High-density spinal cord stimulation selectively activates lower urinary tract nerves.

Objective.Epidural spinal cord stimulation (SCS) is a potential intervention to improve limb and autonomic functions, with lumbar stimulation improving locomotion and thoracic stimulation regulating blood pressure. Here, we asked whether sacral SCS could be used to target the lower urinary tract (LUT) and used a high-density epidural electrode array to test whether individual electrodes could selectively recruit LUT nerves.Approach. We placed a high-density epidural SCS array on the dorsal surface of the sacral spinal cord and cauda equina of anesthetized cats and recorded the stimulation-evoked activity from nerve cuffs on the pelvic, pudendal and sciatic nerves.Main results. Here we show that sacral SCS evokes responses in nerves innervating the bladder and urethra and that these nerves can be activated selectively. Sacral SCS always recruited the pelvic and pudendal nerves and selectively recruited both of these nerves in all but one animal. Individual branches of the pudendal nerve were always recruited as well. Electrodes that selectively recruited specific peripheral nerves were spatially clustered on the arrays, suggesting anatomically organized sensory pathways.Significance.This selective recruitment demonstrates a mechanism to directly modulate bladder and urethral function through known reflex pathways, which could be used to restore bladder and urethral function after injury or disease.

High-Density, Actively Multiplexed µECoG Array on Reinforced Silicone Substrate.

Simultaneous interrogation of electrical signals from wide areas of the brain is vital for neuroscience research and can aid in understanding the mechanisms of brain function and treatments for neurological disorders. There emerges a demand for development of devices with highly conformal interfaces that can span large cortical regions, have sufficient spatial resolution, and chronic recording capability while keeping a small implantation footprint. In this work, we have designed 61 channel and 48 channel high-density, cortical, micro-electrocorticographic electrode arrays with 400 µm pitch on an ultra-soft but durable substrate. We have also developed a custom multiplexing integrated circuit (IC), methods for packaging the IC in a water-tight liquid crystal polymer casing, and a micro-bonding method for attaching the electronics package to the electrode array. With the integrated multiplexer, the number of external wire connections can be reduced to 16 wires, thereby diminishing the invasive footprint of the device. Both the electrode array and IC were tested in vivo in a rat model to demonstrate the ability to sense finely-localized electrophysiological signals.

Surgical placement of customized abdominal vagus nerve stimulating and gastrointestinal serosal surface recording electrodes.

Bioelectronic medical approaches to control vagus nerve-to-organ signaling have the potential to treat cardiac, respiratory, gastrointestinal (GI) and metabolic diseases, such as obesity. Unlike cervical vagus nerve stimulation (VNS), abdominal VNS could provide specific therapeutic control of the GI tract without off-target effects on thoracic organs; however, surgical approaches for abdominal VNS electrode placement are not well established. Moreover, optimal device configurations and additional placement of GI recording electrodes for closed-loop control are largely unknown. We designed VNS cuff and GI planar serosal electrodes and tested placement of these devices in laparoscopic surgery in two cadavers. We determined that electrode positioning on the ventral abdominal vagus nerve and gastric antrum was feasible but other sites, such as the duodenum and proximal stomach, were more difficult. The current investigation can guide potential placement and design of VNS cuff and GI electrodes for development of closed-loop GI therapeutic devices.

Selective stimulation of the ferret abdominal vagus nerve with multi-contact nerve cuff electrodes.

Dysfunction and diseases of the gastrointestinal (GI) tract are a major driver of medical care. The vagus nerve innervates and controls multiple organs of the GI tract and vagus nerve stimulation (VNS) could provide a means for affecting GI function and treating disease. However, the vagus nerve also innervates many other organs throughout the body, and off-target effects of VNS could cause major side effects such as changes in blood pressure. In this study, we aimed to achieve selective stimulation of populations of vagal afferents using a multi-contact cuff electrode wrapped around the abdominal trunks of the vagus nerve. Four-contact nerve cuff electrodes were implanted around the dorsal (N = 3) or ventral (N = 3) abdominal vagus nerve in six ferrets, and the response to stimulation was measured via a 32-channel microelectrode array (MEA) inserted into the left or right nodose ganglion. Selectivity was characterized by the ability to evoke responses in MEA channels through one bipolar pair of cuff contacts but not through the other bipolar pair. We demonstrated that it was possible to selectively activate subpopulations of vagal neurons using abdominal VNS. Additionally, we quantified the conduction velocity of evoked responses to determine what types of nerve fibers (i.e., Aδ vs. C) responded to stimulation. We also quantified the spatial organization of evoked responses in the nodose MEA to determine if there is somatotopic organization of the neurons in that ganglion. Finally, we demonstrated in a separate set of three ferrets that stimulation of the abdominal vagus via a four-contact cuff could selectively alter gastric myoelectric activity, suggesting that abdominal VNS can potentially be used to control GI function.

Machine learning prediction of emesis and gastrointestinal state in ferrets.

Although electrogastrography (EGG) could be a critical tool in the diagnosis of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived from abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus-to-duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k-nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>75%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching, which could provide a methodology to diagnose GI diseases and symptoms such as nausea and vomiting.