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.

High-performance neuroprosthetic control by an individual with tetraplegia.

BACKGROUND: 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. METHODS: We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. FINDINGS: The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. INTERPRETATION: With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. FUNDING: Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.

Cortical control of a prosthetic arm for self-feeding.

Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.

Functional network reorganization during learning in a brain-computer interface paradigm.

Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.

Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control.

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.

Object type recognition for automated analysis of protein subcellular location.

The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved.

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.

Baseplate for two-stage cranial mounting of BMI connectors.

OBJECTIVE: Intracortical electrode arrays provide the best spatial and temporal resolution signals for brain-machine interfaces. Wireless technologies are being developed to handle this information capacity, but currently the only means to deliver neural information from the implant to a signal processing unit is by a physical connection starting at a skull-mounted connector. The failure rate of the attachment of these connectors is significant. In this study we report an improvement to the traditional connectors. APPROACH: We have designed and applied an intermediary mounting plate that incorporates several features that provide better, more stable fixation to the skull: (1) wide legs allowing distribution of loading forces and distancing the intracranial screws from the skin interface, (2) a thin shelf to allow early osseointegration, (3) a concave interior to accommodate the curvature of the cranium, and (4) two-stage fixation process providing time for osseointegration prior to the application of loading forces from the connector. MAIN RESULTS: Six baseplates, over four design iterations, have now been tested in three non-human primates. The baseplates are associated with a substantially lower attachment failure rate. SIGNIFICANCE: Our baseplate design improves on the current skull-mounted connectors, leading to better outcomes for subjects and fewer catastrophic failure events that can terminate resource intensive intracortical recording experiments.

Making the lifetime connection between brain and machine for restoring and enhancing function.

A reliable neural interface that lasts a lifetime will lead to the development of neural prosthetic devices as well as the possibility that brain function can be enhanced. Our data demonstrate that a reliable neural interface is best achieved when the surrounding neuropil grows into the electrode tip where it is held securely, allowing myelinated axons to be recorded using implanted amplifiers. Stable single and multiunits were recorded from three implanted subjects and classified according to amplitudes and firing rates. In one paralyzed and mute subject implanted for over 5 years with a double electrode in the speech motor cortex, the single units allowed recognition of over half the 39 English language phonemes detected using a variety of decoding methods. These single units were used by the subject in a speech task where vowel phonemes were recognized and fed back to the subject using audio output. Weeks of training resulted in an 80% success rate in producing four vowels in an adaptation of the classic center-out task used in motor control studies. The importance of using single units was shown in a different task using pure tones that the same subject heard and then sung or hummed in his head. Feedback was associated with smoothly coordinated unit firings. The plasticity of the unit firings was demonstrated over several sessions first without, and then with, feedback. These data suggest that units can be reliably recorded over years, that there is an inverse relationship between single unit firing rate and amplitude, that pattern recognition decoding paradigms can allow phoneme recognition, that single units appear more important than multiunits when precision is important, and that units are plastic in their functional relationships. These characteristics of a reliable neural interface are essential for the development of neural prostheses and also for the future enhancement of human brain function.

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics.

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

Dispersal of Golgi matrix proteins during mitotic Golgi disassembly.

During mitosis, the mammalian Golgi disassembles into numerous vesicles and larger membrane structures referred to as clusters or remnants. Following mitosis, the vesicles and clusters reassemble to form an intact Golgi in each daughter cell. One model of Golgi biogenesis states that Golgi matrix proteins remain assembled in mitotic clusters and then serve as a template for Golgi reassembly. To test this idea, we performed a 3D-computational analysis of mitotic cells to determine the extent to which these proteins remain in mitotic clusters. As a control we used brefeldin A-induced Golgi disassembly which causes dispersal of Golgi enzymes, but leaves matrix proteins in remnant structures. Unlike brefeldin A-treated cells, in which matrix proteins were clearly sorted from non-matrix proteins, we observed extensive dispersal of matrix proteins in metaphase cells with no evidence of differential sorting of these proteins from other Golgi proteins. The extensive disassembly of matrix proteins argues against their participation in a stable template and supports a self-assembly mode of Golgi biogenesis.