPace, Inc) is well tolerated in clinical trials, but potential effects on sleep are unknown. METHODS: Six adults with medically refractory focal epilepsy treated for at least six months with the RNS System underwent a single night of polysomnography (PSG). RNS System lead locations included mesial temporal and neocortical targets. Sleep stages and arousals were scored according to standard guidelines. Stimulations delivered by the RNS System in response to detections of epileptiform activity were identified by artifacts on scalp electroencephalography. RESULTS: One subject was excluded for technical reasons related to unreliable identification of stimulation artifact on EEG during PSG. In the remaining five subjects, PSG showed fragmented sleep with frequent arousals. Arousal histograms aligned to stimulations revealed a significant peak in arousals just before stimulation. In one of these subjects, the arousal peak began before stimulation and extended ~1 seconds after stimulation. A peak in arousals occurring only after stimulation was not observed. SIGNIFICANCE: In this small cohort of patients, brain-responsive neurostimulation does not appear to disrupt sleep. If confirmed in larger studies, this could represent a potential clinical advantage of brain-responsive neurostimulation over other neurostimulation modalities.

Replay of Learned Neural Firing Sequences during Rest in Human Motor Cortex.

The offline "replay" of neural firing patterns underlying waking experience, previously observed in non-human animals, is thought to be a mechanism for memory consolidation. Here, we test for replay in the human brain by recording spiking activity from the motor cortex of two participants who had intracortical microelectrode arrays placed chronically as part of a brain-computer interface pilot clinical trial. Participants took a nap before and after playing a neurally controlled sequence-copying game that consists of many repetitions of one "repeated" sequence sparsely interleaved with varying "control" sequences. Both participants performed repeated sequences more accurately than control sequences, consistent with learning. We compare the firing rate patterns that caused the cursor movements when performing each sequence to firing rate patterns throughout both rest periods. Correlations with repeated sequences increase more from pre- to post-task rest than do correlations with control sequences, providing direct evidence of learning-related replay in the human brain.

Brain-computer interfaces for communication.

Locked-in syndrome (LIS) is characterized by an inability to move or speak in the presence of intact cognition and can be caused by brainstem trauma or neuromuscular disease. Quality of life (QoL) in LIS is strongly impaired by the inability to communicate, which cannot always be remedied by traditional augmentative and alternative communication (AAC) solutions if residual muscle activity is insufficient to control the AAC device. Brain-computer interfaces (BCIs) may offer a solution by employing the person's neural signals instead of relying on muscle activity. Here, we review the latest communication BCI research using noninvasive signal acquisition approaches (electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy) and subdural and intracortical implanted electrodes, and we discuss current efforts to translate research knowledge into usable BCI-enabled communication solutions that aim to improve the QoL of individuals with LIS.

Electrocorticographic events from long-term ambulatory brain recordings can potentially supplement seizure diaries.

PURPOSE: To determine the feasibility of using epileptiform events detected by continuous electrocorticographic monitoring via a brain-responsive neurostimulation system to supplement patient-maintained seizure diaries. METHODS: Data were retrospectively analyzed from a randomized controlled trial of brain-responsive neurostimulation (RNS® System) for adjunctive treatment of medically intractable focal onset seizures in 191 subjects. The long-term (≥3 months) correspondence between daily counts of diary-reported seizures and device-recorded "long epileptiform events" (LEs), a proxy for electrographic seizures (ESs), was assessed using cross-correlation and logistic generalized estimating equation models. RESULTS: Diary-reported seizures and LEs significantly co-varied across days in 124 patients whose detection settings were held constant, with a significantly higher correlation in 54 patients (44 %) whose LEs were usually ESs (high concordance patients). There were more days in which LEs were detected than days in which patients reported a seizure (positive predictive value (PPV): 34 %). On days when there were no LEs, there were typically no diary-reported seizures (negative predictive value (NPV): 90 %). In patients with a high concordance between LEs and ESs, the PPV and NPV were both slightly higher, 43 % (35-52 %) and 93 % (95 % CI: 86-97 %) respectively. CONCLUSION: Although LEs can substantially outnumber diary reported seizures, the high across-day correlation and strong NPV between LEs and diary seizures suggests that LEs recorded by the RNS® System could potentially supplement seizure diaries by providing an objective biomarker for relative seizure burden.

Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.

Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.

Brain-responsive neurostimulation for epilepsy (RNS® System).

Controlled clinical trials in adults with medically intractable focal seizures treated with the RNS® System demonstrate that closed-loop responsive neurostimulation to the seizure focus reduces the frequency of disabling seizures, is well tolerated, and is acceptably safe. Seizure reductions begin with initiation of treatment and continue over time, reaching median reductions of 75% after 9 years of treatment. Treatment with responsive cortical stimulation is also associated with improvement in quality of life and cognitive function related to the functional area being treated. In addition, the RNS System's chronic ambulatory electrocorticographic monitoring provides unprecedented insight into each patient's disease management, and into the study of epilepsy itself, in ways that may enhance the treatment of epilepsy in the future.

Cortical control of a tablet computer by people with paralysis.

General-purpose computers have become ubiquitous and important for everyday life, but they are difficult for people with paralysis to use. Specialized software and personalized input devices can improve access, but often provide only limited functionality. In this study, three research participants with tetraplegia who had multielectrode arrays implanted in motor cortex as part of the BrainGate2 clinical trial used an intracortical brain-computer interface (iBCI) to control an unmodified commercial tablet computer. Neural activity was decoded in real time as a point-and-click wireless Bluetooth mouse, allowing participants to use common and recreational applications (web browsing, email, chatting, playing music on a piano application, sending text messages, etc.). Two of the participants also used the iBCI to "chat" with each other in real time. This study demonstrates, for the first time, high-performance iBCI control of an unmodified, commercially available, general-purpose mobile computing device by people with tetraplegia.

A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.

OBJECTIVE: Recent reports indicate that making better assumptions about the user's intended movement can improve the accuracy of decoder calibration for intracortical brain-computer interfaces. Several methods now exist for estimating user intent, including an optimal feedback control model, a piecewise-linear feedback control model, ReFIT, and other heuristics. Which of these methods yields the best decoding performance? METHODS: Using data from the BrainGate2 pilot clinical trial, we measured how a steady-state velocity Kalman filter decoder was affected by the choice of intention estimation method. We examined three separate components of the Kalman filter: dimensionality reduction, temporal smoothing, and output gain (speed scaling). RESULTS: The decoder's dimensionality reduction properties were largely unaffected by the intention estimation method. Decoded velocity vectors differed by <5% in terms of angular error and speed vs. target distance curves across methods. In contrast, the smoothing and gain properties of the decoder were greatly affected (> 50% difference in average values). Since the optimal gain and smoothing properties are task-specific (e.g. lower gains are better for smaller targets but worse for larger targets), no one method was better for all tasks. CONCLUSION: Our results show that, when gain and smoothing differences are accounted for, current intention estimation methods yield nearly equivalent decoders and that simple models of user intent, such as a position error vector (target position minus cursor position), perform comparably to more elaborate models. Our results also highlight that simple differences in gain and smoothing properties have a large effect on online performance and can confound decoder comparisons.

Rapid calibration of an intracortical brain-computer interface for people with tetraplegia.

OBJECTIVE: Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration. APPROACH: We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF). MAIN RESULTS: Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration. SIGNIFICANCE: These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.

Feedback control policies employed by people using intracortical brain-computer interfaces.

OBJECTIVE: When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders. APPROACH: We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI. MAIN RESULTS: We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high. SIGNIFICANCE: Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.

Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces.

Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to "click" during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as "non-click" those periods that the kinematic decoder's retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of "retrospectively labeled" decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.

Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface.

Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

Clinical translation of a high-performance neural prosthesis.

Neural prostheses have the potential to improve the quality of life of individuals with paralysis by directly mapping neural activity to limb- and computer-control signals. We translated a neural prosthetic system previously developed in animal model studies for use by two individuals with amyotrophic lateral sclerosis who had intracortical microelectrode arrays placed in motor cortex. Measured more than 1 year after implant, the neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants.

Reprint of "Non-causal spike filtering improves decoding of movement intention for intracortical BCIs".

BACKGROUND: Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials. NEW METHOD: We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals. RESULTS: Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. CONCLUSIONS: Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.

Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome.

A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs.

BACKGROUND: Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials. NEW METHOD: We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals. RESULTS: Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. CONCLUSIONS: Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.

Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia.

OBJECTIVE: Brain-computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using 'open-loop' (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for 'closed-loop' (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing. APPROACH: Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data. MAIN RESULTS: Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements. SIGNIFICANCE: Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional 'adaptive' advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.