Electrical Stimulation publications

Deep brain stimulation (DBS) for secondary (acquired, combined) dystonia does not reach the high degree of efficacy achieved in primary (genetic, isolated) dystonia. We hypothesize that this may be due to variability in the underlying injury, so that different children may require placement of electrodes in different regions of basal ganglia and thalamus. We describe a new targeting procedure in which temporary depth electrodes are placed at multiple possible targets in basal ganglia and thalamus, and probing for efficacy is performed using test stimulation and recording while children remain for one week in an inpatient Neuromodulation Monitoring Unit (NMU). Nine Children with severe secondary dystonia underwent the NMU targeting procedure. In all cases, 4 electrodes were implanted. We compared the results to 6 children who had previously had 4 electrodes implanted using standard intraoperative microelectrode targeting techniques. Results showed a significant benefit, with 80% of children with NMU targeting achieving greater than 5-point improvement on the Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), compared with 50% of children using intraoperative targeting. NMU targeting improved BFMDRS by an average of 17.1 whereas intraoperative targeting improved by an average of 10.3. These preliminary results support the use of test stimulation and recording in a Neuromodulation Monitoring Unit (NMU) as a new technique with the potential to improve outcomes following DBS in children with secondary (acquired) dystonia. A larger sample size will be needed to confirm these results.

Vocal communication is a sensory-motor process requiring auditory self-monitoring to correct errors and to ensure accurate vocal production. When presented with altered speech feedback, humans rapidly change their speech to compensate. Although previous evidence has demonstrated suppression of auditory cortex during both speech and animal vocalization, the specific role of auditory cortex in such feedback-dependent control is unknown. Here we show the relationship between neural activity in the auditory cortex and feedback-dependent vocal control in marmoset monkeys. We demonstrate that marmosets, like humans, exhibit feedback control of vocal acoustics. We further show that feedback-sensitive activity of auditory cortex neurons predict such compensatory vocal changes. Finally, we demonstrate that electrical microstimulation of auditory cortex rapidly evokes similar changes in vocal production. These results are evidence for a causal role of auditory cortex in vocal self-monitoring and feedback-dependent control, and have implications for understanding human speech motor control.

Neural prostheses that can monitor the physiological state of a subject are becoming clinically viable through improvements in the capacity to record from neural tissue. However, a significant limitation of current devices is that it is difficult to fabricate electrode arrays that have both high channel counts and the appropriate electrical properties required for neural recordings. In earlier work, we demonstrated nitrogen doped ultrananocrystalline diamond (N-UNCD) can provide efficacious electrical stimulation of neural tissue, with high charge injection capacity, surface stability and biocompatibility. In this work, we expand on this functionality to show that N-UNCD electrodes can also record from neural tissue owing to its low electrochemical impedance. We show that N-UNCD electrodes are highly flexible in their application, with successful recordings of action potentials from single neurons in an in vitro retina preparation, as well as local field potential responses from in vivo visual cortex tissue. Key properties of N-UNCD films, combined with scalability of electrode array fabrication with custom sizes for recording or stimulation along with integration through vertical interconnects to silicon based integrated circuits, may in future form the basis for the fabrication of versatile closed-loop neural prostheses that can both record and stimulate.

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.

Understanding the neural mechanisms of loss and recovery of consciousness remains an active area of scientific and clinical interest. Recent models and theories of consciousness implicate thalamus or neocortex as the dominant regions that control global state change, but experimentally testing these theories requires parsing the response properties of the complex thalamocortical network. In this work, we explore information processing using chronic recordings of multiunit activity between thalamus and cortex by targeting either the corticothalamic or thalamocortical pathway with direct electrical stimulation under subhypnotic and just-hypnotic states of isoflurane or dexmedetomidine induced unconsciousness. We found that mutual information between the stimulus and response in the corticothalamic circuit decreases with loss of consciousness, and that the change is not reciprocated in the thalamocortical direction. These changes in mutual information were moderately correlated with changes in evoked rate, and a strong effect of isoflurane on spontaneous and evoked rate accounts for a large portion of the change in mutual information. Further, we found cortical synchrony to increase under sub-and just-hypnotic doses of dexmedetomidine and isoflurane, suggesting cortical responses become more causally dependent on thalamocortical input at reduced levels of consciousness, or unconsciousness. We believe these changes suggest loss of consciousness is associated with a decrease in the number of available cortical network states, and may suggest cognitive unbinding from ascending sensory input.

Retinal neuromodulation is an emerging therapeutic approach to restore functional vision to those suffering retinal photoreceptor degeneration. The retina encodes visual information and transmits it to the brain. Replicating this retinal code through electrical stimulation is essential to improving the performance of visual prostheses. In doing so, the first step relies on precise neural recordings from visual centers that allow studying the response of these neurons to electrical stimulation of the retina. This paper demonstrates the feasibility of a rat model to conduct highly reliable electrophysiological studies in the field of retinal neuromodulation. A disc electrode, implanted in the retrobulbar space was used to stimulate the retina of Long-Evans rats. Buzsaki multi-electro arrays were inserted in the superior colliculus (SC) to record electrical activity propagated from the retinal ganglion cells (RGCs). Activation thresholds calculated from local field potentials (visual cortex) and from neural spikes (SC) were contrasted. Both values were comparable to those in humans and in other animal models, and were slightly higher when estimated from SC recordings. However, differences were not statistically significant.

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm2. The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.

Facial paralysis can be a devastating condition, causing disfiguring facial droop, slurred speech, eye dryness, scarring and blindness. This study investigated the utility of closed-loop functional electric stimulation (FES) for reanimating paralyzed facial muscles in a quantitative rodent model. The right buccal and marginal mandibular branches of the rat facial nerve were transected for selective, unilateral paralysis of whisker muscles. Microwire electrodes were implanted bilaterally into the facial musculature for FES and electromyographic (EMG) recording. With the rats awake and head-fixed, whisker trajectories were tracked bilaterally with optical micrometers. First, the relationship between EMG and volitional whisker movement was quantified on the intact side of the face. Second, the effect of FES on whisker trajectories was quantified on the paralyzed side. Third, closed-loop experiments were performed in which the EMG signal on the intact side triggered FES on the paralyzed side to restore symmetric whisking. The results demonstrate a novel in vivo platform for developing control strategies for neuromuscular facial prostheses.

The pulvinar complex is interconnected extensively with brain regions involved in spatial processing and eye movement control. Recent inactivation studies have shown that the dorsal pulvinar (dPul) plays a role in saccade target selection; however, it remains unknown whether it exerts effects on visual processing or at planning/execution stages. We used electrical microstimulation of the dPul while monkeys performed saccade tasks toward instructed and freely chosen targets. Timing of stimulation was varied, starting before, at, or after onset of target(s). Stimulation affected saccade properties and target selection in a time-dependent manner. Stimulation starting before but overlapping with target onset shortened saccadic reaction times (RTs) for ipsiversive (to the stimulation site) target locations, whereas stimulation starting at and after target onset caused systematic delays for both ipsiversive and contraversive locations. Similarly, stimulation starting before the onset of bilateral targets increased ipsiversive target choices, whereas stimulation after target onset increased contraversive choices. Properties of dPul neurons and stimulation effects were consistent with an overall contraversive drive, with varying outcomes contingent upon behavioral demands. RT and choice effects were largely congruent in the visually-guided task, but stimulation during memory-guided saccades, while influencing RTs and errors, did not affect choice behavior. Together, these results show that the dPul plays a primary role in action planning as opposed to visual processing, that it exerts its strongest influence on spatial choices when decision and action are temporally close, and that this choice effect can be dissociated from motor effects on saccade initiation and execution. SIGNIFICANCE STATEMENT Despite a recent surge of interest, the core function of the pulvinar, the largest thalamic complex in primates, remains elusive. This understanding is crucial given the central role of the pulvinar in current theories of integrative brain functions supporting cognition and goal-directed behaviors, but electrophysiological and causal interference studies of dorsal pulvinar (dPul) are rare. Building on our previous studies that pharmacologically suppressed dPul activity for several hours, here we used transient electrical microstimulation at different periods while monkeys performed instructed and choice eye movement tasks, to determine time-specific contributions of pulvinar to saccade generation and decision making. We show that stimulation effects depend on timing and behavioral state and that effects on choices can be dissociated from motor effects.

We report on the superior electrochemical properties, in-vivo performance and long term stability under electrical stimulation of a new electrode material fabricated from lithographically patterned glassy carbon. For a direct comparison with conventional metal electrodes, similar ultra-flexible, micro-electrocorticography (μ-ECoG) arrays with platinum (Pt) or glassy carbon (GC) electrodes were manufactured. The GC microelectrodes have more than 70% wider electrochemical window and 70% higher CTC (charge transfer capacity) than Pt microelectrodes of similar geometry. Moreover, we demonstrate that the GC microelectrodes can withstand at least 5 million pulses at 0.45 mC/cm 2 charge density with less than 7.5% impedance change, while the Pt microelectrodes delaminated after 1 million pulses. Additionally, poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) was selectively electrodeposited on both sets of devices to specifically reduce their impedances for smaller diameters (<60 μm). We observed that PEDOT-PSS adhered significantly better to GC than Pt, and allowed drastic reduction of electrode size while maintaining same amount of delivered current. The electrode arrays biocompatibility was demonstrated through in-vitro cell viability experiments, while acute in vivo characterization was performed in rats and showed that GC microelectrode arrays recorded somatosensory evoked potentials (SEP) with an almost twice SNR (signal-to-noise ratio) when compared to the Pt ones.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial.

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.

Selective vagal nerve stimulation (sVNS) has been demonstrated to lower blood pressure (BP) in rats without causing major side effects. This method might be adapted for the treatment of therapy-resistant hypertension in patients. Converting enzyme inhibitors (CEIs) are among the first drugs that are administered for arterial hypertension and prominently reduce BP primarily by interacting with the renin-angiotensin system of the kidneys. Beyond the reduction of BP, CEI have a positive effect on the survival rate after myocardial infarction; they reduce the rates of stroke and improve the neurohormonal status in heart-failure patients. If sVNS might be introduced as a therapy against resistant hypertension, patients will at least partially stay on their CEI medication. It is therefore the aim of this study to investigate the influence of the CEI enalapril on the haemodynamic and respiratory effects of sVNS. In 10 male Wistar rats, a polyimide-based multichannel-cuff-electrode was placed around the vagal nerve bundle to selectively stimulate the aortic depressor nerve fibres. Stimulation parameters were adapted to the thresholds of the individual animals and included repetition frequencies between 30 and 50 Hz, amplitudes of 0.5 to 1.5 mA and pulse widths between 0.4 ms and 1.0 ms. BP responses were detected with a microtip transducer in the left carotid artery, and electrocardiography was recorded with subcutaneous electrodes. After intravenous administration of enalapril (2 mg/kg bodyweight), the animals' mean arterial blood pressures (MAPs) decreased significantly, while the heart rates (HRs) were not significantly influenced. The effects of sVNS on BP and HR were attenuated by enalapril but were still present. We conclude that sVNS can lower the MAP during enalapril treatment without relevant side effects.

Although resting-state functional connectivity is a commonly used neuroimaging paradigm, the underlying mechanisms remain unknown. Thalamo-cortical and cortico-cortical circuits generate oscillations at different frequencies during spontaneous activity. However, it remains unclear how the various rhythms interact and whether their interactions are lamina-specific. Here we investigated intra- and inter-laminar spontaneous phase-amplitude coupling (PAC). We recorded local-field potentials using laminar probes inserted in the forelimb representation of rat area S1. We then computed time-series of frequency-band- and lamina-specific current source density (CSD), and PACs of CSD for all possible pairs of the classical frequency bands in the range of 1-150 Hz. We observed both intra- and inter-laminar spontaneous PAC. Of 18 possible combinations, 12 showed PAC, with the highest measures of interaction obtained for the pairs of the theta/gamma and delta/gamma bands. Intra- and inter-laminar PACs involving layers 2/3-5a were higher than those involving layer 6. Current sinks (sources) in the delta band were associated with increased (decreased) amplitudes of high-frequency signals in the beta to fast gamma bands throughout layers 2/3-6. Spontaneous sinks (sources) of the theta and alpha bands in layers 2/3-4 were on average linked to dipoles completed by sources (sinks) in layer 6, associated with high (low) amplitudes of the beta to fast-gamma bands in the entire cortical column. Our findings show that during spontaneous activity, delta, theta, and alpha oscillations are associated with periodic excitability, which for the theta and alpha bands is lamina-dependent. They further emphasize the differences between the function of layer 6 and that of the superficial layers, and the role of layer 6 in controlling activity in those layers. Our study links theories on the involvement of PAC in resting-state functional connectivity with previous work that revealed lamina-specific anatomical thalamo-cortico-cortical connections.

Auditory information relayed by auditory nerve fibers and somatosensory information relayed by granule cell parallel fibers converge on the fusiform cells (FCs) of the dorsal cochlear nucleus, the first brain station of the auditory pathway. In vitro, parallel fiber synapses on FCs exhibit spike-timing-dependent plasticity with Hebbian learning rules, partially mediated by the NMDA receptor (NMDAr). Well-timed bimodal auditory-somatosensory stimulation, in vivo equivalent of spike-timing-dependent plasticity, can induce stimulus-timing-dependent plasticity (StTDP) of the FCs spontaneous and tone-evoked firing rates. In healthy guinea pigs, the resulting distribution of StTDP learning rules across a FC neural population is dominated by a Hebbian profile while anti-Hebbian, suppressive and enhancing LRs are less frequent. In this study, we investigate in vivo, the NMDAr contribution to FC baseline activity and long term plasticity. We find that blocking the NMDAr decreases the synchronization of FC- spontaneous activity and mediates differential modulation of FC rate-level functions such that low, and high threshold units are more likely to increase, and decrease, respectively, their maximum amplitudes. Three significant alterations in mean learning-rule profiles were identified: transitions from an initial Hebbian profile towards (1) an anti-Hebbian; (2) a suppressive profile; and (3) transitions from an anti-Hebbian to a Hebbian profile. FC units preserving their learning rules showed instead, NMDAr-dependent plasticity to unimodal acoustic stimulation, with persistent depression of tone-evoked responses changing to persistent enhancement following the NMDAr antagonist. These results reveal a crucial role of the NMDAr in mediating FC baseline activity and long-term plasticity which have important implications for signal processing and auditory pathologies related to maladaptive plasticity of dorsal cochlear nucleus circuitry.

Selective vagal nerve stimulation (sVNS) has been shown to reduce blood pressure without major side effects in rats. This technology might be the key to non-medical antihypertensive treatment in patients with therapy-resistant hypertension. β-blockers are the first-line therapy of hypertension and have in general a bradycardic effect. As VNS itself can also promote bradycardia, it was the aim of this study to investigate the influence of the β1-selective blocker Metoprolol on the effect of sVNS especially with respect to the heart rate. In 10 male Wistar rats, a polyimide multichannel-cuff electrode was placed around the vagal nerve bundle to selectively stimulate the aortic depressor nerve fibers. The stimulation parameters were adapted to the thresholds of individual animals and were in the following ranges: frequency 30-50 Hz, amplitude 0.3-1.8 mA and pulse width 0.3-1.3 ms. Blood pressure responses were detected with a microtip transducer in the carotid artery, and electrocardiography was recorded with s.c. chest electrodes. After IV administration of Metoprolol (2 mg kg(-1) body weight), the animals' mean arterial blood pressure (MAP) and heart rate (HR) decreased significantly. Although the selective electrical stimulation of the baroreceptive fibers reduced MAP and HR, both effects were significantly alleviated by Metoprolol. As a side effect, the rate of stimulation-induced apnea significantly increased after Metoprolol administration. sVNS can lower the MAP under Metoprolol without causing severe bradycardia.

Fast-scan cyclic voltammetry (FSCV) is often used to measure real-time dopamine (DA) concentrations in awake, behaving rodents. Extending this technique to work in monkeys would provide a platform for advanced behavioral studies and a primate model for preclinical research. The present study demonstrates the feasibility of DA recordings in two awake monkeys (Macaca mulatta) using a mixture of techniques adapted from rodent, primate and brain slice work. We developed a long carbon fiber electrode to operate in the larger primate brain. This electrode was lowered into the striatum each day using a recording chamber and a detachable micromanipulator system. A manipulator also moved one or more tungsten stimulating electrodes into either the nearby striatum or the ventral tegmental area/substantia nigra pars compacta (VTA/SNc). We developed an electrical stimulation controller to reduce artifacts during electrical stimulation. We also introduce a stimulation-based methodology for estimating distances between electrodes in the brain. Dopamine responses within the striatum were evoked by either stimulation of the striatum near the FSCV electrode, or stimulation within the VTA/SNc. Unexpected juice rewards also evoked dopamine responses in the ventral striatum. Thus, we demonstrate that robust dopamine responses can be recorded from awake, behaving primates with FSCV. In addition, we describe how a stimulation technique borrowed from the neuroprosthetics field can activate the distributed monkey midbrain dopamine system in a way that mimics rodent VTA stimulation.

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.