See Kleen and Kirsch (doi:10.1093/awx178) for a scientific commentary on this article.Cognitive deficits are common among epilepsy patients. In these patients, interictal epileptiform discharges, also termed spikes, are seen routinely on electroencephalography and believed to be associated with transient cognitive impairments. In this study, we investigated the effect of spikes on memory encoding and retrieval, taking into account the spatial distribution of spikes in relation to the seizure onset zone as well as anatomical regions of the brain. Sixty-seven patients with medication refractory epilepsy undergoing continuous intracranial electroencephalography monitoring engaged in a delayed free recall task to test short-term memory. In this task, subjects were asked to memorize and recall lists of common nouns. We quantified the effect of each spike on the probability of successful recall using a generalized logistic mixed model. We found that in patients with left lateralized seizure onset zones, spikes outside the seizure onset zone impacted memory encoding, whereas those within the seizure onset zone did not. In addition, spikes in the left inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, and fusiform gyrus during memory encoding reduced odds of recall by as much as 15% per spike. Spikes also reduced the odds of word retrieval, an effect that was stronger with spikes outside of the seizure onset zone. These results suggest that seizure onset regions are dysfunctional at baseline, and support the idea that interictal spikes disrupt cognitive processes related to the underlying tissue.

Restoring somatosensory feedback to people with limb amputations is crucial to improve prosthetic control. Multiple studies have demonstrated that peripheral nerve stimulation and targeted reinnervation can provide somatotopically relevant sensory feedback. While effective, the surgical procedures required for these techniques remain a major barrier to translatability. Here, we demonstrate in four people with upper-limb amputation that epidural spinal cord stimulation (SCS), a common clinical technique to treat pain, evoked somatosensory percepts that were perceived as emanating from the missing arm and hand. Over up to 29 days, stimulation evoked sensory percepts in consistent locations in the missing hand regardless of time since amputation or level of amputation. Evoked sensations were occasionally described as naturalistic (e.g. touch or pressure), but were often paresthesias. Increasing stimulus amplitude increased the perceived intensity linearly, without increasing area of the sensations. These results demonstrate the potential of SCS as a tool to restore somatosensation after amputations.

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.

Objective. We have shown previously that microstimulation of the lumbar dorsal root ganglia (L5-L7 DRG) using penetrating microelectrodes, selectively recruits distal branches of the sciatic and femoral nerves in an acute preparation. However, a variety of challenges limit the clinical translatability of DRG microstimulation via penetrating electrodes. For clinical translation of a DRG somatosensory neural interface, electrodes placed on the epineural surface of the DRG may be a viable path forward. The goal of this study was to evaluate the recruitment properties of epineural electrodes and compare their performance with that of penetrating electrodes. Here, we compare the number of selectively recruited distal nerve branches and the threshold stimulus intensities between penetrating and epineural electrode arrays. Approach. Antidromically propagating action potentials were recorded from multiple distal branches of the femoral and sciatic nerves in response to epineural stimulation on 11 ganglia in four cats to quantify the selectivity of DRG stimulation. Compound action potentials (CAPs) were recorded using nerve cuff electrodes implanted around up to nine distal branches of the femoral and sciatic nerve trunks. We also tested stimulation selectivity with penetrating microelectrode arrays implanted into ten ganglia in four cats. A binary search was carried out to identify the minimum stimulus intensity that evoked a response at any of the distal cuffs, as well as whether the threshold response selectively occurred in only a single distal nerve branch. Main results. Stimulation evoked activity in just a single peripheral nerve through 67% of epineural electrodes (35/52) and through 79% of the penetrating microelectrodes (240/308). The recruitment threshold (median  =  9.67 nC/phase) and dynamic range of epineural stimulation (median  =  1.01 nC/phase) were significantly higher than penetrating stimulation (0.90 nC/phase and 0.36 nC/phase, respectively). However, the pattern of peripheral nerves recruited for each DRG were similar for stimulation through epineural and penetrating electrodes. Significance. Despite higher recruitment thresholds, epineural stimulation provides comparable selectivity and superior dynamic range to penetrating electrodes. These results suggest that it may be possible to achieve a highly selective neural interface with the DRG without penetrating the epineurium.

Post-amputation phantom limb pain (PLP) is a widespread phenomenon that can have physical, psychological, and functional impacts on amputees who experience the condition. The varying presentations and mechanisms of PLP make it difficult to effectively provide long-term pain relief. Multiple neuromodulatory approaches to treating PLP have focused on electrical stimulation of the peripheral nervous system, with varying degrees of success. More recently, research has been done to study the effects of neuroprosthetic approaches on PLP. Neuroprosthetics combine the use of a functional prosthetic with stimulation to the peripheral nerves in the residual limb. Although many of the neuroprosthetic studies focus on improving function, several have shown preliminary evidence for the reduction of severity of PLP. In this review we provide an overview of the current understanding of the neurological mechanisms that initiate and sustain PLP, as well as the neuromodulatory and neuroprosthetic approaches under development for treatment of the condition.Muscle Nerve 59:154-167, 2019

In the United States, over 1.5 million people live with lower-limb amputation. Existing prosthetic limbs do not restore somatosensory feedback from the limb, resulting in functional impairments including balance deficits and an increased risk of falls. Further, these prostheses do not alleviate the severe phantom limb pain that often follows amputation. Leveraging clinically available spinal cord stimulation electrodes, we designed a system that restores somatosensation in the missing limb, thereby improving balance and gait and reducing phantom limb pain. We show that spinal cord stimulation can evoke sensations in the missing foot and that we can control the location and intensity of those sensations. Further, by modulating stimulation intensity in real time based on signals from a wireless pressure-sensitive shoe insole, subjects exhibit improvements in functional measures of balance and gait stability. Finally, over the duration of the implant period, subjects experienced a clinically meaningful decrease in phantom limb pain. These combined results demonstrate that, with an electrode technology that is currently in widespread clinical use, our approach has the potential to become an important intervention for lower-limb amputation.

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.

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment 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 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 (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.