Single Unit publications

Background: Electrical stimulation targeting the motor thalamus (Mthal) represents an emerging strategy for regulating motor cortical activity patterns, however the fundamental mechanisms and optimal stimulation configurations are not well established. Objective: To characterize how burst Mthal stimulation modulates cortical activity and to identify stimulation parameters that maximize cortical co-firing via thalamocortical rebound mechanisms. Methods: We conducted acute electrophysiological experiments in anesthetized rats using Mthal stimulation with systematically manipulated burst stimulation parameters (i.e., number of pulses and pulse width) in the low-frequency band (1-8 Hz) modulation cycle. Cortical responses across large areas covering primary and secondary motor cortex were recorded. Additionally, intracortical recordings were performed for detailed spike activity monitoring during stimulation. Cortical activity patterns in local field potentials and spike activity were analyzed to quantify the effects of Mthal stimulation on motor cortex. Computational thalamocortical circuit models were employed to explore the mechanistic basis of frequency-selective modulation effects. Results: Burst Mthal stimulation elicited tunable cortical excitation, with optimal modulation achieved at 3-4 Hz. This preferred frequency corresponded to the natural rebound timing of thalamocortical (TC) cells and generated maximal cortical co-firing and synchronization. Higher stimulation frequencies (e.g., 8 Hz) resulted in suppressed and diminished responses. Computational modeling further validated that the cellular mechanism of the TC cell rebound excitation leads to frequency-dependent cortical modulation. Conclusion: Burst Mthal stimulation demonstrates frequency-selective modulation of cortical network excitability, with 3-4 Hz protocols providing optimal enhancement of synchronized activity through exploitation of thalamocortical rebound dynamics. These results establish motor thalamic stimulation as a promising and efficient methodology for precise cortical network control and offer mechanistic foundations for developing therapeutic neuromodulation interventions aimed at motor system rehabilitation.

Sympathetic hyperactivity is a common feature observed in both primary and secondary hypertension, and neuromodulation-based therapies targeting this overactivity are under active investigation. The intermediolateral nucleus (IML) in the spinal cord plays a central role in sympathetic regulation, and its direct stimulation induces a transient, frequency-dependent reduction in blood pressure. However, this effect gradually diminishes as the baroreflex restores blood pressure to baseline. Therefore, this study aims to develop a closed-loop (CL) stimulation system that adjusts parameters in real-time based on neural activity recorded from the IML and to enhance and prolong the transient antihypertensive effect. This strategy was evaluated in normotensive and angiotensin II-induced hypertensive rat models and compared to open-loop (OL) stimulation of equal intensity. In both models, CL stimulation produced a more sustained blood pressure reduction than that of OL stimulation. These findings suggest that CL spinal cord stimulation offers a more effective and durable therapeutic option for treating hypertension.

Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.

Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.

The value associated with reward is sensitive to external factors, such as the time between the choice and reward delivery as classically manipulated in temporal discounting tasks. Subjective preference for two reward options is dependent on objective variables of reward magnitude and reward delay. Single neuron correlates of reward value have been observed in regions, including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies show cortico-striatal-limbic network activity related to subjective preferences. To explore how oscillatory dynamics represent reward processing across brain regions, we measured local field potentials of rats performing a temporal discounting task. Our goal was to use a data-driven approach to identify an electrophysiological marker that correlates with reward preference. We found that reward-locked oscillations at beta frequencies signaled the magnitude of reward and decayed with longer temporal delays. Electrodes in orbitofrontal/medial prefrontal cortex, anterior insula, ventral striatum, and amygdala individually increased power and were functionally connected at beta frequencies during reward outcome. Beta power during reward outcome correlated with subjective value as defined by a computational model fit to the discounting behavior. These data suggest that cortico-striatal beta oscillations are a reward signal correlated, which may represent subjective value and hold potential to serve as a biomarker and potential therapeutic target.

This protocol was used to collect data now published in the Journal of Neural Engineering, Spatially selective stimulation of the pig vagus nerve to modulate target effect versus side effect using the multi-contact ImThera stimulating device. https://doi.org/10.1088/1741-2552/acb3fd Dataset has been published to pennsieve.io and can be found at: https://doi.org/10.26275/efbj-8evl

Intracortical microstimulation (ICMS) is commonly used in many experimental and clinical paradigms; however, its effects on the activation of neurons are still not completely understood. To document the responses of cortical neurons in awake nonhuman primates to stimulation, we recorded single-unit activity while delivering single-pulse stimulation via Utah arrays implanted in primary motor cortex (M1) of three macaque monkeys. Stimuli between 5 and 50 μA delivered to single channels reliably evoked spikes in neurons recorded throughout the array with delays of up to 12 ms. ICMS pulses also induced a period of inhibition lasting up to 150 ms that typically followed the initial excitatory response. Higher current amplitudes led to a greater probability of evoking a spike and extended the duration of inhibition. The likelihood of evoking a spike in a neuron was dependent on the spontaneous firing rate as well as the delay between its most recent spike time and stimulus onset. Tonic repetitive stimulation between 2 and 20 Hz often modulated both the probability of evoking spikes and the duration of inhibition; high-frequency stimulation was more likely to change both responses. On a trial-by-trial basis, whether a stimulus evoked a spike did not affect the subsequent inhibitory response; however, their changes over time were often positively or negatively correlated. Our results document the complex dynamics of cortical neural responses to electrical stimulation that need to be considered when using ICMS for scientific and clinical applications.

In humans, the level of consciousness is assessed by quantifying the spatiotemporal complexity of cortical responses using Perturbational Complexity Index (PCI) and related PCIst (st, state transitions). Here we validate PCIst in freely moving rats and mice by showing that it is lower in NREM sleep and slow wave anesthesia than in wake or REM sleep, as in humans. We then show that (1) low PCIst is associated with the occurrence of an OFF period of neuronal silence; (2) stimulation of deep, but not superficial, cortical layers leads to reliable PCIst changes across sleep/wake and anesthesia; (3) consistent PCIst changes are independent of which single area is being stimulated or recorded, except for recordings in mouse prefrontal cortex. These experiments show that PCIst can reliably measure vigilance states in unresponsive animals and support the hypothesis that it is low when an OFF period disrupts causal interactions in cortical networks.

This protocol was used to collect data now published in the Journal of Neural Engineering, Spatially selective stimulation of the pig vagus nerve to modulate target effect versus side effect using the multi-contact ImThera stimulating device. https://doi.org/10.1088/1741-2552/acb3fd Dataset has been published to pennsieve.io and can be found at: https://doi.org/10.26275/efbj-8evl

Animals can capitalize on invariance in the environment by learning and automating highly consistent actions; however, they must also remain flexible and adapt to environmental changes. It remains unclear how primary motor cortex (M1) can drive precise movements, yet also support behavioral exploration when faced with consistent errors. Using a reach-to-grasp task in rats, along with simultaneous electrophysiological monitoring in M1 and dorsolateral striatum (DLS), we find that behavioral exploration to overcome consistent task errors is closely associated with tandem increases in M1 and DLS neural variability; subsequently, consistent ensemble patterning returns with convergence to a new successful strategy. We also show that compared to reliably patterned intracranial microstimulation in M1, variable stimulation patterns result in significantly greater movement variability. Our results thus indicate that motor and striatal areas can flexibly transition between two modes, reliable neural pattern generation for automatic and precise movements versus variable neural patterning for behavioral exploration.

Spatial hearing in cochlear implant (CI) patients remains a major challenge, with many early deaf users reported to have no measurable sensitivity to interaural time differences (ITDs). Deprivation of binaural experience during an early critical period is often hypothesized to be the cause of this shortcoming. However, we show that neonatally deafened (ND) rats provided with precisely synchronized CI stimulation in adulthood can be trained to lateralize ITDs with essentially normal behavioral thresholds near 50 μs. Furthermore, comparable ND rats show high physiological sensitivity to ITDs immediately after binaural implantation in adulthood. Our result that ND-CI rats achieved very good behavioral ITD thresholds, while prelingually deaf human CI patients often fail to develop a useful sensitivity to ITD raises urgent questions concerning the possibility that shortcomings in technology or treatment, rather than missing input during early development, may be behind the usually poor binaural outcomes for current CI patients.

Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top–down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell's spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.

Background: Despite advances in sophisticated robotic hands, intuitive control of and sensory feedback from these prostheses has been limited to only 3-degrees-of-freedom (DOF) with 2 sensory percepts in closed-loop control. A Utah Slanted Electrode Array (USEA) has been used in the past to provide up to 81 sensory percepts for human amputees. Here, we report on the advanced capabilities of multiple USEAs implanted in the residual peripheral arm nerves of human amputees for restoring control of 5 DOF and sensation of up to 131 proprioceptive and cutaneous hand sensory percepts. We also demonstrate that USEA-restored sensory percepts provide a useful source of feedback during closed-loop virtual prosthetic hand control. Methods: Two 100-channel USEAs were implanted for 4-5 weeks, one each in the median and ulnar arm nerves of two human subjects with prior long-duration upper-arm amputations. Intended finger and wrist positions were decoded from neuronal firing patterns via a modified Kalman filter, allowing subjects to control many movements of a virtual prosthetic hand. Additionally, USEA microstimulation was used to evoke numerous sensory percepts spanning the phantom hand. Closed-loop control was achieved by stimulating via an electrode of the ulnar-nerve USEA while recording and decoding movement via the median-nerve USEA. Results: Subjects controlled up to 12 degrees-of-freedom during informal, 'freeform' online movement decode sessions, and experienced up to 131 USEA-evoked proprioceptive and cutaneous sensations spanning the phantom hand. Independent control was achieved for a 5-DOF real-time decode that included flexion/extension of the thumb, index, middle, and ring fingers, and the wrist. Proportional control was achieved for a 4-DOF real-time decode. One subject used a USEA-evoked hand sensation as feedback to complete a 1-DOF closed-loop virtual-hand movement task. There were no observed long-term functional deficits due to the USEA implants. Conclusions: Implantation of high-channel-count USEAs enables multi-degree-of-freedom control of virtual prosthetic hand movement and restoration of a rich selection of both proprioceptive and cutaneous sensory percepts spanning the hand during the short 4-5 week post-implant period. Future USEA use in longer-term implants and in closed-loop may enable restoration of many of the capabilities of an intact hand while contributing to a meaningful embodiment of the prosthesis.

Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron's electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.