Activity in Primary Motor Cortex Related to Visual Feedback.

Neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral events, monkeys performed variations of a center-out reaching task. Monkeys viewed a computer cursor matched to hand position and a radial target at 1 of 16 locations. In some trials, they performed a visuomotor rotation (the cursor moved at an angle to the hand). After adaptation, encoding changes for single units are temporally structured: adaptation could affect one temporal component of a unit's response but not another. In half the normal and perturbed trials, we removed visual feedback before movement. Adaptation-sensitive firing components toward the end of movement are often weak or absent during reaches without feedback. These results show that temporal structure in motor cortical activity is driven by behavior, with a discrete component related to visual feedback.

Distributed processing of movement signaling.

Basic neurophysiological research with monkeys has shown how neurons in the motor cortex have firing rates tuned to movement direction. This original finding would have been difficult to uncover without the use of a behaving primate paradigm in which subjects grasped a handle and moved purposefully to targets in different directions. Subsequent research, again using behaving primate models, extended these findings to continuous drawing and to arm and hand movements encompassing action across multiple joints. This research also led to robust extraction algorithms in which information from neuronal populations is used to decode movement intent. The ability to decode intended movement provided the foundation for neural prosthetics in which brain-controlled interfaces are used by paralyzed human subjects to control computer cursors or high-performance motorized prosthetic arms and hands. This translation of neurophysiological laboratory findings to therapy is a clear example of why using nonhuman primates for basic research is valuable for advancing treatment of neurological disorders. Recent research emphasizes the distribution of intention signaling through neuronal populations and shows how many movement parameters are encoded simultaneously. In addition to direction and velocity, the arm's impedance has now been found to be encoded as well. The ability to decode motion and force from neural populations will make it possible to extend neural prosthetic paradigms to precise interaction with objects, enabling paralyzed individuals to perform many tasks of daily living.

Viral-Mediated Optogenetic Stimulation of Peripheral Motor Nerves in Non-human Primates.

Objective: Reanimation of muscles paralyzed by disease states such as spinal cord injury remains a highly sought therapeutic goal of neuroprosthetic research. Optogenetic stimulation of peripheral motor nerves expressing light-sensitive opsins is a promising approach to muscle reanimation that may overcome several drawbacks of traditional methods such as functional electrical stimulation (FES). However, the utility of these methods has only been demonstrated in rodents to date, while translation to clinical practice will likely first require demonstration and refinement of these gene therapy techniques in non-human primates. Approach: Three rhesus macaques were injected intramuscularly with either one or both of two optogenetic constructs (AAV6-hSyn-ChR2-eYFP and/or AAV6-hSyn-Chronos-eYFP) to transduce opsin expression in the corresponding nerves. Neuromuscular junctions were targeted for virus delivery using an electrical stimulating injection technique. Functional opsin expression was periodically evaluated up to 13 weeks post-injection by optically stimulating targeted nerves with a 472 nm fiber-coupled laser while recording electromyographic (EMG) responses. Main Results: One monkey demonstrated functional expression of ChR2 at 8 weeks post-injection in each of two injected muscles, while the second monkey briefly exhibited contractions coupled to optical stimulation in a muscle injected with the Chronos construct at 10 weeks. A third monkey injected only in one muscle with the ChR2 construct showed strong optically coupled contractions at 5 ½ weeks which then disappeared by 9 weeks. EMG responses to optical stimulation of ChR2-transduced nerves demonstrated graded recruitment relative to both stimulus pulse-width and light intensity, and followed stimulus trains up to 16 Hz. In addition, the EMG response to prolonged stimulation showed delayed fatigue over several minutes. Significance: These results demonstrate the feasibility of viral transduction of peripheral motor nerves for functional optical stimulation of motor activity in non-human primates, a variable timeline of opsin expression in a animal model closer to humans, and fundamental EMG response characteristics to optical nerve stimulation. Together, they represent an important step in translating these optogenetic techniques as a clinically viable gene therapy.

Stiffness as a control factor for object manipulation.

During manipulation, force is exerted with the expectation that an object will move in an intended manner. This prediction is a learned coordination between force and movement. Mechanically, impedance is a way to describe this coordination, and object interaction could be anticipated by setting impedance before the hand moves the object. This strategy would be especially important at the end of a reach, because feedback is ineffective for rapid force changes. Since mechanical impedance is not subject to the time delays of feedback, it can, if set properly, produce the desired motion on impact. We examined this possibility by instructing subjects to move a handle to a specific target position along a track. The handle was locked in place until the subject exerted enough force to cross a threshold; the handle was then released abruptly to move along the track. We hypothesized that this ballistic release task would encourage subjects to modify impedance in anticipation of the upcoming movement and found that one component of impedance, stiffness, varied in a way that matched the behavioral demands of the task. Analysis suggests that this stiffness was set before the handle moved and governed the subsequent motion. We also found separate components of muscle activity that corresponded to stiffness and to changes in force. Our results show that subjects used a robust and efficient strategy to coordinate force and displacement by modulating muscle activity in a way that was behaviorally relevant in the task.NEW & NOTEWORTHY The arm can behave like a spring, and this mechanical behavior can be advantageous in situations requiring rapid changes in force and/or displacement. Selection of a proper "virtual" spring before the occurrence of a rapid transient could facilitate a desired responsive movement. We show that these spring-like arm mechanics, set in anticipation of an instantaneous force change, function as an efficient strategy to control movement when feedback is ineffective.

Decoding arm speed during reaching.

Neural prostheses decode intention from cortical activity to restore upper extremity movement. Typical decoding algorithms extract velocity-a vector quantity with direction and magnitude (speed) -from neuronal firing rates. Standard decoding algorithms accurately recover arm direction, but the extraction of speed has proven more difficult. We show that this difficulty is due to the way speed is encoded by individual neurons and demonstrate how standard encoding-decoding procedures produce characteristic errors. These problems are addressed using alternative brain-computer interface (BCI) algorithms that accommodate nonlinear encoding of speed and direction. Our BCI approach leads to skillful control of both direction and speed as demonstrated by stereotypic bell-shaped speed profiles, straight trajectories, and steady cursor positions before and after the movement.

Intracortical recording stability in human brain-computer interface users.

OBJECTIVE: Intracortical brain-computer interfaces (BCIs) are being developed to assist people with motor disabilities in communicating and interacting with the world around them. This technology relies on recordings from the primary motor cortex, which may vary from day to day. APPROACH: Here we quantify, in two long-term BCI subjects, the length of time that action potentials from the same neuron, or group of neurons, can be recorded from the motor cortex. MAIN RESULTS: These action potentials are identified by their extracellular waveforms and may change within a single day, although some of these identified units can be identified consistently for weeks and even months. Features of the extracellular waveforms allowed us to predict whether a specific unit was more or less likely to remain stable over a prolonged period. SIGNIFICANCE: A greater understanding of unit stability and instability can aid the development of motor BCIs, where the goal is to maintain a high level of performance despite changes in the recorded population. BCIs should be able to be operated without technician intervention for hours, and hopefully days, to provide the most benefit to the end-users of this technology.

Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed?

We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.

Intracortical microstimulation of human somatosensory cortex.

Intracortical microstimulation of the somatosensory cortex offers the potential for creating a sensory neuroprosthesis to restore tactile sensation. Whereas animal studies have suggested that both cutaneous and proprioceptive percepts can be evoked using this approach, the perceptual quality of the stimuli cannot be measured in these experiments. We show that microstimulation within the hand area of the somatosensory cortex of a person with long-term spinal cord injury evokes tactile sensations perceived as originating from locations on the hand and that cortical stimulation sites are organized according to expected somatotopic principles. Many of these percepts exhibit naturalistic characteristics (including feelings of pressure), can be evoked at low stimulation amplitudes, and remain stable for months. Further, modulating the stimulus amplitude grades the perceptual intensity of the stimuli, suggesting that intracortical microstimulation could be used to convey information about the contact location and pressure necessary to perform dexterous hand movements associated with object manipulation.

Movement: How the Brain Communicates with the World.

Voluntary movement is a result of signals transmitted through a communication channel that links the internal world in our minds to the physical world around us. Intention can be considered the desire to effect change on our environment, and this is contained in the signals from the brain, passed through the nervous system to converge on muscles that generate displacements and forces on our surroundings. The resulting changes in the world act to generate sensations that feed back to the nervous system, closing the control loop. This Perspective discusses the experimental and theoretical underpinnings of current models of movement generation and the way they are modulated by external information. Movement systems embody intentionality and prediction, two factors that are propelling a revolution in engineering. Development of movement models that include the complexities of the external world may allow a better understanding of the neuronal populations regulating these processes, as well as the development of solutions for autonomous vehicles and robots, and neural prostheses for those who are motor impaired.

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

BACKGROUND: Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object. METHODS: Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps. RESULTS: Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control. CONCLUSIONS: Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users. TRIAL REGISTRATION: NCT01364480 and NCT01894802 .

Idle state classification using spiking activity and local field potentials in a brain computer interface.

Previous studies of intracortical brain-computer interfaces (BCIs) have often focused on or compared the use of spiking activity and local field potentials (LFPs) for decoding kinematic movement parameters. Conversely, using these signals to detect the initial intention to use a neuroprosthetic device or not has remained a relatively understudied problem. In this study, we examined the relative performance of spiking activity and LFP signals in detecting discrete state changes in attention regarding a user's desire to actively control a BCI device. Preliminary offline results suggest that the beta and high gamma frequency bands of LFP activity demonstrated a capacity for discriminating idle/active BCI control states equal to or greater than firing rate activity on the same channel. Population classifier models using either signal modality demonstrated an indistinguishably high degree of accuracy in decoding rest periods from active BCI reach periods as well as other portions of active BCI task trials. These results suggest that either signal modality may be used to reliably detect discrete state changes on a fine time scale for the purpose of gating neural prosthetic movements.

A MULTIVARIATE GAUSSIAN PROCESS FACTOR MODEL FOR HAND SHAPE DURING REACH-TO-GRASP MOVEMENTS.

We propose a Multivariate Gaussian Process Factor Model to estimate low dimensional spatio-temporal patterns of finger motion in repeated reach-to-grasp movements. Our model decomposes and reduces the dimensionality of variation of the multivariate functional data. We first account for time variability through multivariate functional registration, then decompose finger motion into a term that is shared among replications and a term that encodes the variation per replication. We discuss variants of our model, estimation algorithms, and we evaluate its performance in simulations and in data collected from a non-human primate executing a reach-to-grasp task. We show that by taking advantage of the repeated trial structure of the experiments, our model yields an intuitive way to interpret the time and replication variation in our kinematic dataset.

Automatic scan test for detection of functional connectivity between cortex and muscles.

Postspike effects (PSEs) in averages of spike-triggered EMG snippets provide physiological evidence of connectivity between CMN cells and spinal motoneurons innervating skeletal muscles. They are typically detected by visual inspection of spike-triggered averages (SpTAs) or by multiple-fragment/single-snippet analyses [MFA (Poliakov AV, Schieber MH. J Neurosci Methods 79: 143-150, 1998) and SSA (Perel S, Schwartz AB, Ventura V. Neural Comput 26: 40-56, 2014)]; the latter are automatic tests that yield P values. However, MFA/SSA are only effective to detect PSEs that occur at about 6-16 ms posttrigger. Our first contribution is the scan test, an automatic test that has the same utility as SpTA, i.e., it can detect a wide range of PSEs at any latency, but it also yields a P value. Our second contribution is a thorough investigation of the statistical properties of PSE detection tests. We show that when the PSE is weak or the sample size is small, visual inspections of SpTAs have low power, because it is difficult to distinguish PSEs from background EMG variations. We also show that the scan test has better power and that its rate of spurious detections matches the chosen significance level α. This is especially important for investigators because, when a PSE is detected, this guarantees that the probability of a spurious PSE is less than α. Finally, we illustrate the operational characteristics of the PSE detection tests on 2,059 datasets from 5 experiments. The scan test is particularly useful to identify candidate PSEs, which can then be subject to further evaluation by SpTA inspection, and when PSEs are small and visual detection is ambiguous.

Motor cortical correlates of arm resting in the context of a reaching task and implications for prosthetic control.

Prosthetic devices are being developed to restore movement for motor-impaired individuals. A robotic arm can be controlled based on models that relate motor-cortical ensemble activity to kinematic parameters. The models are typically built and validated on data from structured trial periods during which a subject actively performs specific movements, but real-world prosthetic devices will need to operate correctly during rest periods as well. To develop a model of motor cortical modulation during rest, we trained monkeys (Macaca mulatta) to perform a reaching task with their own arm while recording motor-cortical single-unit activity. When a monkey spontaneously put its arm down to rest between trials, our traditional movement decoder produced a nonzero velocity prediction, which would cause undesired motion when applied to a prosthetic arm. During these rest periods, a marked shift was found in individual units' tuning functions. The activity pattern of the whole population during rest (Idle state) was highly distinct from that during reaching movements (Active state), allowing us to predict arm resting from instantaneous firing rates with 98% accuracy using a simple classifier. By cascading this state classifier and the movement decoder, we were able to predict zero velocity correctly, which would avoid undesired motion in a prosthetic application. Interestingly, firing rates during hold periods followed the Active pattern even though hold kinematics were similar to those during rest with near-zero velocity. These findings expand our concept of motor-cortical function by showing that population activity reflects behavioral context in addition to the direct parameters of the movement itself.

Motor cortical control of movement speed with implications for brain-machine interface control.

Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications.

Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control.

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.

Single-snippet analysis for detection of postspike effects.

Corticomotoneuronal cells (CMN), located predominantly in the primary motor cortex, project directly to alpha motoneuronal pools in the spinal cord. The effects of CMN spikes on motoneuronal excitability are traditionally characterized by visualizing postspike effects (PSEs) in spike-triggered averages (SpTA; Fetz, Cheney, & German, 1976; Fetz & Cheney, 1980; McKiernan, Marcario, Karrer, & Cheney, 1998) of electromyography (EMG) data. Poliakov and Schieber (1998) suggested a formal test, the multiple-fragment analysis (MFA), to automatically detect PSEs. However, MFA's performance was not statistically validated, and it is unclear under what conditions it is valid. This paper's contributions are a power study that validates the MFA; an alternative test, the single-snippet analysis (SSA), which has the same functionality as MFA but is easier to calculate and has better power in small samples; a simple bootstrap simulation to estimate SpTA baselines with simulation bands that help visualize potential PSEs; and a bootstrap adjustment to the MFA and SSA to correct for nonlinear SpTA baselines.

An electrocorticographic brain interface in an individual with tetraplegia.

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.