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Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes.We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort.Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction.Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.

Emerging evidence suggests that the temporal dynamics of cortico-cortical evoked potentials (CCEPs) may be used to characterize the patterns of information flow between and within brain networks. At present, however, the spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that CCEPs propagate as an evoked traveling wave emanating from the site of stimulation. To elicit CCEPs, we applied single-pulse stimulation to stereoelectroencephalography (SEEG) electrodes implanted in 21 adult patients with intractable epilepsy. For each robust CCEP, we measured the timing of the maximal descent in evoked local field potentials and broadband high-gamma power (70-150 Hz) envelopes relative to the distance between the recording and stimulation contacts using three different metrics (i.e., Euclidean distance, path length, geodesic distance), representing direct, subcortical, and transcortical propagation, respectively. Many evoked responses to single-pulse electrical stimulation appear to propagate as traveling waves (∽17-30%), even in the sparsely sampled, three-dimensional SEEG space. These results provide new insights into the spatiotemporal dynamics of CCEP propagation.

BackgroundThe anterior cingulate cortex (ACC) plays an important role in the cognitive and emotional processing of pain. Prior studies have used deep brain stimulation (DBS) to treat chronic pain, but results have been inconsistent. This may be due to network adaptation over time and variable causes of chronic pain. Identifying patient-specific pain network features may be necessary to determine patient candidacy for DBS.HypothesisCingulate stimulation would increase patients’ hot pain thresholds if non-stimulation 70-150 Hz activity encoded psychophysical pain responses.MethodsIn this study, four patients who underwent intracranial monitoring for epilepsy monitoring participated in a pain task. They placed their hand on a device capable of eliciting thermal pain for five seconds and rated their pain. We used these results to determine the individual’s thermal pain threshold with and without electrical stimulation. Two different types of generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics.ResultsThe pain threshold for each patient was determined from the psychometric probability density function. Two patients had a higher pain threshold with stimulation than without, while the other two patients had no difference. We also evaluated the relationship between neural activity and pain responses. We found that patients who responded to stimulation had specific time windows where high-frequency activity was associated with increased pain ratings.ConclusionStimulation of cingulate regions with increased pain-related neural activity was more effective at modulating pain perception than stimulating non-responsive areas. Personalized evaluation of neural activity biomarkers could help identify the best target for stimulation and predict its effectiveness in future studies evaluating DBS.

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks’ relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited.We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded “yes” or “no” to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale.GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset.We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.Significance StatementWe investigated the neural encoding of pain psychophysics across 16 brain regions during a continuous thermal stimulus in humans. Mixed-effects models were used to analyze trends across 20 human subjects. Using intracranial electrodes, we show a parametric relationship between behavioral responses and HFA during ongoing pain. We found that HFA in cognitive and emotional pain processing regions was closely associated with pain evaluation at the stimulus onset, end, or both. The neural encoding of subjective pain intensity, measured by a visual analog scale, differed from that of binary pain intensity. Perception and psychophysical correlates to pain depend on how patients are asked to evaluate it. Our findings provide evidence that HFA can serve as a neural marker within specific brain regions of behavioral pain responses, as measured by sEEG.

In patients with drug-resistant epilepsy, electrical stimulation of the brain in response to epileptiform activity can make seizures less frequent and debilitating. When effective, this therapy, known as closed-loop responsive neurostimulation (RNS), produces long-lasting changes in brain dynamics that correlate with clinical outcomes. Since periods with frequent epileptiform activity are less conducive to neuroplasticity, we hypothesize that stimulation timing, specifically stimulation during brain states with less epileptiform activity, is critical in driving long-term changes that restore healthy brain networks. To test this, we quantified stimulation episodes during low- and high-risk epochs—that is, stimulation during periods with a low or high risk of generating seizures and less or more epileptiform activity—in a cohort of 40 patients treated with RNS. Patients were categorized into three groups: super responders (>90% reduction, n=10), intermediate responders (≥ 50% reduction and ≤ 90% reduction), n=19, and poor responders (One Sentence SummaryIncreased stimulation during periods of reduced seizure risk corresponds with improved therapy in responsive neurostimulation for epilepsy.

Cerebral projections of nociceptive stimuli are of great interest as targets for neuromodulation in chronic pain. To study cerebral networks involved in processing noxious stimuli, researchers often rely on thermo-nociception to induce pain. However, various limitations exist in many pain-inducing techniques, such as not accounting for individual variations in pain and trial structure predictability.We propose an improved and reliable psychometric experimental method to evaluate human nociceptive processing to overcome some of these limitations. The developed testing paradigm leverages a custom-built, open-source, thermoelectric device (TED). The device construction and hardware are described. A maximum-likelihood adaptive algorithm is integrated into the TED software, facilitating individual psychometric functions representative of both hot and cold pain perception. In addition to testing only hot or cold thresholds, the TED may also be used to induce the thermal grill illusion (TGI), where the bars are set to alternating warm and cool temperatures.Here, we validated the TED's capability to adjust between different temperatures and showed that the device quickly and automatically changes temperature without any experimenter input. We also validated the device and integrated psychometric pain task in 21 healthy human subjects. Hot and cold pain thresholds (HPT, CPT) were determined in human subjects with1°C of variation. Thresholds were anticorrelated, meaning a volunteer with a low CPT likely had a high HPT. We also showed how the TED can be used to induce the TGI.The TED can induce thermo-nociception and provide probabilistic measures of hot and cold pain thresholds. Based on the findings presented, we discuss how the TED could be used to study thermo-nociceptive cerebral projections if paired with intracranial electrode monitoring.

Providing user-focused, objective, and quantified metrics for prosthesis usability may help reduce the high (up to 50%) abandonment rates and accelerate the clinical adoption and cost reimbursement for new and improved prosthetic systems. We comparatively evaluated several physiological, behavioral, and subjective cognitive workload measures applied to upper-limb neuroprosthesis use.Users controlled a virtual prosthetic arm via surface electromyography (sEMG) and completed a virtual target control task at easy and hard levels of difficulty (with large and small targets, respectively). As indices of cognitive workload, we took behavioral (Detection Response Task; DRT) and electroencephalographic (EEG; parietal alpha and frontal theta power, and the P3 event-related potential) measures for one group (n = 1 amputee participant, n = 10 non-amputee participants), and electrocardiographic (ECG; low/high frequency heart-rate variability ratio) and pupillometric (task-evoked pupillary response) measures for another group (n = 1 amputee participant, n = 10 non-amputee participants), because all measures could not reasonably be recorded simultaneously. Participants of both groups also completed the subjective NASA Task-Load Index (TLX) survey.Ease of use, setup, piloting, and analysis complexity varied among measures. The DRT required minimal piloting, was simple to set up, and used straightforward analyses. ECG measures required moderate piloting, were simple to set up, and had somewhat complex analyses. Pupillometric measures required extensive piloting but were simple to set up and relatively simple to analyze. EEG measures required extensive piloting, extensive setup and equipment, careful monitoring, and moderately complex analyses.Across subjects, the DRT, low/high frequency heart-rate variability ratio, task-evoked pupillary response, and NASA TLX significantly differentiated between the easy and hard tasks, whereas EEG measures (alpha power, theta power, and P3 event-related potential) did not. Aside from the NASA TLX, the DRT was the easiest to use and most sensitive to cognitive load across and within subjects. Among physiological measures, we recommend ECG, pupillometry, and EEG/ERPs, in that order.This study provides the first evaluation of multiple objective and quantified cognitive workload measures during the same task with prosthesis use. User-focused cognitive workload assessments may increase our understanding of human interactions with advanced upper-limb neuroprostheses and facilitate their improvements and translation to real-world use.Significance StatementThe human arm is dexterous and able to sense objects it contacts. Restoring sensory and motor function to a person with limb loss presents multiple challenges and requires improvements in robotics, biological interfaces, decoding biological signals for prosthesis movement, and sensory restoration. The scientific and engineering communities have made progress toward restoring arm function through advanced neuroprostheses. However, most studies focus solely on task performance, and they typically employ artificial experimental paradigms in which the user can devote full attention to the task, which is often unrealistic for use in everyday activities. To develop neuroprostheses capable of restoring intuitive arm function, engineers and scientists must also consider the difficulty of use, or cognitive burden, of using the neuroprosthesis. Although many measures of cognitive workload have been developed, few studies directly interrogate cognitive workload during neuroprosthesis use. An engineer or scientist seeking to employ cognitive workload measures during neuroprosthesis use will likely wonder, as we did, which measures are most suitable for their needs. To address this question, we empirically assess the practical and functional merits and limitations of several physiological, behavioral, and subjective techniques to measure cognitive workload during use of an advanced prosthesis. We anticipate that these findings may influence other medical and consumer areas of human-computer interaction, such as virtual reality or exoskeleton use.

Background Electrical stimulation of residual afferent nerve fibers can evoke sensations from a missing limb after amputation, and bionic arms endowed with artificial sensory feedback have been shown to confer functional and psychological benefits. Here we explore the extent to which artificial sensations can be discriminated based on location, quality, and intensity. Methods We implanted Utah Slanted Electrode Arrays (USEAs) in the arm nerves of three transradial amputees and delivered electrical stimulation via different electrodes and frequencies to produce sensations on the missing hand with various locations, qualities, and intensities. Participants performed blind discrimination trials to discriminate among these artificial sensations. Results Participants successfully discriminated cutaneous and proprioceptive sensations ranging in location, quality and intensity. Performance was significantly greater than chance for all discrimination tasks, including discrimination among up to ten different cutaneous location-intensity combinations (15/30 successes, p p p p p Conclusion Electrode location, stimulation frequency, and stimulation pattern can be modulated to evoke functionally discriminable sensations with a range of locations, qualities, and intensities. This rich source of artificial sensory feedback may enhance functional performance and embodiment of bionic arms endowed with a sense of touch.

BACKGROUND: A long-held dream of scientists is to transfer information directly to the visual cortex of blind individuals, thereby restoring a rudimentary form of sight. However, no clinically available cortical visual prosthesis yet exists. METHODS: We implanted an intracortical microelectrode array consisting of 96 electrodes in the visual cortex of a 57-year-old person with complete blindness for a six- month period. We measured thresholds and the characteristics of the visual percepts elicited by intracortical microstimulation. RESULTS: Implantation and subsequent explantation of intracortical microelectrodes were carried out without complications. The mean stimulation threshold for single electrodes was 66.8 ± 36.5 μA. We consistently obtained high-quality recordings from visually deprived neurons and the stimulation parameters remained stable over time. Simultaneous stimulation via multiple electrodes were associated with a significant reduction in thresholds (p

ObjectiveThe objective of this study is to quantify the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes.MethodsWe included 65 patients with strip electrodes (n=12), strip and grid electrodes (n=24), strip, grid, and depth electrodes (n=7), or depth electrodes only (n=22) from the University of Utah spanning 2010-2020. Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. The gray and white matter coverage was quantified based on spherical volumes centered on electrode centroids, with radii ranging from 1-15 mm, along with detailed finite element models of local electric fieldsResultsGray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage was greatest for depth electrodes at all RoIs, which is noteworthy for studies involving stimulation mapping. Depth electrodes were able to record significantly more gray matter from the amygdala and hippocampus than subdural electrodes.SignificanceThis study provides the first probabilistic analysis to quantify gray and white matter coverage for multiple categories of intracranial recording configurations. Depth electrodes may offer increased per contact coverage of gray matter over other recording strategies if the desired signals are local to the contact, while subdural grids and strips can sample more gray matter if the desired signals are more diffuse.

Bypass sockets allow researchers to perform tests of prosthetic systems from the prosthetic user's perspective. We designed a modular upper-limb bypass socket with 3D-printed components that can be easily modified for use with a variety of terminal devices. Our bypass socket preserves access to forearm musculature and the hand, which are necessary for surface electromyography and to provide substituted sensory feedback. Our bypass socket allows a sufficient range of motion to complete tasks in the frontal working area, as measured on non-amputee participants. We examined the performance of non-amputee participants using the bypass socket on the original and modified Box and Block Tests. Participants moved 11.3 +/- 2.7 and 11.7 +/- 2.4 blocks in the original and modified Box and Block Tests (mean +/- SD), respectively, within the range of reported scores using amputee participants. Range-of-motion for users wearing the bypass socket meets or exceeds most reported range-of-motion requirements for activities of daily living. The bypass socket was originally designed with a freely rotating wrist; we found that adding elastic resistance to user wrist rotation while wearing the bypass socket had no significant effect on motor decode performance. We have open-sourced the design files and an assembly manual for the bypass socket. We anticipate that the bypass socket will be a useful tool to evaluate and develop sensorized myoelectric prosthesis technology., Comment: 8 pages, 5 figures

Objectives Kilohertz high-frequency alternating current (KHFAC) electrical nerve stimulation produces a reversible nerve block in peripheral nerves in human patients with chronic pain pathologies. Although this stimulation methodology has been verified with nonselective extrafascicular electrodes, the effectiveness of producing a selective nerve block with more-selective intrafascicular electrodes has not been well documented. The objective of this study was to examine whether intrafascicular electrodes can block painful stimuli while preserving conduction of other neural activity within the implanted nerve. Materials and methods We analyzed the effects of various stimulation waveforms delivered through Utah Slanted Electrode Arrays (USEAs) implanted in the median nerve of a male human subject with a left brachial plexus injury. We compared KHFAC stimulation with a sham control. Results KHFAC stimulation through USEA electrodes produced a reduction in pain sensitivity in the palmar aspect of the left middle finger. KHFAC had limited effects on the patient's ability to feel tactile probing in the same area or move the digits of his left hand. Other tested stimulation parameters either increased or showed no reduction in pain. Conclusions KHFAC stimulation in peripheral nerves through intrafascicular electrodes demonstrated a selective reduction in pain sensitivity while preserving other nerve functions. This treatment may benefit patient populations who have chronic pain originating from peripheral nerves, but who do not want to block whole-nerve function in order to preserve sensory and motor function reliant on the implanted nerve. Furthermore, KHFAC may benefit patients who respond negatively to other forms of peripheral nerve stimulation therapy.

Interictal epileptiform discharges (IEDs), also known as interictal spikes, are large intermittent electrophysiological events observed between seizures in patients with epilepsy. While seizures are infrequent and unpredictable, IEDs are far more common, often occurring several times per minute. Yet despite the abundance of IEDs, it remains unknown how they relate to seizures. To better understand this relationship, we examined multi-day recordings of 96-channel microelectrode arrays implanted in human epilepsy patients. These recordings (spanning single cell action potentials to population field potentials) allowed us to study the microscale spatiotemporal organization of over 45,000 IEDs across 10 participants from 2 surgical centers. These recordings showed that the majority of IEDs propagate across neocortex as traveling waves. While all of these traveling wave distributions exhibited a predominant, consistent direction, the majority also exhibited a second, auxiliary, direction. Clustering the IED distributions revealed that their predominant and auxiliary distributions were antipodal, mimicking the spatial microstructure of seizure discharges (SDs) that we have previously reported. We thus compared spatial features of IED sub-distributions to those for SDs, showing a correspondence between ictal and interictal spatial properties in participants whose microelectrode arrays were recruited into the seizure from adjacent cortical tissue. These results reveal fundamental relationships between IEDs and seizures and suggest how IEDs could be used to infer spatial features of seizures.

Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

ObjectiveWe explore the long-term performance and stability of seven percutanous Utah Slanted Electrode Arrays (USEAs) and intramuscular recording leads (iEMGs) implanted chronically in the residual arm nerves and muscles of three human amputees as a means to permanently restore sensorimotor function after upper-limb.ApproachWe quantify the number of functional recording and functional stimulating electrodes over time. We also calculate the signal-to-noise ratio of USEA and iEMG recordings and quantify the stimulation amplitude necessary to evoke detectable sensory percepts. Furthermore, we quantify the consistency of the sensory modality, receptive field location, and receptive field size of USEA-evoked percepts.Main ResultsIn the most recent subject, involving USEAs with technical improvements, neural recordings persisted for 502 days (entire implant duration) and the number of functional recording electrodes for one USEA increased over time. However, for six out of seven USEAs the number of functional recording electrodes decreased within the first two months after implantation. The signal-to-noise ratio of neural recordings and electromyographic recordings stayed relatively consistent over time. Sensory percepts were consistently evoked over the span of 14 months, were not significantly different in size, and highlighted the nerves’ fascicular organization. The percentage of percepts with consistent modality or consistent receptive field location between sessions (~1 month apart) varied between 0–86.2% and 9.1–100%, respectively. Stimulation thresholds and electrode impedances increased initially but then remained relatively stable over time.SignificanceThis work demonstrates improved performance of USEAs, and provides a basis for comparing the longevity and stability of USEAs to that of other neural interfaces. Although USEAs provide a rich repertoire of neural recordings and sensory percepts, performance still generally declines over time. Future work should leverage the results presented here to further improve USEA design or to develop adaptive algorithms that can maintain a high level of performance.

This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use-essential information to determine clinical relevance and improve future research for advanced bionic arms.

Objective Single-pulse electrical stimulation (SPES) of intracranial electrodes evokes responses that may help identify the seizure onset zone (SOZ); however, lack of automation and response variability has limited clinical adoption of this technique. We evaluated whether automated delivery of low-current SPES could evoke delayed high-frequency suppression (DHFS) of ongoing electrocorticography (ECoG) signals that, when combined with objective analytic techniques, may provide a reliable marker of this zone. Methods Low-current SPES (1-ms, 3.5-mA biphasic pulses) was delivered to 652 electrodes across 10 patients undergoing ECoG for seizure focus localization. DHFS was measured by calculating the normalized trial-averaged time-frequency power (70–250 Hz) 0.4–1 sec post-stimulation. Electrodes that evoked suppression when stimulated or recorded suppression when stimulation was nearby were used to estimate the SOZ. Results The estimated SOZ significantly identified the clinical SOZ in 6 of 10 patients (5 of 7 temporal foci) with a false-positive rate of 0–0.06. Stimulation required Conclusions We show that DHFS provides accurate estimates of the clinical SOZ in patients with refractory epilepsy. Significance This approach may increase the safety, speed, and reproducibility of SOZ identification while reducing cost, subjectivity, and patient discomfort.

We describe use of a bidirectional neuromyoelectric prosthetic hand that conveys biomimetic sensory feedback. Electromyographic recordings from residual arm muscles were decoded to provide independent and proportional control of a six-DOF prosthetic hand and wrist-the DEKA LUKE arm. Activation of contact sensors on the prosthesis resulted in intraneural microstimulation of residual sensory nerve fibers through chronically implanted Utah Slanted Electrode Arrays, thereby evoking tactile percepts on the phantom hand. With sensory feedback enabled, the participant exhibited greater precision in grip force and was better able to handle fragile objects. With active exploration, the participant was also able to distinguish between small and large objects and between soft and hard ones. When the sensory feedback was biomimetic-designed to mimic natural sensory signals-the participant was able to identify the objects significantly faster than with the use of traditional encoding algorithms that depended on only the present stimulus intensity. Thus, artificial touch can be sculpted by patterning the sensory feedback, and biologically inspired patterns elicit more interpretable and useful percepts.

Significance: The performance of traditional approaches to decoding movement intent from electromyograms (EMGs) and other biological signals commonly degrade over time. Furthermore, conventional algorithms for training neural network based decoders may not perform well outside the domain of the state transitions observed during training. The work presented in this paper mitigates both these problems, resulting in an approach that has the potential to substantially improve the quality of life of the people with limb loss. Objective: This paper presents and evaluates the performance of four decoding methods for volitional movement intent from intramuscular EMG signals. Methods: The decoders are trained using the dataset aggregation (DAgger) algorithm, in which the training dataset is augmented during each training iteration based on the decoded estimates from previous iterations. Four competing decoding methods, namely polynomial Kalman filters (KFs), multilayer perceptron (MLP) networks, convolutional neural networks (CNN), and long short-term memory (LSTM) networks, were developed. The performances of the four decoding methods were evaluated using EMG datasets recorded from two human volunteers with transradial amputation. Short-term analyses, in which the training and cross-validation data came from the same dataset, and long-term analyses, in which the training and testing were done in different datasets, were performed. Results: Short-term analyses of the decoders demonstrated that CNN and MLP decoders performed significantly better than KF and LSTM decoders, showing an improvement of up to 60% in the normalized mean-square decoding error in cross-validation tests. Long-term analyses indicated that the CNN, MLP, and LSTM decoders performed significantly better than a KF-based decoder at most analyzed cases of temporal separations (0–150 days) between the acquisition of the training and testing datasets. Conclusion: The short-term and long-term performances of MLP- and CNN-based decoders trained with DAgger demonstrated their potential to provide more accurate and naturalistic control of prosthetic hands than alternate approaches.

Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA "LUKE" Arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter. Results: We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gain were significantly greater than one and served to amplify participant effort. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living. Comparison with Existing Methods: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes. Conclusions: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands., Comment: 10 figures. Accepted in J. Neurosci. Methods (2019)

The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area., Epileptic brains display inhibitory restraint as manifested by the spread of synchronized activities being delayed in timing. Here, Elliot Smith and colleagues show fast-moving traveling wave that originates from the edge of ictal wavefront with subsequent depolarization and multiunit firing in the seizing brain regions in epileptic patients.

Millions of people in the United States are affected by chronic pain, and the financial cost of pain treatment is weighing on the healthcare system. In some cases, current pharmacological treatments may do more harm than good, as with the United States opioid crisis. Direct electrical stimulation of the brain is one potential non-pharmacological treatment with a long history of investigation. Yet brain stimulation has been far less successful than peripheral or spinal cord stimulation, perhaps because of our limited understanding of the neural circuits involved in pain perception. In this paper, we review the history of using electrical stimulation of the brain to treat pain, as well as contemporary studies identifying the structures involved in pain networks, such as the thalamus, insula, and anterior cingulate. We propose that the thermal grill illusion, an experimental pain model, can facilitate further investigation of these structures. Pairing this model with intracranial recording will provide insight toward disentangling the neural correlates from the described anatomic areas. Finally, the possibility of altering pain perception with brain stimulation in these regions could be highly informative for the development of novel brain stimulation therapies for chronic pain.

We quantified prosthesis embodiment and phantom pain reduction associated with motor control and sensory feedback from a prosthetic hand in one human with a long-term transradial amputation. Microelectrode arrays were implanted in the residual median and ulnar arm nerves, and intramuscular electromyography recording leads were implanted in residual limb muscles to enable sensory feedback and motor control. Objective measures (proprioceptive drift) and subjective measures (survey answers) were used to assess prosthesis embodiment. For both measures, there was a significant level of embodiment of the physical prosthetic limb after open-loop motor control of the prosthesis (i.e., without sensory feedback), open-loop sensation from the prosthesis (i.e., without motor control), and closed-loop control of the prosthesis (i.e., motor control with sensory feedback). There was also a statistically significant reduction in reported phantom pain after experimental sessions that included open-loop nerve microstimulation, open-loop prosthesis motor control, or closed-loop prosthesis motor control. The closed-loop condition provided no additional significant improvements in phantom pain reduction or prosthesis embodiment relative to the open-loop sensory condition or the open-loop motor condition. This study represents the first long-term (14-month), systematic report of phantom pain reduction and prosthesis embodiment in a human amputee across a variety of prosthesis use cases.

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. Electronic supplementary material The online version of this article (10.1186/s12984-017-0320-4) contains supplementary material, which is available to authorized users.

OBJECTIVE Voice tremor is a common feature of essential tremor (ET) that is difficult to treat medically and significantly affects quality of life. Deep brain stimulation (DBS) of the ventral intermediate nucleus (Vim) of the thalamus is effective in improving contralateral distal limb tremor and has been shown in limited studies to affect voice tremor. Our objective was to retrospectively evaluate whether Vim-DBS used to treat patients with essential motor tremor also effectively treated underlying concurrent voice tremor and assess whether particular lead locations were favorable for treating vocal tremor. MATERIALS AND METHODS In this retrospective cohort study, patients had unilateral or bilateral lead placement and were monitored for up to 12 months. We used the Fahn-Tolosa-Marin (FTM) subscore to assess vocal tremor. Changes in vocal tremor before and after stimulation and over several sessions were assessed. RESULTS Of the 77 patients who met the inclusion criteria and were treated for essential tremor, 20 (26%) patients had vocal tremor prior to stimulation. Active Vim-DBS decreased the amplitude of voice tremor by 80% (p