Implantable neural electrodes must drastically improve chronic recording stability before they can be translated into long-term human clinical prosthetics. Previous studies suggest that sub-cellular sized and mechanically compliant probes may result in improved tissue integration and recording longevity. However, currently these design features are restricted by the opposing mechanical requirements needed for minimally damaging insertions. We designed a non-cytotoxic, carboxymethylcellulose (CMC) based dissolvable delivery vehicle (shuttle) to provide the mechanical support for insertion of ultra-small, ultra-compliant microfabricated neural probes. Stiff CMC-based shuttles rapidly soften immediately after being placed ?1 mm above an open craniotomy as they absorb vapors from the brain. To address this, we developed a sophisticated targeting, high speed insertion (?80 mm/s), and release system to implant these shuttles. After implantation, the goal is for the shuttle to dissolve away leaving only the electrodes behind. Here we show the histology of chronically implanted shuttles of large (300 ?m × 125 ?m) and small (100 ?m × 125 ?m) size at discrete time points over 12 weeks. Early time points show the CMC shuttle expanded after insertion as it absorbed moisture from the brain and slowly dissolved. At later time points neuronal cell bodies populate regions within the original shuttle tract. The large CMC shuttles show that the CMC expansion can cause extended secondary damage. On the other hand, the smaller CMC shuttles show limited secondary damage, wound closure by 4 weeks, absence of activated microglia at 12 weeks, as well as evidence suggesting neural regeneration at the implant site. This shuttle, therefore, shows great promise facilitating the implantation of nontraditional ultra-small, and ultra-compliant probes.
The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.
Biologic scaffolds composed of naturally occurring extracellular matrix (ECM) can provide a microenvironmental niche that alters the default healing response toward a constructive and functional outcome. The present study showed similarities in the remodeling characteristics of xenogeneic ECM scaffolds when used as a surgical treatment for volumetric muscle loss in both a preclinical rodent model and five male patients. Porcine urinary bladder ECM scaffold implantation was associated with perivascular stem cell mobilization and accumulation within the site of injury, and de novo formation of skeletal muscle cells. The ECM-mediated constructive remodeling was associated with stimulus-responsive skeletal muscle in rodents and functional improvement in three of the five human patients.
This study describes results of primary afferent neural microstimulation experiments using microelectrode arrays implanted chronically in the lumbar dorsal root ganglia (DRG) of four cats. The goal was to test the stability and selectivity of these microelectrode arrays as a potential interface for restoration of somatosensory feedback after damage to the nervous system such as amputation.
Our research group recently demonstrated that a person with tetraplegia could use a brain-computer interface (BCI) to control a sophisticated anthropomorphic robotic arm with skill and speed approaching that of an able-bodied person. This multiyear study exemplifies important principles in translating research from foundational theory and animal experiments into a clinical study. We present a roadmap that may serve as an example for other areas of clinical device research as well as an update on study results. Prior to conducting a multiyear clinical trial, years of animal research preceded BCI testing in an epilepsy monitoring unit, and then in a short-term (28 days) clinical investigation. Scientists and engineers developed the necessary robotic and surgical hardware, software environment, data analysis techniques, and training paradigms. Coordination among researchers, funding institutes, and regulatory bodies ensured that the study would provide valuable scientific information in a safe environment for the study participant. Finally, clinicians from neurosurgery, anesthesiology, physiatry, psychology, and occupational therapy all worked in a multidisciplinary team along with the other researchers to conduct a multiyear BCI clinical study. This teamwork and coordination can be used as a model for others attempting to translate basic science into real-world clinical situations.
Pudendal afferent fibers can be excited using electrical stimulation to evoke reflex bladder activity. While this approach shows promise for restoring bladder function, stimulation of desired pathways, and integration of afferent signals for sensory feedback remains challenging. At sacral dorsal root ganglia (DRG), the convergence of pelvic and pudendal afferent fibers provides a unique location for access to lower urinary tract neurons. Our goal in this study was to demonstrate the potential of microstimulation in sacral DRG for evoking reflex bladder responses.
The ventral spinal roots contain the axons of spinal motoneurons and provide the only location in the peripheral nervous system where recorded neural activity can be assured to be motor rather than sensory. This study demonstrates recordings of single unit activity from these ventral root axons using floating microelectrode arrays (FMAs). Ventral root recordings were characterized by examining single unit yield and signal-to-noise ratios (SNR) with 32-channel FMAs implanted chronically in the L6 and L7 spinal roots of nine cats. Single unit recordings were performed for implant periods of up to 12?weeks. Motor units were identified based on active discharge during locomotion and inactivity under anesthesia. Motor unit yield and SNR were calculated for each electrode, and results were grouped by electrode site size, which were varied systematically between 25 and 160??m to determine effects on signal quality. The unit yields and SNR did not differ significantly across this wide range of electrode sizes. Both SNR and yield decayed over time, but electrodes were able to record spikes with SNR >2 up to 12?weeks post-implant. These results demonstrate that it is feasible to record single unit activity from multiple isolated motor units with penetrating microelectrode arrays implanted chronically in the ventral spinal roots. This approach could be useful for creating a spinal nerve interface for advanced neural prostheses, and results of this study will be used to improve design of microelectrodes for chronic neural recording in the ventral spinal roots.
After spinal cord injury (SCI), motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation (AO), in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, AO can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI) even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG)-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz) and the high-gamma band (65-115 Hz) which contains significant movement-related information. We observed significant motor-related high-gamma band activity during AO in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that AO could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with paralysis.
We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification ("spike sorting"). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.
Spinal cord injury (SCI) results in a loss of function and sensation below the level of the lesion. Neuroprosthetic technology has been developed to help restore motor and autonomic functions as well as to provide sensory feedback.
Spinal cord injury (SCI) often affects a persons ability to perform critical activities of daily living and can negatively affect his or her quality of life. Assistive technology aims to bridge this gap in order to augment function and increase independence. It is critical to involve consumers in the design and evaluation process as new technologies such as brain-computer interfaces (BCIs) are developed. In a survey study of 57 veterans with SCI participating in the 2010 National Veterans Wheelchair Games, we found that restoration of bladder and bowel control, walking, and arm and hand function (tetraplegia only) were all high priorities for improving quality of life. Many of the participants had not used or heard of some currently available technologies designed to improve function or the ability to interact with their environment. The majority of participants in this study were interested in using a BCI, particularly for controlling functional electrical stimulation to restore lost function. Independent operation was considered to be the most important design criteria. Interestingly, many participants reported that they would consider surgery to implant a BCI even though noninvasiveness was a high-priority design requirement. This survey demonstrates the interest of individuals with SCI in receiving and contributing to the design of BCIs.
Vascularized composite allotransplantation has become a clinical reality. Patients undergoing vascularized composite allotransplantation have modest functional return. Most patients have had multiple acute rejections. The effect of multiple acute rejections influencing functional outcomes is unknown. This study systematically analyzes the effects of multiple acute rejections on functional outcome.
Functional electrical stimulation (FES) approaches often utilize an open-loop controller to drive state transitions. The addition of sensory feedback may allow for closed-loop control that can respond effectively to perturbations and muscle fatigue.
Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.
The development of bladder and bowel neuroprostheses may benefit from the use of sensory feedback. We evaluated the use of high-density penetrating microelectrode arrays in sacral dorsal root ganglia (DRG) for recording bladder and perineal afferent activity. Arrays were inserted in S1 and S2 DRG in three anesthetized cats. Neural signals were recorded while the bladder volume was modulated and mechanical stimuli were applied to the perineal region. In two experiments, 48 units were observed that tracked bladder pressure with their firing rates (79% from S2). At least 50 additional units in each of the three experiments (274 total; 60% from S2) had a significant change in their firing rates during one or more perineal stimulation trials. This study shows the feasibility of obtaining bladder-state information and other feedback signals from the pelvic region with a sacral DRG electrode interface located in a single level. This natural source of feedback would be valuable for providing closed-loop control of bladder or other pelvic neuroprostheses.
A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.
Patterned microstimulation of muscle and cutaneous afferent neurons may provide tactile and proprioceptive feedback to users of advanced prosthetic limbs. However, it is unclear what types of stimulation patterns will be effective, and the parameter space for creating these patterns is prohibitively large to explore systematically using only psychophysics paradigms. In this study, we used an array of microelectrodes in primary somatosensory cortex (S1) of an isoflurane anesthetized cat to measure responses in a population of neurons evoked by various patterns of primary afferent microstimulation delivered to the L6 and L7 dorsal root ganglia (DRG). Each pattern consisted of a 300 ms train of microstimulation pulses having a fixed amplitude, pulse rate, and location in the array of DRG electrodes. Evoked responses were detectable on many S1 channels at the lowest amplitude tested (5 ?A) and pulse rate (10 pulses per second). Increasing the pulse rate lowered the threshold amplitude for evoking a response on some S1 channels. Location effects were also observed. Adjacent stimulation sites evoked discriminable responses at low but not high (20 ?A) amplitudes. In summary, we observed interactions between stimulation pulse rate, pulse amplitude, and location. Such interactions must be considered when designing stimulation patterns for transmitting sensory feedback by primary afferent microstimulation.
In neuroprostheses that use functional electrical stimulation (FES) to restore motor function, closed-loop feedback control may compensate for muscle fatigue, perturbations and nonlinearities in the behavior of the effected muscles. Kinematic state information is naturally represented in the firing rates of primary afferent neurons, which may be recorded with multi-electrode arrays at the level of the dorsal root ganglia (DRG). Previous work in cats has shown that it is feasible to estimate the kinematic state of the hind limb with a multivariate linear regression model of the neural activity in the DRG. In this study we extend these results to estimate the limb state in real-time during intramuscular stimulation in an anesthetized cat. Furthermore, we used the limb state estimates as feedback to a finite state FES controller to generate rudimentary walking behavior. This work demonstrates the feasibility of using DRG activity in a closed-loop FES system.
Non-penetrating surface electrode recording techniques are typically associated with field potential recordings, while extracellular recordings from single neurons are made using penetrating metal wire or microfabricated microelectrode arrays. Here, we report on single- and multi-unit neuronal recordings made using non-penetrating electrodes placed on the epineural surface of the dorsal root ganglia (DRG). Across four experiments in anesthetized cats, approximately 40% of the electrodes recorded single- and multi-unit spiking activity with spike-rates that covaried significantly with hindlimb movement. In two intraoperative experiments in humans, compound activity was recorded from the DRG surface in response to peripheral stimulation of the common peroneal nerve. This approach may have advantages over penetrating electrode arrays in terms of clinical acceptability and recording longevity.
Individuals with dysfunctional bladders may benefit from devices that track the bladder state. Recordings from pelvic and sacral nerve cuffs can detect bladder contractions, however they often have low signal quality and are susceptible to interference from non-bladder signals. Microelectrode recordings from sacral dorsal root ganglia (DRG) neurons may provide an alternate source for obtaining high quality sensory signals for bladder pressure monitoring. In this study, penetrating microelectrode arrays were inserted in the S1 and S2 DRG in two cats to record afferent spiking activity at different bladder pressures. Multivariate linear regression models were used to estimate bladder pressure from the spiking activity of DRG neurons. The best estimates were obtained with populations of 5-10 units primarily from the S2 DRG, with root mean square errors of 3.0-3.2 cm H(2)O (correlation coefficients of 0.5-0.9). This work demonstrates the feasibility of monitoring bladder pressure from DRG recordings.
To investigate the neural activity corresponding to different cognitive states, it is of great importance to localize the cortical areas that are associated with task-related modulation. In this paper, we propose a novel discriminant pattern source localization (DPSL) method to analyze MEG data. Unlike most traditional source localization methods that aim to find "dominant" sources, DPSL is developed to capture the "differential" sources that distinguish different cognitive states. As will be demonstrated by the experimental results in this paper, the proposed DPSL method offers superior accuracy to identify the spatial locations of task-related sources.
Current treatment principles for muscle injuries with volumetric loss have been largely derived from empirical observations. Differences in severity or anatomic location have determinant effects on the tissue remodeling outcome. Biologic scaffolds composed of extracellular matrix (ECM) have been successfully used to restore vascularized, innervated, and contractile skeletal muscle in animal models but limited anatomic locations have been evaluated. The aim of this study was to determine the ability of a xenogeneic ECM scaffold to restore functional skeletal muscle in a canine model of a complex quadriceps injury involving bone, tendon, and muscle.
This paper demonstrates a synergy-based brain-machine interface that uses low-dimensional command signals to control a high dimensional virtual hand. First, temporal postural synergies were extracted from the angular velocities of finger joints of five healthy subjects when they performed hand movements that were similar to activities of daily living. Two synergies inspired from the extracted synergies, namely, two-finger pinch and whole-hand grasp, were used in real-time brain control, where a virtual hand with 10 degrees of freedom was controlled to grasp or pinch virtual objects. These two synergies were controlled by electrocorticographic (ECoG) signals recorded from two electrodes of an electrode array that spanned motor and speech areas of an individual with intractable epilepsy, thus demonstrating closed loop control of a synergy-based brain-machine interface.
Identifying brain regions with high differential response under multiple experimental conditions is a fundamental goal of functional imaging. In many studies, regions of interest (ROIs) are not determined a priori but are instead discovered from the data, a process that requires care because of the great potential for false discovery. An additional challenge is that magnetoencephalography/electroencephalography sensor signals are very noisy, and brain source images are usually produced by averaging sensor signals across trials. As a consequence, for a given subject, there is only one source data vector for each condition, making it impossible to apply testing methods such as analysis of variance. We solve these problems in several steps. (1) To obtain within-condition uncertainty, we apply the bootstrap across trials, producing many bootstrap source images. To discover hot spots in space and time that could become ROIs, (2) we find source locations where likelihood ratio statistics take unusually large values. We are not interested in isolated brain locations where a test statistic might happen to be large. Instead, (3) we apply a clustering algorithm to identify sources that are contiguous in space and time where the test statistic takes an excursion above some threshold. Having identified possible spatiotemporal ROIs, (4) we evaluate global statistical significance of ROIs by using a permutation test. After these steps, we check performance via simulation, and then illustrate their application in a magnetoencephalography study of four-direction center-out wrist movement, showing that this approach identifies statistically significant spatiotemporal ROIs in the motor and visual cortices of individual subjects.
Rehabilitation engineers apply engineering principles to improve function or to solve challenges faced by persons with disabilities. It is critical to integrate the knowledge of biologics into the process of rehabilitation engineering to advance the field and maximize potential benefits to patients. Some applications in particular demonstrate the value of a symbiotic relationship between biologics and rehabilitation engineering. In this review we illustrate how researchers working with neural interfaces and integrated prosthetics, assistive technology, and biologics data collection are currently integrating these 2 fields. We also discuss the potential for further integration of biologics and rehabilitation engineering to deliver the best technologies and treatments to patients. Engineers and clinicians must work together to develop technologies that meet clinical needs and are accessible to the intended patient population.
In this paper, we propose a clustering linear discriminant analysis algorithm (CLDA) to accurately decode hand movement directions from a small number of training trials for magnetoencephalography-based brain computer interfaces (BCIs). CLDA first applies a spectral clustering algorithm to automatically partition the BCI features into several groups where the within-group correlation is maximized and the between-group correlation is minimized. As such, the covariance matrix of all features can be approximated as a block diagonal matrix, thereby facilitating us to accurately extract the correlation information required by movement decoding from a small set of training data. The efficiency of the proposed CLDA algorithm is theoretically studied and an error bound is derived. Our experiment on movement decoding of five human subjects demonstrates that CLDA achieves superior decoding accuracy over other traditional approaches. The average accuracy of CLDA is 87% for single-trial movement decoding of four directions (i.e., up, down, left, and right).
The aim of this study was to evaluate the long-term effect of localized growth factor delivery on sciatic nerve regeneration in a critical-size (> 1 cm) peripheral nerve defect. Previous work has demonstrated that bioactive proteins can be encapsulated within double-walled, poly(lactic-co-glycolic acid)/poly(lactide) microspheres and embedded within walls of biodegradable polymer nerve guides composed of poly(caprolactone). Within this study, nerve guides containing glial cell line-derived neurotrophic factor (GDNF) were used to bridge a 1.5-cm defect in the male Lewis rat for a 16-week period. Nerve repair was evaluated through functional assessment of joint angle range of motion using video gait kinematics, gastrocnemius twitch force, and gastrocnemius wet weight. Histological evaluation of nerve repair included assessment of Schwann cell and neurofilament location with immunohistochemistry, evaluation of tissue integration and organization throughout the lumen of the regenerated nerve with Massons trichrome stain, and quantification of axon fiber density and g-ratio. Results from this study showed that the measured gastrocnemius twitch force in animals treated with GDNF was significantly higher than negative controls and was not significantly different from the isograft-positive control group. Histological assessment of explanted conduits after 16 weeks showed improved tissue integration within GDNF releasing nerve guides compared to negative controls. Nerve fibers were present across the entire length of GDNF releasing guides, whereas nerve fibers were not detectable beyond the middle region of negative control guides. Therefore, our results support the use of GDNF for improved functional recovery above negative controls following large axonal defects in the peripheral nervous system.
This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.
To date, the majority of studies using magnetoencephalography (MEG) rely on off-line analysis of the spatiotemporal properties of brain activity. Real-time MEG feedback could potentially benefit multiple areas of basic and clinical research: brain-machine interfaces, neurofeedback rehabilitation of stroke and spinal cord injury, and new adaptive paradigm designs, among others. We have developed a software interface to stream MEG signals in real time from the 306-channel Elekta Neuromag MEG system to an external workstation. The signals can be accessed with a minimal delay (?45?ms) when data are sampled at 1000 Hz, which is sufficient for most real-time studies. We also show here that real-time source imaging is possible by demonstrating real-time monitoring and feedback of alpha-band power fluctuations over parieto-occipital and frontal areas. The interface is made available to the academic community as an open-source resource.
The reduction of artifacts in neural data is a key element in improving analysis of brain recordings and the development of effective brain-computer interfaces. This complex problem becomes even more difficult as the number of channels in the neural recording is increased. Here, new techniques based on wavelet thresholding and independent component analysis (ICA) are developed for use in high-dimensional neural data. The wavelet technique uses a discrete wavelet transform with a Haar basis function to localize artifacts in both time and frequency before removing them with thresholding. Wavelet decomposition level is automatically selected based on the smoothness of artifactual wavelet approximation coefficients. The ICA method separates the signal into independent components, detects artifactual components by measuring the offset between the mean and median of each component, and then removing the correct number of components based on the aforementioned offset and the power of the reconstructed signal. A quantitative method for evaluating these techniques is also presented. Through this evaluation, the novel adaptation of wavelet thresholding is shown to produce superior reduction of ocular artifacts when compared to regression, principal component analysis, and ICA.
Magnetoencephalography (MEG) enables a noninvasive interface with the brain that is potentially capable of providing movement-related information similar to that obtained using more invasive neural recording techniques. Previous studies have shown that movement direction can be decoded from multichannel MEG signals recorded in humans performing wrist movements. We studied whether this information can be extracted without overt movement of the subject, because the targeted users of brain-controlled interface (BCI) technology are those with severe motor disabilities. The objectives of this study were twofold: 1) to decode intended movement direction from MEG signals recorded during the planning period before movement onset and during imagined movement and 2) to localize cortical sources modulated by intended movement direction. Ten able-bodied subjects performed both overt and imagined wrist movement while their cortical activities were recorded using a whole head MEG system. The intended movement direction was decoded using linear discriminant analysis and a Bayesian classifier. Minimum current estimation (MCE) in combination with a bootstrapping procedure enabled source-space statistical analysis, which showed that the contralateral motor cortical area was significantly modulated by intended movement direction, and this modulation was the strongest ?100 ms before the onset of overt movement. These results suggest that it is possible to study cortical representation of specific movement information using MEG, and such studies may aid in presurgical localization of optimal sites for implanting electrodes for BCI systems.
The prevailing dogma in tissue engineering is cell-centric. One shortcoming of this approach is the failure to provide the implanted cells with a suitable in vivo microenvironment that promotes tissue reconstruction. Extracellular matrix (ECM)-based scaffolds provide a three-dimensional microenvironment that can promote constructive and functional tissue remodeling rather than inflammation and scarring even in the absence of any implanted cells. The objective of this study was to determine the ability of an ECM-based scaffold to facilitate functional restoration of the distal gastrocnemius musculotendinous junction in a canine model after complete resection of the tissue. Within 6 months, vascularized, innervated skeletal muscle that was similar to normal muscle tissue had formed at the ECM-scaffold implantation site. This neo-tissue generated 48% of the contractile force of contralateral musculotendinous junction and represents the first report of de novo formation of contractile, vascularized, and innervated skeletal muscle in situ after significant tissue loss.
Sensorimotor control is greatly affected by two factors--the time it takes for an animal to sense and respond to stimuli (responsiveness), and the ability of an animal to distinguish between sensory stimuli and generate graded muscle forces (resolution). Here, we demonstrate that anatomical limitations force a necessary trade-off between responsiveness and resolution with increases in animal size. To determine whether responsiveness is prioritized over resolution, or resolution over responsiveness, we studied how size influences the physiological mechanisms underlying sensorimotor control. Using both new electrophysiological experiments and existing data, we determined the maximum axonal conduction velocity (CV) in animals ranging in size from shrews to elephants. Over the 100-fold increase in leg length, CV was nearly constant, increasing proportionally with mass to the 0.04 power. As a consequence, larger animals are burdened with relatively long physiological delays, which may have broad implications for their behaviour, ecology and evolution, including constraining agility and requiring prediction to help control movements.
In this paper, we develop a robust signal space separation (rSSS) algorithm for real-time magnetoencephalography (MEG) data processing. rSSS is based on the spatial signal space separation (SSS) method and it applies robust regression to automatically detect and remove bad MEG channels so that the results of SSS are not distorted. We extend the existing robust regression algorithm via three important new contributions: 1) a low-rank solver that efficiently performs matrix operations; 2) a subspace iteration scheme that selects bad MEG channels using low-order spherical harmonic functions; and 3) a parallel computing implementation that simultaneously runs multiple tasks to further speed up numerical computation. Our experimental results based on both simulation and measurement data demonstrate that rSSS offers superior accuracy over the traditional SSS algorithm, if the MEG data contain significant outliers. Taking advantage of the proposed fast algorithm, rSSS achieves more than 75 x runtime speedup compared to a direct solver of robust regression. Even though rSSS is currently implemented with MATLAB, it already provides sufficient throughput for real-time applications.
Robotic assistive devices are used increasingly to improve the independence and quality of life of persons with disabilities. Devices as varied as robotic feeders, smart-powered wheelchairs, independent mobile robots, and socially assistive robots are becoming more clinically relevant. There is a growing importance for the rehabilitation professional to be aware of available systems and ongoing research efforts. The aim of this article is to describe the advances in assistive robotics that are relevant to professionals serving persons with disabilities. This review breaks down relevant advances into categories of Assistive Robotic Systems, User Interfaces and Control Systems, Sensory and Feedback Systems, and User Perspectives. An understanding of the direction that assistive robotics is taking is important for the clinician and researcher alike; this review is intended to address this need.
This paper presents a numerical approach using principal component analysis (PCA) to quantize and characterize the variance of hand postures in a novel posture transformation task. Five subjects were tested in two tasks in which a cursor can be moved by varying the hand posture. This was accomplished by weighted linear combination of 14 sensors of a data glove. The first task was to move a cursor on computer screen in one dimension horizontally, by posing various hand postures. To increase the complexity of control, in the second task, subjects were asked to move a cursor on computer screen in two dimensions. Joint angles were measured during the experiment by the data glove. In both tasks subjects participated in multiple trials until they achieved smooth cursor movement trajectories. PCA was performed over the postures obtained during the multiple trials of the two tasks. Across the trials, in both the tasks a gradual decrease in the number of principal components was observed. This implies that the variance in the postures decreases with learning. Additionally this might indicate that through learning, subjects adapted postural synergies (or eigen postures) in this novel geometrical environment. Postural synergies when visualized revealed task specific synergies.
To determine whether baseline hand spastic hemiparesis assessed by the Chedoke-McMaster Assessment influences functional improvement after botulinum toxin type A (BTX-A) injections and postinjection therapy.
It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the users intentions to be detected and converted into control signals sent to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation (TMR) can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.
Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.
State-of-the-art upper extremity prostheses include anthropomorphic hands with dexterity that approximates that of a human. To be fully useful, these devices will require an advanced somatosensory neural interface to convey tactile and proprioceptive feedback to the user. To this end, microstimulation methods are being developed using microelectrode arrays implanted at various locations along the somatosensory neuraxis, from peripheral nerves to primary somatosensory cortex. There is presently no consensus as to the best approach, although results from animal and human studies lend support for each. The purpose of this review is to outline practical considerations for the design of a somatosensory interface based on present knowledge of the anatomy and physiology, prior attempts to elicit somatic sensations using electrical stimulation, and lessons learned from successful sensory neuroprostheses such as the cochlear implant.
It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the users intentions to be detected and converted into control signals to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.
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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.