Objective Clinical data suggest that efficacious vagus nerve stimulation (VNS) is limited by side effects such as cough and dyspnea that have stimulation thresholds lower than those for therapeutic outcomes. VNS side effects are putatively caused by activation of nearby muscles within the neck, via direct muscle activation or activation of nerve fibers innervating those muscles. Our goal was to determine the thresholds at which various VNS-evoked effects occur in the domestic pig—an animal model with vagus anatomy similar to human—using the bipolar helical lead deployed clinically. Approach Intrafascicular electrodes were placed within the vagus nerve to record electroneurographic (ENG) responses, and needle electrodes were placed in the vagal-innervated neck muscles to record electromyographic (EMG) responses. Main results Contraction of the cricoarytenoid muscle occurred at low amplitudes (∼0.3 mA) and resulted from activation of motor nerve fibers in the cervical vagus trunk within the electrode cuff which bifurcate into the recurrent laryngeal branch of the vagus. At higher amplitudes (∼1.4 mA), contraction of the cricoarytenoid and cricothyroid muscles was generated by current leakage outside the cuff to activate motor nerve fibers running within the nearby superior laryngeal branch of the vagus. Activation of these muscles generated artifacts in the ENG recordings that may be mistaken for compound action potentials representing slowly conducting Aδ-, B-, and C-fibers. Significance Our data resolve conflicting reports of the stimulation amplitudes required for C-fiber activation in large animal studies (>10 mA) and human studies (<250 μA). After removing muscle-generated artifacts, ENG signals with post-stimulus latencies consistent with Aδ- and B-fibers occurred in only a small subset of animals, and these signals had similar thresholds to those that caused bradycardia. By identifying specific neuroanatomical pathways that cause off-target effects and characterizing the stimulation dose-response curves for on- and off-target effects, we hope to guide interpretation and optimization of clinical VNS.

Implanted neural stimulation and recording devices hold vast potential to treat a variety of neurological conditions, but the invasiveness, complexity, and cost of the implantation procedure greatly reduce access to an otherwise promising therapeutic approach. To address this need, a novel electrode that begins as an uncured, flowable prepolymer that can be injected around a neuroanatomical target to minimize surgical manipulation is developed. Referred to as the Injectrode, the electrode conforms to target structures forming an electrically conductive interface which is orders of magnitude less stiff than conventional neuromodulation electrodes. To validate the Injectrode, detailed electrochemical and microscopy characterization of its material properties is performed and the feasibility of using it to stimulate the nervous system electrically in rats and swine is validated. The silicone-metal-particle composite performs very similarly to pure wire of the same metal (silver) in all measures, including exhibiting a favorable cathodic charge storage capacity (CSC C ) and charge injection limits compared to the clinical LivaNova stimulation electrode and silver wire electrodes. By virtue of its simplicity, the Injectrode has the potential to be less invasive, more robust, and more cost-effective than traditional electrode designs, which could increase the adoption of neuromodulation therapies for existing and new indications.

Clinical data suggest that efficacious vagus nerve stimulation (VNS) is limited by side effects such as cough and dyspnea that have stimulation thresholds lower than those for therapeutic outcomes. VNS side effects are putatively caused by activation of nearby muscles within the neck, via direct muscle activation or activation of nerve fibers innervating those muscles. Our goal was to determine the thresholds at which various VNS-evoked effects occur in the domestic pig—an animal model with vagus anatomy similar to human—using the bipolar helical lead deployed clinically. Intrafascicular electrodes were placed within the vagus nerve to record electroneurographic (ENG) responses, and needle electrodes were placed in the vagal-innervated neck muscles to record electromyographic (EMG) responses. Contraction of the cricoarytenoid muscle occurred at low amplitudes (∼0.3 mA) and resulted from activation of motor nerve fibers in the cervical vagus trunk within the electrode cuff which bifurcate into the recurrent laryngeal branch of the vagus. At higher amplitudes (∼1.4 mA), contraction of the cricoarytenoid and cricothyroid muscles was generated by current leakage outside the cuff to activate motor nerve fibers running within the nearby superior laryngeal branch of the vagus. Activation of these muscles generated artifacts in the ENG recordings that may be mistaken for compound action potentials representing slowly conducting Aδ-, B-, and C-fibers. Our data resolve conflicting reports of the stimulation amplitudes required for C-fiber activation in large animal studies (>10 mA) and human studies (<250 µA). After removing muscle-generated artifacts, ENG signals with post-stimulus latencies consistent with Aδ- and B-fibers occurred in only a small subset of animals, and these signals had similar thresholds to those that caused bradycardia. By identifying specific neuroanatomical pathways that cause off-target effects and characterizing the stimulation dose-response curves for on- and off-target effects, we hope to guide interpretation and optimization of clinical VNS.

Natural scenes often contain multiple objects and surfaces. However, how neurons in the visual cortex represent multiple visual stimuli is not well understood. Previous studies have shown that, when multiple stimuli compete in one feature domain, the evoked neuronal response is biased toward the stimulus that has a stronger signal strength. We recorded from two male macaques to investigate how neurons in the middle temporal cortex (MT) represent multiple stimuli that compete in more than one feature domain. Visual stimuli were two random-dot patches moving in different directions. One stimulus had low luminance contrast and moved with high coherence, whereas the other had high contrast and moved with low coherence. We found that how MT neurons represent multiple stimuli depended on the spatial arrangement. When two stimuli were overlapping, MT responses were dominated by the stimulus component that had high contrast. When two stimuli were spatially separated within the receptive fields, the contrast dominance was abolished. We found the same results when using contrast to compete with motion speed. Our neural data and computer simulations using a V1-MT model suggest that the contrast dominance found with overlapping stimuli is due to normalization occurring at an input stage fed to MT, and MT neurons cannot overturn this bias based on their own feature selectivity. The interaction between spatially separated stimuli can largely be explained by normalization within MT. Our results revealed new rules on stimulus competition and highlighted the impact of hierarchical processing on representing multiple stimuli in the visual cortex.

Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system.

AbstractImplanted neural stimulation and recording devices hold vast potential to treat a variety of neurological conditions, but the invasiveness, complexity, and cost of the implantation procedure greatly reduce access to an otherwise promising therapeutic approach. To address this need, we have developed a novel electrode that begins as an uncured, flowable pre-polymer that can be injected around a neuroanatomical target to minimize surgical manipulation. Referred to as the Injectrode, the electrode conforms to target structures forming an electrically conductive interface which is orders of magnitude less stiff than conventional neuromodulation electrodes. To validate the Injectrode, we performed detailed electrochemical and microscopy characterization of its material properties and validated the feasibility of using it to electrically stimulate the nervous system in rats and swine. The silicone-metal-particle composite performed very similarly to pure wire of the same metal (silver) in all measures, including exhibiting a favorable cathodic charge storage capacity (CSCC) and charge injection limits compared to the clinical LivaNova stimulation electrode and silver wire electrodes. By virtue of being simpler than traditional electrode designs, less invasive, and more cost-effective, the Injectrode has the potential to increase the adoption of neuromodulation therapies for existing and new indications.

Myocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. Supplementing biological neural networks may enable reliable detection, and potentially even facilitate correction, of myocardial ischemia. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia (preclinical experiments in rats). This responsive bioelectronic medicine uses an ANN with a long short-term memory layer to decode spontaneous myocardial ischemia and autonomously trigger vagus nerve stimulation (VNS) for reducing chronotropy, afterload, and myocardial oxygen demand. We first used injections of cardiovascular stress inducing agents (dobutamine and norepinephrine) that produce a model of spontaneous myocardial ischemia. Next, ANNs were trained to decode spontaneous cardiovascular stress and myocardial ischemia, with an overall accuracy of ~92%. ANN-controlled VNS reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations of stress pathophysiology and unsupervised detection of new emerging stress states. Together, this adaptive architecture provides clinically relevant insights as pathophysiology evolves. Overall, these results provide a first-time demonstration that ANNs can supplement deficient biological neural networks to dynamically detect and help bioelectronically reverse cardiovascular pathophysiology.

Natural scenes often contain multiple objects. However, how neurons in the visual cortex represent multiple visual stimuli within their receptive fields (RFs) is not well understood. Previous studies have shown that, when multiple stimuli compete in one feature domain, the evoked neuronal response is dominated by the stimulus component that has a stronger signal strength, which can be explained by response normalization. Here we investigate how neurons in middle-temporal (MT) cortex of the macaque monkey represent multiple stimuli that compete in more than one feature domain within their RFs. Visual stimuli were two random-dot patches moving in different directions. One stimulus moved at high coherence with low luminance contrast, whereas the other moved at low coherence with high contrast. We found that, although the peak MT response elicited by the "low contrast & high coherence" stimulus alone was stronger than by the "high contrast & low coherence" stimulus, MT response to both stimuli when they were overlapping was almost completely dominated by the high-contrast stimulus. When two stimuli were spatially separated within the RF, the contrast dominance was abolished. We found the same results when using contrast to compete with motion speed. Computer simulations using a V1-MT model suggest that the contrast dominance is due to normalization occurring at input stage fed to MT and MT neurons cannot overturn it according to their own feature selectivity. Our results reveal new rules on stimulus competition and highlight the importance of hierarchical processing on the neural representation of multiple visual stimuli in the extrastriate cortex. SIGNIFICANCE STATEMENTWe investigated the rules by which cortical neurons represent multiple visual stimuli that compete in more than one feature domain. We found that the interaction between multiple stimuli within the RFs of neurons in area MT depends on the spatial arrangement of the stimuli. When multiple stimuli are overlapping, the response tuning curves of MT neurons are strongly dominated by the stimulus component that has higher luminance contrast albeit lower motion coherence or a slower speed than a competing stimulus. When multiple stimuli are spatially separated, the contrast dominance is abolished. These results cannot be explained by response normalization only within MT, but reveal the importance of hierarchical processing on the neural representation of multiple visual stimuli in extrastriate cortex.