Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques.

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

Long-term stability of neural signals from microwire arrays implanted in common marmoset motor cortex and striatum.

Current neuroprosthetics rely on stable, high quality recordings from chronically implanted microelectrode arrays (MEAs) in neural tissue. While chronic electrophysiological recordings and electrode failure modes have been reported from rodent and larger non-human primate (NHP) models, chronic recordings from the marmoset model have not been previously described. The common marmoset is a New World primate that is easier to breed and handle compared to larger NHPs and has a similarly organized brain, making it a potentially useful smaller NHP model for neuroscience studies. This study reports recording stability and signal quality of MEAs chronically implanted in behaving marmosets. Six adult male marmosets, trained for reaching tasks, were implanted with either a 16-channel tungsten microwire array (five animals) or a Pt-Ir floating MEA (one animal) in the hand-arm region of the primary motor cortex (M1) and another MEA in the striatum targeting the nucleus accumbens (NAcc). Signal stability and quality was quantified as a function of array yield (active electrodes that recorded action potentials), neuronal yield (isolated single units during a recording session), and signal-to-noise ratio (SNR). Out of 11 implanted MEAs, nine provided functional recordings for at least three months, with two arrays functional for 10 months. In general, implants had high yield, which remained stable for up to several months. However, mechanical failure attributed to MEA connector was the most common failure mode. In the longest implants, signal degradation occurred, which was characterized by gradual decline in array yield, reduced number of isolated single units, and changes in waveform shape of action potentials. This work demonstrates the feasibility of longterm recordings from MEAs implanted in cortical and deep brain structures in the marmoset model. The ability to chronically record cortical signals for neural prosthetics applications in the common marmoset extends the potential of this model in neural interface research.

Common marmoset (Callithrix jacchus) as a primate model for behavioral neuroscience studies.

BACKGROUND: The common marmoset (Callithrix jacchus) has been proposed as a suitable bridge between rodents and larger primates. They have been used in several types of research including auditory, vocal, visual, pharmacological and genetics studies. However, marmosets have not been used as much for behavioral studies. NEW METHOD: Here we present data from training 12 adult marmosets for behavioral neuroscience studies. We discuss the husbandry, food preferences, handling, acclimation to laboratory environments and neurosurgical techniques. In this paper, we also present a custom built "scoop" and a monkey chair suitable for training of these animals. RESULTS: The animals were trained for three tasks: 4 target center-out reaching task, reaching tasks that involved controlling robot actions, and touch screen task. All animals learned the center-out reaching task within 1-2 weeks whereas learning reaching tasks controlling robot actions task took several months of behavioral training where the monkeys learned to associate robot actions with food rewards. COMPARISON TO EXISTING METHOD: We propose the marmoset as a novel model for behavioral neuroscience research as an alternate for larger primate models. This is due to the ease of handling, quick reproduction, available neuroanatomy, sensorimotor system similar to larger primates and humans, and a lissencephalic brain that can enable implantation of microelectrode arrays relatively easier at various cortical locations compared to larger primates. CONCLUSION: All animals were able to learn behavioral tasks well and we present the marmosets as an alternate model for simple behavioral neuroscience tasks.

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.

OBJECTIVE: For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. APPROACH: Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture. MAIN RESULTS: In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold. SIGNIFICANCE: The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank.

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Kernel temporal differences for neural decoding.

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces.

DARPA-funded efforts in the development of novel brain-computer interface technologies.

The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.

A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces.

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.

Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions.

Changes in biotic and abiotic factors can be reflected in the complex impedance spectrum of the microelectrodes chronically implanted into the neural tissue. The recording surface of the tungsten electrode in vivo undergoes abiotic changes due to recording site corrosion and insulation delamination as well as biotic changes due to tissue encapsulation as a result of the foreign body immune response. We reported earlier that large changes in electrode impedance measured at 1 kHz were correlated with poor electrode functional performance, quantified through electrophysiological recordings during the chronic lifetime of the electrode. There is a need to identity the factors that contribute to the chronic impedance variation. In this work, we use numerical simulation and regression to equivalent circuit models to evaluate both the abiotic and biotic contributions to the impedance response over chronic implant duration. COMSOL® simulation of abiotic electrode morphology changes provide a possible explanation for the decrease in the electrode impedance at long implant duration while biotic changes play an important role in the large increase in impedance observed initially.

A tensor-product-kernel framework for multiscale neural activity decoding and control.

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.

Abiotic-biotic characterization of Pt/Ir microelectrode arrays in chronic implants.

Pt/Ir electrodes have been extensively used in neurophysiology research in recent years as they provide a more inert recording surface as compared to tungsten or stainless steel. While floating microelectrode arrays (FMA) consisting of Pt/Ir electrodes are an option for neuroprosthetic applications, long-term in vivo functional performance characterization of these FMAs is lacking. In this study, we have performed comprehensive abiotic-biotic characterization of Pt/Ir arrays in 12 rats with implant periods ranging from 1 week up to 6 months. Each of the FMAs consisted of 16-channel, 1.5 mm long, and 75 µm diameter microwires with tapered tips that were implanted into the somatosensory cortex. Abiotic characterization included (1) pre-implant and post-explant scanning electron microscopy (SEM) to study recording site changes, insulation delamination and cracking, and (2) chronic in vivo electrode impedance spectroscopy. Biotic characterization included study of microglial responses using a panel of antibodies, such as Iba1, ED1, and anti-ferritin, the latter being indicative of blood-brain barrier (BBB) disruption. Significant structural variation was observed pre-implantation among the arrays in the form of irregular insulation, cracks in insulation/recording surface, and insulation delamination. We observed delamination and cracking of insulation in almost all electrodes post-implantation. These changes altered the electrochemical surface area of the electrodes and resulted in declining impedance over the long-term due to formation of electrical leakage pathways. In general, the decline in impedance corresponded with poor electrode functional performance, which was quantified via electrode yield. Our abiotic results suggest that manufacturing variability and insulation material as an important factor contributing to electrode failure. Biotic results show that electrode performance was not correlated with microglial activation (neuroinflammation) as we were able to observe poor performance in the absence of neuroinflammation, as well as good performance in the presence of neuroinflammation. One biotic change that correlated well with poor electrode performance was intraparenchymal bleeding, which was evident macroscopically in some rats and presented microscopically by intense ferritin immunoreactivity in microglia/macrophages. Thus, we currently consider intraparenchymal bleeding, suboptimal electrode fabrication, and insulation delamination as the major factors contributing toward electrode failure.

Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

Intraspinal transplantation and modulation of donor neuron electrophysiological activity.

Rat fetal spinal cord (FSC) tissue, naturally enriched with interneuronal progenitors, was introduced into high cervical, hemi-resection (Hx) lesions. Electrophysiological analyses were conducted to determine if such grafts exhibit physiologically-patterned neuronal activity and if stimuli which increase respiratory motor output also alter donor neuron bursting. Three months following transplantation, the bursting activity of FSC neurons and the contralateral phrenic nerve were recorded in anesthetized rats during a normoxic baseline period and brief respiratory challenges. Spontaneous neuronal activity was detected in 80% of the FSC transplants, and autocorrelation of action potential spikes revealed distinct correlogram peaks in 87% of neurons. At baseline, the average discharge frequency of graft neurons was 13.0 ± 1.7 Hz, and discharge frequency increased during a hypoxic respiratory challenge (p<0.001). Parallel studies in unanesthetized rats showed that FSC tissue recipients had larger inspiratory tidal volumes during brief hypoxic exposures (p<0.05 vs. C2Hx rats). Anatomical connectivity was explored in additional graft recipients by injecting a transsynaptic retrograde viral tracer (pseudorabies virus, PRV) directly into matured transplants. Neuronal labeling occurred throughout graft tissues and also in the host spinal cord and brainstem nuclei, including those associated with respiratory control. These results underscore the neuroplastic potential of host-graft interactions and training approaches to enhance functional integration within targeted spinal circuitry.

An adaptive brain actuated system for augmenting rehabilitation.

For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

OBJECTIVE: Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. APPROACH: Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. MAIN RESULTS: The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. SIGNIFICANCE: By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI.

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.

A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces.

This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.

A highly compliant serpentine shaped polyimide interconnect for front-end strain relief in chronic neural implants.

While the signal quality of recording neural electrodes is observed to degrade over time, the degradation mechanisms are complex and less easily observable. Recording microelectrodes failures are attributed to different biological factors such as tissue encapsulation, immune response, and disruption of blood-brain barrier (BBB) and non-biological factors such as strain due to micromotion, insulation delamination, corrosion, and surface roughness on the recording site (1-4). Strain due to brain micromotion is considered to be one of the important abiotic factors contributing to the failure of the neural implants. To reduce the forces exerted by the electrode on the brain, a high compliance 2D serpentine shaped electrode cable was designed, simulated, and measured using polyimide as the substrate material. Serpentine electrode cables were fabricated using MEMS microfabrication techniques, and the prototypes were subjected to load tests to experimentally measure the compliance. The compliance of the serpentine cable was numerically modeled and quantitatively measured to be up to 10 times higher than the compliance of a straight cable of same dimensions and material.

A trial of scheduled deep brain stimulation for Tourette syndrome: moving away from continuous deep brain stimulation paradigms.

OBJECTIVE: To collect the information necessary to design the methods and outcome variables for a larger trial of scheduled deep brain stimulation (DBS) for Tourette syndrome. DESIGN: We performed a small National Institutes of Health-sponsored clinical trials planning study of the safety and preliminary efficacy of implanted DBS in the bilateral centromedian thalamic region. The study used a cranially contained constant-current device and a scheduled, rather than the classic continuous, DBS paradigm. Baseline vs 6-month outcomes were collected and analyzed. In addition, we compared acute scheduled vs acute continuous vs off DBS. SETTING: A university movement disorders center. PATIENTS: Five patients with implanted DBS. MAIN OUTCOME MEASURE: A 50% improvement in the Yale Global Tic Severity Scale (YGTSS) total score. RESULTS Participating subjects had a mean age of 34.4 (range, 28-39) years and a mean disease duration of 28.8 years. No significant adverse events or hardware-related issues occurred. Baseline vs 6-month data revealed that reductions in the YGTSS total score did not achieve the prestudy criterion of a 50% improvement in the YGTSS total score on scheduled stimulation settings. However, statistically significant improvements were observed in the YGTSS total score (mean [SD] change, -17.8 [9.4]; P=.01), impairment score (-11.3 [5.0]; P=.007), and motor score (-2.8 [2.2]; P=.045); the Modified Rush Tic Rating Scale Score total score (-5.8 [2.9]; P=.01); and the phonic tic severity score (-2.2 [2.6]; P=.04). Continuous, off, and scheduled stimulation conditions were assessed blindly in an acute experiment at 6 months after implantation. The scores in all 3 conditions showed a trend for improvement. Trends for improvement also occurred with continuous and scheduled conditions performing better than the off condition. Tic suppression was commonly seen at ventral (deep) contacts, and programming settings resulting in tic suppression were commonly associated with a subjective feeling of calmness. CONCLUSIONS: This study provides safety and proof of concept that a scheduled DBS approach could improve motor and vocal tics in Tourette syndrome. Refinements in neurostimulator battery life, outcome measure selection, and flexibility in programming settings can be used to enhance outcomes in a future larger study. Scheduled stimulation holds promise as a potential first step for shifting movement and neuropsychiatric disorders toward more responsive neuromodulation approaches. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01329198.

Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate representation of neural signal (i.e., spikernel and generalized linear model). Moreover, after a significant perturbation of the neuron circuit, the control scheme can successfully drive the elicited responses close to the original target responses.