Nicholas A. Sachs

Statistical assessment of the stability of neural movement representations

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

Electrical Stimulation of the Paralyzed Orbicularis Oculi in Rabbit

Dysfunction of the seventh cranial nerve often results in facial paralysis and loss of the ability to blink the eye, which can lead to corneal scarring, diminished vision, and potential loss of the eye. This study investigated the potential of electrical stimulation of the orbicularis oculi muscle as a means of restoring blink function. An animal model of orbicularis paralysis was created by sectioning the seventh cranial nerve in rabbit. Twenty paralyzed and five normal rabbits were acutely implanted with a subcutaneous stimulating electrode near the margin of the upper eyelid. Biphasic current controlled stimulation pulses were delivered between implanted contacts at the medial and lateral edges of the eyelid. Strength-duration curves for lid twitch threshold were generated, and quantitative measurements of lid closure were made for systematically varied parameters including pulse amplitude, pulse width, number of pulses delivered, and duration of paralysis prior to stimulation. Normal rabbits achieved a greater degree of lid closure due to electrical stimulation than rabbits that had been surgically paralyzed. Of rabbits that had been paralyzed, those demonstrating evidence of at least partial reinnervation achieved a greater degree of lid closure than those demonstrating persistent denervation. Trains of 10 ms biphasic pulses delivered at 50 Hz were found to be the most effective means of eliciting lid closure for the range of parameters tested

Development of a BIONic Muscle Spindle for Prosthetic Proprioception

The replacement of proprioceptive function, whether for conscious sensation or feedback control, is likely to be an important aspect of neural prosthetic restoration of limb movements. Thus far, however, it has been hampered by the absence of unobtrusive sensors. We propose a method whereby fully implanted, telemetrically operated BIONstrade monitor muscle movement, and thereby detect changes in joint angle(s) and/or limb posture without requiring the use of secondary components attached to limb segments or external reference frames. The sensor system is designed to detect variations in the electrical coupling between devices implanted in neighboring muscles that result from changes in their relative position as the muscles contract and stretch with joint motion. The goal of this study was to develop and empirically validate mathematical models of the sensing scheme and to use computer simulations to provide an early proof of concept and inform design of the overall sensor system. Results from experiments using paired dipoles in a saline bath and finite element simulations have given insight into the current distribution and potential gradients exhibited within bounded anisotropic environments similar to a human limb segment and demonstrated an anticipated signal to noise ratio of at least 8:1 for submillimeter resolution of relative implant movement over a range of implant displacements up to 15 cm

RF-powered BIONs/spl trade/ for stimulation and sensing

Virtually all bodily functions are controlled by electrical signals in nerves and muscles. Electrical stimulation can restore missing signals but this has been difficult to achieve practically because of limitations in the bioelectric interfaces. Wireless, injectable microdevices are versatile, robust and relatively inexpensive to implant in a variety of sites and applications. Several variants are now in clinical use or under development to perform stimulation and/or sensing functions and to operate autonomously or with continuous coordination and feedback control.

Kinematics of Electrically Elicited Eyelid Movement

Electrical stimulation has demonstrated potential for reanimating eye blink following facial paralysis caused by damage to the seventh cranial nerve. This study investigated the kinematics of lid movement caused by electrical stimulation of the orbicularis oculi muscle in both normal rabbit and rabbit with surgically induced seventh nerve lesion

Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury

Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.

Continuous state-dependent decoders for brain machine interfaces

One of the characteristics of cursor movement controlled via a brain machine interface is a trade-off between the ability to move rapidly between targets and the ability to hold the cursor steadily within a target. We propose to address this limitation by classifying independent movement and posture states, and using neural decoders with optimum dynamical properties for each state. This paper investigates two methods of classifying the state of a limb based on the offline analysis of neural discharge. We also tested the performance of state-dependent decoders that either apply additional smoothing during the posture state or consist of separate filters trained explicitly on data from the different movement states. This work suggests that a state-dependent decoder may provide significantly improved BMI performance.

Dealing with noisy gaze information for a target-dependent neural decoder

We tend to look at targets prior to moving our hand towards them. This means that our eye movements contain information about the movements we are planning to make. This information has been shown to be useful in the context of decoding of movement intent from neural signals. However, this is complicated by the fact that occasionally, subjects may want to move towards targets that have not been foveated, or may be distracted and temporarily look away from the intended target. We have previously accounted for this uncertainty using a probabilistic mixture over targets, where the gaze information is used to identify target candidates. Here we evaluate how the accuracy of prior target information influences decoding accuracy. We also consider a mixture model where we assume that the target may be foveated or, alternatively, that the target may not be foveated. We found that errors due to inaccurate target information were reduced by including a generic model representing movements to all targets into the mixture.

Continuous movement decoding using a target-dependent model with EMG inputs

Trajectory-based models that incorporate target position information have been shown to accurately decode reaching movements from bio-control signals, such as muscle (EMG) and cortical activity (neural spikes). One major hurdle in implementing such models for neuroprosthetic control is that they are inherently designed to decode single reaches from a position of origin to a specific target. Gaze direction can be used to identify appropriate targets, however information regarding movement intent is needed to determine when a reach is meant to begin and when it has been completed. We used linear discriminant analysis to classify limb states into movement classes based on recorded EMG from a sparse set of shoulder muscles. We then used the detected state transitions to update target information in a mixture of Kalman filters that incorporated target position explicitly in the state, and used EMG activity to decode arm movements. Updating the target position initiated movement along new trajectories, allowing a sequence of appropriately timed single reaches to be decoded in series and enabling highly accurate continuous control.