Journal of Neural Engineering

ObjectiveIntraneural nerve interfaces often operate in a monopolar configuration with a common and distant ground electrode. This configuration leads to a wide spreading of the electric field. Therefore, this approach is suboptimal for intraneural nerve interfaces when selective stimulation is required. ApproachWe designed a multilayer electrode array embedding three-dimensional concentric bipolar electrodes. First, we validated the higher stimulation selectivity of this new electrode array compared to classical monopolar stimulation using simulations. Next, we compared them in-vivo by intraneural stimulation of the rabbit optic nerve and recording evoked potentials in the primary visual cortex. Main resultsSimulations showed that three-dimensional concentric bipolar electrodes provide a high localisation of the electric field in the tissue so that electrodes are electrically independent even for high electrode density. Experiments in-vivo highlighted that this configuration leads to evoked responses with lower amplitude and more localised cortical patterns due to the fewer fibres activated by the electric stimulus in the nerve. SignificanceHighly focused electric stimulation is crucial to achieving high selectivity in fibre activation. The multilayer array embedding three-dimensional concentric bipolar electrodes improves selectivity in optic nerve stimulation. This approach is suitable for other neural applications, including bioelectronic medicine.

Retinal prostheses must be able to activate cells in a selective way in order to restore high-fidelity vision. However, inadvertent activation of far-away retinal ganglion cells (RGCs) through electrical stimulation of axon bundles can produce irregular and poorly controlled percepts, limiting artificial vision. Therefore, the problem of axon bundle activation can be defined as the axonal stimulation of RGCs with unknown soma and receptive field locations, typically outside the electrode array. Here, a new algorithm is presented that utilizes electrical recordings to determine the stimulation current amplitudes above which bundle activation occurs. The method exploits several spatiotemporal characteristics of electrically-evoked spikes to overcome the challenge of detecting small axonal spikes in extracellular recordings. The algorithm was validated using large-scale ex vivo stimulation and recording experiments in macaque retina, by comparing algorithmically and manually identified bundle activation thresholds. The algorithm could be used in a closed-loop manner by a future epiretinal prosthesis to reduce poorly controlled visual percepts associated with bundle activation. The method may also be applicable to other types of retinal implants and to cortical implants. ContributionsPT developed the algorithm and analyzed the data, with input from SMi and EJC. NB and NS helped with the analysis. SMa and LG performed dissections and collected the data. PT and VFH performed manual identification. PH, AS and AML developed and supported recording hardware and software. PT, EJC and SMi wrote the manuscript. NS and SMa edited it. EJC and SMi supervised the project.

Brain-computer interfaces (BCIs) can restore function for individuals with neuro-logical disorders and have the potential to transform the way people interact with digital systems. However, the development of advanced BCI applications, such as fluent speech synthesis, is dependent on the underlying information transfer capacity of the physical neural interface employed. A significant barrier to progress has been the lack of standardized, application-agnostic methods for benchmarking BCI system performance prior to clinical trials. Here, we introduce SONIC, a novel preclinical benchmarking paradigm designed to evaluate the information transfer rate (ITR) of a BCI system. This paradigm treats the brain and BCI as a noisy communication channel, where information is sent into the brain via precisely controlled sensory stimuli and read out by the neural interface. We implemented this paradigm in an ovine model by presenting rapid sequences of pure tones while recording neural activity from the primary auditory cortex with the Paradromics Connexus(C) BCI, a fully implanted system utilizing high-density intracortical micro-electrode arrays with wireless power and data transmission. A convolutional neural network was used to decode tones based on neural features. Our results demonstrate an achieved ITR of over 200 bits per second (bps), which is the highest reported BCI ITR to date. For reference, this rate exceeds the linguistic information content of human speech. This ITR is achieved with a total neural interface, filtering, and data aggregation delay of 56 milliseconds. Further analysis demonstrated that ITR remains high (> 100 bps) for the lowest total delay tested (11 ms), supporting the needs of latency-sensitive applications (e.g., direct speech synthesis). This work establishes a new benchmark for BCI performance and demonstrates that the Connexus BCI possesses the bandwidth necessary to support highly advanced applications. This benchmark provides a robust framework for preclinical BCI evaluation, enabling principled system design optimization to accelerate the translation of next-generation neurotechnology.

ObjectivePeripheral nerve neurotechnologies hold significant promise as avenues for new closed-loop clinical treatments. However, analysis tools for nerve recordings - a key component of closed-loop nerve technologies - remain underdeveloped compared to brain-focused methods. This study introduces and explores the performance of two novel nerve signal analysis techniques which rely on a defining feature of peripheral nerve signals: the reliable conduction velocity of signals transmitted by axons in nerves. ApproachWe test the capabilities of the introduced cross-correlation and spike delay velocity analysis techniques both in silico on synthetic nerve signals and on in vivo nerve signals acquired from freely-moving rats. Main resultsOur findings show that both techniques can be successfully employed to extract transmission direction and velocity information from nerve cuff recordings. Notably, cross-correlation analysis can be employed to detect neural signals of very low signal-to-noise ratio, otherwise undetectable by typical spike detection approaches. SignificanceOur findings provide new techniques to both enhance detection and extract new information in the form of velocity data from nerve recordings. As axon signal conduction direction and velocity is tightly linked to neural function, these techniques can support new research into peripheral nervous system function and new therapeutic approaches driven by neural interfaces.

A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (i) access to all cortical regions in a minimally invasive manner; (ii) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (iii) access to spatially distant multiple brain regions that constitute distributed cognitive networks. We present a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode but combined with radially positioned microelectrodes and using the lead body to shield LFP sources, enabling directional sensitivity and scalability, referred to as the DISC array. As predicted by our electro-quasistatic models, DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critically, DISC demonstrated equivalent fidelity to conventional electrodes at the macroscale and uniquely, revealed stereoscopic information about current source density. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting.

Brain-Computer Interfaces (BCI) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and that remain unchanged throughout the BCI use. However, this approach might be inadequate in effectively handling the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use as parameters are expected to change, all the more in a real-time setting. To address this limitation, we have developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI-control setting. Twenty healthy participants performed two BCI-control sessions based on the imagery of two syllables, using a static LDA and the other the adaptive LDA classifier, in randomized order. In this real-time BCI-control task, the adaptive classifier led to better performances than the static one. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets acquired with the same syllable imagery task. These findings highlight the effectiveness and re-liability of adaptive LDA classifiers for real-time imagined speech decoding, and its interest for non-invasive EEG-based BCI notably characterized by low decoding accuracies.

ObjectiveComputational models and visualization toolboxes for Deep Brain Stimulation (DBS) increasingly rely on pre-computed electric field libraries to estimate the Volume of Tissue Activated (VTA). However, the boundary conditions (BCs) and source models used to generate these fields vary widely across studies, and there is currently no experimental consensus regarding which parameters most accurately reflect the physical device output. The objective of this study was to experimentally validate the electric potential distribution of directional DBS leads in order to determine the optimal Finite Element Method (FEM) configuration. ApproachThe voltage distribution surrounding a Boston Scientific Vercise Gevia directional lead was mapped in a saline phantom using a custom high-precision robotic scanning system. Experimental measurements were compared against six FEM configurations that varied in source formulation (Dirichlet vs. Neumann boundary conditions) and ground definitions. For each configuration, the resulting VTA volume was computed to assess the clinical impact of modeling assumptions. ResultsThe FEM configuration implementing a Dirichlet (voltage) boundary condition on the active contact with a grounded implantable pulse generator (IPG) surface demonstrated the highest accuracy, achieving a Symmetric Mean Absolute Percent Error (SMAPE) of less than 9% across all contact levels. In contrast, conventional current-controlled simulations employing Neumann boundary conditions with disparate ground definitions substantially overestimated electric field spread. Suboptimal boundary condition selection resulted in an approximate 67% overestimation of VTA volume (137 mm3 vs. 82 mm3) relative to the experimentally validated model. SignificanceAlthough clinical DBS systems operate as current sources, standard Neumann (current density) boundary conditions do not adequately represent the equipotential behavior of the electrode-tissue interface, resulting in nearly a two-fold error in predicted VTA volume. To improve the validity of predictive clinical models, we recommend the use of Dirichlet boundary conditions derived from the device operating impedance (V = Itarget x Zmeasured) rather than conventional current density specifications.

ObjectiveTo preserve the encoding of visual information in prosthetic vision as close to natural as possible, subretinal photovoltaic implants, which replace the lost photoreceptors, strive to stimulate the second-order retinal neurons, the bipolar cells, while avoiding direct activation of the downstream retinal ganglion cells. To assess the range of such selective subretinal activation, we implanted the devices in rodent models of retinal degeneration and measured the stimulation thresholds based on the visually evoked potentials. After assessment of the bipolar cell-mediated thresholds, direct activation of retinal ganglion cells was measured following intraocular injection of synaptic blockers. Since these chemicals are toxic to the retina, this procedure can only be done once in each animal. ApproachWe developed a machine-learning model that identifies the stimulation pathway directly from the recorded visually evoked potentials, eliminating the need for synaptic blockers. The model was trained on recordings from rats implanted with PRIMA subretinal arrays and evaluated on two additional implant architectures, a second rat species, and a different anesthesia protocol. Main ResultsThe classifier achieved a balanced accuracy of 92% in cross-validation on the training data. Generalization to all unseen experimental conditions yielded an average balanced accuracy of 91%. Integrated Gradients analysis showed that combined bipolar and ganglion cell responses were driven by the early P1 component, while bipolar cell responses relied on later waveform components, consistent with thalamocortical processing dynamics. SignificanceThe described computational alternative to pharmacological blockers should improve the experimental throughput, allow multiple recordings over the lifetime of the same animal, and might be applicable to optimization of the stimulation settings in patients.

Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to electrical stimulation, while minimizing tradeoffs between computational expense and accuracy. Previous work has developed high accuracy artificial neural network (ANN) and convolutional neural network (CNN) surrogate models that predict activation of individual, myelinated axons, to extracellular electrical stimulation for subsets of DBS programming configurations. Moreover, more traditional machine learning methods including extreme gradient boosting (XGBoost) have been used effectively for peripheral-nerve single-fiber activation predictions. We build upon the previous work and compare ANN, CNN and XGBoost methods to a much expanded set of electrode programming configurations including: monopolar, bipolar, tripolar, quadrupolar, multiple monopolar, and multiple cases of directional leads. Training used datasets generated from a finite-element model of an implanted DBS lead together with multi-compartment cable models of synthetically generated axons. We evaluated the machine learning predictors using white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Our ANN and CNN models successfully predicted neural fiber activation for almost all electrode configurations with low error, expanding the scope of our previous predictor model. Results also showed key limitations of XGBoost models and superior performance of CNNs for more complex electrostatic fields of the directional leads.

ObjectivePRIMA subretinal implants provide prosthetic vision to patients blinded by age-related macular degeneration, with acuity closely matching the sampling limit of the pixel pitch: a single 100 {micro}m pixel per line of a letter corresponds to 20/420 acuity. Decreasing the pixel size in the same flat geometry is difficult due to the constrained electric field, especially considering a 40 {micro}m thick debris layer separating the implant from the target neurons. Here we optimize the electrode design to help overcome such limitations. MethodsAn end-to-end modeling pipeline combines the retinal photovoltaic implant simulator (RPSim) based on the Xyce circuit simulator with an interface to COMSOL Multiphysics for electric field modelling. It was used to generate and characterize implants in an open-loop sampling-based optimization. Implant performance was evaluated with respect to voltage drop across bipolar cells (representing the stimulation strength), pattern contrast, and neural selectivity. ResultsThe highest selectivity in stimulation of bipolar cells was achieved with arrays having active electrodes on pillars and return electrodes connected in a mesh surrounding the photovoltaic pixels in the array. Such a design, even with pixels down to 20 {micro}m, provides stimulation strength exceeding, and contrast similar to that of flat 100 {micro}m PRIMA pixels. ConclusionUsing a novel 3-D electrode design, the pitch of the photovoltaic array can be decreased to 20 {micro}m, while providing performance that exceeds the flat 100 {micro}m PRIMA pixels. SignificanceIn humans, 20 {micro}m resolution on the retina corresponds to a visual acuity of 20/80 - a five times improvement compared to the current clinical device.

BackgroundNon-invasive neuromodulation technologies have advanced considerably. Yet, precise and focal activation of deep brain regions remains challenging due to the rapid attenuation of electric fields across the scalp, skull and brain surface. ObjectiveWe present FLOATES (FLOAting Transcranial Electrical Stimulation), a novel approach that employs an untethered wire implanted in the brain which passively relays currents injected transcranially from the brain surface to deep brain regions, achieving focused stimulation deep within the brain. MethodsWe validated FLOATES through a combination of simulations, benchtop testing, and in vivo rodent studies. The benchtop experiments confirmed the ability to relay the field across the floating wire. Rodent studies demonstrated capability to stimulate deep brain regions in vivo. ResultsOur simulation and benchtop testing results indicate that FLOATES can deliver significantly higher electric fields to subcortical regions compared to conventional transcranial stimulation approaches. Further in-vivo results demonstrated deep subthalamic nuclei stimulation to evoke limb motor responses and demonstrated a significantly lower motor threshold compared to transcranial stimulation. Finite element simulations reveal that the efficiency of FLOATES depends on several key parameters including input field strength, wire length and diameter, exposed electrode area, impedance, and tip geometry. Simulations using a human-sized head model suggest that electric fields sufficient for brain stimulation can be obtained with reasonable currents injected to the scalp. ConclusionTogether, these results establish a theoretical and experimental foundation for FLOATES as a minimally invasive and spatially precise brain stimulation platform in modulating deep neural circuits implicated in neuropsychiatric and movement disorders.

BackgroundPeripheral nerve stimulation (PNS) is most effective when specific nerve fiber subpopulations are activated, while minimizing off-target activation, which may cause undesirable side effects. This selectivity depends primarily on electrode design and charge delivery. We hypothesized that selective PNS could be achieved through electrode placement and intrafascicular electric field steering using Longitudinal Intrafascicular Electrodes (LIFEs). MethodsLIFEs were implanted into the tibial fascicle of the sciatic nerve of 17 anesthetized adult rats. We tested whether electrodes positioned at different cross-sectional and longitudinal locations within the same fascicle, together with different electric field-steering approaches produced distinct activation patterns in the gastrocnemius lateralis muscle. Muscle responses were measured using high-density epimysial electromyography (HD-eEMG). ResultsElectrodes placed at different locations within the same fascicle activated distinct muscle regions, demonstrating intrafascicular selectivity. Bipolar stimulation recruited nerve fibers differently than monopolar stimulation, showing that electric field steering can further shape the selective recruitment. In both configurations, increasing the stimulation amplitude produced a graded increase in muscle activation. Furthermore, our findings demonstrated that HD-eEMG is an effective tool for evaluating intrafascicular selectivity. ConclusionThese findings suggest that improving on-target selectivity may support next-generation bioelectronic therapies with better outcomes and fewer side effects, potentially enabling more precise, organ-specific neuromodulation. Using multiple intrafascicular electrodes may provide two complementary strategies for enhancing selectivity: strategic intrafascicular placement to access different fiber subpopulations and bipolar configurations to steer recruitment beyond what a single electrode can achieve.

ObjectiveUtah arrays are widely used in both humans and non-human primates (NHPs) for intracortical brain-computer interfaces (BCIs), primarily for detecting electrical signals from cortical tissue to decode motor commands. Recently, these arrays have also been applied to deliver electrical stimulation aimed at restoring sensory functions. A key challenge limiting their longevity is the micromotion between the array and cortical tissue, which may induce mechanical strain in surrounding tissue and contribute to performance decline. This strain, due to mechanical mismatch, can exacerbate glial scarring around the implant, reducing the efficacy of Utah arrays in recording neuronal activity and delivering electrical stimulation. ApproachTo investigate this, we employed a finite element model (FEM) to predict tissue strains resulting from micromotion. Main ResultsOur findings indicated that strain profiles around edge and corner electrodes were greater than those around interior shanks, affecting both maximum and average strains within 50 {micro}m of the electrode tip. We then correlated these predicted tissue strains with in-vivo electrode performance metrics. We found negative correlations between 1 kHz impedance and tissue strains in human motor arrays and NHP area V4 arrays at 1-mo, 1-yr, and 2-yrs post-implantation. In human motor arrays, the peak-to-peak waveform voltage (PTPV) and signal-to-noise ratio (SNR) of spontaneous activity were also negatively correlated with strain. Conversely, we observed a positive correlation between the evoked SNR of multi-unit activity and strain in NHP area V4 arrays. SignificanceThis study establishes a spatial dependence of electrode performance in Utah arrays that correlates with tissue strain.

A key challenge in correlating neuronal activity with brain function is the limited sampling probability of neuronal activity in real time. It is crucial to increase the sampling probability substantially and in real time. We hypothesized that 3-dimensional (3D) neural probes offer faster and stronger prospects for cell yield than 2D electrode arrays. We simulated a 1000-neuron neuronal network, mimicking the granular layer of the barrel cortical column, recorded signals from inserted 384 electrodes (organized in 3D or 2D), and sorted units using Kilosrt or triangulation localization. We demonstrated that 3D electrode arrays converge more space for triangulation spike sorting than 2D probes do. 3D neural probes, together with triangulation, could isolate up to 80% of the simulated 1000 neurons (as ground truth) and have a cell yield of up to 5, which is, to the best of our knowledge, significantly higher than standard 2D electrodes with Kilosort or triangulation. With a signal-to-noise ratio (SNR) of 10, which is close to the real world, the simulation data suggest that 3D electrode arrays in a face-centric cubic (FCC) arrangement provide a better cell yield. However, larger background noise (e.g. an SNR of 1, which can be improved with lower electrode impedance) has a stronger impact on the triangulation spike sorting. Since only the peak value of spikes are required for triangulation localization, the computing loading is much less than spike waveform-based spike sorting approach. Thus, combining 3D electrode arrays with triangulation localization is ideal for real-time spike sorting. Thus, we demonstrated that adding one more dimension in designing neural probes can dramatically increase cell yield and speed up isolating neuronal unit activity. We, for the first time, provide a tool for utilizing computer simulations to optimize the design of neural electrode arrays before time-consuming probe fabrication.

Objective/BackgroundAcquiring bioelectric signals from many single neurons in primate brain remains challenging. Chronic implants offer a reasonable channel count ([~]100) but sample only a small, fixed region of the cortex. Acutely inserted electrodes can sample from a wider region by making new penetrations each day. The aim of this study was to develop an active dense CMOS probe and experimental procedures to demonstrate acute large-scale single unit recordings from behaving monkeys. MethodsA single-shank CMOS probe was specifically designed for intracortical macaque recordings. The device is based on SiNAPS technology, with additional multiplexing circuits to minimize output lines. Synchronous sampling at 20 kHz/channel from >2k electrode-pixels is achieved with multiple probe systems. Experiments were performed in behaving macaques, achieving multiple day insertions in the motor cortex. Methods were developed to extract spontaneous spiking times of antidromically-identified neurons. ResultsThe probe (10.7 mm in length, 158 m in width, 50 m in thickness) provides a regular array of 1024 electrodes (14 x 14 m2) arranged in 4 columns with an interelectrode pitch of 30 m. Dural penetration is eased by a small pilot hole; the optimized insertion procedure allows recordings from many different sites. Some cells were identified by antidromic activation as pyramidal tract neurons, which project to the spinal cord. ConclusionThis probe configuration can reach the anterior bank of the central sulcus, which contains many corticospinal cells that connect directly to motoneurons. SignificanceThis study is an important advance in the toolkit of primate neurophysiology.

Vagus nerve stimulation (VNS), a well-established application of electrical neuromodulation, has been effective in treating dis-orders such as epilepsy and depression. Recent neuroscientific advancements have identified further potential in VNS, calling for technical advancements to explore these possibilities. To address this, we developed a neuromodulation platform that can perform both neural activity excitation and inhibition, to further explore spatially and directionally selective stimulation. The platform can be powered wirelessly through ultrasound and includes a charge balancing mechanism to enhance stimulation safety. The platform performs biphasic and monophasic voltage-controlled stimulation, using 6 electrodes for excitation and 2 for inhibition. The pulse width can be varied between 0 and 1500 {micro}s, and the pulse repetition rate between 1 Hz and 1 kHz for excitation and 1 kHz to 50 kHz for inhibition. The implemented charge balancing mechanism uses a digital PID controller to reduce the voltage offset at the stimulation site to values as low as 2 mV. The introduced neuromodulation platform provides a versatile experimental tool intended for, but not limited to, VNS in pre-clinical and clinical settings. It is designed using only commercially available components and is easily reproducible, allowing to explore the possibilities of VNS while identifying the necessary components and processes for developing a miniaturized version.

Modern neuroelectronic interfaces have shown great potential to diagnose conditions, address neurological dysfunction, and advance neuroscientific knowledge. However, neural interface systems today require tethered connections that restrict mobility, prevent testing across ecological contexts, and inhibit clinical translation to at-home use. Fully implantable commercial systems have previously been developed, but exhibit significant constraints, including a bulky design, limited modularity, low bandwidth, or unidirectional communication (e.g. deep brain stimulation systems, DBS; spinal cord stimulation systems, SCS). Here, we have developed the Modular Bionic Interface (MBI), a system composed of a fully implantable device and a worn unit for high-bandwidth, bidirectional interfacing with the nervous system. The MBI can record high fidelity electrophysiological signals and deliver spatiotemporally modulated electrical stimulation for clinical and research purposes through flexible interaction with third party implantable devices. We performed benchtop evaluation to validate the recording and stimulation capabilities of the MBI across a diverse range of inputs and outputs. We then evaluated the MBI system in vivo through chronic implantation within a sheep, where results were stable for the length of evaluation, over three months. While connected to an actively powered, third-party high-resolution spinal cord stimulation electrode array, the MBI system was able to deliver stimulation to evoke lower extremity motor responses and record spinal compound action potentials evoked by peripheral nerve and spinal stimulation. Through rigorous evaluation, we demonstrate a fully implantable system with a small footprint capable of high-resolution, bi-directional communication with the nervous system via modular connections to third-party devices. We expect that modular devices will further our ability to treat complex neurological disease and injury.

ObjectiveMultipolar intracranial electrical brain stimulation (iEBS) is a method that has potential to improve clinical applications of mono- and bipolar iEBS. Current tools for researching multipolar iEBS are proprietary, can have high entry costs, lack flexibility in managing different stimulation parameters and electrodes, and can include clinical features unnecessary for the requisite exploratory research. This is a factor limiting the progress in understanding and applying multipolar iEBS effectively. To address these challenges, we developed the Bioelectric Router for Adaptive Isochronous Neuro stimulation (BRAINS) board. ApproachThe BRAINS board is a cost-effective and customizable device designed to facilitate multipolar stimulation experiments across a 16-channel electrode array using common research electrode setups. The BRAINS board interfaces with a microcontroller, allowing users to configure each channel for cathodal or anodal input, establish a grounded connection, or maintain a floating state. The design prioritizes ease of integration by leveraging standard tools like a microcontroller and an analog signal isolators while providing options to customize setups according to experimental conditions. It also ensures output isolation, reduces noise, and supports remote configuration changes for rapid switching of electrode states. To test the efficacy of the board, we performed bench-top validation of monopolar, bipolar, and multipolar stimulation regimes. The same regimes were tested in vivo in mouse primary visual cortex and measured using Neuropixel recordings. Main ResultsThe BRAINS board demonstrates no meaningful differences in Root Mean Square Error (RMSE) noise or signal-to-noise ratio compared to the baseline performance of the isolated stimulator alone. The board supports configuration changes at a rate of up to 600 Hz without introducing residual noise, enabling high-frequency switching necessary for temporally multiplexed multipolar stimulation. SignificanceThe BRAINS board represents a significant advancement in exploratory brain stimulation research by providing a user-friendly, customizable, open source, and cost-effective tool capable of conducting sophisticated, reproducible, and finely controlled stimulation experiments. With a capacity for effectively real-time information processing and efficient parameter exploration the BRAINS board can enhance both exploratory research on iEBS and enable improved clinical use of multipolar and closed-loop iEBS.

ObjectiveOur prior studies have demonstrated that lateral spinal cord stimulation can evoke somatosensory percepts in the missing foot in individuals with a lower-limb amputation. However, subjects reported concurrent sensations in their residual limb. In this study, we evaluate the hypothesis that using high-density paddle electrodes with smaller contact sizes, and multipolar stimulation configurations could evoke more focal sensations in the foot over a wide range of stimulation amplitudes. ApproachWe used a combination of electrophysiology and computational modelling methods to investigate the selective activation of distal nerve branches in response to lateral spinal cord stimulation in cats. In six acute feline experiments, we performed an L3-S1 laminectomy and placed custom 32-electrode paddles laterally over the dura of the spinal cord. We recorded antidromic action potentials in the distal branches of the sciatic and femoral nerve trunks in response to stimulation using three contact diameters (150, 500 and 1000 {micro}m) and two stimulation configurations - monopolar and bipolar stimulation. We replicated the neural recruitment patterns from those experiments in a computational model of the feline lumbar spinal cord. We then used the model to examine neural recruitment with 1.8 mm and 2.5 mm contacts, as well as a tripolar guarded-cathode configuration. Main resultsIn the electrophysiology experiments, the 500 {micro}m-diameter electrodes achieved the most selective nerve activation (68%) compared to 62% for both 150 and 1000 {micro}m-diameter electrodes. The minimum amplitudes for recruiting nerve branches (i.e., threshold) as well as the dynamic ranges were largely similar for the different contact diameters (median: 35 {micro}A) and stimulation configurations (30 {micro}A for bipolar stimulation; 35 {micro}A for monopolar stimulation). The computational model reproduced the finding that selectivity did not differ significantly among the three contact sizes tested in cat experiments, though it revealed that increasing contact diameter above 1000 {micro}m raised the minimum amplitude required for selective activation and reduced spinal root selectivity. Across both approaches, we consistently recruited large-diameter afferents that are critical for somatosensory applications of spinal cord stimulation. SignificanceOur results indicate that, relative to clinical electrodes, reducing the contact diameter of stimulation electrodes can evoke focal sensations, but further reductions below 1000 {micro}m may fail to improve selectivity. This study highlights potential constraints with achieving focal selectivity that are not dependent on the design of the electrodes.

ObjectiveCervical epidural spinal cord stimulation (SCS) can facilitate upper-limb motor recovery, but electrode configurations that optimally recruit motor circuits remain unclear. This study systematically evaluated how electrode position, size, orientation, and waveform influence the efficacy of forelimb motor activation in rats, with the goal of identifying configurations that minimize stimulation thresholds of evoked responses across multiple muscles. ApproachCustom microfabricated arrays of electrodes were implanted over the C6 dorsal root entry zone (DREZ) in eight adult female Sprague Dawley rats. A circular array was used to vary current orientation in 45{degrees} increments, while a linear array was used to optimize mediolateral electrode position. The linear array included both small (250 {micro}m) and large (500 {micro}m) contacts to assess size effects at mediolateral positions. Stimulation consisted of biphasic and pseudomonophasic waveforms with bipolar or distant return, and a high-definition montage to probe spatial focality around the DREZ. Motor-evoked potentials (MEPs) recorded via implanted EMG electrodes were analyzed in six forelimb muscles. Thresholds, estimated from recruitment curves using a hierarchical Bayesian model, were compared within-rat using pairwise t-tests with correction for multiple comparisons. ResultsStimulation over the DREZ yielded the lowest thresholds and efficacy decreased progressively with medial or lateral displacement relative to DREZ. In the circular array, rostro-caudal current orientation was most effective, reducing thresholds by up to 58% relative to latero-medial orientation (p = 0.0007). In the linear array, large contacts were significantly more effective than small contacts at the lateral position, reducing thresholds by 45% (p = 0.034). Cathodal stimulation was more effective than anodal, and high-definition montages reduced efficacy compared to distant returns. Across all tested parameters, position and orientation had the greatest influence on efficacy, with optimal conditions combining DREZ targeting and rostro-caudal oriented current flow. SignificanceMaximum efficacy was achieved for cervical SCS with electrodes positioned over the DREZ, rostro-caudal current flow, larger contacts, and cathodal stimulation. These design principles that more effectively engage spinal circuitry could reduce the current required and thereby improve SCS systems for upper-limb motor restoration.